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Sample records for bee colony optimization

  1. Optimal Grid Scheduling Using Improved Artificial Bee Colony Algorithm

    OpenAIRE

    T. Vigneswari; M. A. Maluk Mohamed

    2015-01-01

    Job Scheduling plays an important role for efficient utilization of grid resources available across different domains and geographical zones. Scheduling of jobs is challenging and NPcomplete. Evolutionary / Swarm Intelligence algorithms have been extensively used to address the NP problem in grid scheduling. Artificial Bee Colony (ABC) has been proposed for optimization problems based on foraging behaviour of bees. This work proposes a modified ABC algorithm, Cluster Hete...

  2. A modified scout bee for artificial bee colony algorithm and its performance on optimization problems

    Directory of Open Access Journals (Sweden)

    Syahid Anuar

    2016-10-01

    Full Text Available The artificial bee colony (ABC is one of the swarm intelligence algorithms used to solve optimization problems which is inspired by the foraging behaviour of the honey bees. In this paper, artificial bee colony with the rate of change technique which models the behaviour of scout bee to improve the performance of the standard ABC in terms of exploration is introduced. The technique is called artificial bee colony rate of change (ABC-ROC because the scout bee process depends on the rate of change on the performance graph, replace the parameter limit. The performance of ABC-ROC is analysed on a set of benchmark problems and also on the effect of the parameter colony size. Furthermore, the performance of ABC-ROC is compared with the state of the art algorithms.

  3. Solving Multiobjective Optimization Problems Using Artificial Bee Colony Algorithm

    Directory of Open Access Journals (Sweden)

    Wenping Zou

    2011-01-01

    Full Text Available Multiobjective optimization has been a difficult problem and focus for research in fields of science and engineering. This paper presents a novel algorithm based on artificial bee colony (ABC to deal with multi-objective optimization problems. ABC is one of the most recently introduced algorithms based on the intelligent foraging behavior of a honey bee swarm. It uses less control parameters, and it can be efficiently used for solving multimodal and multidimensional optimization problems. Our algorithm uses the concept of Pareto dominance to determine the flight direction of a bee, and it maintains nondominated solution vectors which have been found in an external archive. The proposed algorithm is validated using the standard test problems, and simulation results show that the proposed approach is highly competitive and can be considered a viable alternative to solve multi-objective optimization problems.

  4. Artificial bee colony algorithm for constrained possibilistic portfolio optimization problem

    Science.gov (United States)

    Chen, Wei

    2015-07-01

    In this paper, we discuss the portfolio optimization problem with real-world constraints under the assumption that the returns of risky assets are fuzzy numbers. A new possibilistic mean-semiabsolute deviation model is proposed, in which transaction costs, cardinality and quantity constraints are considered. Due to such constraints the proposed model becomes a mixed integer nonlinear programming problem and traditional optimization methods fail to find the optimal solution efficiently. Thus, a modified artificial bee colony (MABC) algorithm is developed to solve the corresponding optimization problem. Finally, a numerical example is given to illustrate the effectiveness of the proposed model and the corresponding algorithm.

  5. Protein structure prediction using bee colony optimization metaheuristic

    DEFF Research Database (Denmark)

    Fonseca, Rasmus; Paluszewski, Martin; Winter, Pawel

    2010-01-01

    of the proteins structure, an energy potential and some optimization algorithm that ¿nds the structure with minimal energy. Bee Colony Optimization (BCO) is a relatively new approach to solving opti- mization problems based on the foraging behaviour of bees. Several variants of BCO have been suggested......Predicting the native structure of proteins is one of the most challenging problems in molecular biology. The goal is to determine the three-dimensional struc- ture from the one-dimensional amino acid sequence. De novo prediction algorithms seek to do this by developing a representation...... our BCO method to generate good solutions to the protein structure prediction problem. The results show that BCO generally ¿nds better solutions than simulated annealing which so far has been the metaheuristic of choice for this problem....

  6. A hybrid artificial bee colony algorithm for numerical function optimization

    Science.gov (United States)

    Alqattan, Zakaria N.; Abdullah, Rosni

    2015-02-01

    Artificial Bee Colony (ABC) algorithm is one of the swarm intelligence algorithms; it has been introduced by Karaboga in 2005. It is a meta-heuristic optimization search algorithm inspired from the intelligent foraging behavior of the honey bees in nature. Its unique search process made it as one of the most competitive algorithm with some other search algorithms in the area of optimization, such as Genetic algorithm (GA) and Particle Swarm Optimization (PSO). However, the ABC performance of the local search process and the bee movement or the solution improvement equation still has some weaknesses. The ABC is good in avoiding trapping at the local optimum but it spends its time searching around unpromising random selected solutions. Inspired by the PSO, we propose a Hybrid Particle-movement ABC algorithm called HPABC, which adapts the particle movement process to improve the exploration of the original ABC algorithm. Numerical benchmark functions were used in order to experimentally test the HPABC algorithm. The results illustrate that the HPABC algorithm can outperform the ABC algorithm in most of the experiments (75% better in accuracy and over 3 times faster).

  7. Modified artificial bee colony algorithm for reactive power optimization

    Science.gov (United States)

    Sulaiman, Noorazliza; Mohamad-Saleh, Junita; Abro, Abdul Ghani

    2015-05-01

    Bio-inspired algorithms (BIAs) implemented to solve various optimization problems have shown promising results which are very important in this severely complex real-world. Artificial Bee Colony (ABC) algorithm, a kind of BIAs has demonstrated tremendous results as compared to other optimization algorithms. This paper presents a new modified ABC algorithm referred to as JA-ABC3 with the aim to enhance convergence speed and avoid premature convergence. The proposed algorithm has been simulated on ten commonly used benchmarks functions. Its performance has also been compared with other existing ABC variants. To justify its robust applicability, the proposed algorithm has been tested to solve Reactive Power Optimization problem. The results have shown that the proposed algorithm has superior performance to other existing ABC variants e.g. GABC, BABC1, BABC2, BsfABC dan IABC in terms of convergence speed. Furthermore, the proposed algorithm has also demonstrated excellence performance in solving Reactive Power Optimization problem.

  8. ABCluster: the artificial bee colony algorithm for cluster global optimization.

    Science.gov (United States)

    Zhang, Jun; Dolg, Michael

    2015-10-07

    Global optimization of cluster geometries is of fundamental importance in chemistry and an interesting problem in applied mathematics. In this work, we introduce a relatively new swarm intelligence algorithm, i.e. the artificial bee colony (ABC) algorithm proposed in 2005, to this field. It is inspired by the foraging behavior of a bee colony, and only three parameters are needed to control it. We applied it to several potential functions of quite different nature, i.e., the Coulomb-Born-Mayer, Lennard-Jones, Morse, Z and Gupta potentials. The benchmarks reveal that for long-ranged potentials the ABC algorithm is very efficient in locating the global minimum, while for short-ranged ones it is sometimes trapped into a local minimum funnel on a potential energy surface of large clusters. We have released an efficient, user-friendly, and free program "ABCluster" to realize the ABC algorithm. It is a black-box program for non-experts as well as experts and might become a useful tool for chemists to study clusters.

  9. Loading pattern optimization of PWR reactors using Artificial Bee Colony

    International Nuclear Information System (INIS)

    Safarzadeh, O.; Zolfaghari, A.; Norouzi, A.; Minuchehr, H.

    2011-01-01

    Highlights: → ABC algorithm is comparable to the canonical GA algorithm and PSO. → The performance of ABC shows that the algorithm is quiet promising. → The final band width of search fitness values by ABC is narrow. → The ABC algorithm is relatively easy to implement. - Abstract: In this paper a core reloading technique using Artificial Bee Colony algorithm, ABC, is presented in the context of finding an optimal configuration of fuel assemblies. The proposed method can be used for in-core fuel management optimization problems in pressurized water reactors. To evaluate the proposed technique, the power flattening of a VVER-1000 core is considered as an objective function although other variables such as K eff , power peaking factor, burn up and cycle length can also be taken into account. The proposed optimization method is applied to a core design optimization problem previously solved with Genetic and Particle Swarm Intelligence Algorithm. The results, convergence rate and reliability of the new method are quite promising and show that the ABC algorithm performs very well and is comparable to the canonical Genetic Algorithm and Particle Swarm Intelligence, hence demonstrating its potential for other optimization applications in nuclear engineering field as, for instance, the cascade problems.

  10. Hierarchical Artificial Bee Colony Algorithm for RFID Network Planning Optimization

    Directory of Open Access Journals (Sweden)

    Lianbo Ma

    2014-01-01

    Full Text Available This paper presents a novel optimization algorithm, namely, hierarchical artificial bee colony optimization, called HABC, to tackle the radio frequency identification network planning (RNP problem. In the proposed multilevel model, the higher-level species can be aggregated by the subpopulations from lower level. In the bottom level, each subpopulation employing the canonical ABC method searches the part-dimensional optimum in parallel, which can be constructed into a complete solution for the upper level. At the same time, the comprehensive learning method with crossover and mutation operators is applied to enhance the global search ability between species. Experiments are conducted on a set of 10 benchmark optimization problems. The results demonstrate that the proposed HABC obtains remarkable performance on most chosen benchmark functions when compared to several successful swarm intelligence and evolutionary algorithms. Then HABC is used for solving the real-world RNP problem on two instances with different scales. Simulation results show that the proposed algorithm is superior for solving RNP, in terms of optimization accuracy and computation robustness.

  11. Hierarchical artificial bee colony algorithm for RFID network planning optimization.

    Science.gov (United States)

    Ma, Lianbo; Chen, Hanning; Hu, Kunyuan; Zhu, Yunlong

    2014-01-01

    This paper presents a novel optimization algorithm, namely, hierarchical artificial bee colony optimization, called HABC, to tackle the radio frequency identification network planning (RNP) problem. In the proposed multilevel model, the higher-level species can be aggregated by the subpopulations from lower level. In the bottom level, each subpopulation employing the canonical ABC method searches the part-dimensional optimum in parallel, which can be constructed into a complete solution for the upper level. At the same time, the comprehensive learning method with crossover and mutation operators is applied to enhance the global search ability between species. Experiments are conducted on a set of 10 benchmark optimization problems. The results demonstrate that the proposed HABC obtains remarkable performance on most chosen benchmark functions when compared to several successful swarm intelligence and evolutionary algorithms. Then HABC is used for solving the real-world RNP problem on two instances with different scales. Simulation results show that the proposed algorithm is superior for solving RNP, in terms of optimization accuracy and computation robustness.

  12. A novel comprehensive learning artificial bee colony optimizer for dynamic optimization biological problems

    Directory of Open Access Journals (Sweden)

    Weixing Su

    2017-03-01

    Full Text Available There are many dynamic optimization problems in the real world, whose convergence and searching ability is cautiously desired, obviously different from static optimization cases. This requires an optimization algorithm adaptively seek the changing optima over dynamic environments, instead of only finding the global optimal solution in the static environment. This paper proposes a novel comprehensive learning artificial bee colony optimizer (CLABC for optimization in dynamic environments problems, which employs a pool of optimal foraging strategies to balance the exploration and exploitation tradeoff. The main motive of CLABC is to enrich artificial bee foraging behaviors in the ABC model by combining Powell’s pattern search method, life-cycle, and crossover-based social learning strategy. The proposed CLABC is a more bee-colony-realistic model that the bee can reproduce and die dynamically throughout the foraging process and population size varies as the algorithm runs. The experiments for evaluating CLABC are conducted on the dynamic moving peak benchmarks. Furthermore, the proposed algorithm is applied to a real-world application of dynamic RFID network optimization. Statistical analysis of all these cases highlights the significant performance improvement due to the beneficial combination and demonstrates the performance superiority of the proposed algorithm.

  13. A novel comprehensive learning artificial bee colony optimizer for dynamic optimization biological problems.

    Science.gov (United States)

    Su, Weixing; Chen, Hanning; Liu, Fang; Lin, Na; Jing, Shikai; Liang, Xiaodan; Liu, Wei

    2017-03-01

    There are many dynamic optimization problems in the real world, whose convergence and searching ability is cautiously desired, obviously different from static optimization cases. This requires an optimization algorithm adaptively seek the changing optima over dynamic environments, instead of only finding the global optimal solution in the static environment. This paper proposes a novel comprehensive learning artificial bee colony optimizer (CLABC) for optimization in dynamic environments problems, which employs a pool of optimal foraging strategies to balance the exploration and exploitation tradeoff. The main motive of CLABC is to enrich artificial bee foraging behaviors in the ABC model by combining Powell's pattern search method, life-cycle, and crossover-based social learning strategy. The proposed CLABC is a more bee-colony-realistic model that the bee can reproduce and die dynamically throughout the foraging process and population size varies as the algorithm runs. The experiments for evaluating CLABC are conducted on the dynamic moving peak benchmarks. Furthermore, the proposed algorithm is applied to a real-world application of dynamic RFID network optimization. Statistical analysis of all these cases highlights the significant performance improvement due to the beneficial combination and demonstrates the performance superiority of the proposed algorithm.

  14. Design and economic optimization of shell and tube heat exchangers using Artificial Bee Colony (ABC) algorithm

    International Nuclear Information System (INIS)

    Sencan Sahin, Arzu; Kilic, Bayram; Kilic, Ulas

    2011-01-01

    Highlights: → Artificial Bee Colony for shell and tube heat exchanger optimization is used. → The total cost is minimized by varying design variables. → This new approach can be applied for optimization of heat exchangers. - Abstract: In this study, a new shell and tube heat exchanger optimization design approach is developed. Artificial Bee Colony (ABC) has been applied to minimize the total cost of the equipment including capital investment and the sum of discounted annual energy expenditures related to pumping of shell and tube heat exchanger by varying various design variables such as tube length, tube outer diameter, pitch size, baffle spacing, etc. Finally, the results are compared to those obtained by literature approaches. The obtained results indicate that Artificial Bee Colony (ABC) algorithm can be successfully applied for optimal design of shell and tube heat exchangers.

  15. Design and economic optimization of shell and tube heat exchangers using Artificial Bee Colony (ABC) algorithm

    Energy Technology Data Exchange (ETDEWEB)

    Sencan Sahin, Arzu, E-mail: sencan@tef.sdu.edu.tr [Department of Mechanical Education, Technical Education Faculty, Sueleyman Demirel University, 32260 Isparta (Turkey); Kilic, Bayram, E-mail: bayramkilic@hotmail.com [Bucak Emin Guelmez Vocational School, Mehmet Akif Ersoy University, Bucak (Turkey); Kilic, Ulas, E-mail: ulaskilic@mehmetakif.edu.tr [Bucak Emin Guelmez Vocational School, Mehmet Akif Ersoy University, Bucak (Turkey)

    2011-10-15

    Highlights: {yields} Artificial Bee Colony for shell and tube heat exchanger optimization is used. {yields} The total cost is minimized by varying design variables. {yields} This new approach can be applied for optimization of heat exchangers. - Abstract: In this study, a new shell and tube heat exchanger optimization design approach is developed. Artificial Bee Colony (ABC) has been applied to minimize the total cost of the equipment including capital investment and the sum of discounted annual energy expenditures related to pumping of shell and tube heat exchanger by varying various design variables such as tube length, tube outer diameter, pitch size, baffle spacing, etc. Finally, the results are compared to those obtained by literature approaches. The obtained results indicate that Artificial Bee Colony (ABC) algorithm can be successfully applied for optimal design of shell and tube heat exchangers.

  16. A Multistrategy Optimization Improved Artificial Bee Colony Algorithm

    Directory of Open Access Journals (Sweden)

    Wen Liu

    2014-01-01

    Full Text Available Being prone to the shortcomings of premature and slow convergence rate of artificial bee colony algorithm, an improved algorithm was proposed. Chaotic reverse learning strategies were used to initialize swarm in order to improve the global search ability of the algorithm and keep the diversity of the algorithm; the similarity degree of individuals of the population was used to characterize the diversity of population; population diversity measure was set as an indicator to dynamically and adaptively adjust the nectar position; the premature and local convergence were avoided effectively; dual population search mechanism was introduced to the search stage of algorithm; the parallel search of dual population considerably improved the convergence rate. Through simulation experiments of 10 standard testing functions and compared with other algorithms, the results showed that the improved algorithm had faster convergence rate and the capacity of jumping out of local optimum faster.

  17. A new quantum inspired chaotic artificial bee colony algorithm for optimal power flow problem

    International Nuclear Information System (INIS)

    Yuan, Xiaohui; Wang, Pengtao; Yuan, Yanbin; Huang, Yuehua; Zhang, Xiaopan

    2015-01-01

    Highlights: • Quantum theory is introduced to artificial bee colony algorithm (ABC) to increase population diversity. • A chaotic local search operator is used to enhance local search ability of ABC. • Quantum inspired chaotic ABC method (QCABC) is proposed to solve optimal power flow. • The feasibility and effectiveness of the proposed QCABC is verified by examples. - Abstract: This paper proposes a new artificial bee colony algorithm with quantum theory and the chaotic local search strategy (QCABC), and uses it to solve the optimal power flow (OPF) problem. Under the quantum computing theory, the QCABC algorithm encodes each individual with quantum bits to form a corresponding quantum bit string. By determining each quantum bits value, we can get the value of the individual. After the scout bee stage of the artificial bee colony algorithm, we begin the chaotic local search in the vicinity of the best individual found so far. Finally, the quantum rotation gate is used to process each quantum bit so that all individuals can update toward the direction of the best individual. The QCABC algorithm is carried out to deal with the OPF problem in the IEEE 30-bus and IEEE 118-bus standard test systems. The results of the QCABC algorithm are compared with other algorithms (artificial bee colony algorithm, genetic algorithm, particle swarm optimization algorithm). The comparison shows that the QCABC algorithm can effectively solve the OPF problem and it can get the better optimal results than those of other algorithms

  18. Implementation of the Bee Colony Optimization method for the design of fuel cells; Implementacion del metodo Bee Colony Optimization para el diseno de celdas de combustible

    Energy Technology Data Exchange (ETDEWEB)

    Esquivel E, J.; Ortiz S, J. J., E-mail: jaime.esquivel@fi.uaemex.mx [ININ, Carretera Mexico-Toluca s/n, 52750 Ocoyoacac, Estado de Mexico (Mexico)

    2011-11-15

    The present work shows the results obtained after applying the Bee Colony Optimization algorithm in the design of fuel cells for a BWR. The algorithm that is implemented, works following the behavior that have the bees when pollinating a flowers field. The bees carry out an exhaustive analysis in the cell, so they leave generating diverse configurations where different fuel bars are placed with different uranium enrichments to reach a value mean, with a specific number of gadolinium bars. The behavior of the generated cell is evaluated by means of the use of the commercial code CASMO-4, which shows the variables that allow fixing if the cell fulfills the requirements. Such variables are the local potential peak factor and the neutrons multiplication factor in an infinite medium. (Author)

  19. Cancer Classification Based on Support Vector Machine Optimized by Particle Swarm Optimization and Artificial Bee Colony.

    Science.gov (United States)

    Gao, Lingyun; Ye, Mingquan; Wu, Changrong

    2017-11-29

    Intelligent optimization algorithms have advantages in dealing with complex nonlinear problems accompanied by good flexibility and adaptability. In this paper, the FCBF (Fast Correlation-Based Feature selection) method is used to filter irrelevant and redundant features in order to improve the quality of cancer classification. Then, we perform classification based on SVM (Support Vector Machine) optimized by PSO (Particle Swarm Optimization) combined with ABC (Artificial Bee Colony) approaches, which is represented as PA-SVM. The proposed PA-SVM method is applied to nine cancer datasets, including five datasets of outcome prediction and a protein dataset of ovarian cancer. By comparison with other classification methods, the results demonstrate the effectiveness and the robustness of the proposed PA-SVM method in handling various types of data for cancer classification.

  20. Dynamic population artificial bee colony algorithm for multi-objective optimal power flow

    Directory of Open Access Journals (Sweden)

    Man Ding

    2017-03-01

    Full Text Available This paper proposes a novel artificial bee colony algorithm with dynamic population (ABC-DP, which synergizes the idea of extended life-cycle evolving model to balance the exploration and exploitation tradeoff. The proposed ABC-DP is a more bee-colony-realistic model that the bee can reproduce and die dynamically throughout the foraging process and population size varies as the algorithm runs. ABC-DP is then used for solving the optimal power flow (OPF problem in power systems that considers the cost, loss, and emission impacts as the objective functions. The 30-bus IEEE test system is presented to illustrate the application of the proposed algorithm. The simulation results, which are also compared to nondominated sorting genetic algorithm II (NSGAII and multi-objective ABC (MOABC, are presented to illustrate the effectiveness and robustness of the proposed method.

  1. A Novel Rough Set Reduct Algorithm for Medical Domain Based on Bee Colony Optimization

    OpenAIRE

    Suguna, N.; Thanushkodi, K.

    2010-01-01

    Feature selection refers to the problem of selecting relevant features which produce the most predictive outcome. In particular, feature selection task is involved in datasets containing huge number of features. Rough set theory has been one of the most successful methods used for feature selection. However, this method is still not able to find optimal subsets. This paper proposes a new feature selection method based on Rough set theory hybrid with Bee Colony Optimization (BCO) in an attempt...

  2. A new improved artificial bee colony algorithm for ship hull form optimization

    Science.gov (United States)

    Huang, Fuxin; Wang, Lijue; Yang, Chi

    2016-04-01

    The artificial bee colony (ABC) algorithm is a relatively new swarm intelligence-based optimization algorithm. Its simplicity of implementation, relatively few parameter settings and promising optimization capability make it widely used in different fields. However, it has problems of slow convergence due to its solution search equation. Here, a new solution search equation based on a combination of the elite solution pool and the block perturbation scheme is proposed to improve the performance of the algorithm. In addition, two different solution search equations are used by employed bees and onlooker bees to balance the exploration and exploitation of the algorithm. The developed algorithm is validated by a set of well-known numerical benchmark functions. It is then applied to optimize two ship hull forms with minimum resistance. The tested results show that the proposed new improved ABC algorithm can outperform the ABC algorithm in most of the tested problems.

  3. Discrete and Continuous Optimization Based on Hierarchical Artificial Bee Colony Optimizer

    Directory of Open Access Journals (Sweden)

    Lianbo Ma

    2014-01-01

    Full Text Available This paper presents a novel optimization algorithm, namely, hierarchical artificial bee colony optimization (HABC, to tackle complex high-dimensional problems. In the proposed multilevel model, the higher-level species can be aggregated by the subpopulations from lower level. In the bottom level, each subpopulation employing the canonical ABC method searches the part-dimensional optimum in parallel, which can be constructed into a complete solution for the upper level. At the same time, the comprehensive learning method with crossover and mutation operator is applied to enhance the global search ability between species. Experiments are conducted on a set of 20 continuous and discrete benchmark problems. The experimental results demonstrate remarkable performance of the HABC algorithm when compared with other six evolutionary algorithms.

  4. Optimization of type-2 fuzzy controllers using the bee colony algorithm

    CERN Document Server

    Amador, Leticia

    2017-01-01

    This book focuses on the fields of fuzzy logic, bio-inspired algorithm; especially bee colony optimization algorithm and also considering the fuzzy control area. The main idea is that this areas together can to solve various control problems and to find better results. In this book we test the proposed method using two benchmark problems; the problem for filling a water tank and the problem for controlling the trajectory in an autonomous mobile robot. When Interval Type-2 Fuzzy Logic System is implemented to model the behavior of systems, the results show a better stabilization, because the analysis of uncertainty is better. For this reason we consider in this book the proposed method using fuzzy systems, fuzzy controllers, and bee colony optimization algorithm improve the behavior of the complex control problems.

  5. Directed Bee Colony Optimization Algorithm to Solve the Nurse Rostering Problem.

    Science.gov (United States)

    Rajeswari, M; Amudhavel, J; Pothula, Sujatha; Dhavachelvan, P

    2017-01-01

    The Nurse Rostering Problem is an NP-hard combinatorial optimization, scheduling problem for assigning a set of nurses to shifts per day by considering both hard and soft constraints. A novel metaheuristic technique is required for solving Nurse Rostering Problem (NRP). This work proposes a metaheuristic technique called Directed Bee Colony Optimization Algorithm using the Modified Nelder-Mead Method for solving the NRP. To solve the NRP, the authors used a multiobjective mathematical programming model and proposed a methodology for the adaptation of a Multiobjective Directed Bee Colony Optimization (MODBCO). MODBCO is used successfully for solving the multiobjective problem of optimizing the scheduling problems. This MODBCO is an integration of deterministic local search, multiagent particle system environment, and honey bee decision-making process. The performance of the algorithm is assessed using the standard dataset INRC2010, and it reflects many real-world cases which vary in size and complexity. The experimental analysis uses statistical tools to show the uniqueness of the algorithm on assessment criteria.

  6. APPLYING TEACHING-LEARNING TO ARTIFICIAL BEE COLONY FOR PARAMETER OPTIMIZATION OF SOFTWARE EFFORT ESTIMATION MODEL

    Directory of Open Access Journals (Sweden)

    THANH TUNG KHUAT

    2017-05-01

    Full Text Available Artificial Bee Colony inspired by the foraging behaviour of honey bees is a novel meta-heuristic optimization algorithm in the community of swarm intelligence algorithms. Nevertheless, it is still insufficient in the speed of convergence and the quality of solutions. This paper proposes an approach in order to tackle these downsides by combining the positive aspects of TeachingLearning based optimization and Artificial Bee Colony. The performance of the proposed method is assessed on the software effort estimation problem, which is the complex and important issue in the project management. Software developers often carry out the software estimation in the early stages of the software development life cycle to derive the required cost and schedule for a project. There are a large number of methods for effort estimation in which COCOMO II is one of the most widely used models. However, this model has some restricts because its parameters have not been optimized yet. In this work, therefore, we will present the approach to overcome this limitation of COCOMO II model. The experiments have been conducted on NASA software project dataset and the obtained results indicated that the improvement of parameters provided better estimation capabilities compared to the original COCOMO II model.

  7. An Improved Multi-Objective Artificial Bee Colony Optimization Algorithm with Regulation Operators

    Directory of Open Access Journals (Sweden)

    Jiuyuan Huo

    2017-02-01

    Full Text Available To achieve effective and accurate optimization for multi-objective optimization problems, a multi-objective artificial bee colony algorithm with regulation operators (RMOABC inspired by the intelligent foraging behavior of honey bees was proposed in this paper. The proposed algorithm utilizes the Pareto dominance theory and takes advantage of adaptive grid and regulation operator mechanisms. The adaptive grid technique is used to adaptively assess the Pareto front maintained in an external archive and the regulation operator is used to balance the weights of the local search and the global search in the evolution of the algorithm. The performance of RMOABC was evaluated in comparison with other nature inspired algorithms includes NSGA-II and MOEA/D. The experiments results demonstrated that the RMOABC approach has better accuracy and minimal execution time.

  8. New Enhanced Artificial Bee Colony (JA-ABC5 Algorithm with Application for Reactive Power Optimization

    Directory of Open Access Journals (Sweden)

    Noorazliza Sulaiman

    2015-01-01

    Full Text Available The standard artificial bee colony (ABC algorithm involves exploration and exploitation processes which need to be balanced for enhanced performance. This paper proposes a new modified ABC algorithm named JA-ABC5 to enhance convergence speed and improve the ability to reach the global optimum by balancing exploration and exploitation processes. New stages have been proposed at the earlier stages of the algorithm to increase the exploitation process. Besides that, modified mutation equations have also been introduced in the employed and onlooker-bees phases to balance the two processes. The performance of JA-ABC5 has been analyzed on 27 commonly used benchmark functions and tested to optimize the reactive power optimization problem. The performance results have clearly shown that the newly proposed algorithm has outperformed other compared algorithms in terms of convergence speed and global optimum achievement.

  9. New enhanced artificial bee colony (JA-ABC5) algorithm with application for reactive power optimization.

    Science.gov (United States)

    Sulaiman, Noorazliza; Mohamad-Saleh, Junita; Abro, Abdul Ghani

    2015-01-01

    The standard artificial bee colony (ABC) algorithm involves exploration and exploitation processes which need to be balanced for enhanced performance. This paper proposes a new modified ABC algorithm named JA-ABC5 to enhance convergence speed and improve the ability to reach the global optimum by balancing exploration and exploitation processes. New stages have been proposed at the earlier stages of the algorithm to increase the exploitation process. Besides that, modified mutation equations have also been introduced in the employed and onlooker-bees phases to balance the two processes. The performance of JA-ABC5 has been analyzed on 27 commonly used benchmark functions and tested to optimize the reactive power optimization problem. The performance results have clearly shown that the newly proposed algorithm has outperformed other compared algorithms in terms of convergence speed and global optimum achievement.

  10. Performance improvement of optical CDMA networks with stochastic artificial bee colony optimization technique

    Science.gov (United States)

    Panda, Satyasen

    2018-05-01

    This paper proposes a modified artificial bee colony optimization (ABC) algorithm based on levy flight swarm intelligence referred as artificial bee colony levy flight stochastic walk (ABC-LFSW) optimization for optical code division multiple access (OCDMA) network. The ABC-LFSW algorithm is used to solve asset assignment problem based on signal to noise ratio (SNR) optimization in OCDM networks with quality of service constraints. The proposed optimization using ABC-LFSW algorithm provides methods for minimizing various noises and interferences, regulating the transmitted power and optimizing the network design for improving the power efficiency of the optical code path (OCP) from source node to destination node. In this regard, an optical system model is proposed for improving the network performance with optimized input parameters. The detailed discussion and simulation results based on transmitted power allocation and power efficiency of OCPs are included. The experimental results prove the superiority of the proposed network in terms of power efficiency and spectral efficiency in comparison to networks without any power allocation approach.

  11. Optimization of Straight Cylindrical Turning Using Artificial Bee Colony (ABC) Algorithm

    Science.gov (United States)

    Prasanth, Rajanampalli Seshasai Srinivasa; Hans Raj, Kandikonda

    2017-04-01

    Artificial bee colony (ABC) algorithm, that mimics the intelligent foraging behavior of honey bees, is increasingly gaining acceptance in the field of process optimization, as it is capable of handling nonlinearity, complexity and uncertainty. Straight cylindrical turning is a complex and nonlinear machining process which involves the selection of appropriate cutting parameters that affect the quality of the workpiece. This paper presents the estimation of optimal cutting parameters of the straight cylindrical turning process using the ABC algorithm. The ABC algorithm is first tested on four benchmark problems of numerical optimization and its performance is compared with genetic algorithm (GA) and ant colony optimization (ACO) algorithm. Results indicate that, the rate of convergence of ABC algorithm is better than GA and ACO. Then, the ABC algorithm is used to predict optimal cutting parameters such as cutting speed, feed rate, depth of cut and tool nose radius to achieve good surface finish. Results indicate that, the ABC algorithm estimated a comparable surface finish when compared with real coded genetic algorithm and differential evolution algorithm.

  12. A Bee Colony Optimization Approach for Mixed Blocking Constraints Flow Shop Scheduling Problems

    Directory of Open Access Journals (Sweden)

    Mostafa Khorramizadeh

    2015-01-01

    Full Text Available The flow shop scheduling problems with mixed blocking constraints with minimization of makespan are investigated. The Taguchi orthogonal arrays and path relinking along with some efficient local search methods are used to develop a metaheuristic algorithm based on bee colony optimization. In order to compare the performance of the proposed algorithm, two well-known test problems are considered. Computational results show that the presented algorithm has comparative performance with well-known algorithms of the literature, especially for the large sized problems.

  13. Multimodal optimization by using hybrid of artificial bee colony algorithm and BFGS algorithm

    Science.gov (United States)

    Anam, S.

    2017-10-01

    Optimization has become one of the important fields in Mathematics. Many problems in engineering and science can be formulated into optimization problems. They maybe have many local optima. The optimization problem with many local optima, known as multimodal optimization problem, is how to find the global solution. Several metaheuristic methods have been proposed to solve multimodal optimization problems such as Particle Swarm Optimization (PSO), Genetics Algorithm (GA), Artificial Bee Colony (ABC) algorithm, etc. The performance of the ABC algorithm is better than or similar to those of other population-based algorithms with the advantage of employing a fewer control parameters. The ABC algorithm also has the advantages of strong robustness, fast convergence and high flexibility. However, it has the disadvantages premature convergence in the later search period. The accuracy of the optimal value cannot meet the requirements sometimes. Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm is a good iterative method for finding a local optimum. Compared with other local optimization methods, the BFGS algorithm is better. Based on the advantages of the ABC algorithm and the BFGS algorithm, this paper proposes a hybrid of the artificial bee colony algorithm and the BFGS algorithm to solve the multimodal optimization problem. The first step is that the ABC algorithm is run to find a point. In the second step is that the point obtained by the first step is used as an initial point of BFGS algorithm. The results show that the hybrid method can overcome from the basic ABC algorithm problems for almost all test function. However, if the shape of function is flat, the proposed method cannot work well.

  14. Revenue-driven Lightpaths Provisioning over Optical WDM Networks Using Bee Colony Optimization

    Directory of Open Access Journals (Sweden)

    Goran Z. Marković

    2017-01-01

    Full Text Available Revenue-driven Lightpaths Provisioning over Optical WDM Networks Using Bee Colony Optimization Goran Z. Markovic University of Belgrade n Faculty of Transport and Traffic Engineering, Vojvode Stepe 305, Belgrade, 11000, Serbia E-mail: g.markovic@sf.bg.ac.rs Abstract This paper aims to study the lightpaths provisioning problem in optical WDM networks with scarce available wavelengths under the static (off-line traffic demands such that network operatorrs (NOrs revenue is maximized. To achieve this goal, a NO has to be addressed with the issue how to solve the call admission control jointly with the lightpaths routing and wavelength assignment (RWA problem in efficient manner. The improved bee colony optimization (BCOi metaheuristic is applied to solve the considered revenue maximization (Max-Rev problem. We evaluated the performances of the proposed BCOi Max-Rev framework by performing numerous simulation experiments in different realistic WDM optical network topologies. We observed that our BCOi Max-Rev algorithm is an efficient tool to produce high quality solutions within reasonable amount of CPU time. It has been proved that BCOi Max-Rev solutions just slightly deviate from optimal solutions (at most 1% and considerably outperform some heuristic algorithms, such as the Max-Profit and FCFS. In addition, our Max-Rev BCOi algorithm is able to produce better solution quality compared to the constructive BCO approach (up to 3.5% in the case of NSFNet and 5% in the case of EON. Finally, we compared the BCOi to differential evolution (DE approach in the case of more complex networks, such as the USA optical network topology. The results show that our BCOi always outperforms DE metaheuristic, whereby the profit improvement could reach up to 20 % in some instances. Keywords: bee colony optimization (BCO, lightpath, optical network, routing and wavelength assignment (RWA, revenue maximization. 1. Introduction Optical networks employing wavelength

  15. A new technique based on Artificial Bee Colony Algorithm for optimal sizing of stand-alone photovoltaic system

    OpenAIRE

    Mohamed, Ahmed F.; Elarini, Mahdi M.; Othman, Ahmed M.

    2013-01-01

    One of the most recent optimization techniques applied to the optimal design of photovoltaic system to supply an isolated load demand is the Artificial Bee Colony Algorithm (ABC). The proposed methodology is applied to optimize the cost of the PV system including photovoltaic, a battery bank, a battery charger controller, and inverter. Two objective functions are proposed: the first one is the PV module output power which is to be maximized and the second one is the life cycle cost (LCC) whic...

  16. Artificial Bee Colony Algorithm Combined with Grenade Explosion Method and Cauchy Operator for Global Optimization

    Directory of Open Access Journals (Sweden)

    Jian-Guo Zheng

    2015-01-01

    Full Text Available Artificial bee colony (ABC algorithm is a popular swarm intelligence technique inspired by the intelligent foraging behavior of honey bees. However, ABC is good at exploration but poor at exploitation and its convergence speed is also an issue in some cases. To improve the performance of ABC, a novel ABC combined with grenade explosion method (GEM and Cauchy operator, namely, ABCGC, is proposed. GEM is embedded in the onlooker bees’ phase to enhance the exploitation ability and accelerate convergence of ABCGC; meanwhile, Cauchy operator is introduced into the scout bees’ phase to help ABCGC escape from local optimum and further enhance its exploration ability. Two sets of well-known benchmark functions are used to validate the better performance of ABCGC. The experiments confirm that ABCGC is significantly superior to ABC and other competitors; particularly it converges to the global optimum faster in most cases. These results suggest that ABCGC usually achieves a good balance between exploitation and exploration and can effectively serve as an alternative for global optimization.

  17. An Improved Artificial Bee Colony Algorithm and Its Application to Multi-Objective Optimal Power Flow

    Directory of Open Access Journals (Sweden)

    Xuanhu He

    2015-03-01

    Full Text Available Optimal power flow (OPF objective functions involve minimization of the total fuel costs of generating units, minimization of atmospheric pollutant emissions, minimization of active power losses and minimization of voltage deviations. In this paper, a fuzzy multi-objective OPF model is established by the fuzzy membership functions and the fuzzy satisfaction-maximizing method. The improved artificial bee colony (IABC algorithm is applied to solve the model. In the IABC algorithm, the mutation and crossover operations of a differential evolution algorithm are utilized to generate new solutions to improve exploitation capacity; tent chaos mapping is utilized to generate initial swarms, reference mutation solutions and the reference dimensions of crossover operations to improve swarm diversity. The proposed method is applied to multi-objective OPF problems in IEEE 30-bus, IEEE 57-bus and IEEE 300-bus test systems. The results are compared with those obtained by other algorithms, which demonstrates the effectiveness and superiority of the IABC algorithm, and how the optimal scheme obtained by the proposed model can make systems more economical and stable.

  18. Enhancing Artificial Bee Colony Algorithm with Self-Adaptive Searching Strategy and Artificial Immune Network Operators for Global Optimization

    Directory of Open Access Journals (Sweden)

    Tinggui Chen

    2014-01-01

    Full Text Available Artificial bee colony (ABC algorithm, inspired by the intelligent foraging behavior of honey bees, was proposed by Karaboga. It has been shown to be superior to some conventional intelligent algorithms such as genetic algorithm (GA, artificial colony optimization (ACO, and particle swarm optimization (PSO. However, the ABC still has some limitations. For example, ABC can easily get trapped in the local optimum when handing in functions that have a narrow curving valley, a high eccentric ellipse, or complex multimodal functions. As a result, we proposed an enhanced ABC algorithm called EABC by introducing self-adaptive searching strategy and artificial immune network operators to improve the exploitation and exploration. The simulation results tested on a suite of unimodal or multimodal benchmark functions illustrate that the EABC algorithm outperforms ACO, PSO, and the basic ABC in most of the experiments.

  19. Recent Honey Bee Colony Declines

    Science.gov (United States)

    2007-06-20

    podcasts.psu.edu/taxonomy/term/62]. Staple crops such as wheat , corn, and rice do not rely on insect pollination and are mostly wind pollinated...are interacting to weaken bee colonies and are allowing stress-related pathogens, such as fungi , thus causing a final collapse.27 Others note the...possible role of miticide resistance in bees. High levels of bacteria, viruses, and fungi have been found in the guts of the recoverable dead bees

  20. Thermodynamic Optimization of a Geothermal- Based Organic Rankine Cycle System Using an Artificial Bee Colony Algorithm

    Directory of Open Access Journals (Sweden)

    Osman Özkaraca

    2017-10-01

    Full Text Available Geothermal energy is a renewable form of energy, however due to misuse, processing and management issues, it is necessary to use the resource more efficiently. To increase energy efficiency, energy systems engineers carry out careful energy control studies and offer alternative solutions. With this aim, this study was conducted to improve the performance of a real operating air-cooled organic Rankine cycle binary geothermal power plant (GPP and its components in the aspects of thermodynamic modeling, exergy analysis and optimization processes. In-depth information is obtained about the exergy (maximum work a system can make, exergy losses and destruction at the power plant and its components. Thus the performance of the power plant may be predicted with reasonable accuracy and better understanding is gained for the physical process to be used in improving the performance of the power plant. The results of the exergy analysis show that total exergy production rate and exergy efficiency of the GPP are 21 MW and 14.52%, respectively, after removing parasitic loads. The highest amount of exergy destruction occurs, respectively, in condenser 2, vaporizer HH2, condenser 1, pumps 1 and 2 as components requiring priority performance improvement. To maximize the system exergy efficiency, the artificial bee colony (ABC is applied to the model that simulates the actual GPP. Under all the optimization conditions, the maximum exergy efficiency for the GPP and its components is obtained. Two of these conditions such as Case 4 related to the turbine and Case 12 related to the condenser have the best performance. As a result, the ABC optimization method provides better quality information than exergy analysis. Based on the guidance of this study, the performance of power plants based on geothermal energy and other energy resources may be improved.

  1. Hybrid Artificial Bee Colony Algorithm and Particle Swarm Search for Global Optimization

    Directory of Open Access Journals (Sweden)

    Wang Chun-Feng

    2014-01-01

    Full Text Available Artificial bee colony (ABC algorithm is one of the most recent swarm intelligence based algorithms, which has been shown to be competitive to other population-based algorithms. However, there is still an insufficiency in ABC regarding its solution search equation, which is good at exploration but poor at exploitation. To overcome this problem, we propose a novel artificial bee colony algorithm based on particle swarm search mechanism. In this algorithm, for improving the convergence speed, the initial population is generated by using good point set theory rather than random selection firstly. Secondly, in order to enhance the exploitation ability, the employed bee, onlookers, and scouts utilize the mechanism of PSO to search new candidate solutions. Finally, for further improving the searching ability, the chaotic search operator is adopted in the best solution of the current iteration. Our algorithm is tested on some well-known benchmark functions and compared with other algorithms. Results show that our algorithm has good performance.

  2. A hybrid of ant colony optimization and artificial bee colony algorithm for probabilistic optimal placement and sizing of distributed energy resources

    International Nuclear Information System (INIS)

    Kefayat, M.; Lashkar Ara, A.; Nabavi Niaki, S.A.

    2015-01-01

    Highlights: • A probabilistic optimization framework incorporated with uncertainty is proposed. • A hybrid optimization approach combining ACO and ABC algorithms is proposed. • The problem is to deal with technical, environmental and economical aspects. • A fuzzy interactive approach is incorporated to solve the multi-objective problem. • Several strategies are implemented to compare with literature methods. - Abstract: In this paper, a hybrid configuration of ant colony optimization (ACO) with artificial bee colony (ABC) algorithm called hybrid ACO–ABC algorithm is presented for optimal location and sizing of distributed energy resources (DERs) (i.e., gas turbine, fuel cell, and wind energy) on distribution systems. The proposed algorithm is a combined strategy based on the discrete (location optimization) and continuous (size optimization) structures to achieve advantages of the global and local search ability of ABC and ACO algorithms, respectively. Also, in the proposed algorithm, a multi-objective ABC is used to produce a set of non-dominated solutions which store in the external archive. The objectives consist of minimizing power losses, total emissions produced by substation and resources, total electrical energy cost, and improving the voltage stability. In order to investigate the impact of the uncertainty in the output of the wind energy and load demands, a probabilistic load flow is necessary. In this study, an efficient point estimate method (PEM) is employed to solve the optimization problem in a stochastic environment. The proposed algorithm is tested on the IEEE 33- and 69-bus distribution systems. The results demonstrate the potential and effectiveness of the proposed algorithm in comparison with those of other evolutionary optimization methods

  3. Optimizing Drone Fertility With Spring Nutritional Supplements to Honey Bee (Hymenoptera: Apidae) Colonies.

    Science.gov (United States)

    Rousseau, Andrée; Giovenazzo, Pierre

    2016-03-27

    Supplemental feeding of honey bee (Apis melliferaL., Hymenoptera: Apidae) colonies in spring is essential for colony buildup in northern apicultural regions. The impact of pollen and syrup feeding on drone production and sperm quality is not well-documented, but may improve fecundation of early-bred queens. We measured the impact of feeding sucrose syrup, and protein supplements to colonies in early spring in eastern Canada. Drones were reared under different nutritional regimes, and mature individuals were then assessed in regard to size, weight, and semen quality (semen volume, sperm count, and viability). Results showed significant increases in drone weight and abdomen size when colonies were fed sucrose and a protein supplement. Colonies receiving no additional nourishment had significantly less semen volume per drone and lower sperm viability. Our study demonstrates that feeding honey bee colonies in spring with sucrose syrup and a protein supplement is important to enhance drone reproductive quality. RÉSUMÉ: L'administration de suppléments alimentaires aux colonies de l'abeille domestique (Apis melliferaL., Hymenoptera: Apidae) au printemps est essentielle pour le bon développement des colonies dans les régions apicoles nordiques. L'impact de la supplémentation des colonies en pollen et en sirop sur la production des faux-bourdons et la qualité du sperme demeure peu documenté mais pourrait résulter en une meilleure fécondation des reines produites tôt en saison. Nous avons mesuré l'impact de la supplémentation en sirop et/ou en supplément de pollen sur les colonies d'abeilles tôt au printemps dans l'est du Canada. Les faux-bourdons ont été élevé sous différents régimes alimentaires et les individus matures ont ensuite été évalués pour leur taille, leur poids ainsi que la qualité de leur sperme (volume de sperme, nombre et viabilité des spermatozoïdes. Les résultats montrent une augmentation significative du poids et de la taille

  4. Hybrid artificial bee colony algorithm for parameter optimization of five-parameter bidirectional reflectance distribution function model.

    Science.gov (United States)

    Wang, Qianqian; Zhao, Jing; Gong, Yong; Hao, Qun; Peng, Zhong

    2017-11-20

    A hybrid artificial bee colony (ABC) algorithm inspired by the best-so-far solution and bacterial chemotaxis was introduced to optimize the parameters of the five-parameter bidirectional reflectance distribution function (BRDF) model. To verify the performance of the hybrid ABC algorithm, we measured BRDF of three kinds of samples and simulated the undetermined parameters of the five-parameter BRDF model using the hybrid ABC algorithm and the genetic algorithm, respectively. The experimental results demonstrate that the hybrid ABC algorithm outperforms the genetic algorithm in convergence speed, accuracy, and time efficiency under the same conditions.

  5. A new technique based on Artificial Bee Colony Algorithm for optimal sizing of stand-alone photovoltaic system.

    Science.gov (United States)

    Mohamed, Ahmed F; Elarini, Mahdi M; Othman, Ahmed M

    2014-05-01

    One of the most recent optimization techniques applied to the optimal design of photovoltaic system to supply an isolated load demand is the Artificial Bee Colony Algorithm (ABC). The proposed methodology is applied to optimize the cost of the PV system including photovoltaic, a battery bank, a battery charger controller, and inverter. Two objective functions are proposed: the first one is the PV module output power which is to be maximized and the second one is the life cycle cost (LCC) which is to be minimized. The analysis is performed based on measured solar radiation and ambient temperature measured at Helwan city, Egypt. A comparison between ABC algorithm and Genetic Algorithm (GA) optimal results is done. Another location is selected which is Zagazig city to check the validity of ABC algorithm in any location. The ABC is more optimal than GA. The results encouraged the use of the PV systems to electrify the rural sites of Egypt.

  6. A new technique based on Artificial Bee Colony Algorithm for optimal sizing of stand-alone photovoltaic system

    Directory of Open Access Journals (Sweden)

    Ahmed F. Mohamed

    2014-05-01

    Full Text Available One of the most recent optimization techniques applied to the optimal design of photovoltaic system to supply an isolated load demand is the Artificial Bee Colony Algorithm (ABC. The proposed methodology is applied to optimize the cost of the PV system including photovoltaic, a battery bank, a battery charger controller, and inverter. Two objective functions are proposed: the first one is the PV module output power which is to be maximized and the second one is the life cycle cost (LCC which is to be minimized. The analysis is performed based on measured solar radiation and ambient temperature measured at Helwan city, Egypt. A comparison between ABC algorithm and Genetic Algorithm (GA optimal results is done. Another location is selected which is Zagazig city to check the validity of ABC algorithm in any location. The ABC is more optimal than GA. The results encouraged the use of the PV systems to electrify the rural sites of Egypt.

  7. Hierarchical Artificial Bee Colony Optimizer with Divide-and-Conquer and Crossover for Multilevel Threshold Image Segmentation

    Directory of Open Access Journals (Sweden)

    Maowei He

    2014-01-01

    Full Text Available This paper presents a novel optimization algorithm, namely, hierarchical artificial bee colony optimization (HABC, for multilevel threshold image segmentation, which employs a pool of optimal foraging strategies to extend the classical artificial bee colony framework to a cooperative and hierarchical fashion. In the proposed hierarchical model, the higher-level species incorporates the enhanced information exchange mechanism based on crossover operator to enhance the global search ability between species. In the bottom level, with the divide-and-conquer approach, each subpopulation runs the original ABC method in parallel to part-dimensional optimum, which can be aggregated into a complete solution for the upper level. The experimental results for comparing HABC with several successful EA and SI algorithms on a set of benchmarks demonstrated the effectiveness of the proposed algorithm. Furthermore, we applied the HABC to the multilevel image segmentation problem. Experimental results of the new algorithm on a variety of images demonstrated the performance superiority of the proposed algorithm.

  8. Protein Tertiary Structure Prediction Based on Main Chain Angle Using a Hybrid Bees Colony Optimization Algorithm

    Science.gov (United States)

    Mahmood, Zakaria N.; Mahmuddin, Massudi; Mahmood, Mohammed Nooraldeen

    Encoding proteins of amino acid sequence to predict classified into their respective families and subfamilies is important research area. However for a given protein, knowing the exact action whether hormonal, enzymatic, transmembranal or nuclear receptors does not depend solely on amino acid sequence but on the way the amino acid thread folds as well. This study provides a prototype system that able to predict a protein tertiary structure. Several methods are used to develop and evaluate the system to produce better accuracy in protein 3D structure prediction. The Bees Optimization algorithm which inspired from the honey bees food foraging method, is used in the searching phase. In this study, the experiment is conducted on short sequence proteins that have been used by the previous researches using well-known tools. The proposed approach shows a promising result.

  9. HONEY BEE COLONY PHEROMONES

    Directory of Open Access Journals (Sweden)

    M Dražić

    2001-09-01

    Full Text Available ABSTRACT Pheromones are chemicals produced as liquids by specialised cells or glands and transmitted into the environment as liquids or gases. In contrary to hormones, which are excreted in organism and have effect exclusively on organism that produced them, pheromones are excreted outside organism and effect on different individuals of the same species. Pheromones mediate nearly all aspects of honeybee colony life including social defence, brood care, mating, orientation, foraging and reproduction. Pheromone investigation has high economic importance. With use of pheromones it is possible to manipulate with pest insects on crops or to direct honeybees during pollination on target plants.

  10. Protein folding optimization based on 3D off-lattice model via an improved artificial bee colony algorithm.

    Science.gov (United States)

    Li, Bai; Lin, Mu; Liu, Qiao; Li, Ya; Zhou, Changjun

    2015-10-01

    Protein folding is a fundamental topic in molecular biology. Conventional experimental techniques for protein structure identification or protein folding recognition require strict laboratory requirements and heavy operating burdens, which have largely limited their applications. Alternatively, computer-aided techniques have been developed to optimize protein structures or to predict the protein folding process. In this paper, we utilize a 3D off-lattice model to describe the original protein folding scheme as a simplified energy-optimal numerical problem, where all types of amino acid residues are binarized into hydrophobic and hydrophilic ones. We apply a balance-evolution artificial bee colony (BE-ABC) algorithm as the minimization solver, which is featured by the adaptive adjustment of search intensity to cater for the varying needs during the entire optimization process. In this work, we establish a benchmark case set with 13 real protein sequences from the Protein Data Bank database and evaluate the convergence performance of BE-ABC algorithm through strict comparisons with several state-of-the-art ABC variants in short-term numerical experiments. Besides that, our obtained best-so-far protein structures are compared to the ones in comprehensive previous literature. This study also provides preliminary insights into how artificial intelligence techniques can be applied to reveal the dynamics of protein folding. Graphical Abstract Protein folding optimization using 3D off-lattice model and advanced optimization techniques.

  11. An Efficient Approach for Energy Consumption Optimization and Management in Residential Building Using Artificial Bee Colony and Fuzzy Logic

    Directory of Open Access Journals (Sweden)

    Fazli Wahid

    2016-01-01

    Full Text Available The energy management in residential buildings according to occupant’s requirement and comfort is of vital importance. There are many proposals in the literature addressing the issue of user’s comfort and energy consumption (management with keeping different parameters in consideration. In this paper, we have utilized artificial bee colony (ABC optimization algorithm for maximizing user comfort and minimizing energy consumption simultaneously. We propose a complete user friendly and energy efficient model with different components. The user set parameters and the environmental parameters are inputs of the ABC, and the optimized parameters are the output of the ABC. The error differences between the environmental parameters and the ABC optimized parameters are inputs of fuzzy controllers, which give the required energy as the outputs. The purpose of the optimization algorithm is to maximize the comfort index and minimize the error difference between the user set parameters and the environmental parameters, which ultimately decreases the power consumption. The experimental results show that the proposed model is efficient in achieving high comfort index along with minimized energy consumption.

  12. Escalated convergent artificial bee colony

    Science.gov (United States)

    Jadon, Shimpi Singh; Bansal, Jagdish Chand; Tiwari, Ritu

    2016-03-01

    Artificial bee colony (ABC) optimisation algorithm is a recent, fast and easy-to-implement population-based meta heuristic for optimisation. ABC has been proved a rival algorithm with some popular swarm intelligence-based algorithms such as particle swarm optimisation, firefly algorithm and ant colony optimisation. The solution search equation of ABC is influenced by a random quantity which helps its search process in exploration at the cost of exploitation. In order to find a fast convergent behaviour of ABC while exploitation capability is maintained, in this paper basic ABC is modified in two ways. First, to improve exploitation capability, two local search strategies, namely classical unidimensional local search and levy flight random walk-based local search are incorporated with ABC. Furthermore, a new solution search strategy, namely stochastic diffusion scout search is proposed and incorporated into the scout bee phase to provide more chance to abandon solution to improve itself. Efficiency of the proposed algorithm is tested on 20 benchmark test functions of different complexities and characteristics. Results are very promising and they prove it to be a competitive algorithm in the field of swarm intelligence-based algorithms.

  13. Discovery of Transition Rules for Cellular Automata Using Artificial Bee Colony and Particle Swarm Optimization Algorithms in Urban Growth Modeling

    Directory of Open Access Journals (Sweden)

    Fereydoun Naghibi

    2016-12-01

    Full Text Available This paper presents an advanced method in urban growth modeling to discover transition rules of cellular automata (CA using the artificial bee colony (ABC optimization algorithm. Also, comparisons between the simulation results of CA models optimized by the ABC algorithm and the particle swarm optimization algorithms (PSO as intelligent approaches were performed to evaluate the potential of the proposed methods. According to previous studies, swarm intelligence algorithms for solving optimization problems such as discovering transition rules of CA in land use change/urban growth modeling can produce reasonable results. Modeling of urban growth as a dynamic process is not straightforward because of the existence of nonlinearity and heterogeneity among effective involved variables which can cause a number of challenges for traditional CA. ABC algorithm, the new powerful swarm based optimization algorithms, can be used to capture optimized transition rules of CA. This paper has proposed a methodology based on remote sensing data for modeling urban growth with CA calibrated by the ABC algorithm. The performance of ABC-CA, PSO-CA, and CA-logistic models in land use change detection is tested for the city of Urmia, Iran, between 2004 and 2014. Validations of the models based on statistical measures such as overall accuracy, figure of merit, and total operating characteristic were made. We showed that the overall accuracy of the ABC-CA model was 89%, which was 1.5% and 6.2% higher than those of the PSO-CA and CA-logistic model, respectively. Moreover, the allocation disagreement (simulation error of the simulation results for the ABC-CA, PSO-CA, and CA-logistic models are 11%, 12.5%, and 17.2%, respectively. Finally, for all evaluation indices including running time, convergence capability, flexibility, statistical measurements, and the produced spatial patterns, the ABC-CA model performance showed relative improvement and therefore its superiority was

  14. Short-term hydro-thermal-wind complementary scheduling considering uncertainty of wind power using an enhanced multi-objective bee colony optimization algorithm

    International Nuclear Information System (INIS)

    Zhou, Jianzhong; Lu, Peng; Li, Yuanzheng; Wang, Chao; Yuan, Liu; Mo, Li

    2016-01-01

    Highlights: • HTWCS system is established while considering uncertainty of wind power. • An enhanced multi-objective bee colony optimization algorithm is proposed. • Some heuristic repairing strategies are designed to handle various constraints. • HTWCS problem with economic/environment objectives is solved by EMOBCO. - Abstract: This paper presents a short-term economic/environmental hydro-thermal-wind complementary scheduling (HTWCS) system considering uncertainty of wind power, as well as various complicated non-linear constraints. HTWCS system is formulated as a multi-objective optimization problem to optimize conflictive objectives, i.e., economic and environmental criteria. Then an enhanced multi-objective bee colony optimization algorithm (EMOBCO) is proposed to solve this problem, which adopts Elite archive set, adaptive mutation/selection mechanism and local searching strategy to improve global searching ability of standard bee colony optimization (BCO). Especially, a novel constraints-repairing strategy with compressing decision space and a violation-adjustment method are used to handle various hydraulic and electric constraints. Finally, a daily scheduling simulation case of hydro-thermal-wind system is conducted to verify feasibility and effectiveness of the proposed EMOBCO in solving HTWCS problem. The simulation results indicate that the proposed EMOBCO can provide lower economic cost and smaller pollutant emission than other method established recently while considering various complex constraints in HTWCS problem.

  15. On the application of artificial bee colony (ABC algorithm for optimization of well placements in fractured reservoirs; efficiency comparison with the particle swarm optimization (PSO methodology

    Directory of Open Access Journals (Sweden)

    Behzad Nozohour-leilabady

    2016-03-01

    Full Text Available The application of a recent optimization technique, the artificial bee colony (ABC, was investigated in the context of finding the optimal well locations. The ABC performance was compared with the corresponding results from the particle swarm optimization (PSO algorithm, under essentially similar conditions. Treatment of out-of-boundary solution vectors was accomplished via the Periodic boundary condition (PBC, which presumably accelerates convergence towards the global optimum. Stochastic searches were initiated from several random staring points, to minimize starting-point dependency in the established results. The optimizations were aimed at maximizing the Net Present Value (NPV objective function over the considered oilfield production durations. To deal with the issue of reservoir heterogeneity, random permeability was applied via normal/uniform distribution functions. In addition, the issue of increased number of optimization parameters was address, by considering scenarios with multiple injector and producer wells, and cases with deviated wells in a real reservoir model. The typical results prove ABC to excel PSO (in the cases studied after relatively short optimization cycles, indicating the great premise of ABC methodology to be used for well-optimization purposes.

  16. Artificial Bee Colony Algorithm Based on K-Means Clustering for Multiobjective Optimal Power Flow Problem

    Directory of Open Access Journals (Sweden)

    Liling Sun

    2015-01-01

    Full Text Available An improved multiobjective ABC algorithm based on K-means clustering, called CMOABC, is proposed. To fasten the convergence rate of the canonical MOABC, the way of information communication in the employed bees’ phase is modified. For keeping the population diversity, the multiswarm technology based on K-means clustering is employed to decompose the population into many clusters. Due to each subcomponent evolving separately, after every specific iteration, the population will be reclustered to facilitate information exchange among different clusters. Application of the new CMOABC on several multiobjective benchmark functions shows a marked improvement in performance over the fast nondominated sorting genetic algorithm (NSGA-II, the multiobjective particle swarm optimizer (MOPSO, and the multiobjective ABC (MOABC. Finally, the CMOABC is applied to solve the real-world optimal power flow (OPF problem that considers the cost, loss, and emission impacts as the objective functions. The 30-bus IEEE test system is presented to illustrate the application of the proposed algorithm. The simulation results demonstrate that, compared to NSGA-II, MOPSO, and MOABC, the proposed CMOABC is superior for solving OPF problem, in terms of optimization accuracy.

  17. A Clustering Approach Using Cooperative Artificial Bee Colony Algorithm

    Directory of Open Access Journals (Sweden)

    Wenping Zou

    2010-01-01

    Full Text Available Artificial Bee Colony (ABC is one of the most recently introduced algorithms based on the intelligent foraging behavior of a honey bee swarm. This paper presents an extended ABC algorithm, namely, the Cooperative Article Bee Colony (CABC, which significantly improves the original ABC in solving complex optimization problems. Clustering is a popular data analysis and data mining technique; therefore, the CABC could be used for solving clustering problems. In this work, first the CABC algorithm is used for optimizing six widely used benchmark functions and the comparative results produced by ABC, Particle Swarm Optimization (PSO, and its cooperative version (CPSO are studied. Second, the CABC algorithm is used for data clustering on several benchmark data sets. The performance of CABC algorithm is compared with PSO, CPSO, and ABC algorithms on clustering problems. The simulation results show that the proposed CABC outperforms the other three algorithms in terms of accuracy, robustness, and convergence speed.

  18. Optimal Parameter Selection for Support Vector Machine Based on Artificial Bee Colony Algorithm: A Case Study of Grid-Connected PV System Power Prediction

    Directory of Open Access Journals (Sweden)

    Xiang-ming Gao

    2017-01-01

    Full Text Available Predicting the output power of photovoltaic system with nonstationarity and randomness, an output power prediction model for grid-connected PV systems is proposed based on empirical mode decomposition (EMD and support vector machine (SVM optimized with an artificial bee colony (ABC algorithm. First, according to the weather forecast data sets on the prediction date, the time series data of output power on a similar day with 15-minute intervals are built. Second, the time series data of the output power are decomposed into a series of components, including some intrinsic mode components IMFn and a trend component Res, at different scales using EMD. The corresponding SVM prediction model is established for each IMF component and trend component, and the SVM model parameters are optimized with the artificial bee colony algorithm. Finally, the prediction results of each model are reconstructed, and the predicted values of the output power of the grid-connected PV system can be obtained. The prediction model is tested with actual data, and the results show that the power prediction model based on the EMD and ABC-SVM has a faster calculation speed and higher prediction accuracy than do the single SVM prediction model and the EMD-SVM prediction model without optimization.

  19. Optimal Parameter Selection for Support Vector Machine Based on Artificial Bee Colony Algorithm: A Case Study of Grid-Connected PV System Power Prediction.

    Science.gov (United States)

    Gao, Xiang-Ming; Yang, Shi-Feng; Pan, San-Bo

    2017-01-01

    Predicting the output power of photovoltaic system with nonstationarity and randomness, an output power prediction model for grid-connected PV systems is proposed based on empirical mode decomposition (EMD) and support vector machine (SVM) optimized with an artificial bee colony (ABC) algorithm. First, according to the weather forecast data sets on the prediction date, the time series data of output power on a similar day with 15-minute intervals are built. Second, the time series data of the output power are decomposed into a series of components, including some intrinsic mode components IMFn and a trend component Res, at different scales using EMD. The corresponding SVM prediction model is established for each IMF component and trend component, and the SVM model parameters are optimized with the artificial bee colony algorithm. Finally, the prediction results of each model are reconstructed, and the predicted values of the output power of the grid-connected PV system can be obtained. The prediction model is tested with actual data, and the results show that the power prediction model based on the EMD and ABC-SVM has a faster calculation speed and higher prediction accuracy than do the single SVM prediction model and the EMD-SVM prediction model without optimization.

  20. Genetic diversity affects colony survivorship in commercial honey bee colonies

    Science.gov (United States)

    Tarpy, David R.; vanEngelsdorp, Dennis; Pettis, Jeffrey S.

    2013-08-01

    Honey bee ( Apis mellifera) queens mate with unusually high numbers of males (average of approximately 12 drones), although there is much variation among queens. One main consequence of such extreme polyandry is an increased diversity of worker genotypes within a colony, which has been shown empirically to confer significant adaptive advantages that result in higher colony productivity and survival. Moreover, honey bees are the primary insect pollinators used in modern commercial production agriculture, and their populations have been in decline worldwide. Here, we compare the mating frequencies of queens, and therefore, intracolony genetic diversity, in three commercial beekeeping operations to determine how they correlate with various measures of colony health and productivity, particularly the likelihood of queen supersedure and colony survival in functional, intensively managed beehives. We found the average effective paternity frequency ( m e ) of this population of honey bee queens to be 13.6 ± 6.76, which was not significantly different between colonies that superseded their queen and those that did not. However, colonies that were less genetically diverse (headed by queens with m e ≤ 7.0) were 2.86 times more likely to die by the end of the study when compared to colonies that were more genetically diverse (headed by queens with m e > 7.0). The stark contrast in colony survival based on increased genetic diversity suggests that there are important tangible benefits of increased queen mating number in managed honey bees, although the exact mechanism(s) that govern these benefits have not been fully elucidated.

  1. Metatranscriptomic analyses of honey bee colonies.

    Science.gov (United States)

    Tozkar, Cansu Ö; Kence, Meral; Kence, Aykut; Huang, Qiang; Evans, Jay D

    2015-01-01

    Honey bees face numerous biotic threats from viruses to bacteria, fungi, protists, and mites. Here we describe a thorough analysis of microbes harbored by worker honey bees collected from field colonies in geographically distinct regions of Turkey. Turkey is one of the World's most important centers of apiculture, harboring five subspecies of Apis mellifera L., approximately 20% of the honey bee subspecies in the world. We use deep ILLUMINA-based RNA sequencing to capture RNA species for the honey bee and a sampling of all non-endogenous species carried by bees. After trimming and mapping these reads to the honey bee genome, approximately 10% of the sequences (9-10 million reads per library) remained. These were then mapped to a curated set of public sequences containing ca. Sixty megabase-pairs of sequence representing known microbial species associated with honey bees. Levels of key honey bee pathogens were confirmed using quantitative PCR screens. We contrast microbial matches across different sites in Turkey, showing new country recordings of Lake Sinai virus, two Spiroplasma bacterium species, symbionts Candidatus Schmidhempelia bombi, Frischella perrara, Snodgrassella alvi, Gilliamella apicola, Lactobacillus spp.), neogregarines, and a trypanosome species. By using metagenomic analysis, this study also reveals deep molecular evidence for the presence of bacterial pathogens (Melissococcus plutonius, Paenibacillus larvae), Varroa destructor-1 virus, Sacbrood virus, and fungi. Despite this effort we did not detect KBV, SBPV, Tobacco ringspot virus, VdMLV (Varroa Macula like virus), Acarapis spp., Tropilaeleps spp. and Apocephalus (phorid fly). We discuss possible impacts of management practices and honey bee subspecies on microbial retinues. The described workflow and curated microbial database will be generally useful for microbial surveys of healthy and declining honey bees.

  2. A Simple and Efficient Artificial Bee Colony Algorithm

    Directory of Open Access Journals (Sweden)

    Yunfeng Xu

    2013-01-01

    Full Text Available Artificial bee colony (ABC is a new population-based stochastic algorithm which has shown good search abilities on many optimization problems. However, the original ABC shows slow convergence speed during the search process. In order to enhance the performance of ABC, this paper proposes a new artificial bee colony (NABC algorithm, which modifies the search pattern of both employed and onlooker bees. A solution pool is constructed by storing some best solutions of the current swarm. New candidate solutions are generated by searching the neighborhood of solutions randomly chosen from the solution pool. Experiments are conducted on a set of twelve benchmark functions. Simulation results show that our approach is significantly better or at least comparable to the original ABC and seven other stochastic algorithms.

  3. Application of the artificial bee colony algorithm for solving the set covering problem.

    Science.gov (United States)

    Crawford, Broderick; Soto, Ricardo; Cuesta, Rodrigo; Paredes, Fernando

    2014-01-01

    The set covering problem is a formal model for many practical optimization problems. In the set covering problem the goal is to choose a subset of the columns of minimal cost that covers every row. Here, we present a novel application of the artificial bee colony algorithm to solve the non-unicost set covering problem. The artificial bee colony algorithm is a recent swarm metaheuristic technique based on the intelligent foraging behavior of honey bees. Experimental results show that our artificial bee colony algorithm is competitive in terms of solution quality with other recent metaheuristic approaches for the set covering problem.

  4. How can bee colony algorithm serve medicine?

    Science.gov (United States)

    Salehahmadi, Zeinab; Manafi, Amir

    2014-07-01

    Healthcare professionals usually should make complex decisions with far reaching consequences and associated risks in health care fields. As it was demonstrated in other industries, the ability to drill down into pertinent data to explore knowledge behind the data can greatly facilitate superior, informed decisions to ensue the facts. Nature has always inspired researchers to develop models of solving the problems. Bee colony algorithm (BCA), based on the self-organized behavior of social insects is one of the most popular member of the family of population oriented, nature inspired meta-heuristic swarm intelligence method which has been proved its superiority over some other nature inspired algorithms. The objective of this model was to identify valid novel, potentially useful, and understandable correlations and patterns in existing data. This review employs a thematic analysis of online series of academic papers to outline BCA in medical hive, reducing the response and computational time and optimizing the problems. To illustrate the benefits of this model, the cases of disease diagnose system are presented.

  5. Artificial bee colony in neuro - Symbolic integration

    Science.gov (United States)

    Kasihmuddin, Mohd Shareduwan Mohd; Sathasivam, Saratha; Mansor, Mohd. Asyraf

    2017-08-01

    Swarm intelligence is a research area that models the population of the swarm based on natural computation. Artificial bee colony (ABC) algorithm is a swarm based metaheuristic algorithm introduced by Karaboga to optimize numerical problem. Pattern-SAT is a pattern reconstruction paradigm that utilized 2SAT logical rule in representing the behavior of the desired pattern. The information of the desired pattern in terms of 2SAT logic is embedded to Hopfield neural network (HNN-P2SAT) and the desired pattern is reconstructed during the retrieval phase. Since the performance of HNN-P2SAT in Pattern-SAT deteriorates when the number of 2SAT clause increased, newly improved ABC is used to reduce the computation burden during the learning phase of HNN-P2SAT (HNN-P2SATABC). The aim of this study is to investigate the performance of Pattern-SAT produced by ABC incorporated with HNN-P2SAT and compare it with conventional standalone HNN. The comparison is examined by using Microsoft Visual Basic C++ 2013 software. The detailed comparison in doing Pattern-SAT is discussed based on global Pattern-SAT, ratio of activated clauses and computation time. The result obtained from computer simulation indicates the beneficial features of HNN-P2SATABC in doing Pattern-SAT. This finding is expected to result in a significant implication on the choice of searching method used to do Pattern-SAT.

  6. Varroa-Virus Interaction in Collapsing Honey Bee Colonies

    DEFF Research Database (Denmark)

    Francis, Roy Mathew; Nielsen, Steen L.; Kryger, Per

    2013-01-01

    Varroa mites and viruses are the currently the high-profile suspects in collapsing bee colonies. Therefore, seasonal variation in varroa load and viruses (Acute-Kashmir-Israeli complex (AKI) and Deformed Wing Virus (DWV)) were monitored in a year-long study. We investigated the viral titres...... in honey bees and varroa mites from 23 colonies (15 apiaries) under three treatment conditions: Organic acids (11 colonies), pyrethroid (9 colonies) and untreated (3 colonies). Approximately 200 bees were sampled every month from April 2011 to October 2011, and April 2012. The 200 bees were split to 10...... subsamples of 20 bees and analysed separately, which allows us to determine the prevalence of virus-infected bees. The treatment efficacy was often low for both treatments. In colonies where varroa treatment reduced the mite load, colonies overwintered successfully, allowing the mites and viruses...

  7. Iridovirus and microsporidian linked to honey bee colony decline.

    Science.gov (United States)

    Bromenshenk, Jerry J; Henderson, Colin B; Wick, Charles H; Stanford, Michael F; Zulich, Alan W; Jabbour, Rabih E; Deshpande, Samir V; McCubbin, Patrick E; Seccomb, Robert A; Welch, Phillip M; Williams, Trevor; Firth, David R; Skowronski, Evan; Lehmann, Margaret M; Bilimoria, Shan L; Gress, Joanna; Wanner, Kevin W; Cramer, Robert A

    2010-10-06

    In 2010 Colony Collapse Disorder (CCD), again devastated honey bee colonies in the USA, indicating that the problem is neither diminishing nor has it been resolved. Many CCD investigations, using sensitive genome-based methods, have found small RNA bee viruses and the microsporidia, Nosema apis and N. ceranae in healthy and collapsing colonies alike with no single pathogen firmly linked to honey bee losses. We used Mass spectrometry-based proteomics (MSP) to identify and quantify thousands of proteins from healthy and collapsing bee colonies. MSP revealed two unreported RNA viruses in North American honey bees, Varroa destructor-1 virus and Kakugo virus, and identified an invertebrate iridescent virus (IIV) (Iridoviridae) associated with CCD colonies. Prevalence of IIV significantly discriminated among strong, failing, and collapsed colonies. In addition, bees in failing colonies contained not only IIV, but also Nosema. Co-occurrence of these microbes consistently marked CCD in (1) bees from commercial apiaries sampled across the U.S. in 2006-2007, (2) bees sequentially sampled as the disorder progressed in an observation hive colony in 2008, and (3) bees from a recurrence of CCD in Florida in 2009. The pathogen pairing was not observed in samples from colonies with no history of CCD, namely bees from Australia and a large, non-migratory beekeeping business in Montana. Laboratory cage trials with a strain of IIV type 6 and Nosema ceranae confirmed that co-infection with these two pathogens was more lethal to bees than either pathogen alone. These findings implicate co-infection by IIV and Nosema with honey bee colony decline, giving credence to older research pointing to IIV, interacting with Nosema and mites, as probable cause of bee losses in the USA, Europe, and Asia. We next need to characterize the IIV and Nosema that we detected and develop management practices to reduce honey bee losses.

  8. A Knowledge-Informed and Pareto-Based Artificial Bee Colony Optimization Algorithm for Multi-Objective Land-Use Allocation

    Directory of Open Access Journals (Sweden)

    Lina Yang

    2018-02-01

    Full Text Available Land-use allocation is of great significance in urban development. This type of allocation is usually considered to be a complex multi-objective spatial optimization problem, whose optimized result is a set of Pareto-optimal solutions (Pareto front reflecting different tradeoffs in several objectives. However, obtaining a Pareto front is a challenging task, and the Pareto front obtained by state-of-the-art algorithms is still not sufficient. To achieve better Pareto solutions, taking the grid-representative land-use allocation problem with two objectives as an example, an artificial bee colony optimization algorithm for multi-objective land-use allocation (ABC-MOLA is proposed. In this algorithm, the traditional ABC’s search direction guiding scheme and solution maintaining process are modified. In addition, a knowledge-informed neighborhood search strategy, which utilizes the auxiliary knowledge of natural geography and spatial structures to facilitate the neighborhood spatial search around each solution, is developed to further improve the Pareto front’s quality. A series of comparison experiments (a simulated experiment with small data volume and a real-world data experiment for a large area shows that all the Pareto fronts obtained by ABC-MOLA totally dominate the Pareto fronts by other algorithms, which demonstrates ABC-MOLA’s effectiveness in achieving Pareto fronts of high quality.

  9. Optimization and design of an aircraft’s morphing wing-tip demonstrator for drag reduction at low speed, Part I – Aerodynamic optimization using genetic, bee colony and gradient descent algorithms

    Directory of Open Access Journals (Sweden)

    Andreea Koreanschi

    2017-02-01

    Full Text Available In this paper, an ‘in-house’ genetic algorithm is described and applied to an optimization problem for improving the aerodynamic performances of an aircraft wing tip through upper surface morphing. The algorithm’s performances were studied from the convergence point of view, in accordance with design conditions. The algorithm was compared to two other optimization methods, namely the artificial bee colony and a gradient method, for two optimization objectives, and the results of the optimizations with each of the three methods were plotted on response surfaces obtained with the Monte Carlo method, to show that they were situated in the global optimum region. The optimization results for 16 wind tunnel test cases and 2 objective functions were presented. The 16 cases used for the optimizations were included in the experimental test plan for the morphing wing-tip demonstrator, and the results obtained using the displacements given by the optimizations were evaluated.

  10. Hybrid Bee Ant Colony Algorithm for Effective Load Balancing And ...

    African Journals Online (AJOL)

    PROF. OLIVER OSUAGWA

    Ant Colony algorithm is used in this hybrid Bee Ant Colony algorithm to solve load balancing issues ... Genetic Algorithm (MO-GA) for dynamic job scheduling that .... Information Networking and Applications Workshops. [7]. M. Dorigo & T.

  11. Occurrence of Nosema species in honey bee colonies in Kenya ...

    African Journals Online (AJOL)

    While honey bee colonies in North America and Europe are in decline due to parasites and ... Infections levels were higher in the coastal region than in the interior. ... of the impact of this pathogen to the Kenyan honey bee colonies with a view of ... Senegal (6); Sierra Leone (1); South Africa (96); South Sudan (1); Sudan (3) ...

  12. Urban Growth Modeling Using Cellular Automata with Multi-Temporal Remote Sensing Images Calibrated by the Artificial Bee Colony Optimization Algorithm.

    Science.gov (United States)

    Naghibi, Fereydoun; Delavar, Mahmoud Reza; Pijanowski, Bryan

    2016-12-14

    Cellular Automata (CA) is one of the most common techniques used to simulate the urbanization process. CA-based urban models use transition rules to deliver spatial patterns of urban growth and urban dynamics over time. Determining the optimum transition rules of the CA is a critical step because of the heterogeneity and nonlinearities existing among urban growth driving forces. Recently, new CA models integrated with optimization methods based on swarm intelligence algorithms were proposed to overcome this drawback. The Artificial Bee Colony (ABC) algorithm is an advanced meta-heuristic swarm intelligence-based algorithm. Here, we propose a novel CA-based urban change model that uses the ABC algorithm to extract optimum transition rules. We applied the proposed ABC-CA model to simulate future urban growth in Urmia (Iran) with multi-temporal Landsat images from 1997, 2006 and 2015. Validation of the simulation results was made through statistical methods such as overall accuracy, the figure of merit and total operating characteristics (TOC). Additionally, we calibrated the CA model by ant colony optimization (ACO) to assess the performance of our proposed model versus similar swarm intelligence algorithm methods. We showed that the overall accuracy and the figure of merit of the ABC-CA model are 90.1% and 51.7%, which are 2.9% and 8.8% higher than those of the ACO-CA model, respectively. Moreover, the allocation disagreement of the simulation results for the ABC-CA model is 9.9%, which is 2.9% less than that of the ACO-CA model. Finally, the ABC-CA model also outperforms the ACO-CA model with fewer quantity and allocation errors and slightly more hits.

  13. Colony Collapse Disorder (CCD) and bee age impact honey bee pathophysiology.

    Science.gov (United States)

    vanEngelsdorp, Dennis; Traynor, Kirsten S; Andree, Michael; Lichtenberg, Elinor M; Chen, Yanping; Saegerman, Claude; Cox-Foster, Diana L

    2017-01-01

    Honey bee (Apis mellifera) colonies continue to experience high annual losses that remain poorly explained. Numerous interacting factors have been linked to colony declines. Understanding the pathways linking pathophysiology with symptoms is an important step in understanding the mechanisms of disease. In this study we examined the specific pathologies associated with honey bees collected from colonies suffering from Colony Collapse Disorder (CCD) and compared these with bees collected from apparently healthy colonies. We identified a set of pathological physical characteristics that occurred at different rates in CCD diagnosed colonies prior to their collapse: rectum distension, Malpighian tubule iridescence, fecal matter consistency, rectal enteroliths (hard concretions), and venom sac color. The multiple differences in rectum symptomology in bees from CCD apiaries and colonies suggest effected bees had trouble regulating water. To ensure that pathologies we found associated with CCD were indeed pathologies and not due to normal changes in physical appearances that occur as an adult bee ages (CCD colonies are assumed to be composed mostly of young bees), we documented the changes in bees of different ages taken from healthy colonies. We found that young bees had much greater incidences of white nodules than older cohorts. Prevalent in newly-emerged bees, these white nodules or cellular encapsulations indicate an active immune response. Comparing the two sets of characteristics, we determined a subset of pathologies that reliably predict CCD status rather than bee age (fecal matter consistency, rectal distension size, rectal enteroliths and Malpighian tubule iridescence) and that may serve as biomarkers for colony health. In addition, these pathologies suggest that CCD bees are experiencing disrupted excretory physiology. Our identification of these symptoms is an important first step in understanding the physiological pathways that underlie CCD and factors

  14. Colony Collapse Disorder (CCD and bee age impact honey bee pathophysiology.

    Directory of Open Access Journals (Sweden)

    Dennis vanEngelsdorp

    Full Text Available Honey bee (Apis mellifera colonies continue to experience high annual losses that remain poorly explained. Numerous interacting factors have been linked to colony declines. Understanding the pathways linking pathophysiology with symptoms is an important step in understanding the mechanisms of disease. In this study we examined the specific pathologies associated with honey bees collected from colonies suffering from Colony Collapse Disorder (CCD and compared these with bees collected from apparently healthy colonies. We identified a set of pathological physical characteristics that occurred at different rates in CCD diagnosed colonies prior to their collapse: rectum distension, Malpighian tubule iridescence, fecal matter consistency, rectal enteroliths (hard concretions, and venom sac color. The multiple differences in rectum symptomology in bees from CCD apiaries and colonies suggest effected bees had trouble regulating water. To ensure that pathologies we found associated with CCD were indeed pathologies and not due to normal changes in physical appearances that occur as an adult bee ages (CCD colonies are assumed to be composed mostly of young bees, we documented the changes in bees of different ages taken from healthy colonies. We found that young bees had much greater incidences of white nodules than older cohorts. Prevalent in newly-emerged bees, these white nodules or cellular encapsulations indicate an active immune response. Comparing the two sets of characteristics, we determined a subset of pathologies that reliably predict CCD status rather than bee age (fecal matter consistency, rectal distension size, rectal enteroliths and Malpighian tubule iridescence and that may serve as biomarkers for colony health. In addition, these pathologies suggest that CCD bees are experiencing disrupted excretory physiology. Our identification of these symptoms is an important first step in understanding the physiological pathways that underlie CCD and

  15. Penentuan Letak dan Kapasitas Optimal Bank Kapasitor pada Jaring Transmisi 150 kV Sumatera Utara Menggunakan Artificial Bee Colony Algorithm

    Directory of Open Access Journals (Sweden)

    Andita Noor Shafira

    2017-01-01

    Full Text Available Listrik merupakan suatu kebutuhan mutlak yang harus dipenuhi untuk menjamin keberlangsungan hidup masyarakat masa kini. Kebutuhan ini terus meningkat seiring dengan pertumbuhan beban yang semakin bertambah dari tahun ke tahun. Pertumbuhan beban yang diikuti dengan peningkatan permintaan suplai daya reaktif akibat beban bersifat induktif meningkat menyebabkan perencanaan dan operasi dari sistem interkoneksi menjadi lebih kompleks sehingga kualitas sistem menjadi kurang dapat diandalkan. Aliran daya reaktif dapat menyebabkan drop tegangan dan kerugian daya dalam sistem transmisi. Untuk itu dilakukan penentuan letak dan kapasitas kapasitor shunt untuk mengurangi kerugian daya dengan menggunakan Newton-Raphson dan metode optimisasi Artificial Bee Colony Algorithm. Pada percobaan ini dilakukan pemasangan lima kapasitor dengan jumlah koloni sebesar 50 dan Max Cycle Number sebesar 150. Hasil simulasi menggunakan metode Artificial Bee Colony Algorithm menunjukkan bahwa pemasangan kapasitor pada Jaring Transmisi 150 kV Sumatera Utara dapat menurunkan kerugian daya aktif sebesar 8,37%.

  16. Short-term hydro generation scheduling of Xiluodu and Xiangjiaba cascade hydropower stations using improved binary-real coded bee colony optimization algorithm

    International Nuclear Information System (INIS)

    Lu, Peng; Zhou, Jianzhong; Wang, Chao; Qiao, Qi; Mo, Li

    2015-01-01

    Highlights: • STHGS problem is decomposed into two parallel sub-problems of UC and ELD. • Binary coded BCO is used to solve UC sub-problem with 0–1 discrete variables. • Real coded BCO is used to solve ELD sub-problem with continuous variables. • Some heuristic repairing strategies are designed to handle various constraints. • The STHGS of Xiluodu and Xiangjiaba cascade stations is solved by IB-RBCO. - Abstract: Short-term hydro generation scheduling (STHGS) of cascade hydropower stations is a typical nonlinear mixed integer optimization problem to minimize the total water consumption while simultaneously meeting the grid requirements and other hydraulic and electrical constraints. In this paper, STHGS problem is decomposed into two parallel sub-problems of unit commitment (UC) and economic load dispatch (ELD), and the methodology of improved binary-real coded bee colony optimization (IB-RBCO) algorithm is proposed to solve them. Firstly, the improved binary coded BCO is used to solve the UC sub-problem with 0–1 discrete variables, and the heuristic repairing strategy for unit state constrains is applied to generate the feasible unit commitment schedule. Then, the improved real coded BCO is used to solve the ELD sub-problem with continuous variables, and an effective method is introduced to handle various unit operation constraints. Especially, the new updating strategy of DE/best/2/bin method with dynamic parameter control mechanism is applied to real coded BCO to improve the search ability of IB-RBCO. Finally, to verify the feasibility and effectiveness of the proposed IB-RBCO method, it is applied to solve the STHGS problem of Xiluodu and Xiangjiaba cascaded hydropower stations, and the simulating results are compared with other intelligence algorithms. The simulation results demonstrate that the proposed IB-RBCO method can get higher-quality solutions with less water consumption and shorter calculating time when facing the complex STHGS problem

  17. Honey Bee Colonies Remote Monitoring System

    Directory of Open Access Journals (Sweden)

    Sergio Gil-Lebrero

    2016-12-01

    Full Text Available Bees are very important for terrestrial ecosystems and, above all, for the subsistence of many crops, due to their ability to pollinate flowers. Currently, the honey bee populations are decreasing due to colony collapse disorder (CCD. The reasons for CCD are not fully known, and as a result, it is essential to obtain all possible information on the environmental conditions surrounding the beehives. On the other hand, it is important to carry out such information gathering as non-intrusively as possible to avoid modifying the bees’ work conditions and to obtain more reliable data. We designed a wireless-sensor networks meet these requirements. We designed a remote monitoring system (called WBee based on a hierarchical three-level model formed by the wireless node, a local data server, and a cloud data server. WBee is a low-cost, fully scalable, easily deployable system with regard to the number and types of sensors and the number of hives and their geographical distribution. WBee saves the data in each of the levels if there are failures in communication. In addition, the nodes include a backup battery, which allows for further data acquisition and storage in the event of a power outage. Unlike other systems that monitor a single point of a hive, the system we present monitors and stores the temperature and relative humidity of the beehive in three different spots. Additionally, the hive is continuously weighed on a weighing scale. Real-time weight measurement is an innovation in wireless beehive—monitoring systems. We designed an adaptation board to facilitate the connection of the sensors to the node. Through the Internet, researchers and beekeepers can access the cloud data server to find out the condition of their hives in real time.

  18. Image steganalysis using Artificial Bee Colony algorithm

    Science.gov (United States)

    Sajedi, Hedieh

    2017-09-01

    Steganography is the science of secure communication where the presence of the communication cannot be detected while steganalysis is the art of discovering the existence of the secret communication. Processing a huge amount of information takes extensive execution time and computational sources most of the time. As a result, it is needed to employ a phase of preprocessing, which can moderate the execution time and computational sources. In this paper, we propose a new feature-based blind steganalysis method for detecting stego images from the cover (clean) images with JPEG format. In this regard, we present a feature selection technique based on an improved Artificial Bee Colony (ABC). ABC algorithm is inspired by honeybees' social behaviour in their search for perfect food sources. In the proposed method, classifier performance and the dimension of the selected feature vector depend on using wrapper-based methods. The experiments are performed using two large data-sets of JPEG images. Experimental results demonstrate the effectiveness of the proposed steganalysis technique compared to the other existing techniques.

  19. An optimal energy management system for islanded Microgrids based on multi-period artificial bee colony combined with Markov Chain

    DEFF Research Database (Denmark)

    Marzband, Mousa; Azarinejadian, Fatemeh; Savaghebi, Mehdi

    2017-01-01

    ) have been studied in many research activities. Moreover, a demand response (DR) expands customer participation to power systems and results in a paradigm shift from conventional to interactive activities in power systems due to the progress of smart grid technology. Therefore, the modeling......The optimal operation programming of electrical systems through the minimization of the production cost and the market clearing price, as well as the better utilization of renewable energy resources, has attracted the attention of many researchers. To reach this aim, energy management systems (EMSs...... of a consumer characteristic in the DR is becoming a very important issue in these systems. The customer information as the registration and participation information of the DR is used to provide additional indexes for evaluating the customer response, such as consumer's information based on the offer priority...

  20. Longitudinal Effects of Supplemental Forage on the Honey Bee (Apis mellifera) Microbiota and Inter- and Intra-Colony Variability.

    Science.gov (United States)

    Rothman, Jason A; Carroll, Mark J; Meikle, William G; Anderson, Kirk E; McFrederick, Quinn S

    2018-02-03

    Honey bees (Apis mellifera) provide vital pollination services for a variety of agricultural crops around the world and are known to host a consistent core bacterial microbiome. This symbiotic microbial community is essential to many facets of bee health, including likely nutrient acquisition, disease prevention and optimal physiological function. Being that the bee microbiome is likely involved in the digestion of nutrients, we either provided or excluded honey bee colonies from supplemental floral forage before being used for almond pollination. We then used 16S rRNA gene sequencing to examine the effects of forage treatment on the bees' microbial gut communities over four months. In agreement with previous studies, we found that the honey bee gut microbiota is quite stable over time. Similarly, we compared the gut communities of bees from separate colonies and sisters sampled from within the same hive over four months. Surprisingly, we found that the gut microbial communities of individual sisters from the same colony can exhibit as much variation as bees from different colonies. Supplemental floral forage had a subtle effect on the composition of the microbiome during the month of March only, with strains of Gilliamella apicola, Lactobacillus, and Bartonella being less proportionally abundant in bees exposed to forage in the winter. Collectively, our findings show that there is unexpected longitudinal variation within the gut microbial communities of sister honey bees and that supplemental floral forage can subtly alter the microbiome of managed honey bees.

  1. Winter Survival of Individual Honey Bees and Honey Bee Colonies Depends on Level of Varroa destructor Infestation

    Science.gov (United States)

    van Dooremalen, Coby; Gerritsen, Lonne; Cornelissen, Bram; van der Steen, Jozef J. M.; van Langevelde, Frank; Blacquière, Tjeerd

    2012-01-01

    Background Recent elevated winter loss of honey bee colonies is a major concern. The presence of the mite Varroa destructor in colonies places an important pressure on bee health. V. destructor shortens the lifespan of individual bees, while long lifespan during winter is a primary requirement to survive until the next spring. We investigated in two subsequent years the effects of different levels of V. destructor infestation during the transition from short-lived summer bees to long-lived winter bees on the lifespan of individual bees and the survival of bee colonies during winter. Colonies treated earlier in the season to reduce V. destructor infestation during the development of winter bees were expected to have longer bee lifespan and higher colony survival after winter. Methodology/Principal Findings Mite infestation was reduced using acaricide treatments during different months (July, August, September, or not treated). We found that the number of capped brood cells decreased drastically between August and November, while at the same time, the lifespan of the bees (marked cohorts) increased indicating the transition to winter bees. Low V. destructor infestation levels before and during the transition to winter bees resulted in an increase in lifespan of bees and higher colony survival compared to colonies that were not treated and that had higher infestation levels. A variety of stress-related factors could have contributed to the variation in longevity and winter survival that we found between years. Conclusions/Significance This study contributes to theory about the multiple causes for the recent elevated colony losses in honey bees. Our study shows the correlation between long lifespan of winter bees and colony loss in spring. Moreover, we show that colonies treated earlier in the season had reduced V. destructor infestation during the development of winter bees resulting in longer bee lifespan and higher colony survival after winter. PMID:22558421

  2. A Developed Artificial Bee Colony Algorithm Based on Cloud Model

    Directory of Open Access Journals (Sweden)

    Ye Jin

    2018-04-01

    Full Text Available The Artificial Bee Colony (ABC algorithm is a bionic intelligent optimization method. The cloud model is a kind of uncertainty conversion model between a qualitative concept T ˜ that is presented by nature language and its quantitative expression, which integrates probability theory and the fuzzy mathematics. A developed ABC algorithm based on cloud model is proposed to enhance accuracy of the basic ABC algorithm and avoid getting trapped into local optima by introducing a new select mechanism, replacing the onlooker bees’ search formula and changing the scout bees’ updating formula. Experiments on CEC15 show that the new algorithm has a faster convergence speed and higher accuracy than the basic ABC and some cloud model based ABC variants.

  3. Neonicotinoid pesticide reduces bumble bee colony growth and queen production.

    Science.gov (United States)

    Whitehorn, Penelope R; O'Connor, Stephanie; Wackers, Felix L; Goulson, Dave

    2012-04-20

    Growing evidence for declines in bee populations has caused great concern because of the valuable ecosystem services they provide. Neonicotinoid insecticides have been implicated in these declines because they occur at trace levels in the nectar and pollen of crop plants. We exposed colonies of the bumble bee Bombus terrestris in the laboratory to field-realistic levels of the neonicotinoid imidacloprid, then allowed them to develop naturally under field conditions. Treated colonies had a significantly reduced growth rate and suffered an 85% reduction in production of new queens compared with control colonies. Given the scale of use of neonicotinoids, we suggest that they may be having a considerable negative impact on wild bumble bee populations across the developed world.

  4. The neglected bee trees: European beech forests as a home for feral honey bee colonies

    Directory of Open Access Journals (Sweden)

    Patrick Laurenz Kohl

    2018-04-01

    Full Text Available It is a common belief that feral honey bee colonies (Apis mellifera L. were eradicated in Europe through the loss of habitats, domestication by man and spread of pathogens and parasites. Interestingly, no scientific data are available, neither about the past nor the present status of naturally nesting honeybee colonies. We expected near-natural beech (Fagus sylvatica L. forests to provide enough suitable nest sites to be a home for feral honey bee colonies in Europe. Here, we made a first assessment of their occurrence and density in two German woodland areas based on two methods, the tracing of nest sites based on forager flight routes (beelining technique, and the direct inspection of potential cavity trees. Further, we established experimental swarms at forest edges and decoded dances for nest sites performed by scout bees in order to study how far swarms from beekeeper-managed hives would potentially move into a forest. We found that feral honey bee colonies regularly inhabit tree cavities in near-natural beech forests at densities of at least 0.11–0.14 colonies/km2. Colonies were not confined to the forest edges; they were also living deep inside the forests. We estimated a median distance of 2,600 m from the bee trees to the next apiaries, while scout bees in experimental swarms communicated nest sites in close distances (median: 470 m. We extrapolate that there are several thousand feral honey bee colonies in German woodlands. These have to be taken in account when assessing the role of forest areas in providing pollination services to the surrounding land, and their occurrence has implications for the species’ perception among researchers, beekeepers and conservationists. This study provides a starting point for investigating the life-histories and the ecological interactions of honey bees in temperate European forest environments.

  5. The neglected bee trees: European beech forests as a home for feral honey bee colonies.

    Science.gov (United States)

    Kohl, Patrick Laurenz; Rutschmann, Benjamin

    2018-01-01

    It is a common belief that feral honey bee colonies ( Apis mellifera L.) were eradicated in Europe through the loss of habitats, domestication by man and spread of pathogens and parasites. Interestingly, no scientific data are available, neither about the past nor the present status of naturally nesting honeybee colonies. We expected near-natural beech ( Fagus sylvatica L.) forests to provide enough suitable nest sites to be a home for feral honey bee colonies in Europe. Here, we made a first assessment of their occurrence and density in two German woodland areas based on two methods, the tracing of nest sites based on forager flight routes (beelining technique), and the direct inspection of potential cavity trees. Further, we established experimental swarms at forest edges and decoded dances for nest sites performed by scout bees in order to study how far swarms from beekeeper-managed hives would potentially move into a forest. We found that feral honey bee colonies regularly inhabit tree cavities in near-natural beech forests at densities of at least 0.11-0.14 colonies/km 2 . Colonies were not confined to the forest edges; they were also living deep inside the forests. We estimated a median distance of 2,600 m from the bee trees to the next apiaries, while scout bees in experimental swarms communicated nest sites in close distances (median: 470 m). We extrapolate that there are several thousand feral honey bee colonies in German woodlands. These have to be taken in account when assessing the role of forest areas in providing pollination services to the surrounding land, and their occurrence has implications for the species' perception among researchers, beekeepers and conservationists. This study provides a starting point for investigating the life-histories and the ecological interactions of honey bees in temperate European forest environments.

  6. Training Spiking Neural Models Using Artificial Bee Colony

    Science.gov (United States)

    Vazquez, Roberto A.; Garro, Beatriz A.

    2015-01-01

    Spiking neurons are models designed to simulate, in a realistic manner, the behavior of biological neurons. Recently, it has been proven that this type of neurons can be applied to solve pattern recognition problems with great efficiency. However, the lack of learning strategies for training these models do not allow to use them in several pattern recognition problems. On the other hand, several bioinspired algorithms have been proposed in the last years for solving a broad range of optimization problems, including those related to the field of artificial neural networks (ANNs). Artificial bee colony (ABC) is a novel algorithm based on the behavior of bees in the task of exploring their environment to find a food source. In this paper, we describe how the ABC algorithm can be used as a learning strategy to train a spiking neuron aiming to solve pattern recognition problems. Finally, the proposed approach is tested on several pattern recognition problems. It is important to remark that to realize the powerfulness of this type of model only one neuron will be used. In addition, we analyze how the performance of these models is improved using this kind of learning strategy. PMID:25709644

  7. A Multiuser Detector Based on Artificial Bee Colony Algorithm for DS-UWB Systems

    Directory of Open Access Journals (Sweden)

    Zhendong Yin

    2013-01-01

    Full Text Available Artificial Bee Colony (ABC algorithm is an optimization algorithm based on the intelligent behavior of honey bee swarm. The ABC algorithm was developed to solve optimizing numerical problems and revealed premising results in processing time and solution quality. In ABC, a colony of artificial bees search for rich artificial food sources; the optimizing numerical problems are converted to the problem of finding the best parameter which minimizes an objective function. Then, the artificial bees randomly discover a population of initial solutions and then iteratively improve them by employing the behavior: moving towards better solutions by means of a neighbor search mechanism while abandoning poor solutions. In this paper, an efficient multiuser detector based on a suboptimal code mapping multiuser detector and artificial bee colony algorithm (SCM-ABC-MUD is proposed and implemented in direct-sequence ultra-wideband (DS-UWB systems under the additive white Gaussian noise (AWGN channel. The simulation results demonstrate that the BER and the near-far effect resistance performances of this proposed algorithm are quite close to those of the optimum multiuser detector (OMD while its computational complexity is much lower than that of OMD. Furthermore, the BER performance of SCM-ABC-MUD is not sensitive to the number of active users and can obtain a large system capacity.

  8. Colonies of Bumble Bees (Bombus impatiens Produce Fewer Workers, Less Bee Biomass, and Have Smaller Mother Queens Following Fungicide Exposure

    Directory of Open Access Journals (Sweden)

    Olivia M. Bernauer

    2015-06-01

    Full Text Available Bees provide vital pollination services to the majority of flowering plants in both natural and agricultural systems. Unfortunately, both native and managed bee populations are experiencing declines, threatening the persistence of these plants and crops. Agricultural chemicals are one possible culprit contributing to bee declines. Even fungicides, generally considered safe for bees, have been shown to disrupt honey bee development and impair bumble bee behavior. Little is known, however, how fungicides may affect bumble bee colony growth. We conducted a controlled cage study to determine the effects of fungicide exposure on colonies of a native bumble bee species (Bombus impatiens. Colonies of B. impatiens were exposed to flowers treated with field-relevant levels of the fungicide chlorothalonil over the course of one month. Colony success was assessed by the number and biomass of larvae, pupae, and adult bumble bees. Bumble bee colonies exposed to fungicide produced fewer workers, lower total bee biomass, and had lighter mother queens than control colonies. Our results suggest that fungicides negatively affect the colony success of a native bumble bee species and that the use of fungicides during bloom has the potential to severely impact the success of native bumble bee populations foraging in agroecosystems.

  9. Winter Survival of Individual Honey Bees and Honey Bee Colonies Depends on Level of Varroa destructor Infestation

    NARCIS (Netherlands)

    Dooremalen, van C.; Gerritsen, L.J.M.; Cornelissen, B.; Steen, van der J.J.M.; Langevelde, van F.; Blacquiere, T.

    2012-01-01

    Background: Recent elevated winter loss of honey bee colonies is a major concern. The presence of the mite Varroa destructor in colonies places an important pressure on bee health. V. destructor shortens the lifespan of individual bees, while long lifespan during winter is a primary requirement to

  10. Nutritional Effect of Alpha-Linolenic Acid on Honey Bee Colony Development (Apis Mellifera L.

    Directory of Open Access Journals (Sweden)

    Ma Lanting

    2015-12-01

    Full Text Available Alpha-linolenic acid (ALA, which is an n-3 polyunsaturated fatty acid (PUFA, influences honey bee feed intake and longevity. The objective of this study was to research the effect of six dietary ALA levels on the growth and development of Apis mellifera ligustica colonies. In the early spring, a total of 36 honey bee colonies of equal size and queen quality were randomly allocated into 6 groups. The six groups of honey bees were fed a basal diet with supplementation of ALA levels at 0 (group A, 2 (group B, 4 (group C, 6 (group D, 8 (group E, and 10% (group F. In this study, there were significant effects of pollen substitute ALA levels on the feeding amounts of the bee colony, colony population, sealed brood amount, and weight of newly emerged workers (P<0.05. The workers’ midgut Lipase (LPS activity of group C was significantly lower than that of the other groups (P<0.01. The worker bees in groups B, C, and D had significantly longer lifespans than those in the other groups (P<0.05. However, when the diets had ALA concentrations of more than 6%, the mortality of the honey bees increased (P<0.01. These results indicate that ALA levels of 2 ~ 4% of the pollen substitute were optimal for maintaining the highest reproductive performance and the digestion and absorption of fatty acids in honey bees during the period of spring multiplication. Additionally, ALA levels of 2 ~ 6% of the pollen substitute, improved worker bee longevity.

  11. Behavioral Modulation of Infestation by Varroa destructor in Bee Colonies. Implications for Colony Stability.

    Science.gov (United States)

    de Figueiró Santos, Joyce; Coelho, Flávio Codeço; Bliman, Pierre-Alexandre

    2016-01-01

    Colony Collapse Disorder (CCD) has become a global problem for beekeepers and for the crops that depend on bee pollination. While many factors are known to increase the risk of colony collapse, the ectoparasitic mite Varroa destructor is considered to be the most serious one. Although this mite is unlikely to cause the collapse of hives itself, it is the vector for many viral diseases which are among the likely causes for Colony Collapse Disorder. The effects of V. destructor infestation differ from one part of the world to another, with greater morbidity and higher colony losses in European honey bees (EHB) in Europe, Asia and North America. Although this mite has been present in Brazil for many years, there have been no reports of colony losses amongst Africanized Honey Bees (AHB). Studies carried out in Mexico have highlighted different behavioral responses by the AHB to the presence of the mite, notably as far as grooming and hygienic behavior are concerned. Could these explain why the AHB are less susceptible to Colony Collapse Disorder? In order to answer this question, we have developed a mathematical model of the infestation dynamics to analyze the role of resistance behavior by bees in the overall health of the colony, and as a consequence, its ability to face epidemiological challenges.

  12. Behavioral Modulation of Infestation by Varroa destructor in Bee Colonies. Implications for Colony Stability.

    Directory of Open Access Journals (Sweden)

    Joyce de Figueiró Santos

    Full Text Available Colony Collapse Disorder (CCD has become a global problem for beekeepers and for the crops that depend on bee pollination. While many factors are known to increase the risk of colony collapse, the ectoparasitic mite Varroa destructor is considered to be the most serious one. Although this mite is unlikely to cause the collapse of hives itself, it is the vector for many viral diseases which are among the likely causes for Colony Collapse Disorder. The effects of V. destructor infestation differ from one part of the world to another, with greater morbidity and higher colony losses in European honey bees (EHB in Europe, Asia and North America. Although this mite has been present in Brazil for many years, there have been no reports of colony losses amongst Africanized Honey Bees (AHB. Studies carried out in Mexico have highlighted different behavioral responses by the AHB to the presence of the mite, notably as far as grooming and hygienic behavior are concerned. Could these explain why the AHB are less susceptible to Colony Collapse Disorder? In order to answer this question, we have developed a mathematical model of the infestation dynamics to analyze the role of resistance behavior by bees in the overall health of the colony, and as a consequence, its ability to face epidemiological challenges.

  13. Sensitivity analyses for simulating pesticide impacts on honey bee colonies

    Science.gov (United States)

    We employ Monte Carlo simulation and sensitivity analysis techniques to describe the population dynamics of pesticide exposure to a honey bee colony using the VarroaPop + Pesticide model. Simulations are performed of hive population trajectories with and without pesti...

  14. Sensitivity analysis for simulating pesticide impacts on honey bee colonies

    Science.gov (United States)

    Background/Question/Methods Regulatory agencies assess risks to honey bees from pesticides through a tiered process that includes predictive modeling with empirical toxicity and chemical data of pesticides as a line of evidence. We evaluate the Varroapop colony model, proposed by...

  15. Optimasi Penjadwalan Pengerjaan Software Pada Software House Dengan Flow-Shop Problem Menggunakan Artificial Bee Colony

    Directory of Open Access Journals (Sweden)

    Muhammad Fhadli

    2016-12-01

    This research proposed an implementation related to software execution scheduling process at a software house with Flow-Shop Problem (FSP using Artificial Bee Colony (ABC algorithm. Which in FSP required a solution to complete some job/task along with its overall cost at a minimum. There is a constraint that should be kept to note in this research, that is the uncertainty completion time of its jobs. In this research, we will present a solution that is a sequence order of project execution with its overall completion time at a minimum. An experiment will be performed with 3 attempts on each experiment conditions, that is an experiment of iteration parameter and experiment of limit parameter. From this experiment, we concluded that the use of this algorithm explained in this paper can reduce project execution time if we increase the value of total iteration and total colony. Keywords: optimization, flow-shop problem, artificial bee colony, swarm intelligence, meta-heuristic.

  16. An artificial bee colony algorithm for the capacitated vehicle routing problem

    DEFF Research Database (Denmark)

    Szeto, W.Y.; Wu, Yongzhong; Ho, Sin C.

    2011-01-01

    This paper introduces an artificial bee colony heuristic for solving the capacitated vehicle routing problem. The artificial bee colony heuristic is a swarm-based heuristic, which mimics the foraging behavior of a honey bee swarm. An enhanced version of the artificial bee colony heuristic is also...... proposed to improve the solution quality of the original version. The performance of the enhanced heuristic is evaluated on two sets of standard benchmark instances, and compared with the original artificial bee colony heuristic. The computational results show that the enhanced heuristic outperforms...

  17. The fraction haemolymph vitellogenin of a honey bee colony, derived from a pooled haemolymph sample, a colony vitality parameter

    NARCIS (Netherlands)

    Steen, van der J.J.M.; Martel, Anne Claire; Hendrickx, Patrick

    2015-01-01

    The number of bees, amount of brood and haemolymph vitellogenin titre are parameters to establish the vitality of a honey bee colony. Increasing numbers of bees during summer until autumn; increasing amounts of brood in spring towards summer followed by a decrease; and low haemolymph vitellogenin

  18. Comparative testing of different methods for evaluation of Varroa destructor infestation of honey bee colonies

    Directory of Open Access Journals (Sweden)

    Nikolay D. Dobrynin

    2011-09-01

    Full Text Available Different methods for evaluation of the degree of Varroa destructor infestation of honey bee colonies were tested. The methods using in vivo evaluation were the most sparing for the bees but less precise. The methods using evaluation with the killing of the bees or brood were the most precise but less sparing for bees.

  19. Sudden deaths and colony population decline in Greek honey bee colonies.

    Science.gov (United States)

    Bacandritsos, N; Granato, A; Budge, G; Papanastasiou, I; Roinioti, E; Caldon, M; Falcaro, C; Gallina, A; Mutinelli, F

    2010-11-01

    During June and July of 2009, sudden deaths, tremulous movements and population declines of adult honey bees were reported by the beekeepers in the region of Peloponnesus (Mt. Mainalo), Greece. A preliminary study was carried out to investigate these unexplained phenomena in this region. In total, 37 bee samples, two brood frames containing honey bee brood of various ages, eight sugar samples and four sugar patties were collected from the affected colonies. The samples were tested for a range of pests, pathogens and pesticides. Symptomatic adult honey bees tested positive for Varroa destructor, Nosema ceranae, Chronic bee paralysis virus (CBPV), Acute paralysis virus (ABPV), Deformed wing virus (DWV), Sacbrood virus (SBV) and Black queen cell virus (BQCV), but negative for Acarapis woodi. American Foulbrood was absent from the brood samples. Chemical analysis revealed that amitraz, thiametoxan, clothianidin and acetamiprid were all absent from symptomatic adult bees, sugar and sugar patty samples. However, some bee samples, were contaminated with imidacloprid in concentrations between 14 ng/g and 39 ng/g tissue. We present: the infection of Greek honey bees by multiple viruses; the presence of N. ceranae in Greek honey bees and the first record of imidacloprid (neonicotonoid) residues in Greek honey bee tissues. The presence of multiple pathogens and pesticides made it difficult to associate a single specific cause to the depopulation phenomena observed in Greece, although we believe that viruses and N. ceranae synergistically played the most important role. A follow-up in-depth survey across all Greek regions is required to provide context to these preliminary findings. Copyright © 2010 Elsevier Inc. All rights reserved.

  20. Wintering Map for Honey Bee Colonies in El-Behera Governorate ...

    African Journals Online (AJOL)

    The geographical information system (GIS) has been used successfully in many studies to solve apicultural problems. The winter season is considered as a challenge for honey bee colonies due to the cold weather which cause the forfeiture of many colonies. The good wintering of honey bee colonies depends mainly on ...

  1. On the performance of an artificial bee colony optimization algorithm applied to the accident diagnosis in a PWR nuclear power plant

    Energy Technology Data Exchange (ETDEWEB)

    Oliveira, Iona Maghali S. de; Schirru, Roberto; Medeiros, Jose A.C.C., E-mail: maghali@lmp.ufrj.b, E-mail: schirru@lmp.ufrj.b, E-mail: canedo@lmp.ufrj.b [Universidade Federal do Rio de Janeiro (UFRJ), RJ (Brazil). Coordenacao dos Programas de Pos-Graduacao de Engenharia. Programa de Engenharia Nuclear

    2009-07-01

    The swarm-based algorithm described in this paper is a new search algorithm capable of locating good solutions efficiently and within a reasonable running time. The work presents a population-based search algorithm that mimics the food foraging behavior of honey bee swarms and can be regarded as belonging to the category of intelligent optimization tools. In its basic version, the algorithm performs a kind of random search combined with neighborhood search and can be used for solving multi-dimensional numeric problems. Following a description of the algorithm, this paper presents a new event classification system based exclusively on the ability of the algorithm to find the best centroid positions that correctly identifies an accident in a PWR nuclear power plant, thus maximizing the number of correct classification of transients. The simulation results show that the performance of the proposed algorithm is comparable to other population-based algorithms when applied to the same problem, with the advantage of employing fewer control parameters. (author)

  2. On the performance of an artificial bee colony optimization algorithm applied to the accident diagnosis in a PWR nuclear power plant

    International Nuclear Information System (INIS)

    Oliveira, Iona Maghali S. de; Schirru, Roberto; Medeiros, Jose A.C.C.

    2009-01-01

    The swarm-based algorithm described in this paper is a new search algorithm capable of locating good solutions efficiently and within a reasonable running time. The work presents a population-based search algorithm that mimics the food foraging behavior of honey bee swarms and can be regarded as belonging to the category of intelligent optimization tools. In its basic version, the algorithm performs a kind of random search combined with neighborhood search and can be used for solving multi-dimensional numeric problems. Following a description of the algorithm, this paper presents a new event classification system based exclusively on the ability of the algorithm to find the best centroid positions that correctly identifies an accident in a PWR nuclear power plant, thus maximizing the number of correct classification of transients. The simulation results show that the performance of the proposed algorithm is comparable to other population-based algorithms when applied to the same problem, with the advantage of employing fewer control parameters. (author)

  3. Implication of infectious agents and parasites in the Colony Collapse Disorder of the bee Apis mellifera

    OpenAIRE

    Giménez Bonillo, Sara; Universitat Autònoma de Barcelona. Facultat de Veterinària

    2014-01-01

    Pòster The Apis mellifera bee is a pollinator with a very important role and it is indispensable for the growth of the productivity of some agricultural crops. In the last years there is the worry for the increasing loss of mellifera bee colonies all over the world. The CCD (Colony Collapse Disorder) is a sudden death of bee colonies and, in many cases, swarm abandonment

  4. Queen introduction into the queenright honey bee colony

    Directory of Open Access Journals (Sweden)

    Antonín Přidal

    2010-01-01

    Full Text Available One of the actual elementary biologic principles of the introduction of queen is that the recipient co­lo­ny has to be queenless. We accidentally found that a queen can be accepted also in queenright co­lo­ny with using of the queen excluder. Therefore, we conducted two experiments with the introduction of queen in queenright colony.Under defined conditions of the experiment and with application of the queen excluder as a separator of queens we successfully introduced queen in the queenright colony. This result is discussed in relation to the general principle that a queen should be introduced only in a queenless colony. It is possible that there are some exceptions advert to the existence of some unknown biologic patterns in the honey bee biology and pheromones.

  5. Influence of feeding bee colonies on colony strenght and honey authenticity

    Directory of Open Access Journals (Sweden)

    Andreja KANDOLF BOROVŠAK

    2015-12-01

    Full Text Available For the natural development of bee colonies, there is the need for appropriate nutrition. Lack of natural honey flow must be supplemented by feeding bee colonies with sugar syrups or candy paste. This supplementary feeding encourages brood breeding and forage activity, whereby stronger colonies collect more honey. Sugar syrups can cause honey adulteration, which is more frequent with the reversing of the brood combs with the bee food, with the combs moved from the brood chamber to the upper chamber. Authentication of honey from the standpoint of the presence of sugar syrup is very complex, because there is no single method by which honey adulteration can be reliably confirmed. Feeding the colonies in spring should result in stronger colonies and hence the collection of more honey in the brood chambers. The objective of the present study was to determine whether this has effects also on honey authenticity, and to discover a simple method for detection of honey adulteration. The colonies were fed with candy paste that had added yeast and blue dye, to provide markers for detection of honey adulteration. The strength of the colonies and quantity of honey in the brood chambers were monitored. The results of the analysis of stable isotope and activity of foreign enzymes were compared with the results of yeast quantity and colour of the honey (absorbance, L*, a*, b* parameters. Detection of yeast in the honey samples and presence of colour as a consequence of added dye appear to be appropriate methods to follow honey adulteration, and further studies are ongoing.

  6. Territorial biodiversity and consequences on physico-chemical characteristics of pollen collected by honey bee colonies

    OpenAIRE

    Odoux, Jean Francois; Feuillet, Dalila; Aupinel, Pierrick; Loublier, Yves; Tasei, Jean Noel; Mateescu, Cristina

    2012-01-01

    International audience; Pollen resources may become a constraint for the honey bee in cereal farming agrosystems and thus influence honey bee colony development. This survey intended to increase knowledge on bee ecology in order to understand how farming systems can provide bee forage throughout the year. We conducted a 1-year study to investigate the flower range exploited in an agrarian environment in western France, the physico-chemical composition of honey bee-collected pollen, the territ...

  7. Pathogenesis of varroosis at the level of the honey bee (Apis mellifera) colony.

    Science.gov (United States)

    Wegener, J; Ruhnke, H; Scheller, K; Mispagel, S; Knollmann, U; Kamp, G; Bienefeld, K

    2016-01-01

    The parasitic mite Varroa destructor, in interaction with different viruses, is the main cause of honey bee colony mortality in most parts of the world. Here we studied how effects of individual-level parasitization are reflected by the bee colony as a whole. We measured disease progression in an apiary of 24 hives with differing degree of mite infestation, and investigated its relationship to 28 biometrical, physiological and biochemical indicators. In early summer, when the most heavily infested colonies already showed reduced growth, an elevated ratio of brood to bees, as well as a strong presence of phenoloxidase/prophenoloxidase in hive bees were found to be predictors of the time of colony collapse. One month later, the learning performance of worker bees as well as the activity of glucose oxidase measured from head extracts were significantly linked to the timing of colony collapse. Colonies at the brink of collapse were characterized by reduced weight of winter bees and a strong increase in their relative body water content. Our data confirm the importance of the immune system, known from studies of individually-infested bees, for the pathogenesis of varroosis at colony level. However, they also show that single-bee effects cannot always be extrapolated to the colony as a whole. This fact, together with the prominent role of colony-level factors like the ratio between brood and bees for disease progression, stress the importance of the superorganismal dimension of Varroa research. Copyright © 2016 Elsevier Ltd. All rights reserved.

  8. No intracolonial nepotism during colony fissioning in honey bees

    Science.gov (United States)

    Rangel, Juliana; Mattila, Heather R.; Seeley, Thomas D.

    2009-01-01

    Most species of social insects have singly mated queens, but in some species each queen mates with numerous males to create a colony whose workers belong to multiple patrilines. This colony genetic structure creates a potential for intracolonial nepotism. One context with great potential for such nepotism arises in species, like honey bees, whose colonies reproduce by fissioning. During fissioning, workers might nepotistically choose between serving a young (sister) queen or the old (mother) queen, preferring the former if she is a full-sister but the latter if the young queen is only a half-sister. We examined three honeybee colonies that swarmed, and performed paternity analyses on the young (immature) queens and samples of workers who either stayed with the young queens in the nest or left with the mother queen in the swarm. For each colony, we checked whether patrilines represented by immature queens had higher proportions of staying workers than patrilines not represented by immature queens. We found no evidence of this. The absence of intracolonial nepotism during colony fissioning could be because the workers cannot discriminate between full-sister and half-sister queens when they are immature, or because the costs of behaving nepotistically outweigh the benefits. PMID:19692398

  9. Blind Source Separation Based on Covariance Ratio and Artificial Bee Colony Algorithm

    Directory of Open Access Journals (Sweden)

    Lei Chen

    2014-01-01

    Full Text Available The computation amount in blind source separation based on bioinspired intelligence optimization is high. In order to solve this problem, we propose an effective blind source separation algorithm based on the artificial bee colony algorithm. In the proposed algorithm, the covariance ratio of the signals is utilized as the objective function and the artificial bee colony algorithm is used to solve it. The source signal component which is separated out, is then wiped off from mixtures using the deflation method. All the source signals can be recovered successfully by repeating the separation process. Simulation experiments demonstrate that significant improvement of the computation amount and the quality of signal separation is achieved by the proposed algorithm when compared to previous algorithms.

  10. Nutritional aspects of honey bee-collected pollen and constraints on colony development in the eastern Mediterranean

    OpenAIRE

    Shafir, S.; Avni, D.; Hendriksma, H.; Dag, A.; Uni, Z.; Avni, Dorit; Hendriksma, Harmen; Dag, Arnon; Uni, Zehava; Shafir, Sharoni

    2014-01-01

    Pollen is the main protein and lipid source for honey bees (Apis mellifera), and nutritionally impoverished landscapes pose a threat to colony development. To determine colony nutritional demands, we analyzed a yearly cycle of bee-collected pollen from colonies in the field and compared it to colony worker production and honey bee body composition, for the first time in social insects. We monitored monthly bee production in ten colonies at each of seven sites throughout Israel, and trapped po...

  11. Differential gene expression of two extreme honey bee (Apis mellifera) colonies showing varroa tolerance and susceptibility.

    Science.gov (United States)

    Jiang, S; Robertson, T; Mostajeran, M; Robertson, A J; Qiu, X

    2016-06-01

    Varroa destructor, an ectoparasitic mite of honey bees (Apis mellifera), is the most serious pest threatening the apiculture industry. In our honey bee breeding programme, two honey bee colonies showing extreme phenotypes for varroa tolerance/resistance (S88) and susceptibility (G4) were identified by natural selection from a large gene pool over a 6-year period. To investigate potential defence mechanisms for honey bee tolerance to varroa infestation, we employed DNA microarray and real time quantitative (PCR) analyses to identify differentially expressed genes in the tolerant and susceptible colonies at pupa and adult stages. Our results showed that more differentially expressed genes were identified in the tolerant bees than in bees from the susceptible colony, indicating that the tolerant colony showed an increased genetic capacity to respond to varroa mite infestation. In both colonies, there were more differentially expressed genes identified at the pupa stage than at the adult stage, indicating that pupa bees are more responsive to varroa infestation than adult bees. Genes showing differential expression in the colony phenotypes were categorized into several groups based on their molecular functions, such as olfactory signalling, detoxification processes, exoskeleton formation, protein degradation and long-chain fatty acid metabolism, suggesting that these biological processes play roles in conferring varroa tolerance to naturally selected colonies. Identification of differentially expressed genes between the two colony phenotypes provides potential molecular markers for selecting and breeding varroa-tolerant honey bees. © 2016 The Royal Entomological Society.

  12. Bee++: An Object-Oriented, Agent-Based Simulator for Honey Bee Colonies

    Directory of Open Access Journals (Sweden)

    Matthew Betti

    2017-03-01

    Full Text Available We present a model and associated simulation package (www.beeplusplus.ca to capture the natural dynamics of a honey bee colony in a spatially-explicit landscape, with temporally-variable, weather-dependent parameters. The simulation tracks bees of different ages and castes, food stores within the colony, pollen and nectar sources and the spatial position of individual foragers outside the hive. We track explicitly the intake of pesticides in individual bees and their ability to metabolize these toxins, such that the impact of sub-lethal doses of pesticides can be explored. Moreover, pathogen populations (in particular, Nosema apis, Nosema cerenae and Varroa mites have been included in the model and may be introduced at any time or location. The ability to study interactions among pesticides, climate, biodiversity and pathogens in this predictive framework should prove useful to a wide range of researchers studying honey bee populations. To this end, the simulation package is written in open source, object-oriented code (C++ and can be easily modified by the user. Here, we demonstrate the use of the model by exploring the effects of sub-lethal pesticide exposure on the flight behaviour of foragers.

  13. Multiobjective Optimization of Irreversible Thermal Engine Using Mutable Smart Bee Algorithm

    Directory of Open Access Journals (Sweden)

    M. Gorji-Bandpy

    2012-01-01

    Full Text Available A new method called mutable smart bee (MSB algorithm proposed for cooperative optimizing of the maximum power output (MPO and minimum entropy generation (MEG of an Atkinson cycle as a multiobjective, multi-modal mechanical problem. This method utilizes mutable smart bee instead of classical bees. The results have been checked with some of the most common optimizing algorithms like Karaboga’s original artificial bee colony, bees algorithm (BA, improved particle swarm optimization (IPSO, Lukasik firefly algorithm (LFFA, and self-adaptive penalty function genetic algorithm (SAPF-GA. According to obtained results, it can be concluded that Mutable Smart Bee (MSB is capable to maintain its historical memory for the location and quality of food sources and also a little chance of mutation is considered for this bee. These features were found as strong elements for mining data in constraint areas and the results will prove this claim.

  14. Colony Level Prevalence and Intensity of Nosema ceranae in Honey Bees (Apis mellifera L.)

    Science.gov (United States)

    Lucas, Hannah M.; Webster, Thomas C.; Sagili, Ramesh R.

    2016-01-01

    Nosema ceranae is a widely prevalent microsporidian parasite in the western honey bee. There is considerable uncertainty regarding infection dynamics of this important pathogen in honey bee colonies. Understanding the infection dynamics at the colony level may aid in development of a reliable sampling protocol for N. ceranae diagnosis, and provide insights into efficient treatment strategies. The primary objective of this study was to characterize the prevalence (proportion of the sampled bees found infected) and intensity (number of spores per bee) of N. ceranae infection in bees from various age cohorts in a colony. We examined N. ceranae infection in both overwintered colonies that were naturally infected with N. ceranae and in quadruple cohort nucleus colonies that were established and artificially inoculated with N. ceranae. We also examined and quantified effects of N. ceranae infection on hypopharyngeal gland protein content and gut pH. There was no correlation between the prevalence and intensity of N. ceranae infection in composite samples (pooled bee samples used for analysis). Our results indicated that the prevalence and intensity of N. ceranae infection is significantly influenced by honey bee age. The N. ceranae infection prevalence values from composite samples of background bees (unmarked bees collected from four different locations in a colony) were not significantly different from those pertaining to marked-bee age cohorts specific to each sampling date. The foraging-aged bees had a higher prevalence of N. ceranae infection when compared to nurse-aged bees. N. ceranae did not have a significant effect on hypopharyngeal gland protein content. Further, there was no significant difference in mean gut pH of N. ceranae infected bees and non-infected bees. This study provides comprehensive insights into N. ceranae infection dynamics at the colony level, and also demonstrates the effects of N. ceranae infection on hypopharyngeal gland protein content and

  15. Colony Level Prevalence and Intensity of Nosema ceranae in Honey Bees (Apis mellifera L..

    Directory of Open Access Journals (Sweden)

    Cameron J Jack

    Full Text Available Nosema ceranae is a widely prevalent microsporidian parasite in the western honey bee. There is considerable uncertainty regarding infection dynamics of this important pathogen in honey bee colonies. Understanding the infection dynamics at the colony level may aid in development of a reliable sampling protocol for N. ceranae diagnosis, and provide insights into efficient treatment strategies. The primary objective of this study was to characterize the prevalence (proportion of the sampled bees found infected and intensity (number of spores per bee of N. ceranae infection in bees from various age cohorts in a colony. We examined N. ceranae infection in both overwintered colonies that were naturally infected with N. ceranae and in quadruple cohort nucleus colonies that were established and artificially inoculated with N. ceranae. We also examined and quantified effects of N. ceranae infection on hypopharyngeal gland protein content and gut pH. There was no correlation between the prevalence and intensity of N. ceranae infection in composite samples (pooled bee samples used for analysis. Our results indicated that the prevalence and intensity of N. ceranae infection is significantly influenced by honey bee age. The N. ceranae infection prevalence values from composite samples of background bees (unmarked bees collected from four different locations in a colony were not significantly different from those pertaining to marked-bee age cohorts specific to each sampling date. The foraging-aged bees had a higher prevalence of N. ceranae infection when compared to nurse-aged bees. N. ceranae did not have a significant effect on hypopharyngeal gland protein content. Further, there was no significant difference in mean gut pH of N. ceranae infected bees and non-infected bees. This study provides comprehensive insights into N. ceranae infection dynamics at the colony level, and also demonstrates the effects of N. ceranae infection on hypopharyngeal gland

  16. Genetic Bee Colony (GBC) algorithm: A new gene selection method for microarray cancer classification.

    Science.gov (United States)

    Alshamlan, Hala M; Badr, Ghada H; Alohali, Yousef A

    2015-06-01

    Naturally inspired evolutionary algorithms prove effectiveness when used for solving feature selection and classification problems. Artificial Bee Colony (ABC) is a relatively new swarm intelligence method. In this paper, we propose a new hybrid gene selection method, namely Genetic Bee Colony (GBC) algorithm. The proposed algorithm combines the used of a Genetic Algorithm (GA) along with Artificial Bee Colony (ABC) algorithm. The goal is to integrate the advantages of both algorithms. The proposed algorithm is applied to a microarray gene expression profile in order to select the most predictive and informative genes for cancer classification. In order to test the accuracy performance of the proposed algorithm, extensive experiments were conducted. Three binary microarray datasets are use, which include: colon, leukemia, and lung. In addition, another three multi-class microarray datasets are used, which are: SRBCT, lymphoma, and leukemia. Results of the GBC algorithm are compared with our recently proposed technique: mRMR when combined with the Artificial Bee Colony algorithm (mRMR-ABC). We also compared the combination of mRMR with GA (mRMR-GA) and Particle Swarm Optimization (mRMR-PSO) algorithms. In addition, we compared the GBC algorithm with other related algorithms that have been recently published in the literature, using all benchmark datasets. The GBC algorithm shows superior performance as it achieved the highest classification accuracy along with the lowest average number of selected genes. This proves that the GBC algorithm is a promising approach for solving the gene selection problem in both binary and multi-class cancer classification. Copyright © 2015 Elsevier Ltd. All rights reserved.

  17. Late winter feeding stimulates rapid spring development of carniolan honey bee colonies (Apis mellifera carnica

    Directory of Open Access Journals (Sweden)

    Zlatko Puškadija

    2017-01-01

    Full Text Available Unfavourable weather conditions after the queen starts with intensive oviposition during early spring may cause an imbalance in the division of tasks among worker bees in the bee colony. This can lead to slow spring development and poor exploitation of the main spring nectar flows. In order to accelerate the spring development, it is necessary, as a technological measure, to feed supplemental candy to bee colonies. In this research, the necessity of supplemental feeding, as well as the composition of candy (pollen and protein substitute were analysed. Three groups of ten bee colonies each were formed - the control, unfed group, pollen candy fed and protein substitute candy fed. In the period from 22/02/2016 and 04/04/2016 three control measurements were performed during which the number of bees, the number of brood cells and weight of the bee colonies were determined. The research has shown that supplemental feeding of the bee colony in late winter in order to encourage the rapid spring development is justified. Namely, at the final measurements in April, the results showed differences between groups. The treated colonies had higher net hive weight, a greater number of bees and statistically significantly more brood cells. The results of this study confirm that the technological measure of supplemental feeding in late winter should be performed on all commercial apiaries for the production of honey, pollen, royal jelly, queen bees and bee venom.

  18. A Modified Artificial Bee Colony Algorithm Application for Economic Environmental Dispatch

    Science.gov (United States)

    Tarafdar Hagh, M.; Baghban Orandi, Omid

    2018-03-01

    In conventional fossil-fuel power systems, the economic environmental dispatch (EED) problem is a major problem that optimally determines the output power of generating units in a way that cost of total production and emission level be minimized simultaneously, and at the same time all the constraints of units and system are satisfied properly. To solve EED problem which is a non-convex optimization problem, a modified artificial bee colony (MABC) algorithm is proposed in this paper. This algorithm by implementing weighted sum method is applied on two test systems, and eventually, obtained results are compared with other reported results. Comparison of results confirms superiority and efficiency of proposed method clearly.

  19. Detection of Spiroplasma melliferum in honey bee colonies in the US.

    Science.gov (United States)

    Zheng, Huo-Qing; Chen, Yan Ping

    2014-06-01

    Spiroplasma infections in honey bees have been reported in Europe and Asia quite recently, due to intensive studies on the epidemiology of honey bee diseases. The situation in the US is less well analyzed. Here, we examined the honey bee colonies in Beltsville, MD, where Spiroplasmamelliferum was originally reported and found S. melliferum infection in honey bees. Our data showed high variation of S. melliferum infection in honey bees with a peak prevalence in May during the course of one-year study period. The colony prevalence increased from 5% in February to 68% in May and then decreased to 25% in June and 22% in July. Despite that pathogenicity of spiroplasmas in honey bee colonies remains to be determined, our results indicated that spiroplasma infections need to be included for the consideration of the impacts on honey bee health. Published by Elsevier Inc.

  20. Uncertain multiobjective redundancy allocation problem of repairable systems based on artificial bee colony algorithm

    Institute of Scientific and Technical Information of China (English)

    Guo Jiansheng; Wang Zutong; Zheng Mingfa; Wang Ying

    2014-01-01

    Based on the uncertainty theory, this paper is devoted to the redundancy allocation problem in repairable parallel-series systems with uncertain factors, where the failure rate, repair rate and other relative coefficients involved are considered as uncertain variables. The availability of the system and the corresponding designing cost are considered as two optimization objectives. A crisp multiobjective optimization formulation is presented on the basis of uncertainty theory to solve this resultant problem. For solving this problem efficiently, a new multiobjective artificial bee colony algorithm is proposed to search the Pareto efficient set, which introduces rank value and crowding distance in the greedy selection strategy, applies fast non-dominated sort procedure in the exploitation search and inserts tournament selection in the onlooker bee phase. It shows that the proposed algorithm outperforms NSGA-II greatly and can solve multiobjective redundancy allocation problem efficiently. Finally, a numerical example is provided to illustrate this approach.

  1. Parameter identification of piezoelectric hysteresis model based on improved artificial bee colony algorithm

    Science.gov (United States)

    Wang, Geng; Zhou, Kexin; Zhang, Yeming

    2018-04-01

    The widely used Bouc-Wen hysteresis model can be utilized to accurately simulate the voltage-displacement curves of piezoelectric actuators. In order to identify the unknown parameters of the Bouc-Wen model, an improved artificial bee colony (IABC) algorithm is proposed in this paper. A guiding strategy for searching the current optimal position of the food source is proposed in the method, which can help balance the local search ability and global exploitation capability. And the formula for the scout bees to search for the food source is modified to increase the convergence speed. Some experiments were conducted to verify the effectiveness of the IABC algorithm. The results show that the identified hysteresis model agreed well with the actual actuator response. Moreover, the identification results were compared with the standard particle swarm optimization (PSO) method, and it can be seen that the search performance in convergence rate of the IABC algorithm is better than that of the standard PSO method.

  2. Late winter feeding stimulates rapid spring development of carniolan honey bee colonies (Apis mellifera carnica)

    OpenAIRE

    Zlatko Puškadija; Lejla Spiljak; Marin Kovačić

    2017-01-01

    Unfavourable weather conditions after the queen starts with intensive oviposition during early spring may cause an imbalance in the division of tasks among worker bees in the bee colony. This can lead to slow spring development and poor exploitation of the main spring nectar flows. In order to accelerate the spring development, it is necessary, as a technological measure, to feed supplemental candy to bee colonies. In this research, the necessity of supplemental feeding, as well as the com...

  3. Archimedean copula estimation of distribution algorithm based on artificial bee colony algorithm

    Institute of Scientific and Technical Information of China (English)

    Haidong Xu; Mingyan Jiang; Kun Xu

    2015-01-01

    The artificial bee colony (ABC) algorithm is a com-petitive stochastic population-based optimization algorithm. How-ever, the ABC algorithm does not use the social information and lacks the knowledge of the problem structure, which leads to in-sufficiency in both convergent speed and searching precision. Archimedean copula estimation of distribution algorithm (ACEDA) is a relatively simple, time-economic and multivariate correlated EDA. This paper proposes a novel hybrid algorithm based on the ABC algorithm and ACEDA cal ed Archimedean copula estima-tion of distribution based on the artificial bee colony (ACABC) algorithm. The hybrid algorithm utilizes ACEDA to estimate the distribution model and then uses the information to help artificial bees to search more efficiently in the search space. Six bench-mark functions are introduced to assess the performance of the ACABC algorithm on numerical function optimization. Experimen-tal results show that the ACABC algorithm converges much faster with greater precision compared with the ABC algorithm, ACEDA and the global best (gbest)-guided ABC (GABC) algorithm in most of the experiments.

  4. Mobile Ad Hoc Network Energy Cost Algorithm Based on Artificial Bee Colony

    Directory of Open Access Journals (Sweden)

    Mustafa Tareq

    2017-01-01

    Full Text Available A mobile ad hoc network (MANET is a collection of mobile nodes that dynamically form a temporary network without using any existing network infrastructure. MANET selects a path with minimal number of intermediate nodes to reach the destination node. As the distance between each node increases, the quantity of transmission power increases. The power level of nodes affects the simplicity with which a route is constituted between a couple of nodes. This study utilizes the swarm intelligence technique through the artificial bee colony (ABC algorithm to optimize the energy consumption in a dynamic source routing (DSR protocol in MANET. The proposed algorithm is called bee DSR (BEEDSR. The ABC algorithm is used to identify the optimal path from the source to the destination to overcome energy problems. The performance of the BEEDSR algorithm is compared with DSR and bee-inspired protocols (BeeIP. The comparison was conducted based on average energy consumption, average throughput, average end-to-end delay, routing overhead, and packet delivery ratio performance metrics, varying the node speed and packet size. The BEEDSR algorithm is superior in performance than other protocols in terms of energy conservation and delay degradation relating to node speed and packet size.

  5. Electric load forecasting by seasonal recurrent SVR (support vector regression) with chaotic artificial bee colony algorithm

    International Nuclear Information System (INIS)

    Hong, Wei-Chiang

    2011-01-01

    Support vector regression (SVR), with hybrid chaotic sequence and evolutionary algorithms to determine suitable values of its three parameters, not only can effectively avoid converging prematurely (i.e., trapping into a local optimum), but also reveals its superior forecasting performance. Electric load sometimes demonstrates a seasonal (cyclic) tendency due to economic activities or climate cyclic nature. The applications of SVR models to deal with seasonal (cyclic) electric load forecasting have not been widely explored. In addition, the concept of recurrent neural networks (RNNs), focused on using past information to capture detailed information, is helpful to be combined into an SVR model. This investigation presents an electric load forecasting model which combines the seasonal recurrent support vector regression model with chaotic artificial bee colony algorithm (namely SRSVRCABC) to improve the forecasting performance. The proposed SRSVRCABC employs the chaotic behavior of honey bees which is with better performance in function optimization to overcome premature local optimum. A numerical example from an existed reference is used to elucidate the forecasting performance of the proposed SRSVRCABC model. The forecasting results indicate that the proposed model yields more accurate forecasting results than ARIMA and TF-ε-SVR-SA models. Therefore, the SRSVRCABC model is a promising alternative for electric load forecasting. -- Highlights: → Hybridizing the seasonal adjustment and the recurrent mechanism into an SVR model. → Employing chaotic sequence to improve the premature convergence of artificial bee colony algorithm. → Successfully providing significant accurate monthly load demand forecasting.

  6. A stereo remote sensing feature selection method based on artificial bee colony algorithm

    Science.gov (United States)

    Yan, Yiming; Liu, Pigang; Zhang, Ye; Su, Nan; Tian, Shu; Gao, Fengjiao; Shen, Yi

    2014-05-01

    To improve the efficiency of stereo information for remote sensing classification, a stereo remote sensing feature selection method is proposed in this paper presents, which is based on artificial bee colony algorithm. Remote sensing stereo information could be described by digital surface model (DSM) and optical image, which contain information of the three-dimensional structure and optical characteristics, respectively. Firstly, three-dimensional structure characteristic could be analyzed by 3D-Zernike descriptors (3DZD). However, different parameters of 3DZD could descript different complexity of three-dimensional structure, and it needs to be better optimized selected for various objects on the ground. Secondly, features for representing optical characteristic also need to be optimized. If not properly handled, when a stereo feature vector composed of 3DZD and image features, that would be a lot of redundant information, and the redundant information may not improve the classification accuracy, even cause adverse effects. To reduce information redundancy while maintaining or improving the classification accuracy, an optimized frame for this stereo feature selection problem is created, and artificial bee colony algorithm is introduced for solving this optimization problem. Experimental results show that the proposed method can effectively improve the computational efficiency, improve the classification accuracy.

  7. Double evolutsional artificial bee colony algorithm for multiple traveling salesman problem

    Directory of Open Access Journals (Sweden)

    Xue Ming Hao

    2016-01-01

    Full Text Available The double evolutional artificial bee colony algorithm (DEABC is proposed for solving the single depot multiple traveling salesman problem (MTSP. The proposed DEABC algorithm, which takes advantage of the strength of the upgraded operators, is characterized by its guidance in exploitation search and diversity in exploration search. The double evolutional process for exploitation search is composed of two phases of half stochastic optimal search, and the diversity generating operator for exploration search is used for solutions which cannot be improved after limited times. The computational results demonstrated the superiority of our algorithm over previous state-of-the-art methods.

  8. Causes and Scale of Winter Flights in Honey Bee (Apis Mellifera Carnica Colonies

    Directory of Open Access Journals (Sweden)

    Węgrzynowicz Paweł

    2014-06-01

    Full Text Available Winter honey bee losses were evaluated during the two overwintering periods of 2009/2010 and 2010/2011. The research included dead bee workers that fell on the hive bottom board (debris and the ones that flew out of the hive. Differences were observed in the number of bees fallen as debris between the two periods, whereas the number of bees flying out was similar in both years. No differences were found between the numbers of dead bees in strong and weak colonies. The percentage of bees flying out of the colony increased in the presence of Nosema spores, Varroa infestation, increased average air temperature, and insolation during the day. In addition, both the presence of Nosema and insolation during the day had an impact on the number of bees that died and fell on the hive board.

  9. An artificial bee colony algorithm for uncertain portfolio selection.

    Science.gov (United States)

    Chen, Wei

    2014-01-01

    Portfolio selection is an important issue for researchers and practitioners. In this paper, under the assumption that security returns are given by experts' evaluations rather than historical data, we discuss the portfolio adjusting problem which takes transaction costs and diversification degree of portfolio into consideration. Uncertain variables are employed to describe the security returns. In the proposed mean-variance-entropy model, the uncertain mean value of the return is used to measure investment return, the uncertain variance of the return is used to measure investment risk, and the entropy is used to measure diversification degree of portfolio. In order to solve the proposed model, a modified artificial bee colony (ABC) algorithm is designed. Finally, a numerical example is given to illustrate the modelling idea and the effectiveness of the proposed algorithm.

  10. Improved artificial bee colony algorithm based gravity matching navigation method.

    Science.gov (United States)

    Gao, Wei; Zhao, Bo; Zhou, Guang Tao; Wang, Qiu Ying; Yu, Chun Yang

    2014-07-18

    Gravity matching navigation algorithm is one of the key technologies for gravity aided inertial navigation systems. With the development of intelligent algorithms, the powerful search ability of the Artificial Bee Colony (ABC) algorithm makes it possible to be applied to the gravity matching navigation field. However, existing search mechanisms of basic ABC algorithms cannot meet the need for high accuracy in gravity aided navigation. Firstly, proper modifications are proposed to improve the performance of the basic ABC algorithm. Secondly, a new search mechanism is presented in this paper which is based on an improved ABC algorithm using external speed information. At last, modified Hausdorff distance is introduced to screen the possible matching results. Both simulations and ocean experiments verify the feasibility of the method, and results show that the matching rate of the method is high enough to obtain a precise matching position.

  11. Artificial bee colony algorithm with dynamic multi-population

    Science.gov (United States)

    Zhang, Ming; Ji, Zhicheng; Wang, Yan

    2017-07-01

    To improve the convergence rate and make a balance between the global search and local turning abilities, this paper proposes a decentralized form of artificial bee colony (ABC) algorithm with dynamic multi-populations by means of fuzzy C-means (FCM) clustering. Each subpopulation periodically enlarges with the same size during the search process, and the overlapping individuals among different subareas work for delivering information acting as exploring the search space with diffusion of solutions. Moreover, a Gaussian-based search equation with redefined local attractor is proposed to further accelerate the diffusion of the best solution and guide the search towards potential areas. Experimental results on a set of benchmarks demonstrate the competitive performance of our proposed approach.

  12. Lévy flight artificial bee colony algorithm

    Science.gov (United States)

    Sharma, Harish; Bansal, Jagdish Chand; Arya, K. V.; Yang, Xin-She

    2016-08-01

    Artificial bee colony (ABC) optimisation algorithm is a relatively simple and recent population-based probabilistic approach for global optimisation. The solution search equation of ABC is significantly influenced by a random quantity which helps in exploration at the cost of exploitation of the search space. In the ABC, there is a high chance to skip the true solution due to its large step sizes. In order to balance between diversity and convergence in the ABC, a Lévy flight inspired search strategy is proposed and integrated with ABC. The proposed strategy is named as Lévy Flight ABC (LFABC) has both the local and global search capability simultaneously and can be achieved by tuning the Lévy flight parameters and thus automatically tuning the step sizes. In the LFABC, new solutions are generated around the best solution and it helps to enhance the exploitation capability of ABC. Furthermore, to improve the exploration capability, the numbers of scout bees are increased. The experiments on 20 test problems of different complexities and five real-world engineering optimisation problems show that the proposed strategy outperforms the basic ABC and recent variants of ABC, namely, Gbest-guided ABC, best-so-far ABC and modified ABC in most of the experiments.

  13. Pathogen prevalence and abundance in honey bee colonies involved in almond pollination.

    Science.gov (United States)

    Cavigli, Ian; Daughenbaugh, Katie F; Martin, Madison; Lerch, Michael; Banner, Katie; Garcia, Emma; Brutscher, Laura M; Flenniken, Michelle L

    Honey bees are important pollinators of agricultural crops. Since 2006, US beekeepers have experienced high annual honey bee colony losses, which may be attributed to multiple abiotic and biotic factors, including pathogens. However, the relative importance of these factors has not been fully elucidated. To identify the most prevalent pathogens and investigate the relationship between colony strength and health, we assessed pathogen occurrence, prevalence, and abundance in Western US honey bee colonies involved in almond pollination. The most prevalent pathogens were Black queen cell virus (BQCV), Lake Sinai virus 2 (LSV2), Sacbrood virus (SBV), Nosema ceranae , and trypanosomatids. Our results indicated that pathogen prevalence and abundance were associated with both sampling date and beekeeping operation, that prevalence was highest in honey bee samples obtained immediately after almond pollination, and that weak colonies had a greater mean pathogen prevalence than strong colonies.

  14. A Comparative Study of Improved Artificial Bee Colony Algorithms Applied to Multilevel Image Thresholding

    Directory of Open Access Journals (Sweden)

    Kanjana Charansiriphaisan

    2013-01-01

    Full Text Available Multilevel thresholding is a highly useful tool for the application of image segmentation. Otsu’s method, a common exhaustive search for finding optimal thresholds, involves a high computational cost. There has been a lot of recent research into various meta-heuristic searches in the area of optimization research. This paper analyses and discusses using a family of artificial bee colony algorithms, namely, the standard ABC, ABC/best/1, ABC/best/2, IABC/best/1, IABC/rand/1, and CABC, and some particle swarm optimization-based algorithms for searching multilevel thresholding. The strategy for an onlooker bee to select an employee bee was modified to serve our purposes. The metric measures, which are used to compare the algorithms, are the maximum number of function calls, successful rate, and successful performance. The ranking was performed by Friedman ranks. The experimental results showed that IABC/best/1 outperformed the other techniques when all of them were applied to multilevel image thresholding. Furthermore, the experiments confirmed that IABC/best/1 is a simple, general, and high performance algorithm.

  15. Effect of a home-made pollen substitute on honey bee colony development

    NARCIS (Netherlands)

    Steen, van der J.J.M.

    2007-01-01

    In 2001 and 2002, studies were conducted on a pollen substitute formulated for easy home preparation. Tests were done with free flying honey bee colonies. In 2001, pollen supply was restricted with pollen traps in 9 experimental colonies. Colonies were then equally divided among three treatments:

  16. Distribution of Paenibacillus larvae spores inside honey bee colonies and its relevance for diagnosis.

    Science.gov (United States)

    Gillard, M; Charriere, J D; Belloy, L

    2008-09-01

    One of the most important factors affecting the development of honey bee colonies is infectious diseases such as American foulbrood (AFB) caused by the spore forming Gram-positive bacterium Paenibacillus larvae. Colony inspections for AFB clinical symptoms are time consuming. Moreover, diseased cells in the early stages of the infection may easily be overlooked. In this study, we investigated whether it is possible to determine the sanitary status of a colony based on analyses of different materials collected from the hive. We analysed 237 bee samples and 67 honey samples originating from 71 colonies situated in 13 apiaries with clinical AFB occurrences. We tested whether a difference in spore load among bees inside the whole hive exists and which sample material related to its location inside the hive was the most appropriate for an early AFB diagnosis based on the culture method. Results indicated that diagnostics based on analysis of honey samples and bees collected at the hive entrance are of limited value as only 86% and 83%, respectively, of samples from AFB-symptomatic colonies were positive. Analysis of bee samples collected from the brood nest, honey chamber, and edge frame allowed the detection of all colonies showing AFB clinical symptoms. Microbiological analysis showed that more than one quarter of samples collected from colonies without AFB clinical symptoms were positive for P. larvae. Based on these results, we recommend investigating colonies by testing bee samples from the brood nest, edge frame or honey chamber for P. larvae spores.

  17. Application of Artificial Bee Colony in Model Parameter Identification of Solar Cells

    Directory of Open Access Journals (Sweden)

    Rongjie Wang

    2015-07-01

    Full Text Available The identification of values of solar cell parameters is of great interest for evaluating solar cell performances. The algorithm of an artificial bee colony was used to extract model parameters of solar cells from current-voltage characteristics. Firstly, the best-so-for mechanism was introduced to the original artificial bee colony. Then, a method was proposed to identify parameters for a single diode model and double diode model using this improved artificial bee colony. Experimental results clearly demonstrate the effectiveness of the proposed method and its superior performance compared to other competing methods.

  18. Dynamics of the Presence of Israeli Acute Paralysis Virus in Honey Bee Colonies with Colony Collapse Disorder

    Directory of Open Access Journals (Sweden)

    Chunsheng Hou

    2014-05-01

    Full Text Available The determinants of Colony Collapse Disorder (CCD, a particular case of collapse of honey bee colonies, are still unresolved. Viruses including the Israeli acute paralysis virus (IAPV were associated with CCD. We found an apiary with colonies showing typical CCD characteristics that bore high loads of IAPV, recovered some colonies from collapse and tested the hypothesis if IAPV was actively replicating in them and infectious to healthy bees. We found that IAPV was the dominant pathogen and it replicated actively in the colonies: viral titers decreased from April to September and increased from September to December. IAPV extracted from infected bees was highly infectious to healthy pupae: they showed several-fold amplification of the viral genome and synthesis of the virion protein VP3. The health of recovered colonies was seriously compromised. Interestingly, a rise of IAPV genomic copies in two colonies coincided with their subsequent collapse. Our results do not imply IAPV as the cause of CCD but indicate that once acquired and induced to replication it acts as an infectious factor that affects the health of the colonies and may determine their survival. This is the first follow up outside the US of CCD-colonies bearing IAPV under natural conditions.

  19. Manipulation of colony environment modulates honey bee aggression and brain gene expression.

    Science.gov (United States)

    Rittschof, C C; Robinson, G E

    2013-11-01

    The social environment plays an essential role in shaping behavior for most animals. Social effects on behavior are often linked to changes in brain gene expression. In the honey bee (Apis mellifera L.), social modulation of individual aggression allows colonies to adjust the intensity with which they defend their hive in response to predation threat. Previous research has showed social effects on both aggression and aggression-related brain gene expression in honey bees, caused by alarm pheromone and unknown factors related to colony genotype. For example, some bees from less aggressive genetic stock reared in colonies with genetic predispositions toward increased aggression show both increased aggression and more aggressive-like brain gene expression profiles. We tested the hypothesis that exposure to a colony environment influenced by high levels of predation threat results in increased aggression and aggressive-like gene expression patterns in individual bees. We assessed gene expression using four marker genes. Experimentally induced predation threats modified behavior, but the effect was opposite of our predictions: disturbed colonies showed decreased aggression. Disturbed colonies also decreased foraging activity, suggesting that they did not habituate to threats; other explanations for this finding are discussed. Bees in disturbed colonies also showed changes in brain gene expression, some of which paralleled behavioral findings. These results show that bee aggression and associated molecular processes are subject to complex social influences. © 2013 John Wiley & Sons Ltd and International Behavioural and Neural Genetics Society.

  20. Multi Dimensional Honey Bee Foraging Algorithm Based on Optimal Energy Consumption

    Science.gov (United States)

    Saritha, R.; Vinod Chandra, S. S.

    2017-10-01

    In this paper a new nature inspired algorithm is proposed based on natural foraging behavior of multi-dimensional honey bee colonies. This method handles issues that arise when food is shared from multiple sources by multiple swarms at multiple destinations. The self organizing nature of natural honey bee swarms in multiple colonies is based on the principle of energy consumption. Swarms of multiple colonies select a food source to optimally fulfill the requirements of its colonies. This is based on the energy requirement for transporting food between a source and destination. Minimum use of energy leads to maximizing profit in each colony. The mathematical model proposed here is based on this principle. This has been successfully evaluated by applying it on multi-objective transportation problem for optimizing cost and time. The algorithm optimizes the needs at each destination in linear time.

  1. An Improved Artificial Bee Colony-Based Approach for Zoning Protected Ecological Areas.

    Directory of Open Access Journals (Sweden)

    Jing Shao

    Full Text Available China is facing ecological and environmental challenges as its urban growth rate continues to rise, and zoning protected ecological areas is recognized as an effective response measure. Zoning inherently involves both site attributes and aggregation attributes, and the combination of mathematical models and heuristic algorithms have proven advantageous. In this article, an improved artificial bee colony (IABC-based approach is proposed for zoning protected ecological areas at a regional scale. Three main improvements were made: the first is the use of multiple strategies to generate the initial bee population of a specific quality and diversity, the second is an exploitation search procedure to generate neighbor solutions combining "replace" and "alter" operations, and the third is a "swap" strategy to enable a local search for the iterative optimal solution. The IABC algorithm was verified using simulated data. Then it was applied to define an optimum scheme of protected ecological areas of Sanya (in the Hainan province of China, and a reasonable solution was obtained. Finally, a comparison experiment with other methods (agent-based land allocation model, ant colony optimization, and density slicing was conducted and demonstrated that the IABC algorithm was more effective and efficient than the other methods. Through this study, we aimed to provide a scientifically sound, practical approach for zoning procedures.

  2. Researches on the Influence of Some Apicol Stimulators Use in the Supplemental Feeding of Honey Bee Colonies

    Directory of Open Access Journals (Sweden)

    Silvia Patruica

    2013-05-01

    Full Text Available This paper presents the results of supplemental feedings use applied to honey bee colonies in autumn. The experiments were carried out between August 20th 2011 and July 2012, in Berini locality, Timiș County (Romania, on 32 Apis meliffera honey bee colonies, divided into four experimental variants. Honey bee families were fed in order to supplement the honey food reserves with sugar syrup containing medicinal plants, or with APIMERA product. During the experimental period, there were being studied the number of brood combs after hibernation, the quantity of broods at the beginning of spring, as well as the quantity of honey and pollen obtained by the studied bee colonies. The best results regarding the development of honey bee colonies in spring were obtained in honey bee colonies for which food reserves have been supplemented with honey combs, followed by the bee colonies fed with sugar syrup containing medicinal plants supplements.

  3. Wintering Map for Honey Bee Colonies in El-Behera Governorate

    African Journals Online (AJOL)

    MICHAEL HORSFALL

    www.bioline.org.br/ja. Wintering Map for Honey Bee Colonies in El-Behera Governorate, Egypt by using ... F. ABOU-SHAARA. Plant Protection Department, Faculty of Agriculture, Damanhour University, Egypt. ..... Architecture of automatized ...

  4. Monitoring colony-level effects of sublethal pesticide exposure on honey bees

    Science.gov (United States)

    The effects of sublethal pesticide exposure to honey bee colonies may be significant but difficult to detect in the field using standard visual assessment methods. Here we describe methods to measure the quantities of adult bees, brood and food resources by weighing hives and hive parts, by photogra...

  5. Service Composition Instantiation Based on Cross-Modified Artificial Bee Colony Algorithm

    Institute of Scientific and Technical Information of China (English)

    Lei Huo; Zhiliang Wang

    2016-01-01

    Internet of things (IoT) imposes new challenges on service composition as it is difficult to manage a quick instantiation of a complex services from a growing number of dynamic candidate services.A cross-modified Artificial Bee Colony Algorithm (CMABC) is proposed to achieve the optimal solution services in an acceptable time and high accuracy.Firstly,web service instantiation model was established.What is more,to overcome the problem of discrete and chaotic solution space,the global optimal solution was used to accelerate convergence rate by imitating the cross operation of Genetic algorithm (GA).The simulation experiment result shows that CMABC exhibited faster convergence speed and better convergence accuracy than some other intelligent optimization algorithms.

  6. A supplier selection using a hybrid grey based hierarchical clustering and artificial bee colony

    Directory of Open Access Journals (Sweden)

    Farshad Faezy Razi

    2014-06-01

    Full Text Available Selection of one or a combination of the most suitable potential providers and outsourcing problem is the most important strategies in logistics and supply chain management. In this paper, selection of an optimal combination of suppliers in inventory and supply chain management are studied and analyzed via multiple attribute decision making approach, data mining and evolutionary optimization algorithms. For supplier selection in supply chain, hierarchical clustering according to the studied indexes first clusters suppliers. Then, according to its cluster, each supplier is evaluated through Grey Relational Analysis. Then the combination of suppliers’ Pareto optimal rank and costs are obtained using Artificial Bee Colony meta-heuristic algorithm. A case study is conducted for a better description of a new algorithm to select a multiple source of suppliers.

  7. Pathogens as Predictors of Honey Bee Colony Strength in England and Wales.

    Science.gov (United States)

    Budge, Giles E; Pietravalle, Stéphane; Brown, Mike; Laurenson, Lynn; Jones, Ben; Tomkies, Victoria; Delaplane, Keith S

    2015-01-01

    Inspectors with the UK National Bee Unit were asked for 2007-2008 to target problem apiaries in England and Wales for pathogen screening and colony strength measures. Healthy colonies were included in the sampling to provide a continuum of health conditions. A total of 406 adult bee samples was screened and yielded 7 viral, 1 bacterial, and 2 microsporidial pathogens and 1 ectoparasite (Acarapis woodi). In addition, 108 samples of brood were screened and yielded 4 honey bee viruses. Virus prevalence varied from common (deformed wing virus, black queen cell virus) to complete absence (Israeli acute paralysis virus). When colonies were forced into one of two classes, strong or weak, the weak colonies contained more pathogens in adult bees. Among observed pathogens, only deformed wing virus was able to predict colony strength. The effect was negative such that colonies testing positive for deformed wing virus were likely to have fewer combs of bees or brood. This study constitutes the first record for Nosema ceranae in Great Britain. These results contribute to the growing body of evidence linking pathogens to poor honey bee health.

  8. Pathogens as Predictors of Honey Bee Colony Strength in England and Wales.

    Directory of Open Access Journals (Sweden)

    Giles E Budge

    Full Text Available Inspectors with the UK National Bee Unit were asked for 2007-2008 to target problem apiaries in England and Wales for pathogen screening and colony strength measures. Healthy colonies were included in the sampling to provide a continuum of health conditions. A total of 406 adult bee samples was screened and yielded 7 viral, 1 bacterial, and 2 microsporidial pathogens and 1 ectoparasite (Acarapis woodi. In addition, 108 samples of brood were screened and yielded 4 honey bee viruses. Virus prevalence varied from common (deformed wing virus, black queen cell virus to complete absence (Israeli acute paralysis virus. When colonies were forced into one of two classes, strong or weak, the weak colonies contained more pathogens in adult bees. Among observed pathogens, only deformed wing virus was able to predict colony strength. The effect was negative such that colonies testing positive for deformed wing virus were likely to have fewer combs of bees or brood. This study constitutes the first record for Nosema ceranae in Great Britain. These results contribute to the growing body of evidence linking pathogens to poor honey bee health.

  9. The Effect of Open Brood and Colony Strength on the Onset of Oviposition by Queen Bees

    Directory of Open Access Journals (Sweden)

    Gąbka Jakub

    2014-06-01

    Full Text Available In bee colonies without open brood, e.g., after swarming, there is no need for royal jelly, and nurse bees thus do not produce it. According to many beekeepers, adding combs with open brood restarts the production of royal jelly by nurse bees, and the virgin queens then are better fed and start earlier oviposition. The purpose of this study was to investigate whether the presence of open brood and the strength of the colonies affect the onset of oviposition by queen bees. Open brood in colonies with virgins before and during mating flights did not accelerate the initiation of oviposition by the queens. In addition, no differences were identified in starting oviposition by queens in strong colonies of more than 30,000 worker bees, or in weak colonies with up to 1,000 workers. Overall, the results showed that neither open brood in the nests, nor the strength of the colonies affects the onset of oviposition by queen bees.

  10. Honey bees as indicators of radionuclide contamination: exploring colony variability and temporal contaminant accumulation

    International Nuclear Information System (INIS)

    Haarmann, T.K.

    1997-01-01

    Two aspects of using honey bees, Apis mellifera, as indicators of environmental radionuclide contamination were investigated: colony variability and temporal contaminant accumulation. Two separate field experiments were conducted in areas with bioavailable radionuclide contamination. Bees were collected from colonies, analysed for concentrations of radionuclides, and the results were compared using graphical and statistical methods. The first experiment indicates that generally a low variability exists between samples collected within the same colony. A higher variability exists between samples collected from adjacent colonies. Levels of tritium and sodium-22 found in samples taken from similar colonies were inconsistent, while levels of cobalt-57, cobalt-60 and manganese-54 were consistent. A second experiment investigated the accumulation of radionuclides over time by comparing colonies that had been in the study area for different periods of time. This experiment demonstrated that there is indeed a significant accumulation of radionuclides within colonies

  11. Vibration reduction of composite plates by piezoelectric patches using a modified artificial bee colony algorithm

    Directory of Open Access Journals (Sweden)

    Hadi Ghashochi-Bargh

    Full Text Available In Current paper, power consumption and vertical displacement optimization of composite plates subject to a step load are carried out by piezoelectric patches using the modified multi-objective Elitist-Artificial Bee Colony (E-ABC algorithm. The motivation behind this concept is to well balance the exploration and exploitation capability for attaining better convergence to the optimum. In order to reduce the calculation time, the elitist strategy is also used in Artificial Bee Colony algorithm. The voltages of patches, plate length/width ratios, ply angles, plate thickness/length ratios, number of layers and edge conditions are chosen as design variables. The formulation is based on the classical laminated plate theory (CLPT and Hamilton's principle. The performance of the new ABC approach is compared with the PSO algorithm and shows the good efficiency of the new ABC approach. To check the validity, the transient responses of isotropic and orthotropic plates are compared with those available in the literature and show a good agreement.

  12. Design of fuzzy classifier for diabetes disease using Modified Artificial Bee Colony algorithm.

    Science.gov (United States)

    Beloufa, Fayssal; Chikh, M A

    2013-10-01

    In this study, diagnosis of diabetes disease, which is one of the most important diseases, is conducted with artificial intelligence techniques. We have proposed a novel Artificial Bee Colony (ABC) algorithm in which a mutation operator is added to an Artificial Bee Colony for improving its performance. When the current best solution cannot be updated, a blended crossover operator (BLX-α) of genetic algorithm is applied, in order to enhance the diversity of ABC, without compromising with the solution quality. This modified version of ABC is used as a new tool to create and optimize automatically the membership functions and rules base directly from data. We take the diabetes dataset used in our work from the UCI machine learning repository. The performances of the proposed method are evaluated through classification rate, sensitivity and specificity values using 10-fold cross-validation method. The obtained classification rate of our method is 84.21% and it is very promising when compared with the previous research in the literature for the same problem. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

  13. Artificial bee colony algorithm for single-trial electroencephalogram analysis.

    Science.gov (United States)

    Hsu, Wei-Yen; Hu, Ya-Ping

    2015-04-01

    In this study, we propose an analysis system combined with feature selection to further improve the classification accuracy of single-trial electroencephalogram (EEG) data. Acquiring event-related brain potential data from the sensorimotor cortices, the system comprises artifact and background noise removal, feature extraction, feature selection, and feature classification. First, the artifacts and background noise are removed automatically by means of independent component analysis and surface Laplacian filter, respectively. Several potential features, such as band power, autoregressive model, and coherence and phase-locking value, are then extracted for subsequent classification. Next, artificial bee colony (ABC) algorithm is used to select features from the aforementioned feature combination. Finally, selected subfeatures are classified by support vector machine. Comparing with and without artifact removal and feature selection, using a genetic algorithm on single-trial EEG data for 6 subjects, the results indicate that the proposed system is promising and suitable for brain-computer interface applications. © EEG and Clinical Neuroscience Society (ECNS) 2014.

  14. Protein levels and colony development of Africanized and European honey bees fed natural and artificial diets

    OpenAIRE

    Morais, Michelle Manfrini [UNIFESP; Turcatto, Aline Patricia; Pereira, Rogerio Aparecido; Francoy, Tiago Mauricio; Guidugli-Lazzarini, Karina Rosa; Goncalves, Lionel Segui; Almeida, Joyce Mayra Volpini de; Ellis, J. D.; De Jong, David

    2013-01-01

    Pollen substitute diets are a valuable resource for maintaining strong and health honey bee colonies. Specific diets may be useful in one region or country and inadequate or economically unviable in others. We compared two artificial protein diets that had been formulated from locally-available ingredients in Brazil with bee bread and a non-protein sucrose diet. Groups of 100 newly-emerged, adult workers of Africanized honey bees in Brazil and European honey bees in the USA were confined in s...

  15. Availability of environmental radioactivity to honey bee colonies at Los Alamos

    International Nuclear Information System (INIS)

    Hakonson, T.E.; Bostick, K.V.

    1976-01-01

    Data are presented on the availability of tritium, cesium 137, and plutonium to honey bee colonies foraging in the environment surrounding the Los Alamos Scientific Laboratory. Sources of these radionuclides in the laboratory environs include liquid and atmospheric effluents and buried solid waste. Honey bee colonies were placed in three canyon liquid waste disposal areas and were sampled frequently, along with honey, surface water, and surrounding vegetation, to qualitatively determine the availability of these radionuclides to bees (Apis mellifera) and to identify potential food chain sources of the elements. Tritium concentrations in bee and honey samples from the canyons increased rapidly from initial values of 137 Cs in the environs. The existence of at least three radionuclide sources in the Los Alamos Scientific Laboratory (LASL) environs complicates the interpretation of the data. However, it is apparent that honey bees can acquire 3 H, 137 Cs, and Pu from multiple sources in the environs

  16. Infestation and distribution of the mite Varroa destructor in colonies of africanized bees

    Directory of Open Access Journals (Sweden)

    G. Moretto

    Full Text Available Whereas in several parts of the world varroa is the major pest affecting apiculture, in others the parasite is unknown to many beekeepers because its damage to bees is minor. The impact of the mite Varroa destructor is related to the climatic conditions and the races of Apis mellifera bees in each region where the pest exists. In the present study, the current level of infestation by the mite was assessed to determine the evolution of the pest in Africanized bee colonies in Southern Brazil. This level of infestation was considered low: approximately two mites per one hundred adult bees. This result is similar to that obtained for the same apiary almost five years ago and for others distributed in various regions of Brazil. In the present study, we also estimated the total varroa population and its distribution among brood and adults in each bee colony.

  17. Cooperative path planning for multi-USV based on improved artificial bee colony algorithm

    Science.gov (United States)

    Cao, Lu; Chen, Qiwei

    2018-03-01

    Due to the complex constraints, more uncertain factors and critical real-time demand of path planning for multiple unmanned surface vehicle (multi-USV), an improved artificial bee colony (I-ABC) algorithm were proposed to solve the model of cooperative path planning for multi-USV. First the Voronoi diagram of battle field space is conceived to generate the optimal area of USVs paths. Then the chaotic searching algorithm is used to initialize the collection of paths, which is regard as foods of the ABC algorithm. With the limited data, the initial collection can search the optimal area of paths perfectly. Finally simulations of the multi-USV path planning under various threats have been carried out. Simulation results verify that the I-ABC algorithm can improve the diversity of nectar source and the convergence rate of algorithm. It can increase the adaptability of dynamic battlefield and unexpected threats for USV.

  18. Back Analysis of Geomechanical Parameters in Underground Engineering Using Artificial Bee Colony

    Directory of Open Access Journals (Sweden)

    Changxing Zhu

    2014-01-01

    Full Text Available Accurate geomechanical parameters are critical in tunneling excavation, design, and supporting. In this paper, a displacements back analysis based on artificial bee colony (ABC algorithm is proposed to identify geomechanical parameters from monitored displacements. ABC was used as global optimal algorithm to search the unknown geomechanical parameters for the problem with analytical solution. To the problem without analytical solution, optimal back analysis is time-consuming, and least square support vector machine (LSSVM was used to build the relationship between unknown geomechanical parameters and displacement and improve the efficiency of back analysis. The proposed method was applied to a tunnel with analytical solution and a tunnel without analytical solution. The results show the proposed method is feasible.

  19. A novel artificial bee colony based clustering algorithm for categorical data.

    Science.gov (United States)

    Ji, Jinchao; Pang, Wei; Zheng, Yanlin; Wang, Zhe; Ma, Zhiqiang

    2015-01-01

    Data with categorical attributes are ubiquitous in the real world. However, existing partitional clustering algorithms for categorical data are prone to fall into local optima. To address this issue, in this paper we propose a novel clustering algorithm, ABC-K-Modes (Artificial Bee Colony clustering based on K-Modes), based on the traditional k-modes clustering algorithm and the artificial bee colony approach. In our approach, we first introduce a one-step k-modes procedure, and then integrate this procedure with the artificial bee colony approach to deal with categorical data. In the search process performed by scout bees, we adopt the multi-source search inspired by the idea of batch processing to accelerate the convergence of ABC-K-Modes. The performance of ABC-K-Modes is evaluated by a series of experiments in comparison with that of the other popular algorithms for categorical data.

  20. Viral prevalence increases with regional colony abundance in honey bee drones (Apis mellifera L).

    Science.gov (United States)

    Forfert, Nadège; Natsopoulou, Myrsini E; Paxton, Robert J; Moritz, Robin F A

    2016-10-01

    Transmission among colonies is a central feature for the epidemiology of honey bee pathogens. High colony abundance may promote transmission among colonies independently of apiary layout, making colony abundance a potentially important parameter determining pathogen prevalence in populations of honey bees. To test this idea, we sampled male honey bees (drones) from seven distinct drone congregation areas (DCA), and used their genotypes to estimate colony abundance at each site. A multiplex ligation dependent probe amplification assay (MLPA) was used to assess the prevalence of ten viruses, using five common viral targets, in individual drones. There was a significant positive association between colony abundance and number of viral infections. This result highlights the potential importance of high colony abundance for pathogen prevalence, possibly because high population density facilitates pathogen transmission. Pathogen prevalence in drones collected from DCAs may be a useful means of estimating the disease status of a population of honey bees during the mating season, especially for localities with a large number of wild or feral colonies. Copyright © 2016 Elsevier B.V. All rights reserved.

  1. Social immunity and the superorganism: Behavioral defenses protecting honey bee colonies from pathogens and parasites

    Science.gov (United States)

    Honey bees (Apis mellifera) have a number of traits that effectively reduce the spread of pathogens and parasites throughout the colony. These mechanisms of social immunity are often analogous to the individual immune system. As such social immune defences function to protect the colony or superorga...

  2. Naturally selected honey bee (Apis mellifera) colonies resistant to Varroa destructor do not groom more intensively

    NARCIS (Netherlands)

    Kruitwagen, Astrid; Langevelde, van Frank; Dooremalen, van Coby; Blacquière, Tjeerd

    2017-01-01

    The ectoparasitic mite Varroa destructor is an important cause of high colony losses of the honey bee Apis mellifera. In The Netherlands, two resistant A. mellifera populations developed naturally after ceasing varroa control. As a result, mite infestation levels of the colonies of these populations

  3. Protein levels and colony development of Africanized and European honey bees fed natural and artificial diets.

    Science.gov (United States)

    Morais, M M; Turcatto, A P; Pereira, R A; Francoy, T M; Guidugli-Lazzarini, K R; Gonçalves, L S; de Almeida, J M V; Ellis, J D; De Jong, D

    2013-12-19

    Pollen substitute diets are a valuable resource for maintaining strong and health honey bee colonies. Specific diets may be useful in one region or country and inadequate or economically unviable in others. We compared two artificial protein diets that had been formulated from locally-available ingredients in Brazil with bee bread and a non-protein sucrose diet. Groups of 100 newly-emerged, adult workers of Africanized honey bees in Brazil and European honey bees in the USA were confined in small cages and fed on one of four diets for seven days. The artificial diets included a high protein diet made of soy milk powder and albumin, and a lower protein level diet consisting of soy milk powder, brewer's yeast and rice bran. The initial protein levels in newly emerged bees were approximately 18-21 µg/µL hemolymph. After feeding on the diets for seven days, the protein levels in the hemolymph were similar among the protein diet groups (~37-49 µg/µL after seven days), although Africanized bees acquired higher protein levels, increasing 145 and 100% on diets D1 and D2, respectively, versus 83 and 60% in the European bees. All the protein diets resulted in significantly higher levels of protein than sucrose solution alone. In the field, the two pollen substitute diets were tested during periods of low pollen availability in the field in two regions of Brazil. Food consumption, population development, colony weight, and honey production were evaluated to determine the impact of the diets on colony strength parameters. The colonies fed artificial diets had a significant improvement in all parameters, while control colonies dwindled during the dearth period. We conclude that these two artificial protein diets have good potential as pollen substitutes during dearth periods and that Africanized bees more efficiently utilize artificial protein diets than do European honey bees.

  4. Intrinsic colony conditions affect the provisioning and oviposition process in the stingless bee Melipona scutellaris.

    Science.gov (United States)

    Pereira, R A; Morais, M M; Nascimento, F S; Bego, L R

    2009-01-01

    The cell provisioning and oviposition process (POP) is a unique characteristic of stingless bees (Meliponini), in which coordinated interactions between workers and queen regulate the filling of brood cells with larval resources and subsequent egg laying. Environmental conditions seem to regulate reproduction in stingless bees; however, little is known about how the amount of food affects quantitative sequences of the process. We examined intrinsic variables by comparing three colonies in distinct conditions (strong, intermediate and weak state). We predicted that some of these variables are correlated with temporal events of POP in Melipona scutellaris colonies. The results demonstrated that the strong colony had shorter periods of POP.

  5. The effect of Agaricus brasiliensis extract supplementation on honey bee colonies

    Directory of Open Access Journals (Sweden)

    JEVROSIMA STEVANOVIC

    2018-02-01

    Full Text Available ABSTRACT This study was done to discover any beneficial effect of a medicinal mushroom Agaricus brasiliensis extract on the honey bee. Firstly, a laboratory experiment was conducted on 640 bees reared in 32 single-use plastic rearing cups. A. brasiliensis extract proved safe in all doses tested (50, 100 and 150 mg/kg/day irrespective of feeding mode (sugar syrup or candy. Secondly, a three-year field experiment was conducted on 26 colonies treated with a single dose of A. brasiliensis extract (100 mg/kg/day added to syrup. Each year the colonies were treated once in autumn and twice in spring. The treatments significantly increased colony strength parameters: brood rearing improvement and adult population growth were noticed more often than the increase in honey production and pollen reserves. These positive effects were mainly observed in April. In conclusion, A. brasiliensis extract is safe for the bees and helps maintaining strong colonies, especially in spring.

  6. The effect of Agaricus brasiliensis extract supplementation on honey bee colonies.

    Science.gov (United States)

    Stevanovic, Jevrosima; Stanimirovic, Zoran; Simeunovic, Predrag; Lakic, Nada; Radovic, Ivica; Sokovic, Marina; Griensven, Leo J L D VAN

    2018-01-01

    This study was done to discover any beneficial effect of a medicinal mushroom Agaricus brasiliensis extract on the honey bee. Firstly, a laboratory experiment was conducted on 640 bees reared in 32 single-use plastic rearing cups. A. brasiliensis extract proved safe in all doses tested (50, 100 and 150 mg/kg/day) irrespective of feeding mode (sugar syrup or candy). Secondly, a three-year field experiment was conducted on 26 colonies treated with a single dose of A. brasiliensis extract (100 mg/kg/day) added to syrup. Each year the colonies were treated once in autumn and twice in spring. The treatments significantly increased colony strength parameters: brood rearing improvement and adult population growth were noticed more often than the increase in honey production and pollen reserves. These positive effects were mainly observed in April. In conclusion, A. brasiliensis extract is safe for the bees and helps maintaining strong colonies, especially in spring.

  7. Ant Colony Optimization for Control

    NARCIS (Netherlands)

    Van Ast, J.M.

    2010-01-01

    The very basis of this thesis is the collective behavior of ants in colonies. Ants are an excellent example of how rather simple behavior on a local level can lead to complex behavior on a global level that is beneficial for the individuals. The key in the self-organization of ants is communication

  8. Planting of neonicotinoid-coated corn raises honey bee mortality and sets back colony development

    Directory of Open Access Journals (Sweden)

    Olivier Samson-Robert

    2017-08-01

    Full Text Available Worldwide occurrences of honey bee colony losses have raised concerns about bee health and the sustainability of pollination-dependent crops. While multiple causal factors have been identified, seed coating with insecticides of the neonicotinoid family has been the focus of much discussion and research. Nonetheless, few studies have investigated the impacts of these insecticides under field conditions or in commercial beekeeping operations. Given that corn-seed coating constitutes the largest single use of neonicotinoid, our study compared honey bee mortality from commercial apiaries located in two different agricultural settings, i.e. corn-dominated areas and corn-free environments, during the corn planting season. Data was collected in 2012 and 2013 from 26 bee yards. Dead honey bees from five hives in each apiary were counted and collected, and samples were analyzed using a multi-residue LC-MS/MS method. Long-term effects on colony development were simulated based on a honey bee population dynamic model. Mortality survey showed that colonies located in a corn-dominated area had daily mortality counts 3.51 times those of colonies from corn crop-free sites. Chemical analyses revealed that honey bees were exposed to various agricultural pesticides during the corn planting season, but were primarily subjected to neonicotinoid compounds (54% of analysed samples contained clothianidin, and 31% contained both clothianidin and thiamethoxam. Performance development simulations performed on hive populations’ show that increased mortality during the corn planting season sets back colony development and bears contributions to collapse risk but, most of all, reduces the effectiveness and value of colonies for pollination services. Our results also have implications for the numerous large-scale and worldwide-cultivated crops that currently rely on pre-emptive use of neonicotinoid seed treatments.

  9. Honey bee (Apis mellifera) colony health and pathogen composition in migratory beekeeping operations involved in California almond pollination.

    Science.gov (United States)

    Glenny, William; Cavigli, Ian; Daughenbaugh, Katie F; Radford, Rosemarie; Kegley, Susan E; Flenniken, Michelle L

    2017-01-01

    Honey bees are important pollinators of agricultural crops. Pathogens and other factors have been implicated in high annual losses of honey bee colonies in North America and some European countries. To further investigate the relationship between multiple factors, including pathogen prevalence and abundance and colony health, we monitored commercially managed migratory honey bee colonies involved in California almond pollination in 2014. At each sampling event, honey bee colony health was assessed, using colony population size as a proxy for health, and the prevalence and abundance of seven honey bee pathogens was evaluated using PCR and quantitative PCR, respectively. In this sample cohort, pathogen prevalence and abundance did not correlate with colony health, but did correlate with the date of sampling. In general, pathogen prevalence (i.e., the number of specific pathogens harbored within a colony) was lower early in the year (January-March) and was greater in the summer, with peak prevalence occurring in June. Pathogen abundance in individual honey bee colonies varied throughout the year and was strongly associated with the sampling date, and was influenced by beekeeping operation, colony health, and mite infestation level. Together, data from this and other observational cohort studies that monitor individual honey bee colonies and precisely account for sampling date (i.e., day of year) will lead to a better understanding of the influence of pathogens on colony mortality and the effects of other factors on these associations.

  10. Honey bee (Apis mellifera colony health and pathogen composition in migratory beekeeping operations involved in California almond pollination.

    Directory of Open Access Journals (Sweden)

    William Glenny

    Full Text Available Honey bees are important pollinators of agricultural crops. Pathogens and other factors have been implicated in high annual losses of honey bee colonies in North America and some European countries. To further investigate the relationship between multiple factors, including pathogen prevalence and abundance and colony health, we monitored commercially managed migratory honey bee colonies involved in California almond pollination in 2014. At each sampling event, honey bee colony health was assessed, using colony population size as a proxy for health, and the prevalence and abundance of seven honey bee pathogens was evaluated using PCR and quantitative PCR, respectively. In this sample cohort, pathogen prevalence and abundance did not correlate with colony health, but did correlate with the date of sampling. In general, pathogen prevalence (i.e., the number of specific pathogens harbored within a colony was lower early in the year (January-March and was greater in the summer, with peak prevalence occurring in June. Pathogen abundance in individual honey bee colonies varied throughout the year and was strongly associated with the sampling date, and was influenced by beekeeping operation, colony health, and mite infestation level. Together, data from this and other observational cohort studies that monitor individual honey bee colonies and precisely account for sampling date (i.e., day of year will lead to a better understanding of the influence of pathogens on colony mortality and the effects of other factors on these associations.

  11. A new collaborative recommendation approach based on users clustering using artificial bee colony algorithm.

    Science.gov (United States)

    Ju, Chunhua; Xu, Chonghuan

    2013-01-01

    Although there are many good collaborative recommendation methods, it is still a challenge to increase the accuracy and diversity of these methods to fulfill users' preferences. In this paper, we propose a novel collaborative filtering recommendation approach based on K-means clustering algorithm. In the process of clustering, we use artificial bee colony (ABC) algorithm to overcome the local optimal problem caused by K-means. After that we adopt the modified cosine similarity to compute the similarity between users in the same clusters. Finally, we generate recommendation results for the corresponding target users. Detailed numerical analysis on a benchmark dataset MovieLens and a real-world dataset indicates that our new collaborative filtering approach based on users clustering algorithm outperforms many other recommendation methods.

  12. A New Collaborative Recommendation Approach Based on Users Clustering Using Artificial Bee Colony Algorithm

    Directory of Open Access Journals (Sweden)

    Chunhua Ju

    2013-01-01

    Full Text Available Although there are many good collaborative recommendation methods, it is still a challenge to increase the accuracy and diversity of these methods to fulfill users’ preferences. In this paper, we propose a novel collaborative filtering recommendation approach based on K-means clustering algorithm. In the process of clustering, we use artificial bee colony (ABC algorithm to overcome the local optimal problem caused by K-means. After that we adopt the modified cosine similarity to compute the similarity between users in the same clusters. Finally, we generate recommendation results for the corresponding target users. Detailed numerical analysis on a benchmark dataset MovieLens and a real-world dataset indicates that our new collaborative filtering approach based on users clustering algorithm outperforms many other recommendation methods.

  13. Transit Network Design: a Hybrid Enhanced Artificial Bee Colony Approach and a Case Study

    Directory of Open Access Journals (Sweden)

    Y. Jiang

    2013-09-01

    Full Text Available A bus network design problem in a suburban area of Hong Kong is studied. The objective is to minimize the weighted sum of the number of transfers and the total travel time of passengers by restructuring bus routes and determining new frequencies. A mixed integer optimization model is developed and was solved by a Hybrid Enhanced Artificial Bee Colony algorithm (HEABC. A case study was conducted to investigate the effects of different design parameters, including the total number of bus routes available, the maximum route duration within the study area and the maximum allowable number of bus routes that originated from each terminal. The model and results are useful for improving bus service policies.

  14. Colony size evolution and the origin of eusociality in corbiculate bees (Hymenoptera: Apinae.

    Directory of Open Access Journals (Sweden)

    Enrique Rodriguez-Serrano

    Full Text Available Recently, it has been proposed that the one of the main determinants of complex societies in Hymenoptera is colony size, since the existence of large colonies reduces the direct reproductive success of an average individual, given a decreased chance of being part of the reproductive caste. In this study, we evaluate colony size evolution in corbiculate bees and their relationship with the sociality level shown by these bees. Specifically i the correlation between colony size and level of sociality considering the phylogenetic relationship to evaluate a general evolutionary tendency, and ii the hypothetical ancestral forms of several clades within a phylogeny of corbiculate bees, to address idiosyncratic process occurring at important nodes. We found that the level of social complexity in corbiculate bees is phylogenetically correlated with colony size. Additionally, another process is invoked to propose why colony size evolved concurrently with the level of social complexity. The study of this trait improves the understanding of the evolutionary transition from simple to complex societies, and highlights the importance of explicit probabilistic models to test the evolution of other important characters involved in the origin of eusociality.

  15. Nest initiation in three North American bumble bees (Bombus): gyne number and presence of honey bee workers influence establishment success and colony size.

    Science.gov (United States)

    Strange, James P

    2010-01-01

    Three species of bumble bees, Bombus appositus Cresson, Bombus bifarius, Cresson and Bombus centralis Cresson (Hymenoptera: Apidae) were evaluated for nest initiation success under three sets of initial conditions. In the spring, gynes of each species were caught in the wild and introduced to nest boxes in one of three ways. Gynes were either introduced in conspecific pairs, singly with two honey bees, Apis mellifera L. (Hymenoptera: Apidae) workers, or alone. Nesting success and colony growth parameters were measured to understand the effects of the various treatments on nest establishment. Colonies initiated from pairs of conspecific gynes were most successful in producing worker bees (59.1%), less successful were colonies initiated with honey bee workers (33.3%), and least successful were bumble bee gynes initiating colonies alone (16.7%). There was a negative correlation between the numbers of days to the emergence of the first worker in a colony to the attainment of ultimate colony size, indicating that gynes that have not commenced oviposition in 21 days are unlikely to result in colonies exceeding 50 workers. B. appositus had the highest rate of nest establishment followed by B. bifarius and B. centralis. Nest establishment rates in three western bumble bee species can be increased dramatically by the addition of either honey bee workers or a second gyne to nesting boxes at colony initiation.

  16. First report of sacbrood virus in honey bee (Apis mellifera) colonies in Brazil.

    Science.gov (United States)

    Freiberg, M; De Jong, D; Message, D; Cox-Foster, D

    2012-09-13

    Sacbrood disease, an affliction of honey bees (Apis mellifera) characterized by brood that fails to pupate and subsequently dies, is an important threat to honey bee health. The disease is caused by the sacbrood virus (SBV), a positive-, single-stranded RNA virus in the order Picornavirales. Because of the economic importance of honey bees for both pollination and honey production, it is vital to understand and monitor the spread of viruses such as SBV. This virus has been found in many places across the globe, including recently in some South American countries, and it is likely that it will continue to spread. We performed a preliminary study to search for SBV in two apiaries of Africanized honey bees in the State of São Paulo, Brazil, using RT-PCR and Sanger sequencing and found the first evidence of SBV in honey bee colonies in Brazil. The virus was detected in larvae, foraging and nurse bees from two colonies, one of which had symptoms of sacbrood disease, at the beginning of the winter season in June 2011. No SBV was found in samples from nine other nearby colonies.

  17. Organization model for Mobile Wireless Sensor Networks inspired in Artificial Bee Colony

    International Nuclear Information System (INIS)

    Julio de Mesquita Filho, Sao Jose do Rio Preto, SP (Brazil))" data-affiliation=" (Sao Paulo State Univerity Julio de Mesquita Filho, Sao Jose do Rio Preto, SP (Brazil))" >Roberto, Guilherme Freire; Julio de Mesquita Filho, Sao Jose do Rio Preto, SP (Brazil))" data-affiliation=" (Sao Paulo State Univerity Julio de Mesquita Filho, Sao Jose do Rio Preto, SP (Brazil))" >Neves, Leandro Alves; Maschi, Luis Fernando Castilho; Pigatto, Daniel Fernando; Branco, Kalinka Regina Lucas Jaquie Castelo; Montez, Carlos; Pinto, Alex Sandro Roschildt

    2015-01-01

    The purpose of this study is to find a self-organizing model for MWSN based on bee colonies in order to reduce the number of messages transmitted among nodes, and thus reduce the overall consumption energy while maintaining the efficiency of message delivery. The results obtained in this article are originated from simulations carried out with SINALGO software, which demonstrates the effectiveness of the proposed approach. The BeeAODV (Bee Ad-Hoc On Demand Distance Vector) proposed in this paper allows to considerably reduce message exchanges whether compared to AODV (Ad-Hoc On Demand Distance Vector)

  18. Risk factors associated with honey bee colony loss in apiaries in Galicia, NW Spain

    Directory of Open Access Journals (Sweden)

    Aranzazu Meana

    2017-04-01

    Full Text Available A cross-sectional study was carried out in Galicia, NW Spain, in order to estimate the magnitude of honey bee colony losses and to identify potential risk factors involved. A total of 99 samples from 99 apiaries were collected in spring using simple random sampling. According to international guidelines, the apiaries were classified as affected by colony loss or asymptomatic. Each sample consisted of worker bees, brood and comb-stored pollen. All worker bees and brood samples were analysed individually in order to detect the main honey bee pathogens. Moreover, the presence of residues of the most prevalent agrotoxic insecticides and acaricides was assessed in comb-stored pollen. The general characteristics of the apiaries and sanitary information regarding previous years was evaluated through questionnaires, while the vegetation surrounding the apiaries sampled was assessed by palynological analysis of comb-stored pollen. The colony loss prevalence was 53.5% (CI95%=43.2-63.9 and Nosema ceranae was found to be the only risk factor strongly associated with colony loss. The decision tree also pointed out the impact of the Varroa mite presence while variables such as apiary size, the incorrect application of Varroa mite treatments, and the presence of Acarapis woodi and Kashmir bee virus (KBV were identified as possible co-factors.

  19. Longitudinal effects of supplemental forage on the honey bee (Apis 1 mellifera) microbiota and inter- and intra-colony variability

    Science.gov (United States)

    Honey bee colonies obtain much of their gut bacteria (gut microbiota) from fresh nectar and pollen collected from flowering plants (forage). Honey bee colonies often go for long periods of time without fresh forage during winter and early spring. We examined the effects of mid-winter supplemental fo...

  20. Stable genetic diversity despite parasite and pathogen spread in honey bee colonies

    Science.gov (United States)

    Jara, Laura; Muñoz, Irene; Cepero, Almudena; Martín-Hernández, Raquel; Serrano, José; Higes, Mariano; De la Rúa, Pilar

    2015-10-01

    In the last decades, the rapid spread of diseases, such as varroosis and nosemosis, associated with massive honey bee colonies mortality around the world has significantly decreased the number and size of honey bee populations and possibly their genetic diversity. Here, we compare the genetic diversity of Iberian honey bee colonies in two samplings performed in 2006 and 2010 in relation to the presence of the pathogenic agents Nosema apis, Nosema ceranae, and Varroa destructor in order to determine whether parasite and pathogen spread in honey bee colonies reflects changes in genetic diversity. We found that the genetic diversity remained similar, while the incidence of N. ceranae increased and the incidence of N. apis and V. destructor decreased slightly. These results indicate that the genetic diversity was not affected by the presence of these pathogenic agents in the analyzed period. However, the two groups of colonies with and without Nosema/Varroa detected showed significant genetic differentiation (G test). A detailed analysis of the allelic segregation of microsatellite loci in Nosema/Varroa-negative colonies and parasitized ones revealed two outlier loci related to genes involved in immune response.

  1. Stable genetic diversity despite parasite and pathogen spread in honey bee colonies.

    Science.gov (United States)

    Jara, Laura; Muñoz, Irene; Cepero, Almudena; Martín-Hernández, Raquel; Serrano, José; Higes, Mariano; De la Rúa, Pilar

    2015-10-01

    In the last decades, the rapid spread of diseases, such as varroosis and nosemosis, associated with massive honey bee colonies mortality around the world has significantly decreased the number and size of honey bee populations and possibly their genetic diversity. Here, we compare the genetic diversity of Iberian honey bee colonies in two samplings performed in 2006 and 2010 in relation to the presence of the pathogenic agents Nosema apis, Nosema ceranae, and Varroa destructor in order to determine whether parasite and pathogen spread in honey bee colonies reflects changes in genetic diversity. We found that the genetic diversity remained similar, while the incidence of N. ceranae increased and the incidence of N. apis and V. destructor decreased slightly. These results indicate that the genetic diversity was not affected by the presence of these pathogenic agents in the analyzed period. However, the two groups of colonies with and without Nosema/Varroa detected showed significant genetic differentiation (G test). A detailed analysis of the allelic segregation of microsatellite loci in Nosema/Varroa-negative colonies and parasitized ones revealed two outlier loci related to genes involved in immune response.

  2. Changes in learning and foraging behaviour within developing bumble bee (Bombus terrestris colonies.

    Directory of Open Access Journals (Sweden)

    Lisa J Evans

    Full Text Available Organisation in eusocial insect colonies emerges from the decisions and actions of its individual members. In turn, these decisions and actions are influenced by the individual's behaviour (or temperament. Although there is variation in the behaviour of individuals within a colony, we know surprisingly little about how (or indeed if the types of behaviour present in a colony change over time. Here, for the first time, we assessed potential changes in the behavioural type of foragers during colony development. Using an ecologically relevant foraging task, we measured the decision speed and learning ability of bumble bees (Bombus terrestris at different stages of colony development. We determined whether individuals that forage early in the colony life cycle (the queen and early emerging workers behaved differently from workers that emerge and forage at the end of colony development. Whilst we found no overall change in the foraging behaviour of workers with colony development, there were strong differences in foraging behaviour between queens and their workers. Queens appeared to forage more cautiously than their workers and were also quicker to learn. These behaviours could allow queens to maximise their nectar collecting efficiency whilst avoiding predation. Because the foundress queen is crucial to the survival and success of a bumble bee colony, more efficient foraging behaviour in queens may have strong adaptive value.

  3. Voltage Profile Enhancement and Reduction of Real Power loss by Hybrid Biogeography Based Artificial Bee Colony algorithm

    Directory of Open Access Journals (Sweden)

    K. Lenin

    2014-04-01

    Full Text Available This paper presents Hybrid Biogeography algorithm for solving the multi-objective reactive power dispatch problem in a power system. Real Power Loss minimization and maximization of voltage stability margin are taken as the objectives. Artificial bee colony optimization (ABC is quick and forceful algorithm for global optimization. Biogeography-Based Optimization (BBO is a new-fangled biogeography inspired algorithm. It mainly utilizes the biogeography-based relocation operator to share the information among solutions. In this work, a hybrid algorithm with BBO and ABC is projected, and named as HBBABC (Hybrid Biogeography based Artificial Bee Colony Optimization, for the universal numerical optimization problem. HBBABC merge the searching behavior of ABC with that of BBO. Both the algorithms have different solution probing tendency like ABC have good exploration probing tendency while BBO have good exploitation probing tendency.  HBBABC used to solve the reactive power dispatch problem and the proposed technique has been tested in standard IEEE30 bus test system.

  4. Characterization of the active microbiotas associated with honey bees reveals healthier and broader communities when colonies are genetically diverse.

    Directory of Open Access Journals (Sweden)

    Heather R Mattila

    Full Text Available Recent losses of honey bee colonies have led to increased interest in the microbial communities that are associated with these important pollinators. A critical function that bacteria perform for their honey bee hosts, but one that is poorly understood, is the transformation of worker-collected pollen into bee bread, a nutritious food product that can be stored for long periods in colonies. We used 16S rRNA pyrosequencing to comprehensively characterize in genetically diverse and genetically uniform colonies the active bacterial communities that are found on honey bees, in their digestive tracts, and in bee bread. This method provided insights that have not been revealed by past studies into the content and benefits of honey bee-associated microbial communities. Colony microbiotas differed substantially between sampling environments and were dominated by several anaerobic bacterial genera never before associated with honey bees, but renowned for their use by humans to ferment food. Colonies with genetically diverse populations of workers, a result of the highly promiscuous mating behavior of queens, benefited from greater microbial diversity, reduced pathogen loads, and increased abundance of putatively helpful bacteria, particularly species from the potentially probiotic genus Bifidobacterium. Across all colonies, Bifidobacterium activity was negatively correlated with the activity of genera that include pathogenic microbes; this relationship suggests a possible target for understanding whether microbes provide protective benefits to honey bees. Within-colony diversity shapes microbiotas associated with honey bees in ways that may have important repercussions for colony function and health. Our findings illuminate the importance of honey bee-bacteria symbioses and examine their intersection with nutrition, pathogen load, and genetic diversity, factors that are considered key to understanding honey bee decline.

  5. Performance of honey bee colonies under a long-lasting dietary exposure to sublethal concentrations of the neonicotinoid insecticide thiacloprid.

    Science.gov (United States)

    Siede, Reinhold; Faust, Lena; Meixner, Marina D; Maus, Christian; Grünewald, Bernd; Büchler, Ralph

    2017-07-01

    Substantial honey bee colony losses have occurred periodically in the last decades. The drivers for these losses are not fully understood. The influence of pests and pathogens are beyond dispute, but in addition, chronic exposure to sublethal concentrations of pesticides has been suggested to affect the performance of honey bee colonies. This study aims to elucidate the potential effects of a chronic exposure to sublethal concentrations (one realistic worst-case concentration) of the neonicotinoid thiacloprid to honey bee colonies in a three year replicated colony feeding study. Thiacloprid did not significantly affect the colony strength. No differences between treatment and control were observed for the mortality of bees, the infestation with the parasitic mite Varroa destructor and the infection levels of viruses. No colony losses occurred during the overwintering seasons. Furthermore, thiacloprid did not influence the constitutive expression of the immunity-related hymenoptaecin gene. However, upregulation of hymenoptaecin expression as a response to bacterial challenge was less pronounced in exposed bees than in control bees. Under field conditions, bee colonies are not adversely affected by a long-lasting exposure to sublethal concentrations of thiacloprid. No indications were found that field-realistic and higher doses exerted a biologically significant effect on colony performance. © 2017 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry. © 2017 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

  6. Phenotypic and genetic analyses of the varroa sensitive hygienic trait in Russian honey bee (Hymenoptera: Apidae) colonies

    OpenAIRE

    Kirrane, Maria J.; de Guzman, Lilia I.; Holloway, Beth; Frake, Amanda M.; Rinderer, Thomas E.; Whelan, Padraig M.

    2015-01-01

    Varroa destructorcontinues to threaten colonies of European honey bees. General hygiene, and more specific Varroa Sensitive Hygiene (VSH), provide resistance towards the Varroa mite in a number of stocks. In this study, 32 Russian (RHB) and 14 Italian honey bee colonies were assessed for the VSH trait using two different assays. Firstly, colonies were assessed using the standard VSH behavioural assay of the change in infestation of a highly infested donor comb after a one-week exposure. Secon...

  7. Integrated varroa control in honey bee colonies (Apis mellifera carnica) with or without brood

    Science.gov (United States)

    Studies were conducted in two apiaries in order to assess the comparative efficacy of oxalic acid (OA), formic acid (FA) and Thymovar against varroa mites in honey bee colonies. Treatments were performed using 85% FA and OA consisted of 2.9% oxalic acid dihydrate and 31.9% sugar in water. Consecutiv...

  8. Swarm prevention and spring treatment against Varroa destructor in honey bee colonies (Apis mellifera)

    NARCIS (Netherlands)

    Cornelissen, B.; Gerritsen, L.J.M.

    2006-01-01

    In 2004 and 2005 experiments were carried out to test the efficacy and efficiency of Varroa control combined with swarm prevention methods in spring. Honey bee colonies were split in an artificial swarm and a brood carrier. Hereafter the swarms were treated with oxalic acid and the brood carriers

  9. Assessment of chronic sublethal effects of imidacloprid on honey bee colony health

    Science.gov (United States)

    Here we present results of a three-year study to determine the fate of imidacloprid residues in hive matrices and to assess chronic sublethal effects on whole honey bee colonies fed supplemental pollen diet containing imidacloprid at 5, 20 and 100 µg/kg over multiple brood cycles. Various endpoints ...

  10. Chasing your honey: Worldwide diaspora of the small hive beetle, a parasite of honey bee colonies

    Science.gov (United States)

    Endemic to sub-Saharan Africa, small hive beetles (Aethina tumida) are now an invasive pest of honey bee colonies in Australia and North America. Knowledge on the introduction(s) from Africa into and between the current ranges will shed light on pest populations, invasion pathways and contribute to ...

  11. Using within-day hive weight changes to measure environmental effects on honey bee colonies

    Science.gov (United States)

    Patterns in within-day hive weight data from two independent datasets in Arizona and California were modeled using piecewise regression, and analyzed with respect to honey bee colony behavior and landscape effects. The regression analysis yielded information on the start and finish of a colony’s dai...

  12. Artificial Bee Colony Algorithm for Transient Performance Augmentation of Grid Connected Distributed Generation

    Science.gov (United States)

    Chatterjee, A.; Ghoshal, S. P.; Mukherjee, V.

    In this paper, a conventional thermal power system equipped with automatic voltage regulator, IEEE type dual input power system stabilizer (PSS) PSS3B and integral controlled automatic generation control loop is considered. A distributed generation (DG) system consisting of aqua electrolyzer, photovoltaic cells, diesel engine generator, and some other energy storage devices like flywheel energy storage system and battery energy storage system is modeled. This hybrid distributed system is connected to the grid. While integrating this DG with the onventional thermal power system, improved transient performance is noticed. Further improvement in the transient performance of this grid connected DG is observed with the usage of superconducting magnetic energy storage device. The different tunable parameters of the proposed hybrid power system model are optimized by artificial bee colony (ABC) algorithm. The optimal solutions offered by the ABC algorithm are compared with those offered by genetic algorithm (GA). It is also revealed that the optimizing performance of the ABC is better than the GA for this specific application.

  13. A novel artificial bee colony approach of live virtual machine migration policy using Bayes theorem.

    Science.gov (United States)

    Xu, Gaochao; Ding, Yan; Zhao, Jia; Hu, Liang; Fu, Xiaodong

    2013-01-01

    Green cloud data center has become a research hotspot of virtualized cloud computing architecture. Since live virtual machine (VM) migration technology is widely used and studied in cloud computing, we have focused on the VM placement selection of live migration for power saving. We present a novel heuristic approach which is called PS-ABC. Its algorithm includes two parts. One is that it combines the artificial bee colony (ABC) idea with the uniform random initialization idea, the binary search idea, and Boltzmann selection policy to achieve an improved ABC-based approach with better global exploration's ability and local exploitation's ability. The other one is that it uses the Bayes theorem to further optimize the improved ABC-based process to faster get the final optimal solution. As a result, the whole approach achieves a longer-term efficient optimization for power saving. The experimental results demonstrate that PS-ABC evidently reduces the total incremental power consumption and better protects the performance of VM running and migrating compared with the existing research. It makes the result of live VM migration more high-effective and meaningful.

  14. A Novel Artificial Bee Colony Approach of Live Virtual Machine Migration Policy Using Bayes Theorem

    Directory of Open Access Journals (Sweden)

    Gaochao Xu

    2013-01-01

    Full Text Available Green cloud data center has become a research hotspot of virtualized cloud computing architecture. Since live virtual machine (VM migration technology is widely used and studied in cloud computing, we have focused on the VM placement selection of live migration for power saving. We present a novel heuristic approach which is called PS-ABC. Its algorithm includes two parts. One is that it combines the artificial bee colony (ABC idea with the uniform random initialization idea, the binary search idea, and Boltzmann selection policy to achieve an improved ABC-based approach with better global exploration’s ability and local exploitation’s ability. The other one is that it uses the Bayes theorem to further optimize the improved ABC-based process to faster get the final optimal solution. As a result, the whole approach achieves a longer-term efficient optimization for power saving. The experimental results demonstrate that PS-ABC evidently reduces the total incremental power consumption and better protects the performance of VM running and migrating compared with the existing research. It makes the result of live VM migration more high-effective and meaningful.

  15. Heuristic Artificial Bee Colony Algorithm for Uncovering Community in Complex Networks

    Directory of Open Access Journals (Sweden)

    Yuquan Guo

    2017-01-01

    Full Text Available Community structure is important for us to understand the functions and structure of the complex networks. In this paper, Heuristic Artificial Bee Colony (HABC algorithm based on swarm intelligence is proposed for uncovering community. The proposed HABC includes initialization, employed bee searching, onlooker searching, and scout bee searching. In initialization stage, the nectar sources with simple community structure are generated through network dynamic algorithm associated with complete subgraph. In employed bee searching and onlooker searching stages, the searching function is redefined to address the community problem. The efficiency of searching progress can be improved by a heuristic function which is an average agglomerate probability of two neighbor communities. Experiments are carried out on artificial and real world networks, and the results demonstrate that HABC will have better performance in terms of comparing with the state-of-the-art algorithms.

  16. Ant colony optimization and constraint programming

    CERN Document Server

    Solnon, Christine

    2013-01-01

    Ant colony optimization is a metaheuristic which has been successfully applied to a wide range of combinatorial optimization problems. The author describes this metaheuristic and studies its efficiency for solving some hard combinatorial problems, with a specific focus on constraint programming. The text is organized into three parts. The first part introduces constraint programming, which provides high level features to declaratively model problems by means of constraints. It describes the main existing approaches for solving constraint satisfaction problems, including complete tree search

  17. Molecular identification of chronic bee paralysis virus infection in Apis mellifera colonies in Japan.

    Science.gov (United States)

    Morimoto, Tomomi; Kojima, Yuriko; Yoshiyama, Mikio; Kimura, Kiyoshi; Yang, Bu; Kadowaki, Tatsuhiko

    2012-07-01

    Chronic bee paralysis virus (CBPV) infection causes chronic paralysis and loss of workers in honey bee colonies around the world. Although CBPV shows a worldwide distribution, it had not been molecularly detected in Japan. Our investigation of Apis mellifera and Apis cerana japonica colonies with RT-PCR has revealed CBPV infection in A. mellifera but not A. c. japonica colonies in Japan. The prevalence of CBPV is low compared with that of other viruses: deformed wing virus (DWV), black queen cell virus (BQCV), Israel acute paralysis virus (IAPV), and sac brood virus (SBV), previously reported in Japan. Because of its low prevalence (5.6%) in A. mellifera colonies, the incidence of colony losses by CBPV infection must be sporadic in Japan. The presence of the (-) strand RNA in dying workers suggests that CBPV infection and replication may contribute to their symptoms. Phylogenetic analysis demonstrates a geographic separation of Japanese isolates from European, Uruguayan, and mainland US isolates. The lack of major exchange of honey bees between Europe/mainland US and Japan for the recent 26 years (1985-2010) may have resulted in the geographic separation of Japanese CBPV isolates.

  18. Four Categories of Viral Infection Describe the Health Status of Honey Bee Colonies.

    Directory of Open Access Journals (Sweden)

    Esmaeil Amiri

    Full Text Available Honey bee virus prevalence data are an essential prerequisite for managing epidemic events in a population. A survey study was carried out for seven viruses in colonies representing a healthy Danish honey bee population. In addition, colonies from apiaries with high level Varroa infestation or high level of winter mortality were also surveyed. Results from RT-qPCR showed a considerable difference of virus levels between healthy and sick colonies. In the group of healthy colonies, no virus was detected in 36% of cases, while at least one virus was found in each of the sick colonies. Virus titers varied among the samples, and multiple virus infections were common in both groups with a high prevalence of Sacbrood virus (SBV, Black queen cell virus (BQCV and Deformed wing virus (DWV. Based on the distribution of virus titers, we established four categories of infection: samples free of virus (C = 0, samples with low virus titer (estimated number of virus copies 0 < C < 103, samples with medium virus titer (103 ≤ C < 107 and samples with high virus titer (C ≥ 107. This allowed us to statistically compare virus levels in healthy and sick colonies. Using categories to communicate virus diagnosis results to beekeepers may help them to reach an informed decision on management strategies to prevent further spread of viruses among colonies.

  19. The chaotic global best artificial bee colony algorithm for the multi-area economic/emission dispatch

    International Nuclear Information System (INIS)

    Secui, Dinu Calin

    2015-01-01

    This paper suggests a chaotic optimizing method, based on the GBABC (global best artificial bee colony algorithm), where the random sequences used in updating the solutions of this algorithm are replaced with chaotic sequences generated by chaotic maps. The new algorithm, called chaotic CGBABC (global best artificial bee colony algorithm), is used to solving the multi-area economic/emission dispatch problem taking into consideration the valve-point effects, the transmission line losses, multi-fuel sources, prohibited operating zones, tie line capacity and power transfer cost between different areas of the system. The behaviour of the CGBABC algorithm is studied considering ten chaotic maps both one-dimensional and bi-dimensional, with various probability density functions. The CGBABC algorithm's performance including a variety of chaotic maps is tested on five systems (6-unit, 10-unit, 16-unit, 40-unit and 120-unit) with different characteristics, constraints and sizes. The results comparison highlights a hierarchy in the chaotic maps included in the CGBABC algorithm and shows that it performs better than the classical ABC algorithm, the GBABC algorithm and other optimization techniques. - Highlights: • A chaotic global best ABC algorithm (CGBABC) is presented. • CGBABC is applied for solving the multi-area economic/emission dispatch problem. • Valve-point effects, multi-fuel sources, POZ, transmission losses were considered. • The algorithm is tested on five systems having 6, 10, 16, 40 and 120 thermal units. • CGBABC algorithm outperforms several optimization techniques.

  20. Chronic exposure of a honey bee colony to 2.45 GHz continuous wave microwaves

    Science.gov (United States)

    Westerdahl, B. B.; Gary, N. E.

    1981-01-01

    A honey bee colony (Apis mellifera L.) was exposed 28 days to 2.45 GHz continuous wave microwaves at a power density (1 mW/sq cm) expected to be associated with rectennae in the solar power satellite power transmission system. Differences found between the control and microwave-treated colonies were not large, and were in the range of normal variation among similar colonies. Thus, there is an indication that microwave treatment had little, if any, effect on (1) flight and pollen foraging activity, (2) maintenance of internal colony temperature, (3) brood rearing activity, (4) food collection and storage, (5) colony weight, and (6) adult populations. Additional experiments are necessary before firm conclusions can be made.

  1. Image Edge Tracking via Ant Colony Optimization

    Science.gov (United States)

    Li, Ruowei; Wu, Hongkun; Liu, Shilong; Rahman, M. A.; Liu, Sanchi; Kwok, Ngai Ming

    2018-04-01

    A good edge plot should use continuous thin lines to describe the complete contour of the captured object. However, the detection of weak edges is a challenging task because of the associated low pixel intensities. Ant Colony Optimization (ACO) has been employed by many researchers to address this problem. The algorithm is a meta-heuristic method developed by mimicking the natural behaviour of ants. It uses iterative searches to find the optimal solution that cannot be found via traditional optimization approaches. In this work, ACO is employed to track and repair broken edges obtained via conventional Sobel edge detector to produced a result with more connected edges.

  2. A new modified artificial bee colony algorithm for the economic dispatch problem

    International Nuclear Information System (INIS)

    Secui, Dinu Calin

    2015-01-01

    Highlights: • A new modified ABC algorithm (MABC) is proposed to solve the EcD/EmD problem. • Valve-point effects, ramp-rate limits, POZ, transmission losses were considered. • The algorithm is tested on four systems having 6, 13, 40 and 52 thermal units. • MABC algorithm outperforms several optimization techniques. - Abstract: In this paper a new modified artificial bee colony algorithm (MABC) is proposed to solve the economic dispatch problem by taking into account the valve-point effects, the emission pollutions and various operating constraints of the generating units. The MABC algorithm introduces a new relation to update the solutions within the search space, in order to increase the algorithm ability to avoid premature convergence and to find stable and high quality solutions. Moreover, to strengthen the MABC algorithm performance, it is endowed with a chaotic sequence generated by both a cat map and a logistic map. The MABC algorithm behavior is investigated for several combinations resulting from three generating modalities of the chaotic sequences and two selection schemes of the solutions. The performance of the MABC variants is tested on four systems having six units, thirteen units, forty units and fifty-two thermal generating units. The comparison of the results shows that the MABC variants have a better performance than the classical ABC algorithm and other optimization techniques

  3. Screening alternative therapies to control Nosemosis type C in honey bee (Apis mellifera iberiensis) colonies.

    Science.gov (United States)

    Botías, Cristina; Martín-Hernández, Raquel; Meana, Aránzazu; Higes, Mariano

    2013-12-01

    Nosemosis type C caused by the microsporidium Nosema ceranae is one of the most widespread of the adult honey bee diseases, and due to its detrimental effects on both strength and productivity of honey bee colonies, an appropriate control of this disease is advisable. Fumagillin is the only veterinary medicament recommended by the World Organization for Animal Health (OIE) to suppress infections by Nosema, but the use of this antibiotic is prohibited in the European Union and few alternatives are available at present to control the disease. In the present study three therapeutic agents (Nosestat®, Phenyl salicylate and Vitafeed Gold®) have been tested to control N. ceranae infection in honey bee colonies, and have been compared to the use of fumagillin. None of the products tested was effective against Nosema under our experimental conditions. Low consumption of the different doses of treatments may have had a strong influence on the results obtained, highlighting the importance of this issue and emphasizing that this should be evaluated in studies to test therapeutic treatments of honey bee colonies. Copyright © 2013 Elsevier Ltd. All rights reserved.

  4. A Mathematical Model of Forager Loss in Honeybee Colonies Infested with Varroa destructor and the Acute Bee Paralysis Virus.

    Science.gov (United States)

    Ratti, Vardayani; Kevan, Peter G; Eberl, Hermann J

    2017-06-01

    We incorporate a mathematical model of Varroa destructor and the Acute Bee Paralysis Virus with an existing model for a honeybee colony, in which the bee population is divided into hive bees and forager bees based on tasks performed in the colony. The model is a system of five ordinary differential equations with dependent variables: uninfected hive bees, uninfected forager bees, infected hive bees, virus-free mites and virus-carrying mites. The interplay between forager loss and disease infestation is studied. We study the stability of the disease-free equilibrium of the bee-mite-virus model and observe that the disease cannot be fought off in the absence of varroacide treatment. However, the disease-free equilibrium can be stable if the treatment is strong enough and also if the virus-carrying mites become virus-free at a rate faster than the mite birth rate. The critical forager loss due to homing failure, above which the colony fails, is calculated using simulation experiments for disease-free, treated and untreated mite-infested, and treated virus-infested colonies. A virus-infested colony without varroacide treatment fails regardless of the forager mortality rate.

  5. Linking Measures of Colony and Individual Honey Bee Health to Survival among Apiaries Exposed to Varying Agricultural Land Use.

    Science.gov (United States)

    Smart, Matthew; Pettis, Jeff; Rice, Nathan; Browning, Zac; Spivak, Marla

    2016-01-01

    We previously characterized and quantified the influence of land use on survival and productivity of colonies positioned in six apiaries and found that colonies in apiaries surrounded by more land in uncultivated forage experienced greater annual survival, and generally more honey production. Here, detailed metrics of honey bee health were assessed over three years in colonies positioned in the same six apiaries. The colonies were located in North Dakota during the summer months and were transported to California for almond pollination every winter. Our aim was to identify relationships among measures of colony and individual bee health that impacted and predicted overwintering survival of colonies. We tested the hypothesis that colonies in apiaries surrounded by more favorable land use conditions would experience improved health. We modeled colony and individual bee health indices at a critical time point (autumn, prior to overwintering) and related them to eventual spring survival for California almond pollination. Colony measures that predicted overwintering apiary survival included the amount of pollen collected, brood production, and Varroa destructor mite levels. At the individual bee level, expression of vitellogenin, defensin1, and lysozyme2 were important markers of overwinter survival. This study is a novel first step toward identifying pertinent physiological responses in honey bees that result from their positioning near varying landscape features in intensive agricultural environments.

  6. Linking Measures of Colony and Individual Honey Bee Health to Survival among Apiaries Exposed to Varying Agricultural Land Use

    Science.gov (United States)

    Smart, Matthew; Pettis, Jeff; Rice, Nathan; Browning, Zac; Spivak, Marla

    2016-01-01

    We previously characterized and quantified the influence of land use on survival and productivity of colonies positioned in six apiaries and found that colonies in apiaries surrounded by more land in uncultivated forage experienced greater annual survival, and generally more honey production. Here, detailed metrics of honey bee health were assessed over three years in colonies positioned in the same six apiaries. The colonies were located in North Dakota during the summer months and were transported to California for almond pollination every winter. Our aim was to identify relationships among measures of colony and individual bee health that impacted and predicted overwintering survival of colonies. We tested the hypothesis that colonies in apiaries surrounded by more favorable land use conditions would experience improved health. We modeled colony and individual bee health indices at a critical time point (autumn, prior to overwintering) and related them to eventual spring survival for California almond pollination. Colony measures that predicted overwintering apiary survival included the amount of pollen collected, brood production, and Varroa destructor mite levels. At the individual bee level, expression of vitellogenin, defensin1, and lysozyme2 were important markers of overwinter survival. This study is a novel first step toward identifying pertinent physiological responses in honey bees that result from their positioning near varying landscape features in intensive agricultural environments. PMID:27027871

  7. Linking Measures of Colony and Individual Honey Bee Health to Survival among Apiaries Exposed to Varying Agricultural Land Use.

    Directory of Open Access Journals (Sweden)

    Matthew Smart

    Full Text Available We previously characterized and quantified the influence of land use on survival and productivity of colonies positioned in six apiaries and found that colonies in apiaries surrounded by more land in uncultivated forage experienced greater annual survival, and generally more honey production. Here, detailed metrics of honey bee health were assessed over three years in colonies positioned in the same six apiaries. The colonies were located in North Dakota during the summer months and were transported to California for almond pollination every winter. Our aim was to identify relationships among measures of colony and individual bee health that impacted and predicted overwintering survival of colonies. We tested the hypothesis that colonies in apiaries surrounded by more favorable land use conditions would experience improved health. We modeled colony and individual bee health indices at a critical time point (autumn, prior to overwintering and related them to eventual spring survival for California almond pollination. Colony measures that predicted overwintering apiary survival included the amount of pollen collected, brood production, and Varroa destructor mite levels. At the individual bee level, expression of vitellogenin, defensin1, and lysozyme2 were important markers of overwinter survival. This study is a novel first step toward identifying pertinent physiological responses in honey bees that result from their positioning near varying landscape features in intensive agricultural environments.

  8. Summary of winter honey bee colony losses in Slovakia between the years 2009 and 2015

    Directory of Open Access Journals (Sweden)

    Róbert Chlebo

    2016-03-01

    Full Text Available Between the seasons 2009/2010 and 2014/2015 was evaluated 1305 questionnaires in total, received from Slovak beekeepers. Standard questionnaires of COST working group COLOSS were used with sets of questions related to overwintering of bee colonies and possible reasons of its losses. In season 2009/2010 winter losses in Slovakia reached 7.10 %, subsequently in 2010/2011 - 5.96 %, 2011/2012 - 9.70 %, 2012/2013 - 9.50 %, 2013/2014 - 8.84 %, 2014/2015 - 10.00 %. Expected causes of winter mortality (starvation, poor queen´s quality, parasitism, robbery were evaluated in the study to detect the presence of depopulation syndrome of bee colonies - CCD (colony collapse disorder reported from some North American and European areas. As acceptable level of winter losses is generally considered level 10 %, which was not exceeded in any season, thereby Slovakia ranks among countries with the lowest winter mortality of bee colonies worldwide. Possible reason of this situation is most probably multiple Varroa treatment throughout the year, but other reasons are discussed as well in the study.

  9. Toxicity of Selected Acaricides to Honey Bees (Apis mellifera) and Varroa (Varroa destructor Anderson and Trueman) and Their Use in Controlling Varroa within Honey Bee Colonies.

    Science.gov (United States)

    Gregorc, Aleš; Alburaki, Mohamed; Sampson, Blair; Knight, Patricia R; Adamczyk, John

    2018-05-10

    The efficacies of various acaricides in order to control a parasitic mite, the Varroa mite, Varroa destructor , of honey bees, were measured in two different settings, namely, in laboratory caged honey bees and in queen-right honey bee colonies. The Varroa infestation levels before, during, and after the acaricide treatments were determined in two ways, namely: (1) using the sugar shake protocol to count mites on bees and (2) directly counting the dead mites on the hive bottom inserts. The acaricides that were evaluated were coumaphos, tau-fluvalinate, amitraz, thymol, and natural plant compounds (hop acids), which were the active ingredients. The acaricide efficacies in the colonies were evaluated in conjunction with the final coumaphos applications. All of the tested acaricides significantly increased the overall Varroa mortality in the laboratory experiment. Their highest efficiencies were recorded at 6 h post-treatment, except for coumaphos and thymol, which exhibited longer and more consistent activity. In the honey bee colonies, a higher Varroa mortality was recorded in all of the treatments, compared with the natural Varroa mortality during the pretreatment period. The acaricide toxicity to the Varroa mites was consistent in both the caged adult honey bees and workers in the queen-right colonies, although, two of these acaricides, coumaphos at the highest doses and hop acids, were comparatively more toxic to the worker bees.

  10. Fungicide contamination reduces beneficial fungi in bee bread based on an area-wide field study in honey bee, Apis mellifera, colonies.

    Science.gov (United States)

    Yoder, Jay A; Jajack, Andrew J; Rosselot, Andrew E; Smith, Terrance J; Yerke, Mary Clare; Sammataro, Diana

    2013-01-01

    Fermentation by fungi converts stored pollen into bee bread that is fed to honey bee larvae, Apis mellifera, so the diversity of fungi in bee bread may be related to its food value. To explore the relationship between fungicide exposure and bee bread fungi, samples of bee bread collected from bee colonies pollinating orchards from 7 locations over 2 years were analyzed for fungicide residues and fungus composition. There were detectable levels of fungicides from regions that were sprayed before bloom. An organic orchard had the highest quantity and variety of fungicides, likely due to the presence of treated orchards within bees' flight range. Aspergillus, Penicillium, Rhizopus, and Cladosporium (beneficial fungi) were the primary fungal isolates found, regardless of habitat differences. There was some variation in fungal components amongst colonies, even within the same apiary. The variable components were Absidia, Alternaria, Aureobasidium, Bipolaris, Fusarium, Geotrichum, Mucor, Nigrospora, Paecilomyces, Scopulariopsis, and Trichoderma. The number of fungal isolates was reduced as an effect of fungicide contamination. Aspergillus abundance was particularly affected by increased fungicide levels, as indicated by Simpson's diversity index. Bee bread showing fungicide contamination originated from colonies, many of which showed chalkbrood symptoms.

  11. Assessment of Chronic Sublethal Effects of Imidacloprid on Honey Bee Colony Health

    Science.gov (United States)

    Dively, Galen P.; Embrey, Michael S.; Kamel, Alaa; Hawthorne, David J.; Pettis, Jeffery S.

    2015-01-01

    Here we present results of a three-year study to determine the fate of imidacloprid residues in hive matrices and to assess chronic sublethal effects on whole honey bee colonies fed supplemental pollen diet containing imidacloprid at 5, 20 and 100 μg/kg over multiple brood cycles. Various endpoints of colony performance and foraging behavior were measured during and after exposure, including winter survival. Imidacloprid residues became diluted or non-detectable within colonies due to the processing of beebread and honey and the rapid metabolism of the chemical. Imidacloprid exposure doses up to 100 μg/kg had no significant effects on foraging activity or other colony performance indicators during and shortly after exposure. Diseases and pest species did not affect colony health but infestations of Varroa mites were significantly higher in exposed colonies. Honey stores indicated that exposed colonies may have avoided the contaminated food. Imidacloprid dose effects was delayed later in the summer, when colonies exposed to 20 and 100 μg/kg experienced higher rates of queen failure and broodless periods, which led to weaker colonies going into the winter. Pooled over two years, winter survival of colonies averaged 85.7, 72.4, 61.2 and 59.2% in the control, 5, 20 and 100 μg/kg treatment groups, respectively. Analysis of colony survival data showed a significant dose effect, and all contrast tests comparing survival between control and treatment groups were significant, except for colonies exposed to 5 μg/kg. Given the weight of evidence, chronic exposure to imidacloprid at the higher range of field doses (20 to 100 μg/kg) in pollen of certain treated crops could cause negative impacts on honey bee colony health and reduced overwintering success, but the most likely encountered high range of field doses relevant for seed-treated crops (5 μg/kg) had negligible effects on colony health and are unlikely a sole cause of colony declines. PMID:25786127

  12. Effects of Wintering Environment and Parasite-Pathogen Interactions on Honey Bee Colony Loss in North Temperate Regions.

    Directory of Open Access Journals (Sweden)

    Suresh D Desai

    Full Text Available Extreme winter losses of honey bee colonies are a major threat to beekeeping but the combinations of factors underlying colony loss remain debatable. We monitored colonies in two environments (colonies wintered indoors or outdoors and characterized the effects of two parasitic mites, seven viruses, and Nosema on honey bee colony mortality and population loss over winter. Samples were collected from two locations within hives in fall, mid-winter and spring of 2009/2010. Although fall parasite and pathogen loads were similar in outdoor and indoor-wintered colonies, the outdoor-wintered colonies had greater relative reductions in bee population score over winter. Seasonal patterns in deformed wing virus (DWV, black queen cell virus (BQCV, and Nosema level also differed with the wintering environment. DWV and Nosema levels decreased over winter for indoor-wintered colonies but BQCV did not. Both BQCV and Nosema concentration increased over winter in outdoor-wintered colonies. The mean abundance of Varroa decreased and concentration of Sacbrood virus (SBV, Kashmir bee virus (KBV, and Chronic bee paralysis virus (CBPV increased over winter but seasonal patterns were not affected by wintering method. For most viruses, either entrance or brood area samples were reasonable predictors of colony virus load but there were significant season*sample location interactions for Nosema and BQCV, indicating that care must be taken when selecting samples from a single location. For Nosema spp., the fall entrance samples were better predictors of future infestation levels than were fall brood area samples. For indoor-wintered colonies, Israeli acute paralysis virus IAPV concentration was negatively correlated with spring population size. For outdoor-wintered hives, spring Varroa abundance and DWV concentration were positively correlated with bee loss and negatively correlated with spring population size. Multivariate analyses for fall collected samples indicated

  13. Organophosphorus insecticides in honey, pollen and bees (Apis mellifera L.) and their potential hazard to bee colonies in Egypt.

    Science.gov (United States)

    Al Naggar, Yahya; Codling, Garry; Vogt, Anja; Naiem, Elsaied; Mona, Mohamed; Seif, Amal; Giesy, John P

    2015-04-01

    There is no clear single factor to date that explains colony loss in bees, but one factor proposed is the wide-spread application of agrochemicals. Concentrations of 14 organophosphorous insecticides (OPs) in honey bees (Apis mellifera) and hive matrices (honey and pollen) were measured to assess their hazard to honey bees. Samples were collected during spring and summer of 2013, from 5 provinces in the middle delta of Egypt. LC/MS-MS was used to identify and quantify individual OPs by use of a modified Quick Easy Cheap Effective Rugged Safe (QuEChERS) method. Pesticides were detected more frequently in samples collected during summer. Pollen contained the greatest concentrations of OPs. Profenofos, chlorpyrifos, malation and diazinon were the most frequently detected OPs. In contrast, ethoprop, phorate, coumaphos and chlorpyrifos-oxon were not detected. A toxic units approach, with lethality as the endpoint was used in an additive model to assess the cumulative potential for adverse effects posed by OPs. Hazard quotients (HQs) in honey and pollen ranged from 0.01-0.05 during spring and from 0.02-0.08 during summer, respectively. HQs based on lethality due to direct exposure of adult worker bees to OPs during spring and summer ranged from 0.04 to 0.1 for best and worst case respectively. It is concluded that direct exposure and/or dietary exposure to OPs in honey and pollen pose little threat due to lethality of bees in Egypt. Copyright © 2014 Elsevier Inc. All rights reserved.

  14. A Europe-wide experiment for assessing the impact of genotype-environment interactions on the vitality and performance of honey bee colonies

    DEFF Research Database (Denmark)

    Costa, Cecilia; Büchler, Ralph; Berg, Stefan

    2012-01-01

    An international experiment to estimate the importance of genotype-environment interactions on vitality and performance of honey bees and on colony losses was run between July 2009 and March 2012. Altogether 621 bee colonies, involving 16 different genetic origins of European honey bees, were...

  15. Queen rearing and selection practices and their impact on the genetic diversity and fitness of honey bee colonies

    OpenAIRE

    Bouga, Maria; Arnold, Gerard; Bienkowska, Malgorzata; Büchler, Ralph; Garnery, Lionel; Ivanova, Evgeniya; De Jong, David; De la Rúa, Pilar; Kence, Meral; Kezic, Nikola; Kryger, Per; Murilhas, António; Oldroyd, Benjamin; Oliver, Randy; Palacio, María

    2011-01-01

    The Apimondia working group on honey bee diversity and fitness (AWG 7) was created on October 25, 2010 as a Scientific Working Group of Apimondia. The aim of this AWG is to collect information on honey bee queen rearing practices, and examine their impact on the genetic variability and general health of honey bee colonies. The AWG consists of 23 members from 16 different countries. The world wide survey being conducted by this AWG is focused on gathering information on how selection methods, ...

  16. Glucose oxidase production does not increase after colony infection: Testing its role in honey bee social immunity

    Science.gov (United States)

    Honey bees rely on a variety of defense mechanisms to reduce disease infection and spread throughout the colony. Hygienic behavior, resin collection, and antimicrobial peptide production are some examples of defenses that bees use against parasites (Evans & Spivak, 2010 J Invertebr Pathol 103:S62). ...

  17. Statistical methods to quantify the effect of mite parasitism on the probability of death in honey bee colonies

    Science.gov (United States)

    Varroa destructor is a mite parasite of European honey bees, Apis mellifera, that weakens the population, can lead to the death of an entire honey bee colony, and is believed to be the parasite with the most economic impact on beekeeping. The purpose of this study was to estimate the probability of ...

  18. Characterization of the active microbiotas associated with honey bees reveals healthier and broader communities when colonies are genetically diverse

    NARCIS (Netherlands)

    Mattila, H.R.; Rios, D.; Walker-Sperling, V.E.; Roeselers, G.; Newton, I.L.G.

    2012-01-01

    Recent losses of honey bee colonies have led to increased interest in the microbial communities that are associated with these important pollinators. A critical function that bacteria perform for their honey bee hosts, but one that is poorly understood, is the transformation of worker-collected

  19. Within-Colony Variation in the Immunocompetency of Managed and Feral Honey Bees (Apis mellifera L.) in Different Urban Landscapes

    OpenAIRE

    Appler, R.; Frank, Steven; Tarpy, David

    2015-01-01

    Urbanization has the potential to dramatically affect insect populations worldwide, although its effects on pollinator populations are just beginning to be understood. We compared the immunocompetency of honey bees sampled from feral (wild-living) and managed (beekeeper-owned) honey bee colonies. We sampled foragers from feral and managed colonies in rural, suburban, and urban landscapes in and around Raleigh, NC, USA. We then analyzed adult workers using two standard bioassays for insect imm...

  20. Mating Frequencies of Honey Bee Queens (Apis mellifera L.) in a Population of Feral Colonies in the Northeastern United States

    OpenAIRE

    Tarpy, David R.; Delaney, Deborah A.; Seeley, Thomas D.

    2015-01-01

    Across their introduced range in North America, populations of feral honey bee (Apis mellifera L.) colonies have supposedly declined in recent decades as a result of exotic parasites, most notably the ectoparasitic mite Varroa destructor. Nonetheless, recent studies have documented several wild populations of colonies that have persisted. The extreme polyandry of honey bee queens-and the increased intracolony genetic diversity it confers-has been attributed, in part, to improved disease resis...

  1. An Enhanced Artificial Bee Colony Algorithm with Solution Acceptance Rule and Probabilistic Multisearch

    Directory of Open Access Journals (Sweden)

    Alkın Yurtkuran

    2016-01-01

    Full Text Available The artificial bee colony (ABC algorithm is a popular swarm based technique, which is inspired from the intelligent foraging behavior of honeybee swarms. This paper proposes a new variant of ABC algorithm, namely, enhanced ABC with solution acceptance rule and probabilistic multisearch (ABC-SA to address global optimization problems. A new solution acceptance rule is proposed where, instead of greedy selection between old solution and new candidate solution, worse candidate solutions have a probability to be accepted. Additionally, the acceptance probability of worse candidates is nonlinearly decreased throughout the search process adaptively. Moreover, in order to improve the performance of the ABC and balance the intensification and diversification, a probabilistic multisearch strategy is presented. Three different search equations with distinctive characters are employed using predetermined search probabilities. By implementing a new solution acceptance rule and a probabilistic multisearch approach, the intensification and diversification performance of the ABC algorithm is improved. The proposed algorithm has been tested on well-known benchmark functions of varying dimensions by comparing against novel ABC variants, as well as several recent state-of-the-art algorithms. Computational results show that the proposed ABC-SA outperforms other ABC variants and is superior to state-of-the-art algorithms proposed in the literature.

  2. An Enhanced Artificial Bee Colony Algorithm with Solution Acceptance Rule and Probabilistic Multisearch.

    Science.gov (United States)

    Yurtkuran, Alkın; Emel, Erdal

    2016-01-01

    The artificial bee colony (ABC) algorithm is a popular swarm based technique, which is inspired from the intelligent foraging behavior of honeybee swarms. This paper proposes a new variant of ABC algorithm, namely, enhanced ABC with solution acceptance rule and probabilistic multisearch (ABC-SA) to address global optimization problems. A new solution acceptance rule is proposed where, instead of greedy selection between old solution and new candidate solution, worse candidate solutions have a probability to be accepted. Additionally, the acceptance probability of worse candidates is nonlinearly decreased throughout the search process adaptively. Moreover, in order to improve the performance of the ABC and balance the intensification and diversification, a probabilistic multisearch strategy is presented. Three different search equations with distinctive characters are employed using predetermined search probabilities. By implementing a new solution acceptance rule and a probabilistic multisearch approach, the intensification and diversification performance of the ABC algorithm is improved. The proposed algorithm has been tested on well-known benchmark functions of varying dimensions by comparing against novel ABC variants, as well as several recent state-of-the-art algorithms. Computational results show that the proposed ABC-SA outperforms other ABC variants and is superior to state-of-the-art algorithms proposed in the literature.

  3. Safety assessment of sugar dusting treatments by analysis of hygienic behavior in honey bee colonies

    Directory of Open Access Journals (Sweden)

    Stevanovic Jevrosima

    2011-01-01

    Full Text Available The hygienic behavior in honey bees is a dominant natural defense mechanism against brood diseases. In this study, the influence of sugar dusting treatments on hygienic behavior was evaluated in 44 strong honey bee colonies. Three doses of pulverized sugar, 20, 30 and 40 g, each applied at three-, seven- and fourteen-day intervals were tested. The percentage of cleaned cells (PCC in the total number of those with pin-killed brood served as a measure of the hygienic potential. The effect was dependent on the frequency of treatments: all doses applied every third and seventh day significantly (p<0.001 decreased the PCC in comparison with the untreated control colonies. Nevertheless, sugar did not threaten the hygienic potential, as PPC values remained above 94% following all treatments. Thus, it can be concluded that the tested sugar treatments are safe and can be justifiably implemented into integrated pest management strategies to control Varroa destructor.

  4. A Modified Artificial Bee Colony Algorithm for p-Center Problems

    Directory of Open Access Journals (Sweden)

    Alkın Yurtkuran

    2014-01-01

    Full Text Available The objective of the p-center problem is to locate p-centers on a network such that the maximum of the distances from each node to its nearest center is minimized. The artificial bee colony algorithm is a swarm-based meta-heuristic algorithm that mimics the foraging behavior of honey bee colonies. This study proposes a modified ABC algorithm that benefits from a variety of search strategies to balance exploration and exploitation. Moreover, random key-based coding schemes are used to solve the p-center problem effectively. The proposed algorithm is compared to state-of-the-art techniques using different benchmark problems, and computational results reveal that the proposed approach is very efficient.

  5. Chaotic Artificial Bee Colony Algorithm for System Identification of a Small-Scale Unmanned Helicopter

    Directory of Open Access Journals (Sweden)

    Li Ding

    2015-01-01

    Full Text Available The purpose of this paper is devoted to developing a chaotic artificial bee colony algorithm (CABC for the system identification of a small-scale unmanned helicopter state-space model in hover condition. In order to avoid the premature of traditional artificial bee colony algorithm (ABC, which is stuck in local optimum and can not reach the global optimum, a novel chaotic operator with the characteristics of ergodicity and irregularity was introduced to enhance its performance. With input-output data collected from actual flight experiments, the identification results showed the superiority of CABC over the ABC and the genetic algorithm (GA. Simulations are presented to demonstrate the effectiveness of our proposed algorithm and the accuracy of the identified helicopter model.

  6. Division of labor associated with brood rearing in the honey bee: how does it translate to colony fitness?

    Directory of Open Access Journals (Sweden)

    Ramesh R Sagili

    2011-02-01

    Full Text Available Division of labor is a striking feature observed in honey bees and many other social insects. Division of labor has been claimed to benefit fitness. In honey bees, the adult work force may be viewed as divided between non-foraging hive bees that rear brood and maintain the nest, and foragers that collect food outside the nest. Honey bee brood pheromone is a larval pheromone that serves as an excellent empirical tool to manipulate foraging behaviors and thus division of labor in the honey bee. Here we use two different doses of brood pheromone to alter the foraging stimulus environment, thus changing demographics of colony division of labor, to demonstrate how division of labor associated with brood rearing affects colony growth rate. We examine the effects of these different doses of brood pheromone on individual foraging ontogeny and specialization, colony level foraging behavior, and individual glandular protein synthesis. Low brood pheromone treatment colonies exhibited significantly higher foraging population, decreased age of first foraging and greater foraging effort, resulting in greater colony growth compared to other treatments. This study demonstrates how division of labor associated with brood rearing affects honey bee colony growth rate, a token of fitness.

  7. Swarm intelligence of artificial bees applied to In-Core Fuel Management Optimization

    Energy Technology Data Exchange (ETDEWEB)

    Santos de Oliveira, Iona Maghali, E-mail: ioliveira@con.ufrj.br [Nuclear Engineering Program, Federal University of Rio de Janeiro, P.O. Box 68509, Zip Code 21945-970, Rio de Janeiro, RJ (Brazil); Schirru, Roberto, E-mail: schirru@lmp.ufrj.br [Nuclear Engineering Program, Federal University of Rio de Janeiro, P.O. Box 68509, Zip Code 21945-970, Rio de Janeiro, RJ (Brazil)

    2011-05-15

    Research highlights: > We present Artificial Bee Colony with Random Keys (ABCRK) for In-Core Fuel Management Optimization. > Its performance is examined through the optimization of a Brazilian '2-loop' PWR. > Feasibility of using ABCRK is shown against some well known population-based algorithms. > Additional advantage includes the utilization of fewer control parameters. - Abstract: Artificial Bee Colony (ABC) algorithm is a relatively new member of swarm intelligence. ABC tries to simulate the intelligent behavior of real honey bees in food foraging and can be used for solving continuous optimization and multi-dimensional numeric problems. This paper introduces the Artificial Bee Colony with Random Keys (ABCRK), a modified ABC algorithm for solving combinatorial problems such as the In-Core Fuel Management Optimization (ICFMO). The ICFMO is a hard combinatorial optimization problem in Nuclear Engineering which during many years has been solved by expert knowledge. It aims at getting the best arrangement of fuel in the nuclear reactor core that leads to a maximization of the operating time. As a consequence, the operation cost decreases and money is saved. In this study, ABCRK is used for optimizing the ICFMO problem of a Brazilian '2-loop' Pressurized Water Reactor (PWR) Nuclear Power Plant (NPP) and the results obtained with the proposed algorithm are compared with those obtained by Genetic Algorithms (GA) and Particle Swarm Optimization (PSO). The results show that the performance of the ABCRK algorithm is better than or similar to that of other population-based algorithms, with the advantage of employing fewer control parameters.

  8. Swarm intelligence of artificial bees applied to In-Core Fuel Management Optimization

    International Nuclear Information System (INIS)

    Santos de Oliveira, Iona Maghali; Schirru, Roberto

    2011-01-01

    Research highlights: → We present Artificial Bee Colony with Random Keys (ABCRK) for In-Core Fuel Management Optimization. → Its performance is examined through the optimization of a Brazilian '2-loop' PWR. → Feasibility of using ABCRK is shown against some well known population-based algorithms. → Additional advantage includes the utilization of fewer control parameters. - Abstract: Artificial Bee Colony (ABC) algorithm is a relatively new member of swarm intelligence. ABC tries to simulate the intelligent behavior of real honey bees in food foraging and can be used for solving continuous optimization and multi-dimensional numeric problems. This paper introduces the Artificial Bee Colony with Random Keys (ABCRK), a modified ABC algorithm for solving combinatorial problems such as the In-Core Fuel Management Optimization (ICFMO). The ICFMO is a hard combinatorial optimization problem in Nuclear Engineering which during many years has been solved by expert knowledge. It aims at getting the best arrangement of fuel in the nuclear reactor core that leads to a maximization of the operating time. As a consequence, the operation cost decreases and money is saved. In this study, ABCRK is used for optimizing the ICFMO problem of a Brazilian '2-loop' Pressurized Water Reactor (PWR) Nuclear Power Plant (NPP) and the results obtained with the proposed algorithm are compared with those obtained by Genetic Algorithms (GA) and Particle Swarm Optimization (PSO). The results show that the performance of the ABCRK algorithm is better than or similar to that of other population-based algorithms, with the advantage of employing fewer control parameters.

  9. Ant colony search algorithm for optimal reactive power optimization

    Directory of Open Access Journals (Sweden)

    Lenin K.

    2006-01-01

    Full Text Available The paper presents an (ACSA Ant colony search Algorithm for Optimal Reactive Power Optimization and voltage control of power systems. ACSA is a new co-operative agents’ approach, which is inspired by the observation of the behavior of real ant colonies on the topic of ant trial formation and foraging methods. Hence, in the ACSA a set of co-operative agents called "Ants" co-operates to find good solution for Reactive Power Optimization problem. The ACSA is applied for optimal reactive power optimization is evaluated on standard IEEE, 30, 57, 191 (practical test bus system. The proposed approach is tested and compared to genetic algorithm (GA, Adaptive Genetic Algorithm (AGA.

  10. Robust Fault-Tolerant Control for Satellite Attitude Stabilization Based on Active Disturbance Rejection Approach with Artificial Bee Colony Algorithm

    Directory of Open Access Journals (Sweden)

    Fei Song

    2014-01-01

    Full Text Available This paper proposed a robust fault-tolerant control algorithm for satellite stabilization based on active disturbance rejection approach with artificial bee colony algorithm. The actuating mechanism of attitude control system consists of three working reaction flywheels and one spare reaction flywheel. The speed measurement of reaction flywheel is adopted for fault detection. If any reaction flywheel fault is detected, the corresponding fault flywheel is isolated and the spare reaction flywheel is activated to counteract the fault effect and ensure that the satellite is working safely and reliably. The active disturbance rejection approach is employed to design the controller, which handles input information with tracking differentiator, estimates system uncertainties with extended state observer, and generates control variables by state feedback and compensation. The designed active disturbance rejection controller is robust to both internal dynamics and external disturbances. The bandwidth parameter of extended state observer is optimized by the artificial bee colony algorithm so as to improve the performance of attitude control system. A series of simulation experiment results demonstrate the performance superiorities of the proposed robust fault-tolerant control algorithm.

  11. Optimizing ZigBee Security using Stochastic Model Checking

    DEFF Research Database (Denmark)

    Yuksel, Ender; Nielson, Hanne Riis; Nielson, Flemming

    , we identify an important gap in the specification on key updates, and present a methodology for determining optimal key update policies and security parameters. We exploit the stochastic model checking approach using the probabilistic model checker PRISM, and assess the security needs for realistic......ZigBee is a fairly new but promising wireless sensor network standard that offers the advantages of simple and low resource communication. Nevertheless, security is of great concern to ZigBee, and enhancements are prescribed in the latest ZigBee specication: ZigBee-2007. In this technical report...

  12. Risk factors for the presence of Deformed wing virus and Acute bee paralysis virus under temperate and subtropical climate in Argentinian bee colonies.

    Science.gov (United States)

    Molineri, Ana; Giacobino, Agostina; Pacini, Adriana; Bulacio Cagnolo, Natalia; Fondevila, Norberto; Ferrufino, Cecilia; Merke, Julieta; Orellano, Emanuel; Bertozzi, Ezequiel; Masciángelo, Germán; Pietronave, Hernán; Signorini, Marcelo

    2017-05-01

    Beekeepers all across the world are suffering important losses of their colonies, and the parasitic mites Varroa destructor and Nosema sp, as well as several bee viruses, are being pointed out as the possible causes of these losses, generally associated with environmental and management factors. The objective of the present study was to evaluate the presence of seven virus species (Deformed wing virus -DWV-, Acute bee paralysis virus -ABPV-, Chronic bee paralysis virus -CBPV-, Black queen cell virus -BQCV-, Kashmir bee virus -KBV-, Israeli acute bee paralysis virus -IAPV-, and Sacbrood bee virus -SBV), as well as the prevalence of Nosema sp. and Varroa destructor, and their possible associated factors, under temperate and subtropical climate conditions in Argentinean colonies. A total of 385 colonies distributed in five Argentinean eco-regions were examined after honey harvest. The final multivariable model revealed only one variable associated with the presence of DWV and two with the presence of ABPV. The apiary random effect was significant in both cases (P=0.018; P=0.006, respectively). Colonies with a Varroa infestation rate >3% showed higher presence of DWV than colonies with <3% of Varroa infestation level (OR=1.91; 95% CI: 1.02-3.57; P<0.044). The same pattern was observed for the presence of ABPV (OR=2.23; 95% CI: 1.04-4.77; P<0.039). Also, colonies where replacement of old combs was not a common practice had higher presence of ABPV (OR=6.02; 95% CI: 1.16-31.25; P<0.033). Regardless of the location of the colonies, virus presence was strongly associated with V. destructor level. Therefore, all the factors that directly or indirectly influence the levels of mites will be also influencing the presence of the viruses. Copyright © 2017 Elsevier B.V. All rights reserved.

  13. An improved artificial bee colony algorithm based on balance-evolution strategy for unmanned combat aerial vehicle path planning.

    Science.gov (United States)

    Li, Bai; Gong, Li-gang; Yang, Wen-lun

    2014-01-01

    Unmanned combat aerial vehicles (UCAVs) have been of great interest to military organizations throughout the world due to their outstanding capabilities to operate in dangerous or hazardous environments. UCAV path planning aims to obtain an optimal flight route with the threats and constraints in the combat field well considered. In this work, a novel artificial bee colony (ABC) algorithm improved by a balance-evolution strategy (BES) is applied in this optimization scheme. In this new algorithm, convergence information during the iteration is fully utilized to manipulate the exploration/exploitation accuracy and to pursue a balance between local exploitation and global exploration capabilities. Simulation results confirm that BE-ABC algorithm is more competent for the UCAV path planning scheme than the conventional ABC algorithm and two other state-of-the-art modified ABC algorithms.

  14. An Improved Artificial Bee Colony Algorithm Based on Balance-Evolution Strategy for Unmanned Combat Aerial Vehicle Path Planning

    Directory of Open Access Journals (Sweden)

    Bai Li

    2014-01-01

    Full Text Available Unmanned combat aerial vehicles (UCAVs have been of great interest to military organizations throughout the world due to their outstanding capabilities to operate in dangerous or hazardous environments. UCAV path planning aims to obtain an optimal flight route with the threats and constraints in the combat field well considered. In this work, a novel artificial bee colony (ABC algorithm improved by a balance-evolution strategy (BES is applied in this optimization scheme. In this new algorithm, convergence information during the iteration is fully utilized to manipulate the exploration/exploitation accuracy and to pursue a balance between local exploitation and global exploration capabilities. Simulation results confirm that BE-ABC algorithm is more competent for the UCAV path planning scheme than the conventional ABC algorithm and two other state-of-the-art modified ABC algorithms.

  15. Transcriptional signatures of parasitization and markers of colony decline in Varroa-infested honey bees (Apis mellifera).

    Science.gov (United States)

    Zanni, Virginia; Galbraith, David A; Annoscia, Desiderato; Grozinger, Christina M; Nazzi, Francesco

    2017-08-01

    Extensive annual losses of honey bee colonies (Apis mellifera L.) reported in the northern hemisphere represent a global problem for agriculture and biodiversity. The parasitic mite Varroa destructor, in association with deformed wing virus (DWV), plays a key role in this phenomenon, but the underlying mechanisms are still unclear. To elucidate these mechanisms, we analyzed the gene expression profile of uninfested and mite infested bees, under laboratory and field conditions, highlighting the effects of parasitization on the bee's transcriptome under a variety of conditions and scenarios. Parasitization was significantly correlated with higher viral loads. Honey bees exposed to mite infestation exhibited an altered expression of genes related to stress response, immunity, nervous system function, metabolism and behavioural maturation. Additionally, mite infested young bees showed a gene expression profile resembling that of forager bees. To identify potential molecular markers of colony decline, the expression of genes that were commonly regulated across the experiments were subsequently assessed in colonies experiencing increasing mite infestation levels. These studies suggest that PGRP-2, hymenoptaecin, a glucan recognition protein, UNC93 and a p450 cytocrome maybe suitable general biomarkers of Varroa-induced colony decline. Furthermore, the reliability of vitellogenin, a yolk protein previously identified as a good marker of colony survival, was confirmed here. Copyright © 2017 Elsevier Ltd. All rights reserved.

  16. Loading pattern optimization using ant colony algorithm

    International Nuclear Information System (INIS)

    Hoareau, Fabrice

    2008-01-01

    Electricite de France (EDF) operates 58 nuclear power plants (NPP), of the Pressurized Water Reactor type. The loading pattern optimization of these NPP is currently done by EDF expert engineers. Within this framework, EDF R and D has developed automatic optimization tools that assist the experts. LOOP is an industrial tool, developed by EDF R and D and based on a simulated annealing algorithm. In order to improve the results of such automatic tools, new optimization methods have to be tested. Ant Colony Optimization (ACO) algorithms are recent methods that have given very good results on combinatorial optimization problems. In order to evaluate the performance of such methods on loading pattern optimization, direct comparisons between LOOP and a mock-up based on the Max-Min Ant System algorithm (a particular variant of ACO algorithms) were made on realistic test-cases. It is shown that the results obtained by the ACO mock-up are very similar to those of LOOP. Future research will consist in improving these encouraging results by using parallelization and by hybridizing the ACO algorithm with local search procedures. (author)

  17. An Observational Study of Honey Bee Colony Winter Losses and Their Association with Varroa destructor, Neonicotinoids and Other Risk Factors

    NARCIS (Netherlands)

    Zee, van der R.; Gray, A.; Rijk, de T.C.

    2015-01-01

    This article presents results of an analysis of honey bee losses over the winter of 2011-2012 in the Netherlands, from a sample of 86 colonies, located at 43 apiaries. The apiaries were selected using spatially stratified random sampling. Colony winter loss data were collected and related to various

  18. Optic disc detection using ant colony optimization

    Science.gov (United States)

    Dias, Marcy A.; Monteiro, Fernando C.

    2012-09-01

    The retinal fundus images are used in the treatment and diagnosis of several eye diseases, such as diabetic retinopathy and glaucoma. This paper proposes a new method to detect the optic disc (OD) automatically, due to the fact that the knowledge of the OD location is essential to the automatic analysis of retinal images. Ant Colony Optimization (ACO) is an optimization algorithm inspired by the foraging behaviour of some ant species that has been applied in image processing for edge detection. Recently, the ACO was used in fundus images to detect edges, and therefore, to segment the OD and other anatomical retinal structures. We present an algorithm for the detection of OD in the retina which takes advantage of the Gabor wavelet transform, entropy and ACO algorithm. Forty images of the retina from DRIVE database were used to evaluate the performance of our method.

  19. Modified artificial bee colony for the vehicle routing problems with time windows.

    Science.gov (United States)

    Alzaqebah, Malek; Abdullah, Salwani; Jawarneh, Sana

    2016-01-01

    The natural behaviour of the honeybee has attracted the attention of researchers in recent years and several algorithms have been developed that mimic swarm behaviour to solve optimisation problems. This paper introduces an artificial bee colony (ABC) algorithm for the vehicle routing problem with time windows (VRPTW). A Modified ABC algorithm is proposed to improve the solution quality of the original ABC. The high exploration ability of the ABC slows-down its convergence speed, which may due to the mechanism used by scout bees in replacing abandoned (unimproved) solutions with new ones. In the Modified ABC a list of abandoned solutions is used by the scout bees to memorise the abandoned solutions, then the scout bees select a solution from the list based on roulette wheel selection and replace by a new solution with random routs selected from the best solution. The performance of the Modified ABC is evaluated on Solomon benchmark datasets and compared with the original ABC. The computational results demonstrate that the Modified ABC outperforms the original ABC also produce good solutions when compared with the best-known results in the literature. Computational investigations show that the proposed algorithm is a good and promising approach for the VRPTW.

  20. An enhanced artificial bee colony algorithm (EABC) for solving dispatching of hydro-thermal system (DHTS) problem.

    Science.gov (United States)

    Yu, Yi; Wu, Yonggang; Hu, Binqi; Liu, Xinglong

    2018-01-01

    The dispatching of hydro-thermal system is a nonlinear programming problem with multiple constraints and high dimensions and the solution techniques of the model have been a hotspot in research. Based on the advantage of that the artificial bee colony algorithm (ABC) can efficiently solve the high-dimensional problem, an improved artificial bee colony algorithm has been proposed to solve DHTS problem in this paper. The improvements of the proposed algorithm include two aspects. On one hand, local search can be guided in efficiency by the information of the global optimal solution and its gradient in each generation. The global optimal solution improves the search efficiency of the algorithm but loses diversity, while the gradient can weaken the loss of diversity caused by the global optimal solution. On the other hand, inspired by genetic algorithm, the nectar resource which has not been updated in limit generation is transformed to a new one by using selection, crossover and mutation, which can ensure individual diversity and make full use of prior information for improving the global search ability of the algorithm. The two improvements of ABC algorithm are proved to be effective via a classical numeral example at last. Among which the genetic operator for the promotion of the ABC algorithm's performance is significant. The results are also compared with those of other state-of-the-art algorithms, the enhanced ABC algorithm has general advantages in minimum cost, average cost and maximum cost which shows its usability and effectiveness. The achievements in this paper provide a new method for solving the DHTS problems, and also offer a novel reference for the improvement of mechanism and the application of algorithms.

  1. An enhanced artificial bee colony algorithm (EABC for solving dispatching of hydro-thermal system (DHTS problem.

    Directory of Open Access Journals (Sweden)

    Yi Yu

    Full Text Available The dispatching of hydro-thermal system is a nonlinear programming problem with multiple constraints and high dimensions and the solution techniques of the model have been a hotspot in research. Based on the advantage of that the artificial bee colony algorithm (ABC can efficiently solve the high-dimensional problem, an improved artificial bee colony algorithm has been proposed to solve DHTS problem in this paper. The improvements of the proposed algorithm include two aspects. On one hand, local search can be guided in efficiency by the information of the global optimal solution and its gradient in each generation. The global optimal solution improves the search efficiency of the algorithm but loses diversity, while the gradient can weaken the loss of diversity caused by the global optimal solution. On the other hand, inspired by genetic algorithm, the nectar resource which has not been updated in limit generation is transformed to a new one by using selection, crossover and mutation, which can ensure individual diversity and make full use of prior information for improving the global search ability of the algorithm. The two improvements of ABC algorithm are proved to be effective via a classical numeral example at last. Among which the genetic operator for the promotion of the ABC algorithm's performance is significant. The results are also compared with those of other state-of-the-art algorithms, the enhanced ABC algorithm has general advantages in minimum cost, average cost and maximum cost which shows its usability and effectiveness. The achievements in this paper provide a new method for solving the DHTS problems, and also offer a novel reference for the improvement of mechanism and the application of algorithms.

  2. How Honey Bee Colonies Survive in the Wild: Testing the Importance of Small Nests and Frequent Swarming.

    Directory of Open Access Journals (Sweden)

    J Carter Loftus

    Full Text Available The ectoparasitic mite, Varroa destructor, and the viruses that it transmits, kill the colonies of European honey bees (Apis mellifera kept by beekeepers unless the bees are treated with miticides. Nevertheless, there exist populations of wild colonies of European honey bees that are persisting without being treated with miticides. We hypothesized that the persistence of these wild colonies is due in part to their habits of nesting in small cavities and swarming frequently. We tested this hypothesis by establishing two groups of colonies living either in small hives (42 L without swarm-control treatments or in large hives (up to 168 L with swarm-control treatments. We followed the colonies for two years and compared the two groups with respect to swarming frequency, Varroa infesttion rate, disease incidence, and colony survival. Colonies in small hives swarmed more often, had lower Varroa infestation rates, had less disease, and had higher survival compared to colonies in large hives. These results indicate that the smaller nest cavities and more frequent swarming of wild colonies contribute to their persistence without mite treatments.

  3. How Honey Bee Colonies Survive in the Wild: Testing the Importance of Small Nests and Frequent Swarming

    Science.gov (United States)

    Loftus, J. Carter; Smith, Michael L.; Seeley, Thomas D.

    2016-01-01

    The ectoparasitic mite, Varroa destructor, and the viruses that it transmits, kill the colonies of European honey bees (Apis mellifera) kept by beekeepers unless the bees are treated with miticides. Nevertheless, there exist populations of wild colonies of European honey bees that are persisting without being treated with miticides. We hypothesized that the persistence of these wild colonies is due in part to their habits of nesting in small cavities and swarming frequently. We tested this hypothesis by establishing two groups of colonies living either in small hives (42 L) without swarm-control treatments or in large hives (up to 168 L) with swarm-control treatments. We followed the colonies for two years and compared the two groups with respect to swarming frequency, Varroa infesttion rate, disease incidence, and colony survival. Colonies in small hives swarmed more often, had lower Varroa infestation rates, had less disease, and had higher survival compared to colonies in large hives. These results indicate that the smaller nest cavities and more frequent swarming of wild colonies contribute to their persistence without mite treatments. PMID:26968000

  4. Estimates of hydroelectric generation using neural networks with the artificial bee colony algorithm for Turkey

    International Nuclear Information System (INIS)

    Uzlu, Ergun; Akpınar, Adem; Özturk, Hasan Tahsin; Nacar, Sinan; Kankal, Murat

    2014-01-01

    The primary objective of this study was to apply the ANN (artificial neural network) model with the ABC (artificial bee colony) algorithm to estimate annual hydraulic energy production of Turkey. GEED (gross electricity energy demand), population, AYT (average yearly temperature), and energy consumption were selected as independent variables in the model. The first part of the study compared ANN-ABC model performance with results of classical ANN models trained with the BP (back propagation) algorithm. Mean square and relative error were applied to evaluate model accuracy. The test set errors emphasized positive differences between the ANN-ABC and classical ANN models. After determining optimal configurations, three different scenarios were developed to predict future hydropower generation values for Turkey. Results showed the ANN-ABC method predicted hydroelectric generation better than the classical ANN trained with the BP algorithm. Furthermore, results indicated future hydroelectric generation in Turkey will range from 69.1 to 76.5 TWh in 2021, and the total annual electricity demand represented by hydropower supply rates will range from 14.8% to 18.0%. However, according to Vision 2023 agenda goals, the country plans to produce 30% of its electricity demand from renewable energy sources by 2023, and use 20% less energy than in 2010. This percentage renewable energy provision cannot be accomplished unless changes in energy policy and investments are not addressed and implemented. In order to achieve this goal, the Turkish government must reconsider and raise its own investments in hydropower, wind, solar, and geothermal energy, particularly hydropower. - Highlights: • This study is associated with predicting hydropower generation in Turkey. • Sensitivity analysis was performed to determine predictor variables. • GEED, population, energy consumption and AYT were used as predictor variables. • ANN-ABC predicted the hydropower generation more accurately

  5. Fearful foragers: honey bees tune colony and individual foraging to multi-predator presence and food quality.

    Directory of Open Access Journals (Sweden)

    Ken Tan

    Full Text Available Fear can have strong ecosystem effects by giving predators a role disproportionate to their actual kill rates. In bees, fear is shown through foragers avoiding dangerous food sites, thereby reducing the fitness of pollinated plants. However, it remains unclear how fear affects pollinators in a complex natural scenario involving multiple predator species and different patch qualities. We studied hornets, Vespa velutina (smaller and V. tropica (bigger preying upon the Asian honey bee, Apis cerana in China. Hornets hunted bees on flowers and were attacked by bee colonies. Bees treated the bigger hornet species (which is 4 fold more massive as more dangerous. It received 4.5 fold more attackers than the smaller hornet species. We tested bee responses to a three-feeder array with different hornet species and varying resource qualities. When all feeders offered 30% sucrose solution (w/w, colony foraging allocation, individual visits, and individual patch residence times were reduced according to the degree of danger. Predator presence reduced foraging visits by 55-79% and residence times by 17-33%. When feeders offered different reward levels (15%, 30%, or 45% sucrose, colony and individual foraging favored higher sugar concentrations. However, when balancing food quality against multiple threats (sweeter food corresponding to higher danger, colonies exhibited greater fear than individuals. Colonies decreased foraging at low and high danger patches. Individuals exhibited less fear and only decreased visits to the high danger patch. Contrasting individual with emergent colony-level effects of fear can thus illuminate how predators shape pollination by social bees.

  6. An observation study on the effects of queen age on some characteristics of honey bee colonies

    Directory of Open Access Journals (Sweden)

    Ibrahim Çakmak

    2010-01-01

    Full Text Available This study was conducted to determine the effects of the queen’s age on performance of the honeybee (A. mellifera anatoliaca colonies at nomad beekeeping conditions. Performances of the colonies, which had 0, 1, 2 and 3 year-old queens, were compared. The number of combs, brood areas, wintering ability survival rate and honey yield were determined as performance criteria. The average number of combs with bees throughout the experiment in Group I, Group II, Group III and Group IV was 10.92±0.78, 14.68±0.55, 10.10±0.60, 7.88±0.45 number combs/colony; the average of brood areas was 3078±372.5 cm2, 3668±460.3 cm2, 2215±294.0 cm2, 1665.38±241.8 cm2; the average of wintering ability was 84.3±2.9%, 88.0±3.7%, 46.6±19.0%, 26.8±16.5%; the survival rate was 100%, 100%, 60%, 40%; and the average of honey yields was 31.4±1.89 kg, 41.5±1.05 kg, 20.4±2.62 kg and 12.0±1.41 kg per colony, respectively. A significant and negative correlation between queen age and brood production (r=-80.2, colony strength (r=-62.5, wintering ability (r=-66 and honey yield (r=-75.6 were calculated (P<0.01. The colonies headed by young queens had more brood areas, longer worker colony population, better wintering ability and greater honey yield in comparison to colonies headed by old queens.

  7. Sublethal Effects of Imidacloprid on Honey Bee Colony Growth and Activity at Three Sites in the U.S.

    Science.gov (United States)

    Meikle, William G; Adamczyk, John J; Weiss, Milagra; Gregorc, Ales; Johnson, Don R; Stewart, Scott D; Zawislak, Jon; Carroll, Mark J; Lorenz, Gus M

    2016-01-01

    Imidacloprid is a neonicotinoid pesticide heavily used by the agricultural industry and shown to have negative impacts on honey bees above certain concentrations. We evaluated the effects of different imidacloprid concentrations in sugar syrup using cage and field studies, and across different environments. Honey bee colonies fed sublethal concentrations of imidicloprid (0, 5, 20 and 100 ppb) over 6 weeks in field trials at a desert site (Arizona), a site near intensive agriculture (Arkansas) and a site with little nearby agriculture but abundant natural forage (Mississippi) were monitored with respect to colony metrics, such as adult bee and brood population sizes, as well as pesticide residues. Hive weight and internal hive temperature were monitored continuously over two trials in Arizona. Colonies fed 100 ppb imidacloprid in Arizona had significantly lower adult bee populations, brood surface areas and average frame weights, and reduced temperature control, compared to colonies in one or more of the other treatment groups, and consumption rates of those colonies were lower compared to other colonies in Arizona and Arkansas, although no differences in capped brood or average frame weight were observed among treatments in Arkansas. At the Mississippi site, also rich in alternative forage, colonies fed 5 ppb imidacloprid had less capped brood than control colonies, but contamination of control colonies was detected. In contrast, significantly higher daily hive weight variability among colonies fed 5 ppb imidacloprid in Arizona suggested greater foraging activity during a nectar flow post treatment, than any other treatment group. Imidacloprid concentrations in stored honey corresponded well with the respective syrup concentrations fed to the colonies and remained stable within the hive for at least 7 months after the end of treatment.

  8. Nutritional aspects of honey bee-collected pollen and constraints on colony development in the eastern Mediterranean.

    Science.gov (United States)

    Avni, Dorit; Hendriksma, Harmen P; Dag, Arnon; Uni, Zehava; Shafir, Sharoni

    2014-10-01

    Pollen is the main protein and lipid source for honey bees (Apis mellifera), and nutritionally impoverished landscapes pose a threat to colony development. To determine colony nutritional demands, we analyzed a yearly cycle of bee-collected pollen from colonies in the field and compared it to colony worker production and honey bee body composition, for the first time in social insects. We monitored monthly bee production in ten colonies at each of seven sites throughout Israel, and trapped pollen bi-monthly in five additional colonies at each of four of these sites. Pollen mixtures from each sampling date and site were analyzed for weight, total protein, total fatty acids (FAs), and FA composition. Compared to more temperate climates, the eastern Mediterranean allows a relatively high yearly colony growth of ca. 300,000-400,000 bees. Colonies at higher elevation above sea level showed lower growth rates. Queen egg-laying rate did not seem to limit growth, as peaks in capped brood areas showed that queens lay a prolific 2000 eggs a day on average, with up to 3300 eggs in individual cases. Pollen uptake varied significantly among sites and seasons, with an overall annual mean total 16.8kg per colony, containing 7.14kg protein and 677g fat. Overall mean pollen protein content was high (39.8%), and mean total FA content was 3.8%. Production cost, as expressed by the amount of nutrient used per bee, was least variable for linoleic acid and protein, suggesting these as the best descriptive variables for total number of bees produced. Linolenic acid levels in pollen during the autumn were relatively low, and supplementing colonies with this essential FA may mitigate potential nutritional deficiency. The essentiality of linoleic and linolenic acids was consistent with these FAs' tendency to be present at higher levels in collected pollen than in the expected nutrients in bee bodies, demonstrating a well-developed adjustment between pollinator nutritional demands and the

  9. Radar Waveform Recognition Based on Time-Frequency Analysis and Artificial Bee Colony-Support Vector Machine

    Directory of Open Access Journals (Sweden)

    Lutao Liu

    2018-04-01

    Full Text Available In this paper, a system for identifying eight kinds of radar waveforms is explored. The waveforms are the binary phase shift keying (BPSK, Costas codes, linear frequency modulation (LFM and polyphase codes (including P1, P2, P3, P4 and Frank codes. The features of power spectral density (PSD, moments and cumulants, instantaneous properties and time-frequency analysis are extracted from the waveforms and three new features are proposed. The classifier is support vector machine (SVM, which is optimized by artificial bee colony (ABC algorithm. The system shows well robustness, excellent computational complexity and high recognition rate under low signal-to-noise ratio (SNR situation. The simulation results indicate that the overall recognition rate is 92% when SNR is −4 dB.

  10. Effects of brood pheromone (SuperBoost) on consumption of protein supplement and growth of honey bee (Hymenoptera: Apidae) colonies during fall in a northern temperate climate.

    Science.gov (United States)

    Sagili, Ramesh R; Breece, Carolyn R

    2012-08-01

    Honey bee, Apis mellifera L. (Hymenoptera: Apidae), nutrition is vital for colony growth and maintenance of a robust immune system. Brood rearing in honey bee colonies is highly dependent on protein availability. Beekeepers in general provide protein supplement to colonies during periods of pollen dearth. Honey bee brood pheromone is a blend of methyl and ethyl fatty acid esters extractable from cuticle of honey bee larvae that communicates the presence of larvae in a colony. Honey bee brood pheromone has been shown to increase protein supplement consumption and growth of honey bee colonies in a subtropical winter climate. Here, we tested the hypothesis that synthetic brood pheromone (SuperBoost) has the potential to increase protein supplement consumption during fall in a temperate climate and thus increase colony growth. The experiments were conducted in two locations in Oregon during September and October 2009. In both the experiments, colonies receiving brood pheromone treatment consumed significantly higher protein supplement and had greater brood area and adult bees than controls. Results from this study suggest that synthetic brood pheromone may be used to stimulate honey bee colony growth by stimulating protein supplement consumption during fall in a northern temperate climate, when majority of the beekeepers feed protein supplement to their colonies.

  11. A survey of honey bee colony losses in the U.S., fall 2007 to spring 2008.

    Directory of Open Access Journals (Sweden)

    Dennis van Engelsdorp

    Full Text Available BACKGROUND: Honey bees are an essential component of modern agriculture. A recently recognized ailment, Colony Collapse Disorder (CCD, devastates colonies, leaving hives with a complete lack of bees, dead or alive. Up to now, estimates of honey bee population decline have not included losses occurring during the wintering period, thus underestimating actual colony mortality. Our survey quantifies the extent of colony losses in the United States over the winter of 2007-2008. METHODOLOGY/PRINCIPAL FINDINGS: Surveys were conducted to quantify and identify management factors (e.g. operation size, hive migration that contribute to high colony losses in general and CCD symptoms in particular. Over 19% of the country's estimated 2.44 million colonies were surveyed. A total loss of 35.8% of colonies was recorded; an increase of 11.4% compared to last year. Operations that pollinated almonds lost, on average, the same number of colonies as those that did not. The 37.9% of operations that reported having at least some of their colonies die with a complete lack of bees had a total loss of 40.8% of colonies compared to the 17.1% loss reported by beekeepers without this symptom. Large operations were more likely to have this symptom suggesting that a contagious condition may be a causal factor. Sixty percent of all colonies that were reported dead in this survey died without dead bees, and thus possibly suffered from CCD. In PA, losses varied with region, indicating that ambient temperature over winter may be an important factor. CONCLUSIONS/SIGNIFICANCE: Of utmost importance to understanding the recent losses and CCD is keeping track of losses over time and on a large geographic scale. Given that our surveys are representative of the losses across all beekeeping operations, between 0.75 and 1.00 million honey bee colonies are estimated to have died in the United States over the winter of 2007-2008. This article is an extensive survey of U.S. beekeepers

  12. Prediction of social structure and genetic relatedness in colonies of the facultative polygynous stingless bee Melipona bicolor (Hymenoptera, Apidae).

    Science.gov (United States)

    Dos Reis, Evelyze Pinheiro; de Oliveira Campos, Lucio Antonio; Tavares, Mara Garcia

    2011-04-01

    Stingless bee colonies typically consist of one single-mated mother queen and her worker offspring. The stingless bee Melipona bicolor (Hymenoptera: Apidae) shows facultative polygyny, which makes this species particularly suitable for testing theoretical expectations concerning social behavior. In this study, we investigated the social structure and genetic relatedness among workers from eight natural and six manipulated colonies of M. bicolor over a period of one year. The populations of M. bicolor contained monogynous and polygynous colonies. The estimated genetic relatedness among workers from monogynous and polygynous colonies was 0.75 ± 0.12 and 0.53 ± 0.16 (mean ± SEM), respectively. Although the parental genotypes had significant effects on genetic relatedness in monogynous and polygynous colonies, polygyny markedly decreased the relatedness among nestmate workers. Our findings also demonstrate that polygyny in M. bicolor may arise from the adoption of related or unrelated queens.

  13. Prediction of social structure and genetic relatedness in colonies of the facultative polygynous stingless bee Melipona bicolor (Hymenoptera, Apidae

    Directory of Open Access Journals (Sweden)

    Evelyze Pinheiro dos Reis

    2011-01-01

    Full Text Available Stingless bee colonies typically consist of one single-mated mother queen and her worker offspring. The stingless bee Melipona bicolor (Hymenoptera: Apidae shows facultative polygyny, which makes this species particularly suitable for testing theoretical expectations concerning social behavior. In this study, we investigated the social structure and genetic relatedness among workers from eight natural and six manipulated colonies of M. bicolor over a period of one year. The populations of M. bicolor contained monogynous and polygynous colonies. The estimated genetic relatedness among workers from monogynous and polygynous colonies was 0.75 ± 0.12 and 0.53 ± 0.16 (mean ± SEM, respectively. Although the parental genotypes had significant effects on genetic relatedness in monogynous and polygynous colonies, polygyny markedly decreased the relatedness among nestmate workers. Our findings also demonstrate that polygyny in M. bicolor may arise from the adoption of related or unrelated queens.

  14. Warehouse stocking optimization based on dynamic ant colony genetic algorithm

    Science.gov (United States)

    Xiao, Xiaoxu

    2018-04-01

    In view of the various orders of FAW (First Automotive Works) International Logistics Co., Ltd., the SLP method is used to optimize the layout of the warehousing units in the enterprise, thus the warehouse logistics is optimized and the external processing speed of the order is improved. In addition, the relevant intelligent algorithms for optimizing the stocking route problem are analyzed. The ant colony algorithm and genetic algorithm which have good applicability are emphatically studied. The parameters of ant colony algorithm are optimized by genetic algorithm, which improves the performance of ant colony algorithm. A typical path optimization problem model is taken as an example to prove the effectiveness of parameter optimization.

  15. Colony-level variation in pollen collection and foraging preferences among wild-caught bumble bees (Hymenoptera: Apidae).

    Science.gov (United States)

    Saifuddin, Mustafa; Jha, Shalene

    2014-04-01

    Given that many pollinators have exhibited dramatic declines related to habitat destruction, an improved understanding of pollinator resource collection across human-altered landscapes is essential to conservation efforts. Despite the importance of bumble bees (Bombus spp.) as global pollinators, little is known regarding how pollen collection patterns vary between individuals, colonies, and landscapes. In this study, Vosnesensky bumble bees (Bombus vosnesenskii Radoszkowski) were collected from a range of human-altered and natural landscapes in northern California. Extensive vegetation surveys and Geographic Information System (GIS)-based habitat classifications were conducted at each site, bees were genotyped to identify colony mates, and pollen loads were examined to identify visited plants. In contrast to predictions based on strong competitive interactions, pollen load composition was significantly more similar for bees captured in a shared study region compared with bees throughout the research area but was not significantly more similar for colony mates. Preference analyses revealed that pollen loads were not composed of the most abundant plant species per study region. The majority of ranked pollen preference lists were significantly correlated for pairwise comparisons of colony mates and individuals within a study region, whereas the majority of pairwise comparisons of ranked pollen preference lists between individuals located at separate study regions were uncorrelated. Results suggest that pollen load composition and foraging preferences are similar for bees throughout a shared landscape regardless of colony membership. The importance of native plant species in pollen collection is illustrated through preference analyses, and we suggest prioritization of specific rare native plant species for enhanced bumble bee pollen collection.

  16. Aircraft technology portfolio optimization using ant colony optimization

    Science.gov (United States)

    Villeneuve, Frederic J.; Mavris, Dimitri N.

    2012-11-01

    Technology portfolio selection is a combinatorial optimization problem often faced with a large number of combinations and technology incompatibilities. The main research question addressed in this article is to determine if Ant Colony Optimization (ACO) is better suited than Genetic Algorithms (GAs) and Simulated Annealing (SA) for technology portfolio optimization when incompatibility constraints between technologies are present. Convergence rate, capability to find optima, and efficiency in handling of incompatibilities are the three criteria of comparison. The application problem consists of finding the best technology portfolio from 29 aircraft technologies. The results show that ACO and GAs converge faster and find optima more easily than SA, and that ACO can optimize portfolios with technology incompatibilities without using penalty functions. This latter finding paves the way for more use of ACO when the number of constraints increases, such as in the technology and concept selection for complex engineering systems.

  17. Correlations between land covers and honey bee colony losses in a country with industrialized and rural regions.

    Science.gov (United States)

    Clermont, Antoine; Eickermann, Michael; Kraus, François; Hoffmann, Lucien; Beyer, Marco

    2015-11-01

    High levels of honey bee colony losses were recently reported from Canada, China, Europe, Israel, Turkey and the United States, raising concerns of a global pollinator decline and questioning current land use practices, in particular intense agricultural cropping systems. Sixty-seven crops (data from the years 2010-2012) and 66 mid-term stable land cover classes (data from 2007) were analysed for statistical relationships with the honey bee colony losses experienced over the winters 2010/11-2012/13 in Luxembourg (Western Europe). The area covered by each land cover class, the shortest distance between each land cover class and the respective apiary, the number of plots covered by each land use class and the size of the biggest plot of each land cover class within radii of 2 km and 5 km around 166 apiaries (2010), 184 apiaries (2011) and 188 apiaries (2012) were tested for correlations with honey bee colony losses (% per apiary) experienced in the winter following the season when the crops were grown. Artificial water bodies, open urban areas, large industrial facilities including heavy industry, railways and associated installations, buildings and installations with socio-cultural purpose, camping-, sports-, playgrounds, golf courts, oilseed crops other than oilseed rape like sunflower or linseed, some spring cereals and former forest clearcuts or windthrows were the land cover classes most frequently associated with high honey bee colony losses. Grain maize, mixed forest and mixed coniferous forest were the land cover classes most frequently associated with low honey bee colony losses. The present data suggest that land covers related to transport, industry and leisure may have made a more substantial contribution to winter honey bee colony losses in developed countries than anticipated so far. Recommendations for the positioning of apiaries are discussed. Copyright © 2015. Published by Elsevier B.V.

  18. A Comparative Study of Environmental Conditions, Bee Management and the Epidemiological Situation in Apiaries Varying in the Level of Colony Losses

    Directory of Open Access Journals (Sweden)

    Pohorecka Krystyna

    2014-12-01

    Full Text Available Explaining the reasons for the increased mortality of the honey bee (Apis mellifera L. in recent years, in Europe and North America, has become a global research priority in apicultural science. Our project was aimed at determining the relationship between environmental conditions, beekeeping techniques, the epidemiological situation of pathogens, and the mortality rate of bee colonies. Dead bee samples were collected by beekeepers from 2421 colonies. The samples were examined for the presence of V. destructor, Nosema spp. (Nosema apis and Nosema ceranae, chronic bee paralysis virus (CBPV, acute bee paralysis virus (ABPV, deformed wing virus (DWV, and Israeli acute paralysis virus (IAPV.

  19. The effect of queen pheromone status on Varroa mite removal from honey bee colonies with different grooming ability.

    Science.gov (United States)

    Bahreini, Rassol; Currie, Robert W

    2015-07-01

    The objective of this study was to assess the effects of honey bees (Apis mellifera L.) with different grooming ability and queen pheromone status on mortality rates of Varroa mites (Varroa destructor Anderson and Trueman), mite damage, and mortality rates of honey bees. Twenty-four small queenless colonies containing either stock selected for high rates of mite removal (n = 12) or unselected stock (n = 12) were maintained under constant darkness at 5 °C. Colonies were randomly assigned to be treated with one of three queen pheromone status treatments: (1) caged, mated queen, (2) a synthetic queen mandibular pheromone lure (QMP), or (3) queenless with no queen substitute. The results showed overall mite mortality rate was greater in stock selected for grooming than in unselected stock. There was a short term transitory increase in bee mortality rates in selected stock when compared to unselected stock. The presence of queen pheromone from either caged, mated queens or QMP enhanced mite removal from clusters of bees relative to queenless colonies over short periods of time and increased the variation in mite mortality over time relative to colonies without queen pheromone, but did not affect the proportion of damaged mites. The effects of source of bees on mite damage varied with time but damage to mites was not reliably related to mite mortality. In conclusion, this study showed differential mite removal of different stocks was possible under low temperature. Queen status should be considered when designing experiments using bioassays for grooming response.

  20. Towards integrated control of varroa: effect of variation in hygienic behaviour among honey bee colonies on mite population increase and deformed wing virus incidence

    DEFF Research Database (Denmark)

    Toufailia, Hasan M Al; Amiri, Esmaeil; Scandian, Luciano

    2014-01-01

    by varroa. We treated 42 broodless honey bee colonies with oxalic acid in early January 2013 to reduce varroa populations to low levels, which we quantified by extracting mites from a sample of worker bees. We quantified varroa levels, again when the colonies were broodless, 48 weeks later. During...

  1. A Novel Artificial Bee Colony Algorithm Based on Internal-Feedback Strategy for Image Template Matching

    Directory of Open Access Journals (Sweden)

    Bai Li

    2014-01-01

    Full Text Available Image template matching refers to the technique of locating a given reference image over a source image such that they are the most similar. It is a fundamental mission in the field of visual target recognition. In general, there are two critical aspects of a template matching scheme. One is similarity measurement and the other is best-match location search. In this work, we choose the well-known normalized cross correlation model as a similarity criterion. The searching procedure for the best-match location is carried out through an internal-feedback artificial bee colony (IF-ABC algorithm. IF-ABC algorithm is highlighted by its effort to fight against premature convergence. This purpose is achieved through discarding the conventional roulette selection procedure in the ABC algorithm so as to provide each employed bee an equal chance to be followed by the onlooker bees in the local search phase. Besides that, we also suggest efficiently utilizing the internal convergence states as feedback guidance for searching intensity in the subsequent cycles of iteration. We have investigated four ideal template matching cases as well as four actual cases using different searching algorithms. Our simulation results show that the IF-ABC algorithm is more effective and robust for this template matching mission than the conventional ABC and two state-of-the-art modified ABC algorithms do.

  2. Mating frequencies of honey bee queens (Apis mellifera L.) in a population of feral colonies in the Northeastern United States.

    Science.gov (United States)

    Tarpy, David R; Delaney, Deborah A; Seeley, Thomas D

    2015-01-01

    Across their introduced range in North America, populations of feral honey bee (Apis mellifera L.) colonies have supposedly declined in recent decades as a result of exotic parasites, most notably the ectoparasitic mite Varroa destructor. Nonetheless, recent studies have documented several wild populations of colonies that have persisted. The extreme polyandry of honey bee queens-and the increased intracolony genetic diversity it confers-has been attributed, in part, to improved disease resistance and may be a factor in the survival of these populations of feral colonies. We estimated the mating frequencies of queens in feral colonies in the Arnot Forest in New York State to determine if the level of polyandry of these queens is especially high and so might contribute to their survival success. We genotyped the worker offspring from 10 feral colonies in the Arnot Forest of upstate New York, as well as those from 20 managed colonies closest to this forest. We found no significant differences in mean mating frequency between the feral and managed queens, suggesting that queens in the remote, low-density population of colonies in the Arnot Forest are neither mate-limited nor adapted to mate at an especially high frequency. These findings support the hypothesis that the hyperpolyandry of honey bees has been shaped on an evolutionary timescale rather than on an ecological one.

  3. Mating frequencies of honey bee queens (Apis mellifera L. in a population of feral colonies in the Northeastern United States.

    Directory of Open Access Journals (Sweden)

    David R Tarpy

    Full Text Available Across their introduced range in North America, populations of feral honey bee (Apis mellifera L. colonies have supposedly declined in recent decades as a result of exotic parasites, most notably the ectoparasitic mite Varroa destructor. Nonetheless, recent studies have documented several wild populations of colonies that have persisted. The extreme polyandry of honey bee queens-and the increased intracolony genetic diversity it confers-has been attributed, in part, to improved disease resistance and may be a factor in the survival of these populations of feral colonies. We estimated the mating frequencies of queens in feral colonies in the Arnot Forest in New York State to determine if the level of polyandry of these queens is especially high and so might contribute to their survival success. We genotyped the worker offspring from 10 feral colonies in the Arnot Forest of upstate New York, as well as those from 20 managed colonies closest to this forest. We found no significant differences in mean mating frequency between the feral and managed queens, suggesting that queens in the remote, low-density population of colonies in the Arnot Forest are neither mate-limited nor adapted to mate at an especially high frequency. These findings support the hypothesis that the hyperpolyandry of honey bees has been shaped on an evolutionary timescale rather than on an ecological one.

  4. Mating Frequencies of Honey Bee Queens (Apis mellifera L.) in a Population of Feral Colonies in the Northeastern United States

    Science.gov (United States)

    Tarpy, David R.; Delaney, Deborah A.; Seeley, Thomas D.

    2015-01-01

    Across their introduced range in North America, populations of feral honey bee (Apis mellifera L.) colonies have supposedly declined in recent decades as a result of exotic parasites, most notably the ectoparasitic mite Varroa destructor. Nonetheless, recent studies have documented several wild populations of colonies that have persisted. The extreme polyandry of honey bee queens—and the increased intracolony genetic diversity it confers—has been attributed, in part, to improved disease resistance and may be a factor in the survival of these populations of feral colonies. We estimated the mating frequencies of queens in feral colonies in the Arnot Forest in New York State to determine if the level of polyandry of these queens is especially high and so might contribute to their survival success. We genotyped the worker offspring from 10 feral colonies in the Arnot Forest of upstate New York, as well as those from 20 managed colonies closest to this forest. We found no significant differences in mean mating frequency between the feral and managed queens, suggesting that queens in the remote, low-density population of colonies in the Arnot Forest are neither mate-limited nor adapted to mate at an especially high frequency. These findings support the hypothesis that the hyperpolyandry of honey bees has been shaped on an evolutionary timescale rather than on an ecological one. PMID:25775410

  5. Identifying nuclear power plant transients using the Discrete Binary Artificial Bee Colony (DBABC) algorithm

    Energy Technology Data Exchange (ETDEWEB)

    Oliveira, Iona M.S. de; Schirru, Roberto, E-mail: ioliveira@con.ufrj.br, E-mail: schirru@lmp.ufrj.br [Coordenacoa dos Programas de Pos-Graduacao em Engenharia (UFRJ/PEN/COPPE), Rio de Janeiro, RJ (Brazil). Programa de Engenharia Nuclear

    2011-07-01

    The identification of possible transients in a nuclear power plant is a highly relevant problem. This is mainly due to the fact that the operation of a nuclear power plant involves a large number of state variables whose behaviors are extremely dynamic. In risk situations, besides the huge cognitive overload that operators are submitted to, there is also the problem related with the considerable decrease in the effective time for correct decision making. To minimize these problems and help operators to make the corrective actions in due time, this paper presents a new contribution in this area and introduces an experimental transient identification system based exclusively on the abilities of the Discrete Binary Artificial Bee Colony (DBABC) algorithm to find the best centroid positions that correctly identifies a transient in a nuclear power plant. The DBABC is a reworking of the Artificial Bee Colony (ABC) algorithm which presents the advantage of operating in both continuous and discrete search spaces. Through the analysis of experimental results, the effective performance of the proposed DBABC algorithm is shown against some well known best performing algorithms from the literature. (author)

  6. Evaluation of Cutting Performance of Diamond Saw Machine Using Artificial Bee Colony (ABC Algorithm

    Directory of Open Access Journals (Sweden)

    Masoud Akhyani

    2017-12-01

    Full Text Available Artificial Intelligence (AI techniques are used for solving the intractable engineering problems. In this study, it is aimed to study the application of artificial bee colony algorithm for predicting the performance of circular diamond saw in sawing of hard rocks. For this purpose, varieties of fourteen types of hard rocks were cut in laboratory using a cutting rig at 5 mm depth of cut, 40 cm/min feed rate and 3000 rpm peripheral speed. Four major mechanical and physical properties of studied rocks such as uniaxial compressive strength (UCS, Schimazek abrasivity factor (SF-a, Mohs hardness (Mh, and Young’s modulus (Ym were determined in rock mechanic laboratory. Artificial bee colony (ABC was used to classify the performance of circular diamond saw based on mentioned mechanical properties of rocks. Ampere consumption and wear rate of diamond saw were selected as criteria to evaluate the result of ABC algorithm. Ampere consumption was determined during cutting process and the average wear rate of diamond saw was calculated from width, length and height loss. The results of comparison between ABC’s results and cutting performance (ampere consumption and wear rate of diamond saw indicated the ability of metaheuristic algorithm such as ABC to evaluate the cutting performance.

  7. Identifying nuclear power plant transients using the Discrete Binary Artificial Bee Colony (DBABC) algorithm

    International Nuclear Information System (INIS)

    Oliveira, Iona M.S. de; Schirru, Roberto

    2011-01-01

    The identification of possible transients in a nuclear power plant is a highly relevant problem. This is mainly due to the fact that the operation of a nuclear power plant involves a large number of state variables whose behaviors are extremely dynamic. In risk situations, besides the huge cognitive overload that operators are submitted to, there is also the problem related with the considerable decrease in the effective time for correct decision making. To minimize these problems and help operators to make the corrective actions in due time, this paper presents a new contribution in this area and introduces an experimental transient identification system based exclusively on the abilities of the Discrete Binary Artificial Bee Colony (DBABC) algorithm to find the best centroid positions that correctly identifies a transient in a nuclear power plant. The DBABC is a reworking of the Artificial Bee Colony (ABC) algorithm which presents the advantage of operating in both continuous and discrete search spaces. Through the analysis of experimental results, the effective performance of the proposed DBABC algorithm is shown against some well known best performing algorithms from the literature. (author)

  8. Honey bee colonies act as reservoirs for two Spiroplasma facultative symbionts and incur complex, multiyear infection dynamics

    Science.gov (United States)

    Schwarz, Ryan S; Teixeira, Érica Weinstein; Tauber, James P; Birke, Juliane M; Martins, Marta Fonseca; Fonseca, Isabela; Evans, Jay D

    2014-01-01

    Two species of Spiroplasma (Mollicutes) bacteria were isolated from and described as pathogens of the European honey bee, Apis mellifera, ∼30 years ago but recent information on them is lacking despite global concern to understand bee population declines. Here we provide a comprehensive survey for the prevalence of these two Spiroplasma species in current populations of honey bees using improved molecular diagnostic techniques to assay multiyear colony samples from North America (U.S.A.) and South America (Brazil). Significant annual and seasonal fluctuations of Spiroplasma apis and Spiroplasma melliferum prevalence in colonies from the U.S.A. (n = 616) and Brazil (n = 139) occurred during surveys from 2011 through 2013. Overall, 33% of U.S.A. colonies and 54% of Brazil colonies were infected by Spiroplasma spp., where S. melliferum predominated over S. apis in both countries (25% vs. 14% and 44% vs. 38% frequency, respectively). Colonies were co-infected by both species more frequently than expected in both countries and at a much higher rate in Brazil (52%) compared to the U.S.A. (16.5%). U.S.A. samples showed that both species were prevalent not only during spring, as expected from prior research, but also during other seasons. These findings demonstrate that the model of honey bee spiroplasmas as springtime-restricted pathogens needs to be broadened and their role as occasional pathogens considered in current contexts. PMID:24771723

  9. Lower virus infections in Varroa destructor-infested and uninfested brood and adult honey bees (Apis mellifera) of a low mite population growth colony compared to a high mite population growth colony.

    Science.gov (United States)

    Emsen, Berna; Hamiduzzaman, Mollah Md; Goodwin, Paul H; Guzman-Novoa, Ernesto

    2015-01-01

    A comparison was made of the prevalence and relative quantification of deformed wing virus (DWV), Israeli acute paralysis virus (IAPV), black queen cell virus (BQCV), Kashmir bee virus (KBV), acute bee paralysis virus (ABPV) and sac brood virus (SBV) in brood and adult honey bees (Apis mellifera) from colonies selected for high (HMP) and low (LMP) Varroa destructor mite population growth. Two viruses, ABPV and SBV, were never detected. For adults without mite infestation, DWV, IAPV, BQCV and KBV were detected in the HMP colony; however, only BQCV was detected in the LMP colony but at similar levels as in the HMP colony. With mite infestation, the four viruses were detected in adults of the HMP colony but all at higher amounts than in the LMP colony. For brood without mite infestation, DWV and IAPV were detected in the HMP colony, but no viruses were detected in the LMP colony. With mite infestation of brood, the four viruses were detected in the HMP colony, but only DWV and IAPV were detected and at lower amounts in the LMP colony. An epidemiological explanation for these results is that pre-experiment differences in virus presence and levels existed between the HMP and LMP colonies. It is also possible that low V. destructor population growth in the LMP colony resulted in the bees being less exposed to the mite and thus less likely to have virus infections. LMP and HMP bees may have also differed in susceptibility to virus infection.

  10. Lower Virus Infections in Varroa destructor-Infested and Uninfested Brood and Adult Honey Bees (Apis mellifera) of a Low Mite Population Growth Colony Compared to a High Mite Population Growth Colony

    Science.gov (United States)

    Emsen, Berna; Hamiduzzaman, Mollah Md.; Goodwin, Paul H.; Guzman-Novoa, Ernesto

    2015-01-01

    A comparison was made of the prevalence and relative quantification of deformed wing virus (DWV), Israeli acute paralysis virus (IAPV), black queen cell virus (BQCV), Kashmir bee virus (KBV), acute bee paralysis virus (ABPV) and sac brood virus (SBV) in brood and adult honey bees (Apis mellifera) from colonies selected for high (HMP) and low (LMP) Varroa destructor mite population growth. Two viruses, ABPV and SBV, were never detected. For adults without mite infestation, DWV, IAPV, BQCV and KBV were detected in the HMP colony; however, only BQCV was detected in the LMP colony but at similar levels as in the HMP colony. With mite infestation, the four viruses were detected in adults of the HMP colony but all at higher amounts than in the LMP colony. For brood without mite infestation, DWV and IAPV were detected in the HMP colony, but no viruses were detected in the LMP colony. With mite infestation of brood, the four viruses were detected in the HMP colony, but only DWV and IAPV were detected and at lower amounts in the LMP colony. An epidemiological explanation for these results is that pre-experiment differences in virus presence and levels existed between the HMP and LMP colonies. It is also possible that low V. destructor population growth in the LMP colony resulted in the bees being less exposed to the mite and thus less likely to have virus infections. LMP and HMP bees may have also differed in susceptibility to virus infection. PMID:25723540

  11. An Observational Study of Honey Bee Colony Winter Losses and Their Association with Varroa destructor, Neonicotinoids and Other Risk Factors

    Science.gov (United States)

    van der Zee, Romée; Gray, Alison; Pisa, Lennard; de Rijk, Theo

    2015-01-01

    This article presents results of an analysis of honey bee losses over the winter of 2011-2012 in the Netherlands, from a sample of 86 colonies, located at 43 apiaries. The apiaries were selected using spatially stratified random sampling. Colony winter loss data were collected and related to various measures of colony strength recorded in summer, as well as data from laboratory analysis of sample material taken from two selected colonies in each of the 43 apiaries. The logistic regression model which best explained the risk of winter loss included, in order of statistical importance, the variables (1) Varroa destructor mite infestation rate in October 2011, (2) presence of the cyano-substituted neonicotinoids acetamiprid or thiacloprid in the first 2 weeks of August 2011 in at least one of the honey bee matrices honey, bees or bee bread (pollen), (3) presence of Brassica napus (oilseed rape) or Sinapis arvensis (wild mustard) pollen in bee bread in early August 2011, and (4) a measure of the unexplained winter losses for the postal code area where the colonies were located, obtained from a different dataset. We consider in the discussion that reduced opportunities for foraging in July and August because of bad weather may have added substantially to the adverse effects of acetamiprid and thiacloprid. A novel feature of this work is its use of postal code random effects from two other independent datasets collected in the annual national monitoring by questionnaires of winter losses of honey bees in the Netherlands. These were used to plan the sample selection and also in the model fitting of the data in this study. It should however be noted that the results of the present pilot study are based on limited data, which may consequently reveal strong factors but fail to demonstrate possible interaction effects. PMID:26154346

  12. An Observational Study of Honey Bee Colony Winter Losses and Their Association with Varroa destructor, Neonicotinoids and Other Risk Factors.

    Directory of Open Access Journals (Sweden)

    Romée van der Zee

    Full Text Available This article presents results of an analysis of honey bee losses over the winter of 2011-2012 in the Netherlands, from a sample of 86 colonies, located at 43 apiaries. The apiaries were selected using spatially stratified random sampling. Colony winter loss data were collected and related to various measures of colony strength recorded in summer, as well as data from laboratory analysis of sample material taken from two selected colonies in each of the 43 apiaries. The logistic regression model which best explained the risk of winter loss included, in order of statistical importance, the variables (1 Varroa destructor mite infestation rate in October 2011, (2 presence of the cyano-substituted neonicotinoids acetamiprid or thiacloprid in the first 2 weeks of August 2011 in at least one of the honey bee matrices honey, bees or bee bread (pollen, (3 presence of Brassica napus (oilseed rape or Sinapis arvensis (wild mustard pollen in bee bread in early August 2011, and (4 a measure of the unexplained winter losses for the postal code area where the colonies were located, obtained from a different dataset. We consider in the discussion that reduced opportunities for foraging in July and August because of bad weather may have added substantially to the adverse effects of acetamiprid and thiacloprid. A novel feature of this work is its use of postal code random effects from two other independent datasets collected in the annual national monitoring by questionnaires of winter losses of honey bees in the Netherlands. These were used to plan the sample selection and also in the model fitting of the data in this study. It should however be noted that the results of the present pilot study are based on limited data, which may consequently reveal strong factors but fail to demonstrate possible interaction effects.

  13. An Observational Study of Honey Bee Colony Winter Losses and Their Association with Varroa destructor, Neonicotinoids and Other Risk Factors.

    Science.gov (United States)

    van der Zee, Romée; Gray, Alison; Pisa, Lennard; de Rijk, Theo

    2015-01-01

    This article presents results of an analysis of honey bee losses over the winter of 2011-2012 in the Netherlands, from a sample of 86 colonies, located at 43 apiaries. The apiaries were selected using spatially stratified random sampling. Colony winter loss data were collected and related to various measures of colony strength recorded in summer, as well as data from laboratory analysis of sample material taken from two selected colonies in each of the 43 apiaries. The logistic regression model which best explained the risk of winter loss included, in order of statistical importance, the variables (1) Varroa destructor mite infestation rate in October 2011, (2) presence of the cyano-substituted neonicotinoids acetamiprid or thiacloprid in the first 2 weeks of August 2011 in at least one of the honey bee matrices honey, bees or bee bread (pollen), (3) presence of Brassica napus (oilseed rape) or Sinapis arvensis (wild mustard) pollen in bee bread in early August 2011, and (4) a measure of the unexplained winter losses for the postal code area where the colonies were located, obtained from a different dataset. We consider in the discussion that reduced opportunities for foraging in July and August because of bad weather may have added substantially to the adverse effects of acetamiprid and thiacloprid. A novel feature of this work is its use of postal code random effects from two other independent datasets collected in the annual national monitoring by questionnaires of winter losses of honey bees in the Netherlands. These were used to plan the sample selection and also in the model fitting of the data in this study. It should however be noted that the results of the present pilot study are based on limited data, which may consequently reveal strong factors but fail to demonstrate possible interaction effects.

  14. An Improved Marriage in Honey Bees Optimization Algorithm for Single Objective Unconstrained Optimization

    Directory of Open Access Journals (Sweden)

    Yuksel Celik

    2013-01-01

    Full Text Available Marriage in honey bees optimization (MBO is a metaheuristic optimization algorithm developed by inspiration of the mating and fertilization process of honey bees and is a kind of swarm intelligence optimizations. In this study we propose improved marriage in honey bees optimization (IMBO by adding Levy flight algorithm for queen mating flight and neighboring for worker drone improving. The IMBO algorithm’s performance and its success are tested on the well-known six unconstrained test functions and compared with other metaheuristic optimization algorithms.

  15. Within-Colony Variation in the Immunocompetency of Managed and Feral Honey Bees (Apis mellifera L. in Different Urban Landscapes

    Directory of Open Access Journals (Sweden)

    R. Holden Appler

    2015-10-01

    Full Text Available Urbanization has the potential to dramatically affect insect populations worldwide, although its effects on pollinator populations are just beginning to be understood. We compared the immunocompetency of honey bees sampled from feral (wild-living and managed (beekeeper-owned honey bee colonies. We sampled foragers from feral and managed colonies in rural, suburban, and urban landscapes in and around Raleigh, NC, USA. We then analyzed adult workers using two standard bioassays for insect immune function (encapsulation response and phenoloxidase activity. We found that there was far more variation within colonies for encapsulation response or phenoloxidase activity than among rural to urban landscapes, and we did not observe any significant difference in immune response between feral and managed bees. These findings suggest that social pollinators, like honey bees, may be sufficiently robust or variable in their immune responses to obscure any subtle effects of urbanization. Additional studies of immune physiology and disease ecology of social and solitary bees in urban, suburban, and natural ecosystems will provide insights into the relative effects of changing urban environments on several important factors that influence pollinator productivity and health.

  16. Within-Colony Variation in the Immunocompetency of Managed and Feral Honey Bees (Apis mellifera L.) in Different Urban Landscapes.

    Science.gov (United States)

    Appler, R Holden; Frank, Steven D; Tarpy, David R

    2015-10-29

    Urbanization has the potential to dramatically affect insect populations worldwide, although its effects on pollinator populations are just beginning to be understood. We compared the immunocompetency of honey bees sampled from feral (wild-living) and managed (beekeeper-owned) honey bee colonies. We sampled foragers from feral and managed colonies in rural, suburban, and urban landscapes in and around Raleigh, NC, USA. We then analyzed adult workers using two standard bioassays for insect immune function (encapsulation response and phenoloxidase activity). We found that there was far more variation within colonies for encapsulation response or phenoloxidase activity than among rural to urban landscapes, and we did not observe any significant difference in immune response between feral and managed bees. These findings suggest that social pollinators, like honey bees, may be sufficiently robust or variable in their immune responses to obscure any subtle effects of urbanization. Additional studies of immune physiology and disease ecology of social and solitary bees in urban, suburban, and natural ecosystems will provide insights into the relative effects of changing urban environments on several important factors that influence pollinator productivity and health.

  17. Differential performance of honey bee colonies selected for bee-pollen production through instrumental insemination and free-mating technique

    Directory of Open Access Journals (Sweden)

    I.M. de Mattos

    Full Text Available ABSTRACT The use of bee-pollen as a nutritional supplement or as a production-enhancing agent in livestock has increased the demand for this product worldwide. Despite the current importance of this niche within the apiculture industry, few studies have addressed the pollen production. We tested the performance of free-mated (FM and instrumentally inseminated queens (IQ in order to establish the effect of different breeding systems on pollen production. The F1 generation of IQ queens produced 153.95±42.83g/day, showing a significant improvement on the pollen production (2.74 times when compared to the parental generation (51.83±7.84g/day. The F1 generation of free-mated queens produced 100.07±8.23 g/day, which increased by 1.78 times when compared to the parental generation. Furthermore, we observed a statistically significant difference between the pollen production between colonies from the IQ and FM treatments. This study suggests that inseminated queens should be considered by beekeepers that aim to increase pollen production.

  18. Inducible versus constitutive immunity: Examining effects of colony infection on glucose oxidase and Defensin-1 production in honey bees

    Science.gov (United States)

    Honey bees use a variety of defense mechanisms to reduce disease infection and spread throughout the colony. Many of these defenses rely on the collective action of multiple individuals to prevent, reduce or eradicate pathogens—often referred as 'social immunity'. Glucose oxidase (GOX) and some anti...

  19. Phenotypic and genetic analyses of the varroa sensitive hygienic trait in Russian honey bee (hymenoptera: apidae) colonies.

    Science.gov (United States)

    Kirrane, Maria J; de Guzman, Lilia I; Holloway, Beth; Frake, Amanda M; Rinderer, Thomas E; Whelan, Pádraig M

    2014-01-01

    Varroa destructor continues to threaten colonies of European honey bees. General hygiene, and more specific Varroa Sensitive Hygiene (VSH), provide resistance towards the Varroa mite in a number of stocks. In this study, 32 Russian (RHB) and 14 Italian honey bee colonies were assessed for the VSH trait using two different assays. Firstly, colonies were assessed using the standard VSH behavioural assay of the change in infestation of a highly infested donor comb after a one-week exposure. Secondly, the same colonies were assessed using an "actual brood removal assay" that measured the removal of brood in a section created within the donor combs as a potential alternative measure of hygiene towards Varroa-infested brood. All colonies were then analysed for the recently discovered VSH quantitative trait locus (QTL) to determine whether the genetic mechanisms were similar across different stocks. Based on the two assays, RHB colonies were consistently more hygienic toward Varroa-infested brood than Italian honey bee colonies. The actual number of brood cells removed in the defined section was negatively correlated with the Varroa infestations of the colonies (r2 = 0.25). Only two (percentages of brood removed and reproductive foundress Varroa) out of nine phenotypic parameters showed significant associations with genotype distributions. However, the allele associated with each parameter was the opposite of that determined by VSH mapping. In this study, RHB colonies showed high levels of hygienic behaviour towards Varroa -infested brood. The genetic mechanisms are similar to those of the VSH stock, though the opposite allele associates in RHB, indicating a stable recombination event before the selection of the VSH stock. The measurement of brood removal is a simple, reliable alternative method of measuring hygienic behaviour towards Varroa mites, at least in RHB stock.

  20. Phenotypic and genetic analyses of the varroa sensitive hygienic trait in Russian honey bee (hymenoptera: apidae colonies.

    Directory of Open Access Journals (Sweden)

    Maria J Kirrane

    Full Text Available Varroa destructor continues to threaten colonies of European honey bees. General hygiene, and more specific Varroa Sensitive Hygiene (VSH, provide resistance towards the Varroa mite in a number of stocks. In this study, 32 Russian (RHB and 14 Italian honey bee colonies were assessed for the VSH trait using two different assays. Firstly, colonies were assessed using the standard VSH behavioural assay of the change in infestation of a highly infested donor comb after a one-week exposure. Secondly, the same colonies were assessed using an "actual brood removal assay" that measured the removal of brood in a section created within the donor combs as a potential alternative measure of hygiene towards Varroa-infested brood. All colonies were then analysed for the recently discovered VSH quantitative trait locus (QTL to determine whether the genetic mechanisms were similar across different stocks. Based on the two assays, RHB colonies were consistently more hygienic toward Varroa-infested brood than Italian honey bee colonies. The actual number of brood cells removed in the defined section was negatively correlated with the Varroa infestations of the colonies (r2 = 0.25. Only two (percentages of brood removed and reproductive foundress Varroa out of nine phenotypic parameters showed significant associations with genotype distributions. However, the allele associated with each parameter was the opposite of that determined by VSH mapping. In this study, RHB colonies showed high levels of hygienic behaviour towards Varroa -infested brood. The genetic mechanisms are similar to those of the VSH stock, though the opposite allele associates in RHB, indicating a stable recombination event before the selection of the VSH stock. The measurement of brood removal is a simple, reliable alternative method of measuring hygienic behaviour towards Varroa mites, at least in RHB stock.

  1. Land use in the Northern Great Plains region of the U.S. influences the survival and productivity of honey bee colonies

    Science.gov (United States)

    Smart, Matthew; Pettis, Jeff S.; Euliss, Ned H. Jr.; Spivak, Marla S.

    2016-01-01

    The Northern Great Plains region of the US annually hosts a large portion of commercially managed U.S. honey bee colonies each summer. Changing land use patterns over the last several decades have contributed to declines in the availability of bee forage across the region, and the future sustainability of the region to support honey bee colonies is unclear. We examined the influence of varying land use on the survivorship and productivity of honey bee colonies located in six apiaries within the Northern Great Plains state of North Dakota, an area of intensive agriculture and high density of beekeeping operations. Land use surrounding the apiaries was quantified over three years, 2010–2012, and survival and productivity of honey bee colonies were determined in response to the amount of bee forage land within a 3.2-km radius of each apiary. The area of uncultivated forage land (including pasture, USDA conservation program fields, fallow land, flowering woody plants, grassland, hay land, and roadside ditches) exerted a positive impact on annual apiary survival and honey production. Taxonomic diversity of bee-collected pollen and pesticide residues contained therein varied seasonally among apiaries, but overall were not correlated to large-scale land use patterns or survival and honey production. The predominant flowering plants utilized by honey bee colonies for pollen were volunteer species present in unmanaged (for honey bees), and often ephemeral, lands; thus placing honey bee colonies in a precarious situation for acquiring forage and nutrients over the entire growing season. We discuss the implications for land management, conservation, and beekeeper site selection in the Northern Great Plains to adequately support honey bee colonies and insure long term security for pollinator-dependent crops across the entire country.

  2. Artificial bee colony algorithm for economic load dispatch with wind power energy

    Directory of Open Access Journals (Sweden)

    Safari Amin

    2016-01-01

    Full Text Available This paper presents an efficient Artificial Bee Colony (ABC algorithm for solving large scale economic load dispatch (ELD problems in power networks. To realize the ELD, the valve-point loading effect, system load demand, power losses, ramp rate limits and prohibited operation zones are considered here. Simulations were performed on four different power systems with 3, 6, 15 and 40 generating units and the results are compared with two forms of power systems, one power system is with a wind power generator and other power system is without a wind power generator. The results of this study reveal that the proposed approach is able to find appreciable ELD solutions than those of previous algorithms.

  3. An innovative artificial bee colony algorithm and its application to a practical intercell scheduling problem

    Science.gov (United States)

    Li, Dongni; Guo, Rongtao; Zhan, Rongxin; Yin, Yong

    2018-06-01

    In this article, an innovative artificial bee colony (IABC) algorithm is proposed, which incorporates two mechanisms. On the one hand, to provide the evolutionary process with a higher starting level, genetic programming (GP) is used to generate heuristic rules by exploiting the elements that constitute the problem. On the other hand, to achieve a better balance between exploration and exploitation, a leading mechanism is proposed to attract individuals towards a promising region. To evaluate the performance of IABC in solving practical and complex problems, it is applied to the intercell scheduling problem with limited transportation capacity. It is observed that the GP-generated rules incorporate the elements of the most competing human-designed rules, and they are more effective than the human-designed ones. Regarding the leading mechanism, the strategies of the ageing leader and multiple challengers make the algorithm less likely to be trapped in local optima.

  4. An Artificial Bee Colony Algorithm for the Job Shop Scheduling Problem with Random Processing Times

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    Rui Zhang

    2011-09-01

    Full Text Available Due to the influence of unpredictable random events, the processing time of each operation should be treated as random variables if we aim at a robust production schedule. However, compared with the extensive research on the deterministic model, the stochastic job shop scheduling problem (SJSSP has not received sufficient attention. In this paper, we propose an artificial bee colony (ABC algorithm for SJSSP with the objective of minimizing the maximum lateness (which is an index of service quality. First, we propose a performance estimate for preliminary screening of the candidate solutions. Then, the K-armed bandit model is utilized for reducing the computational burden in the exact evaluation (through Monte Carlo simulation process. Finally, the computational results on different-scale test problems validate the effectiveness and efficiency of the proposed approach.

  5. Molecular Identification of Chronic Bee Paralysis Virus Infection in Apis mellifera Colonies in Japan

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    Tomomi Morimoto

    2012-06-01

    Full Text Available Chronic bee paralysis virus (CBPV infection causes chronic paralysis and loss of workers in honey bee colonies around the world. Although CBPV shows a worldwide distribution, it had not been molecularly detected in Japan. Our investigation of Apis mellifera and Apis cerana japonica colonies with RT-PCR has revealed CBPV infection in A. mellifera but not A. c. japonica colonies in Japan. The prevalence of CBPV is low compared with that of other viruses: deformed wing virus (DWV, black queen cell virus (BQCV, Israel acute paralysis virus (IAPV, and sac brood virus (SBV, previously reported in Japan. Because of its low prevalence (5.6% in A. mellifera colonies, the incidence of colony losses by CBPV infection must be sporadic in Japan. The presence of the (− strand RNA in dying workers suggests that CBPV infection and replication may contribute to their symptoms. Phylogenetic analysis demonstrates a geographic separation of Japanese isolates from European, Uruguayan, and mainland US isolates. The lack of major exchange of honey bees between Europe/mainland US and Japan for the recent 26 years (1985–2010 may have resulted in the geographic separation of Japanese CBPV isolates.

  6. Retrospective study of the Nosema ceranae infection of honey bee colonies in Iran (2004-2013

    Directory of Open Access Journals (Sweden)

    Modirrousta, H.

    2014-11-01

    Full Text Available Nosemosis is the most common disease in adult bees. Nosema apis and Nosema ceranae species are agents of important economic losses to beekeepers around the world. The severity of disease at various area is different. Previously, N. apis was observed in areas with a long winter, especially in late winter and early spring. But in recent years, disease has been reported in the warm seasons. The studies indicated that a new species as N. ceranae is involvement in loss and mortality in adult bees. Therefore, diagnosis and differentiation of Nosema species is importance at colony collapse disorders (CCD. The aim of this Research was a retrospective study on Nosema samples isolated from apiaries. Forty- one Nosema Sp. Positive samples were collected from five provinces during 2004 to 2013. The samples were tested by multiplex PCR method using both primers of N. ceranea and N. apis were simultaneously. All of samples were positive for N. ceranea. The products were sent for sequencing. The results show that N. ceranea has spread in Iran, from previous years almost simultaneously with other parts of the world.

  7. Foraging Activity in Plebeia remota, a Stingless Bees Species, Is Influenced by the Reproductive State of a Colony

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    Patrícia Nunes-Silva

    2010-01-01

    Full Text Available Colonies of the Brazilian stingless bee Plebeia remota show a reproductive diapause in autumn and winter. Therefore, they present two distinct reproductive states, during which colony needs are putatively different. Consequently, foraging should be adapted to the different needs. We recorded the foraging activity of two colonies for 30 days in both phases. Indeed, it presented different patterns during the two phases. In the reproductive diapause, the resource predominantly collected by the foragers was nectar. The majority of the bees were nectar foragers, and the peak of collecting activity occurred around noon. Instead, in the reproductive phase, the predominantly collected resource was pollen, and the peak of activity occurred around 10:00 am. Although the majority of the foragers were not specialized in this phase, there were a larger number of pollen foragers compared to the phase of reproductive diapause. The temperature and relative humidity also influenced the foraging activity.

  8. Ant colony algorithm for clustering in portfolio optimization

    Science.gov (United States)

    Subekti, R.; Sari, E. R.; Kusumawati, R.

    2018-03-01

    This research aims to describe portfolio optimization using clustering methods with ant colony approach. Two stock portfolios of LQ45 Indonesia is proposed based on the cluster results obtained from ant colony optimization (ACO). The first portfolio consists of assets with ant colony displacement opportunities beyond the defined probability limits of the researcher, where the weight of each asset is determined by mean-variance method. The second portfolio consists of two assets with the assumption that each asset is a cluster formed from ACO. The first portfolio has a better performance compared to the second portfolio seen from the Sharpe index.

  9. A pan-European epidemiological study reveals honey bee colony survival depends on beekeeper education and disease control.

    Directory of Open Access Journals (Sweden)

    Antoine Jacques

    Full Text Available Reports of honey bee population decline has spurred many national efforts to understand the extent of the problem and to identify causative or associated factors. However, our collective understanding of the factors has been hampered by a lack of joined up trans-national effort. Moreover, the impacts of beekeeper knowledge and beekeeping management practices have often been overlooked, despite honey bees being a managed pollinator. Here, we established a standardised active monitoring network for 5 798 apiaries over two consecutive years to quantify honey bee colony mortality across 17 European countries. Our data demonstrate that overwinter losses ranged between 2% and 32%, and that high summer losses were likely to follow high winter losses. Multivariate Poisson regression models revealed that hobbyist beekeepers with small apiaries and little experience in beekeeping had double the winter mortality rate when compared to professional beekeepers. Furthermore, honey bees kept by professional beekeepers never showed signs of disease, unlike apiaries from hobbyist beekeepers that had symptoms of bacterial infection and heavy Varroa infestation. Our data highlight beekeeper background and apicultural practices as major drivers of honey bee colony losses. The benefits of conducting trans-national monitoring schemes and improving beekeeper training are discussed.

  10. Frequency of Varroa destructor, Nosema spp and Acarapis woodi in commercial colonies of bees (Apis mellifera in Yucatan, Mexico

    Directory of Open Access Journals (Sweden)

    Martínez-Puc Jesús Froylán

    2015-10-01

    Full Text Available Today it has been observed that diseases affecting bees (Apis mellifera have caused significant economic losses in the European continent and in parts of the United States due to high mortality in honey bee colonies without a cause apparent, which is known as the syndrome of depopulation of hives. It is noteworthy that this mortality is not yet presented in Yucatan. In order to determine the frequency and levels of infestation Acarapis woodi and Varroa destructor, and the frequency and levels of infection Nosema spp. commercial colonies of bees (A. mellifera in Yucatan, was collected from June to December 2006, a total of 165 samples distributed in 13 towns of Yucatan. V. destructor frequency was 63.6%, with an average level of infestation of 2.85 ± 0.79 (mites / 100 bees. The frequency of Nosema spp. was 81.8%, with an average infection level = 1'234000 ± 118000 (spores / bee, the presence of A. woodi in the samples analyzed was detected. The existence of an association between V. destructor and Nosema spp was observed. (X2 = 6.53, df = 1, p = 0.01.

  11. Investigation on the Inversion of the Atmospheric Duct Using the Artificial Bee Colony Algorithm Based on Opposition-Based Learning

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    Chao Yang

    2016-01-01

    Full Text Available The artificial bee colony (ABC algorithm is a recently introduced optimization method in the research field of swarm intelligence. This paper presents an improved ABC algorithm named as OGABC based on opposition-based learning (OBL and global best search equation to overcome the shortcomings of the slow convergence rate and sinking into local optima in the process of inversion of atmospheric duct. Taking the inversion of the surface duct using refractivity from clutter (RFC technique as an example to validate the performance of the proposed OGABC, the inversion results are compared with those of the modified invasive weed optimization (MIWO and ABC. The radar sea clutter power calculated by parabolic equation method using the simulated and measured refractivity profile is utilized to carry out the inversion of the surface duct, respectively. The comparative investigation results indicate that the performance of OGABC is superior to that of MIWO and ABC in terms of stability, accuracy, and convergence rate during the process of inversion.

  12. Pheromone-modulated behavioral suites influence colony growth in the honey bee (Apis mellifera)

    Science.gov (United States)

    Pankiw, Tanya; Roman, Roman; Sagili, Ramesh R.; Zhu-Salzman, Keyan

    2004-12-01

    The success of a species depends on its ability to assess its environment and to decide accordingly which behaviors are most appropriate. Many animal species, from bacteria to mammals, are able to communicate using interspecies chemicals called pheromones. In addition to exerting physiological effects on individuals, for social species, pheromones communicate group social structure. Communication of social structure is important to social insects for the allocation of its working members into coordinated suites of behaviors. We tested effects of long-term treatment with brood pheromone on suites of honey bee brood rearing and foraging behaviors. Pheromone-treated colonies reared significantly greater brood areas and more adults than controls, while amounts of stored pollen and honey remained statistically similar. Brood pheromone increased the number of pollen foragers and the pollen load weights they returned. It appeared that the pheromone-induced increase in pollen intake was directly canalized into more brood rearing. A two-way pheromone priming effect was observed, such that some workers from the same age cohorts showed an increased and extended capacity to rear larvae, while others were recruited at significantly younger ages into pollen-specific foraging. Brood pheromone affected suites of nursing and foraging behaviors allocating worker and pollen resources associated with an important fitness trait, colony growth.

  13. Regional distribution of Paenibacillus larvae subspecies larvae, the causative organism of American foulbrood, in honey bee colonies of the Western United States.

    Science.gov (United States)

    Eischen, Frank A; Graham, R Henry; Cox, Robert

    2005-08-01

    We examined honey bee, Apis mellifera L., colonies pollinating almonds in California during February 2003 for Paenibacillus larvae subsp. Larvae, the causative organism of the virulent brood disease American foulbrood. Colonies originating from the Rocky Mountain area and California had significantly higher numbers (P bees, respectively) than colonies from the upper Midwest (1.28). Colonies from the northwestern, central, and southwestern United States had intermediate CFU or bacterial colony levels. Operations positive for P. larvae larvae were relatively uniform at approximately 70-80%, and no regional significant differences were found. Percentages of colonies with high CFUs (> or = 400 per 30 bees) differed significantly, with those from the Rocky Mountain region having 8.73% compared with those of the upper Midwest with 0%. The significance of CFU levels was evaluated by inoculating healthy colonies with diseased immatures and sampling adult bees. The number of CFUs detected per diseased immature was conservatively estimated to be approximately 399 CFUs per 30 adult bees. We defined this spore level as 1 disease equivalent. Based on this, 3.86% colonies in our survey had 1 or more disease equivalent number of P. larvae larvae CFUs. Operations with high P. larvae larvae spore levels in their colonies will likely observe American foulbrood if prophylaxis is not practiced diligently.

  14. Using Colony Monitoring Devices to Evaluate the Impacts of Land Use and Nutritional Value of Forage on Honey Bee Health

    Directory of Open Access Journals (Sweden)

    Matthew Smart

    2017-12-01

    Full Text Available Colony monitoring devices used to track and assess the health status of honey bees are becoming more widely available and used by both beekeepers and researchers. These devices monitor parameters relevant to colony health at frequent intervals, often approximating real time. The fine-scale record of hive condition can be further related to static or dynamic features of the landscape, such as weather, climate, colony density, land use, pesticide use, vegetation class, and forage quality. In this study, we fit commercial honey bee colonies in two apiaries with pollen traps and digital scales to monitor floral resource use, pollen quality, and honey production. One apiary was situated in low-intensity agriculture; the other in high-intensity agriculture. Pollen traps were open for 72 h every two weeks while scales recorded weight every 15 min throughout the growing season. From collected pollen, we determined forage quantity per day, species identity using DNA sequencing, pesticide residues, amino acid content, and total protein content. From scales, we determined the accumulated hive weight change over the growing season, relating to honey production and final colony weight going into winter. Hive scales may also be used to identify the occurrence of environmental pollen and nectar dearth, and track phenological changes in plant communities. We provide comparisons of device-derived data between two apiaries over the growing season and discuss the potential for employing apiary monitoring devices to infer colony health in the context of divergent agricultural land use conditions.

  15. Using colony monitoring devices to evaluate the impacts of land use and nutritional value of forage on honey bee health

    Science.gov (United States)

    Smart, Matthew; Otto, Clint R.; Cornman, Robert S.; Iwanowicz, Deborah

    2018-01-01

    Colony monitoring devices used to track and assess the health status of honey bees are becoming more widely available and used by both beekeepers and researchers. These devices monitor parameters relevant to colony health at frequent intervals, often approximating real time. The fine-scale record of hive condition can be further related to static or dynamic features of the landscape, such as weather, climate, colony density, land use, pesticide use, vegetation class, and forage quality. In this study, we fit commercial honey bee colonies in two apiaries with pollen traps and digital scales to monitor floral resource use, pollen quality, and honey production. One apiary was situated in low-intensity agriculture; the other in high-intensity agriculture. Pollen traps were open for 72 h every two weeks while scales recorded weight every 15 min throughout the growing season. From collected pollen, we determined forage quantity per day, species identity using DNA sequencing, pesticide residues, amino acid content, and total protein content. From scales, we determined the accumulated hive weight change over the growing season, relating to honey production and final colony weight going into winter. Hive scales may also be used to identify the occurrence of environmental pollen and nectar dearth, and track phenological changes in plant communities. We provide comparisons of device-derived data between two apiaries over the growing season and discuss the potential for employing apiary monitoring devices to infer colony health in the context of divergent agricultural land use conditions.

  16. Bees for development: Brazilian survey reveals how to optimize stingless beekeeping.

    Directory of Open Access Journals (Sweden)

    Rodolfo Jaffé

    Full Text Available Stingless bees are an important asset to assure plant biodiversity in many natural ecosystems, and fulfill the growing agricultural demand for pollination. However, across developing countries stingless beekeeping remains an essentially informal activity, technical knowledge is scarce, and management practices lack standardization. Here we profited from the large diversity of stingless beekeepers found in Brazil to assess the impact of particular management practices on productivity and economic revenues from the commercialization of stingless bee products. Our study represents the first large-scale effort aiming at optimizing stingless beekeeping for honey/colony production based on quantitative data. Survey data from 251 beekeepers scattered across 20 Brazilian States revealed the influence of specific management practices and other confounding factors over productivity and income indicators. Specifically, our results highlight the importance of teaching beekeepers how to inspect and feed their colonies, how to multiply them and keep track of genetic lineages, how to harvest and preserve the honey, how to use vinegar traps to control infestation by parasitic flies, and how to add value by labeling honey containers. Furthermore, beekeeping experience and the network of known beekeepers were found to be key factors influencing productivity and income. Our work provides clear guidelines to optimize stingless beekeeping and help transform the activity into a powerful tool for sustainable development.

  17. Bees for development: Brazilian survey reveals how to optimize stingless beekeeping.

    Science.gov (United States)

    Jaffé, Rodolfo; Pope, Nathaniel; Torres Carvalho, Airton; Madureira Maia, Ulysses; Blochtein, Betina; de Carvalho, Carlos Alfredo Lopes; Carvalho-Zilse, Gislene Almeida; Freitas, Breno Magalhães; Menezes, Cristiano; Ribeiro, Márcia de Fátima; Venturieri, Giorgio Cristino; Imperatriz-Fonseca, Vera Lucia

    2015-01-01

    Stingless bees are an important asset to assure plant biodiversity in many natural ecosystems, and fulfill the growing agricultural demand for pollination. However, across developing countries stingless beekeeping remains an essentially informal activity, technical knowledge is scarce, and management practices lack standardization. Here we profited from the large diversity of stingless beekeepers found in Brazil to assess the impact of particular management practices on productivity and economic revenues from the commercialization of stingless bee products. Our study represents the first large-scale effort aiming at optimizing stingless beekeeping for honey/colony production based on quantitative data. Survey data from 251 beekeepers scattered across 20 Brazilian States revealed the influence of specific management practices and other confounding factors over productivity and income indicators. Specifically, our results highlight the importance of teaching beekeepers how to inspect and feed their colonies, how to multiply them and keep track of genetic lineages, how to harvest and preserve the honey, how to use vinegar traps to control infestation by parasitic flies, and how to add value by labeling honey containers. Furthermore, beekeeping experience and the network of known beekeepers were found to be key factors influencing productivity and income. Our work provides clear guidelines to optimize stingless beekeeping and help transform the activity into a powerful tool for sustainable development.

  18. Managed European-Derived Honey Bee, Apis mellifera sspp, Colonies Reduce African-Matriline Honey Bee, A. m. scutellata, Drones at Regional Mating Congregations.

    Science.gov (United States)

    Mortensen, Ashley N; Ellis, James D

    2016-01-01

    African honey bees (Apis mellifera scutellata) dramatically changed the South American beekeeping industry as they rapidly spread through the Americas following their introduction into Brazil. In the present study, we aimed to determine if the management of European-derived honey bees (A. mellifera sspp.) could reduce the relative abundance of African-matriline drones at regional mating sites known as drone congregation areas (DCAs). We collected 2,400 drones at six DCAs either 0.25 km or >2.8 km from managed European-derived honey bee apiaries. The maternal ancestry of each drone was determined by Bgl II enzyme digestion of an amplified portion of the mitochondrial Cytochrome b gene. Furthermore, sibship reconstruction via nuclear microsatellites was conducted for a subset of 1,200 drones to estimate the number of colonies contributing drones to each DCA. Results indicate that DCAs distant to managed European apiaries (>2.8 km) had significantly more African-matriline drones (34.33% of the collected drones had African mitochondrial DNA) than did DCAs close (0.25 km) to managed European apiaries (1.83% of the collected drones had African mitochondrial DNA). Furthermore, nuclear sibship reconstruction demonstrated that the reduction in the proportion of African matriline drones at DCAs near apiaries was not simply an increase in the number of European matriline drones at the DCAs but also the result of fewer African matriline colonies contributing drones to the DCAs. Our data demonstrate that the management of European honey bee colonies can dramatically influence the proportion of drones with African matrilines at nearby drone congregation areas, and would likely decreasing the probability that virgin European queens will mate with African drones at those drone congregation areas.

  19. Managed European-Derived Honey Bee, Apis mellifera sspp, Colonies Reduce African-Matriline Honey Bee, A. m. scutellata, Drones at Regional Mating Congregations

    Science.gov (United States)

    Mortensen, Ashley N.; Ellis, James D.

    2016-01-01

    African honey bees (Apis mellifera scutellata) dramatically changed the South American beekeeping industry as they rapidly spread through the Americas following their introduction into Brazil. In the present study, we aimed to determine if the management of European-derived honey bees (A. mellifera sspp.) could reduce the relative abundance of African-matriline drones at regional mating sites known as drone congregation areas (DCAs). We collected 2,400 drones at six DCAs either 0.25 km or >2.8 km from managed European-derived honey bee apiaries. The maternal ancestry of each drone was determined by Bgl II enzyme digestion of an amplified portion of the mitochondrial Cytochrome b gene. Furthermore, sibship reconstruction via nuclear microsatellites was conducted for a subset of 1,200 drones to estimate the number of colonies contributing drones to each DCA. Results indicate that DCAs distant to managed European apiaries (>2.8 km) had significantly more African−matriline drones (34.33% of the collected drones had African mitochondrial DNA) than did DCAs close (0.25 km) to managed European apiaries (1.83% of the collected drones had African mitochondrial DNA). Furthermore, nuclear sibship reconstruction demonstrated that the reduction in the proportion of African matriline drones at DCAs near apiaries was not simply an increase in the number of European matriline drones at the DCAs but also the result of fewer African matriline colonies contributing drones to the DCAs. Our data demonstrate that the management of European honey bee colonies can dramatically influence the proportion of drones with African matrilines at nearby drone congregation areas, and would likely decreasing the probability that virgin European queens will mate with African drones at those drone congregation areas. PMID:27518068

  20. Virus Status, Varroa Levels, and Survival of 20 Managed Honey Bee Colonies Monitored in Luxembourg Between the Summer of 2011 and the Spring of 2013

    Directory of Open Access Journals (Sweden)

    Clermont Antoine

    2015-06-01

    Full Text Available Twenty managed honey bee colonies, split between 5 apiaries with 4 hives each, were monitored between the summer of 2011 and spring of 2013. Living bees were sampled in July 2011, July 2012, and August 2012. Twenty-five, medium-aged bees, free of varroa mites, were pooled per colony and date, to form one sample. Unlike in France and Belgium, Chronic Bee Paralysis Virus (CBPV has not been found in Luxembourg. Slow Bee Paralysis Virus (SBPV and Israeli Acute Paralysis Virus (IAPV levels were below detection limits. Traces of Kashmir Bee Virus (KBV were amplified. Black Queen Cell Virus (BQCV, Varroa destructor Virus-1 (VDV-1, and SacBrood Virus (SBV were detected in all samples and are reported from Luxembourg for the first time. Varroa destructor Macula- Like Virus (VdMLV, Deformed Wing Virus (DWV, and Acute Bee Paralysis Virus (ABPV were detected at all locations, and in most but not all samples. There was a significant increase in VDV-1 and DWV levels within the observation period. A principal component analysis was unable to separate the bees of colonies that survived the following winter from bees that died, based on their virus contents in summer. The number of dead varroa mites found below colonies was elevated in colonies that died in the following winter. Significant positive relationships were found between the log-transformed virus levels of the bees and the log-transformed number of mites found below the colonies per week, for VDV-1 and DWV. Sacbrood virus levels were independent of varroa levels, suggesting a neutral or competitive relationship between this virus and varroa.

  1. Weight of evidence evaluation of a network of adverse outcome pathways linking activaiton of the nicotinic acetylcholine receptor in honey bees to colony death

    Data.gov (United States)

    U.S. Environmental Protection Agency — Ongoing honey bee colony losses are of significant international concern because of the essential role these insects play in pollinating many high nutrient crops,...

  2. The distribution of Paenibacillus larvae spores in adult bees and honey and larval mortality, following the addition of American foulbrood diseased brood or spore-contaminated honey in honey bee (Apis mellifera) colonies.

    Science.gov (United States)

    Lindström, Anders; Korpela, Seppo; Fries, Ingemar

    2008-09-01

    Within colony transmission of Paenibacillus larvae spores was studied by giving spore-contaminated honey comb or comb containing 100 larvae killed by American foulbrood to five experimental colonies respectively. We registered the impact of the two treatments on P. larvae spore loads in adult bees and honey and on larval mortality by culturing for spores in samples of adult bees and honey, respectively, and by measuring larval survival. The results demonstrate a direct effect of treatment on spore levels in adult bees and honey as well as on larval mortality. Colonies treated with dead larvae showed immediate high spore levels in adult bee samples, while the colonies treated with contaminated honey showed a comparable spore load but the effect was delayed until the bees started to utilize the honey at the end of the flight season. During the winter there was a build up of spores in the adult bees, which may increase the risk for infection in spring. The results confirm that contaminated honey can act as an environmental reservoir of P. larvae spores and suggest that less spores may be needed in honey, compared to in diseased brood, to produce clinically diseased colonies. The spore load in adult bee samples was significantly related to larval mortality but the spore load of honey samples was not.

  3. Application of ant colony optimization in NPP classification fault location

    International Nuclear Information System (INIS)

    Xie Chunli; Liu Yongkuo; Xia Hong

    2009-01-01

    Nuclear Power Plant is a highly complex structural system with high safety requirements. Fault location appears to be particularly important to enhance its safety. Ant Colony Optimization is a new type of optimization algorithm, which is used in the fault location and classification of nuclear power plants in this paper. Taking the main coolant system of the first loop as the study object, using VB6.0 programming technology, the NPP fault location system is designed, and is tested against the related data in the literature. Test results show that the ant colony optimization can be used in the accurate classification fault location in the nuclear power plants. (authors)

  4. Edge detection in digital images using Ant Colony Optimization

    Directory of Open Access Journals (Sweden)

    Marjan Kuchaki Rafsanjani

    2015-11-01

    Full Text Available Ant Colony Optimization (ACO is an optimization algorithm inspired by the behavior of real ant colonies to approximate the solutions of difficult optimization problems. In this paper, ACO is introduced to tackle the image edge detection problem. The proposed approach is based on the distribution of ants on an image; ants try to find possible edges by using a state transition function. Experimental results show that the proposed method compared to standard edge detectors is less sensitive to Gaussian noise and gives finer details and thinner edges when compared to earlier ant-based approaches.

  5. Population growth of Varroa destructor (Acari: Varroidae) in colonies of Russian and unselected honey bee (Hymenoptera: Apidae) stock as related to numbers of foragers with mites

    Science.gov (United States)

    Varroa mites are an external parasite of honey bees and a leading cause of colony losses worldwide. Varroa populations can be controlled with miticides, but mite resistant stocks such as the Russian honey bee (RHB) also are available. RHB and other mite resistant stock limit Varroa population growth...

  6. Ant Colony Optimization and the Minimum Cut Problem

    DEFF Research Database (Denmark)

    Kötzing, Timo; Lehre, Per Kristian; Neumann, Frank

    2010-01-01

    Ant Colony Optimization (ACO) is a powerful metaheuristic for solving combinatorial optimization problems. With this paper we contribute to the theoretical understanding of this kind of algorithm by investigating the classical minimum cut problem. An ACO algorithm similar to the one that was prov...

  7. A large-scale field study examining effects of exposure to clothianidin seed-treated canola on honey bee colony health, development, and overwintering success

    OpenAIRE

    Cutler, G. Christopher; Scott-Dupree, Cynthia D.; Sultan, Maryam; McFarlane, Andrew D.; Brewer, Larry

    2014-01-01

    In summer 2012, we initiated a large-scale field experiment in southern Ontario, Canada, to determine whether exposure to clothianidin seed-treated canola (oil seed rape) has any adverse impacts on honey bees. Colonies were placed in clothianidin seed-treated or control canola fields during bloom, and thereafter were moved to an apiary with no surrounding crops grown from seeds treated with neonicotinoids. Colony weight gain, honey production, pest incidence, bee mortality, number of adults, ...

  8. Modeling design iteration in product design and development and its solution by a novel artificial bee colony algorithm.

    Science.gov (United States)

    Chen, Tinggui; Xiao, Renbin

    2014-01-01

    Due to fierce market competition, how to improve product quality and reduce development cost determines the core competitiveness of enterprises. However, design iteration generally causes increases of product cost and delays of development time as well, so how to identify and model couplings among tasks in product design and development has become an important issue for enterprises to settle. In this paper, the shortcomings existing in WTM model are discussed and tearing approach as well as inner iteration method is used to complement the classic WTM model. In addition, the ABC algorithm is also introduced to find out the optimal decoupling schemes. In this paper, firstly, tearing approach and inner iteration method are analyzed for solving coupled sets. Secondly, a hybrid iteration model combining these two technologies is set up. Thirdly, a high-performance swarm intelligence algorithm, artificial bee colony, is adopted to realize problem-solving. Finally, an engineering design of a chemical processing system is given in order to verify its reasonability and effectiveness.

  9. An integrated portfolio optimisation procedure based on data envelopment analysis, artificial bee colony algorithm and genetic programming

    Science.gov (United States)

    Hsu, Chih-Ming

    2014-12-01

    Portfolio optimisation is an important issue in the field of investment/financial decision-making and has received considerable attention from both researchers and practitioners. However, besides portfolio optimisation, a complete investment procedure should also include the selection of profitable investment targets and determine the optimal timing for buying/selling the investment targets. In this study, an integrated procedure using data envelopment analysis (DEA), artificial bee colony (ABC) and genetic programming (GP) is proposed to resolve a portfolio optimisation problem. The proposed procedure is evaluated through a case study on investing in stocks in the semiconductor sub-section of the Taiwan stock market for 4 years. The potential average 6-month return on investment of 9.31% from 1 November 2007 to 31 October 2011 indicates that the proposed procedure can be considered a feasible and effective tool for making outstanding investment plans, and thus making profits in the Taiwan stock market. Moreover, it is a strategy that can help investors to make profits even when the overall stock market suffers a loss.

  10. Fipronil promotes motor and behavioral changes in honey bees (Apis mellifera) and affects the development of colonies exposed to sublethal doses.

    Science.gov (United States)

    Zaluski, Rodrigo; Kadri, Samir Moura; Alonso, Diego Peres; Martins Ribolla, Paulo Eduardo; de Oliveira Orsi, Ricardo

    2015-05-01

    Bees play a crucial role in pollination and generate honey and other hive products; therefore, their worldwide decline is cause for concern. New broad-spectrum systemic insecticides such as fipronil can harm bees and their use has been discussed as a potential threat to bees' survival. In the present study, the authors evaluate the in vitro toxicity of fipronil and note behavioral and motor activity changes in Africanized adult Apis mellifera that ingest or come into contact with lethal or sublethal doses of fipronil. The effects of sublethal doses on brood viability, population growth, behavior, and the expression of the defensin 1 gene in adult bees were studied in colonies fed with contaminated sugar syrup (8 µg fipronil L(-1) ). Fipronil is highly toxic to bees triggering agitation, seizures, tremors, and paralysis. Bees that are exposed to a lethal or sublethal doses showed reduced motor activity. The number of eggs that hatched, the area occupied by worker eggs, and the number of larvae and pupae that developed were reduced, adult bees showed lethargy, and colonies were abandoned when they were exposed to sublethal doses of fipronil. No change was seen in the bees' expression of defensin 1. The authors conclude that fipronil is highly toxic to honey bees and even sublethal doses may negatively affect the development and maintenance of colonies. © 2015 SETAC.

  11. Mitochondrial DNA diversity of honey bees (Apis mellifera) from unmanaged colonies and swarms in the United States.

    Science.gov (United States)

    Magnus, Roxane M; Tripodi, Amber D; Szalanski, Allen L

    2014-06-01

    To study the genetic diversity of honey bees (Apis mellifera L.) from unmanaged colonies in the United States, we sequenced a portion of the mitochondrial DNA COI-COII region. From the 530 to 1,230 bp amplicon, we observed 23 haplotypes from 247 samples collected from 12 states, representing three of the four A. mellifera lineages known to have been imported into the United States (C, M, and O). Six of the 13 C lineage haplotypes were not found in previous queen breeder studies in the United States. The O lineage accounted for 9% of unmanaged colonies which have not yet been reported in queen breeder studies. The M lineage accounted for a larger portion of unmanaged samples (7%) than queen breeder samples (3%). Based on our mitochondrial DNA data, the genetic diversity of unmanaged honey bees in the United States differs significantly from that of queen breeder populations (p < 0.00001). The detection of genetically distinct maternal lineages of unmanaged honey bees suggests that these haplotypes may have existed outside the managed honey bee population for a long period.

  12. Out with the garbage: the parasitic strategy of the mantisfly Plega hagenella mass-infesting colonies of the eusocial bee Melipona subnitida in northeastern Brazil

    Science.gov (United States)

    Maia-Silva, Camila; Hrncir, Michael; Koedam, Dirk; Machado, Renato Jose Pires; Imperatriz-Fonseca, Vera Lucia

    2013-01-01

    Between April and June of 2012 mantisflies ( Plega hagenella) were found to be extensively parasitizing the nests of two groups of managed colonzies of eusocial stingless bees ( Melipona subnitida) in the semi-arid region of northeastern Brazil. The mantisfly larvae developed inside closed brood cells of the bee comb, where each mantispid larva fed on the bee larva or pupa present in a single brood cell. Mature mantispid larvae pupated inside silken cocoons spun in place within their hosts' brood cells then emerged as pharate adults inside the bee colony. Pharate adults were never attacked and killed by host colony workers. Instead, colony workers picked up the pharates and removed them from the nest unharmed, treating them similar to the way that the general refuse is removed from the nest. Adult mantispids subsequently eclosed from their pupal exuviae outside the nest. Manipulative experiments showed that post-eclosion adult mantispids placed back within active bee colonies were quickly attacked and killed. These observations demonstrate that pharate and post-eclosion adults of P. hagenella are perceived differently by colony workers and that delayed adult eclosion is an important functional element in the parasitic life strategy of P. hagenella, allowing adults to escape without injury from the bee colonies they parasitize.

  13. On the q-Weibull distribution for reliability applications: An adaptive hybrid artificial bee colony algorithm for parameter estimation

    International Nuclear Information System (INIS)

    Xu, Meng; Droguett, Enrique López; Lins, Isis Didier; Chagas Moura, Márcio das

    2017-01-01

    The q-Weibull model is based on the Tsallis non-extensive entropy and is able to model various behaviors of the hazard rate function, including bathtub curves, by using a single set of parameters. Despite its flexibility, the q-Weibull has not been widely used in reliability applications partly because of the complicated parameters estimation. In this work, the parameters of the q-Weibull are estimated by the maximum likelihood (ML) method. Due to the intricate system of nonlinear equations, derivative-based optimization methods may fail to converge. Thus, the heuristic optimization method of artificial bee colony (ABC) is used instead. To deal with the slow convergence of ABC, it is proposed an adaptive hybrid ABC (AHABC) algorithm that dynamically combines Nelder-Mead simplex search method with ABC for the ML estimation of the q-Weibull parameters. Interval estimates for the q-Weibull parameters, including confidence intervals based on the ML asymptotic theory and on bootstrap methods, are also developed. The AHABC is validated via numerical experiments involving the q-Weibull ML for reliability applications and results show that it produces faster and more accurate convergence when compared to ABC and similar approaches. The estimation procedure is applied to real reliability failure data characterized by a bathtub-shaped hazard rate. - Highlights: • Development of an Adaptive Hybrid ABC (AHABC) algorithm for q-Weibull distribution. • AHABC combines local Nelder-Mead simplex method with ABC to enhance local search. • AHABC efficiently finds the optimal solution for the q-Weibull ML problem. • AHABC outperforms ABC and self-adaptive hybrid ABC in accuracy and convergence speed. • Useful model for reliability data with non-monotonic hazard rate.

  14. Longevity extension of worker honey bees (Apis mellifera by royal jelly: optimal dose and active ingredient

    Directory of Open Access Journals (Sweden)

    Wenchao Yang

    2017-03-01

    Full Text Available In the Western honey bee, Apis mellifera, queens and workers have different longevity although they share the same genome. Queens consume royal jelly (RJ as the main food throughout their life, including as adults, but workers only eat worker jelly when they are larvae less than 3 days old. In order to explore the effect of RJ and the components affecting longevity of worker honey bees, we first determined the optimal dose for prolonging longevity of workers as 4% RJ in 50% sucrose solution, and developed a method of obtaining long lived workers. We then compared the effects of longevity extension by RJ 4% with bee-collected pollen from rapeseed (Brassica napus. Lastly, we determined that a water soluble RJ protein obtained by precipitation with 60% ammonium sulfate (RJP60 contained the main component for longevity extension after comparing the effects of RJ crude protein extract (RJCP, RJP30 (obtained by precipitation with 30% ammonium sulfate, and RJ ethanol extract (RJEE. Understanding what regulates worker longevity has potential to help increase colony productivity and improve crop pollination efficiency.

  15. Longevity extension of worker honey bees (Apis mellifera) by royal jelly: optimal dose and active ingredient.

    Science.gov (United States)

    Yang, Wenchao; Tian, Yuanyuan; Han, Mingfeng; Miao, Xiaoqing

    2017-01-01

    In the Western honey bee, Apis mellifera , queens and workers have different longevity although they share the same genome. Queens consume royal jelly (RJ) as the main food throughout their life, including as adults, but workers only eat worker jelly when they are larvae less than 3 days old. In order to explore the effect of RJ and the components affecting longevity of worker honey bees, we first determined the optimal dose for prolonging longevity of workers as 4% RJ in 50% sucrose solution, and developed a method of obtaining long lived workers. We then compared the effects of longevity extension by RJ 4% with bee-collected pollen from rapeseed ( Brassica napus ). Lastly, we determined that a water soluble RJ protein obtained by precipitation with 60% ammonium sulfate (RJP 60 ) contained the main component for longevity extension after comparing the effects of RJ crude protein extract (RJCP), RJP 30 (obtained by precipitation with 30% ammonium sulfate), and RJ ethanol extract (RJEE). Understanding what regulates worker longevity has potential to help increase colony productivity and improve crop pollination efficiency.

  16. Application of colony complex algorithm to nuclear component optimization design

    International Nuclear Information System (INIS)

    Yan Changqi; Li Guijing; Wang Jianjun

    2014-01-01

    Complex algorithm (CA) has got popular application to the region of nuclear engineering. In connection with the specific features of the application of traditional complex algorithm (TCA) to the optimization design in engineering structures, an improved method, colony complex algorithm (CCA), was developed based on the optimal combination of many complexes, in which the disadvantages of TCA were overcame. The optimized results of benchmark function show that CCA has better optimizing performance than TCA. CCA was applied to the high-pressure heater optimization design, and the optimization effect is obvious. (authors)

  17. Brood pheromone effects on colony protein supplement consumption and growth in the honey bee (Hymenoptera: Apidae) in a subtropical winter climate.

    Science.gov (United States)

    Pankiw, Tanya; Sagili, Ramesh R; Metz, Bradley N

    2008-12-01

    Fatty acid esters extractable from the surface of honey bee, Apis mellifera L. (Hymenoptera: Apidae), larvae, called brood pheromone, significantly increase rate of colony growth in the spring and summer when flowering plant pollen is available in the foraging environment. Increased colony growth rate occurs as a consequence of increased pollen intake through mechanisms such as increasing number of pollen foragers and pollen load weights returned. Here, we tested the hypothesis that addition of brood pheromone during the winter pollen dearth period of a humid subtropical climate increases rate of colony growth in colonies provisioned with a protein supplement. Experiments were conducted in late winter (9 February-9 March 2004) and mid-winter (19 January-8 February 2005). In both years, increased brood area, number of bees, and amount of protein supplement consumption were significantly greater in colonies receiving daily treatments of brood pheromone versus control colonies. Amount of extractable protein from hypopharyngeal glands measured in 2005 was significantly greater in bees from pheromone-treated colonies. These results suggest that brood pheromone may be used as a tool to stimulate colony growth in the southern subtropical areas of the United States where the package bee industry is centered and a large proportion of migratory colonies are overwintered.

  18. Queen Quality and the Impact of Honey Bee Diseases on Queen Health: Potential for Interactions between Two Major Threats to Colony Health

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    Esmaeil Amiri

    2017-05-01

    Full Text Available Western honey bees, Apis mellifera, live in highly eusocial colonies that are each typically headed by a single queen. The queen is the sole reproductive female in a healthy colony, and because long-term colony survival depends on her ability to produce a large number of offspring, queen health is essential for colony success. Honey bees have recently been experiencing considerable declines in colony health. Among a number of biotic and abiotic factors known to impact colony health, disease and queen failure are repeatedly reported as important factors underlying colony losses. Surprisingly, there are relatively few studies on the relationship and interaction between honey bee diseases and queen quality. It is critical to understand the negative impacts of pests and pathogens on queen health, how queen problems might enable disease, and how both factors influence colony health. Here, we review the current literature on queen reproductive potential and the impacts of honey bee parasites and pathogens on queens. We conclude by highlighting gaps in our knowledge on the combination of disease and queen failure to provide a perspective and prioritize further research to mitigate disease, improve queen quality, and ensure colony health.

  19. Effects of honey bee (Hymenoptera: Apidae) and bumble bee (Hymenoptera: Apidae) presence on cranberry (Ericales: Ericaceae) pollination.

    Science.gov (United States)

    Evans, E C; Spivak, M

    2006-06-01

    Honey bees, Apis mellifera L., are frequently used to pollinate commercial cranberries, Vaccinium macrocarpon Ait., but information is lacking on the relative contribution of honey bees and native bees, the effects of surrounding vegetation on bee visitation, and on optimal timing for honey bee introduction. We begin with a descriptive study of numbers of honey bees, bumble bees, and other bees visiting cranberry blossoms, and their subsequent effect on cranberry yield, on three cranberry properties in 1999. The property surrounded by agricultural land, as opposed to wetlands and woodlands, had fewer numbers of all bee types. In 2000, one property did not introduce honey bee colonies, providing an opportunity to document the effect of lack of honey bees on yield. With no honey bees, plants along the edge of the bed had significantly higher berry weights compared with nonedge plants, suggesting that wild pollinators were only effective along the edge. Comparing the same bed between 1999, with three honey bee colonies per acre, and 2000, with no honey bees, we found a significant reduction in average berry size. In 2000, we compared stigma loading on properties with and without honey bees. Significantly more stigmas received the minimum number of tetrads required for fruit set on the property with honey bees. Significantly more tetrads were deposited during mid-bloom compared with early bloom, indicating that mid-bloom was the best time to have honey bees present. This study emphasizes the importance and effectiveness of honey bees as pollinators of commercial size cranberry plantings.

  20. Optimum Assembly Sequence Planning System Using Discrete Artificial Bee Colony Algorithm

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    Özkan Özmen

    2018-01-01

    Full Text Available Assembly refers both to the process of combining parts to create a structure and to the product resulting therefrom. The complexity of this process increases with the number of pieces in the assembly. This paper presents the assembly planning system design (APSD program, a computer program developed based on a matrix-based approach and the discrete artificial bee colony (DABC algorithm, which determines the optimum assembly sequence among numerous feasible assembly sequences (FAS. Specifically, the assembly sequences of three-dimensional (3D parts prepared in the computer-aided design (CAD software AutoCAD are first coded using the matrix-based methodology and the resulting FAS are assessed and the optimum assembly sequence is selected according to the assembly time optimisation criterion using DABC. The results of comparison of the performance of the proposed method with other methods proposed in the literature verify its superiority in finding the sequence with the lowest overall time. Further, examination of the results of application of APSD to assemblies consisting of parts in different numbers and shapes shows that it can select the optimum sequence from among hundreds of FAS.

  1. Use of artificial bee colonies algorithm as numerical approximation of differential equations solution

    Science.gov (United States)

    Fikri, Fariz Fahmi; Nuraini, Nuning

    2018-03-01

    The differential equation is one of the branches in mathematics which is closely related to human life problems. Some problems that occur in our life can be modeled into differential equations as well as systems of differential equations such as the Lotka-Volterra model and SIR model. Therefore, solving a problem of differential equations is very important. Some differential equations are difficult to solve, so numerical methods are needed to solve that problems. Some numerical methods for solving differential equations that have been widely used are Euler Method, Heun Method, Runge-Kutta and others. However, some of these methods still have some restrictions that cause the method cannot be used to solve more complex problems such as an evaluation interval that we cannot change freely. New methods are needed to improve that problems. One of the method that can be used is the artificial bees colony algorithm. This algorithm is one of metaheuristic algorithm method, which can come out from local search space and do exploration in solution search space so that will get better solution than other method.

  2. Are dispersal mechanisms changing the host-parasite relationship and increasing the virulence of Varroa destructor [Acari:Varroidae] in managed honey bee [Hymenoptera: Apidae] colonies?

    Science.gov (United States)

    Varroa mites are the most serious pest of honey bees worldwide, and difficult to control in managed colonies. We show in a longitudinal study that even with multiple miticide treatments in the summer and fall, mite numbers remained high and colony losses exceeded 55%. Furthermore, large heavily infe...

  3. Weight-of-evidence evaluation of an adverse outcome pathway network linking activation of the nicotinic acetylcholine receptor in bees to colony loss

    Science.gov (United States)

    Ongoing honey bee colony losses are of significant international concern because of the essential role these insects play in pollinating staple food crops. Chemical and non-chemical stressors both have been implicated as possible contributors to colony failure, however, the pote...

  4. PARAMETER ESTIMATION OF VALVE STICTION USING ANT COLONY OPTIMIZATION

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    S. Kalaivani

    2012-07-01

    Full Text Available In this paper, a procedure for quantifying valve stiction in control loops based on ant colony optimization has been proposed. Pneumatic control valves are widely used in the process industry. The control valve contains non-linearities such as stiction, backlash, and deadband that in turn cause oscillations in the process output. Stiction is one of the long-standing problems and it is the most severe problem in the control valves. Thus the measurement data from an oscillating control loop can be used as a possible diagnostic signal to provide an estimate of the stiction magnitude. Quantification of control valve stiction is still a challenging issue. Prior to doing stiction detection and quantification, it is necessary to choose a suitable model structure to describe control-valve stiction. To understand the stiction phenomenon, the Stenman model is used. Ant Colony Optimization (ACO, an intelligent swarm algorithm, proves effective in various fields. The ACO algorithm is inspired from the natural trail following behaviour of ants. The parameters of the Stenman model are estimated using ant colony optimization, from the input-output data by minimizing the error between the actual stiction model output and the simulated stiction model output. Using ant colony optimization, Stenman model with known nonlinear structure and unknown parameters can be estimated.

  5. Honey Bee Colonies Headed by Hyperpolyandrous Queens Have Improved Brood Rearing Efficiency and Lower Infestation Rates of Parasitic Varroa Mites.

    Directory of Open Access Journals (Sweden)

    Keith S Delaplane

    Full Text Available A honey bee queen mates on wing with an average of 12 males and stores their sperm to produce progeny of mixed paternity. The degree of a queen's polyandry is positively associated with measures of her colony's fitness, and observed distributions of mating number are evolutionary optima balancing risks of mating flights against benefits to the colony. Effective mating numbers as high as 40 have been documented, begging the question of the upper bounds of this behavior that can be expected to confer colony benefit. In this study we used instrumental insemination to create three classes of queens with exaggerated range of polyandry--15, 30, or 60 drones. Colonies headed by queens inseminated with 30 or 60 drones produced more brood per bee and had a lower proportion of samples positive for Varroa destructor mites than colonies whose queens were inseminated with 15 drones, suggesting benefits of polyandry at rates higher than those normally obtaining in nature. Our results are consistent with two hypotheses that posit conditions that reward such high expressions of polyandry: (1 a queen may mate with many males in order to promote beneficial non-additive genetic interactions among subfamilies, and (2 a queen may mate with many males in order to capture a large number of rare alleles that regulate resistance to pathogens and parasites in a breeding population. Our results are unique for identifying the highest levels of polyandry yet detected that confer colony-level benefit and for showing a benefit of polyandry in particular toward the parasitic mite V. destructor.

  6. Genetic control of the honey bee (Apis mellifera) dance language: segregating dance forms in a backcrossed colony.

    Science.gov (United States)

    Johnson, R N; Oldroyd, B P; Barron, A B; Crozier, R H

    2002-01-01

    We studied the genetic control of the dance dialects that exist in the different subspecies of honey bees (Apis mellifera) by observing the variation in dance form observed in a backcross between two lines that showed widely different dance dialects. To do this we generated the reciprocal of the cross performed by Rinderer and Beaman (1995), thus producing phenotypic segregation of dance forms within a single colony rather than between colonies. Our results are consistent with Rinderer and Beaman (1995) in that inheritance of the transition point from round dancing --> waggle dancing is consistent with control by a single locus with more than one allele. That is, we found one dance type to be dominant in the F(1), and observed a 1:1 segregation of dance in a backcross involving the F(1) and the recessive parent. However, we found some minor differences in dance dialect inheritance, with the most significant being an apparent reversal of dominance between our cross (for us "black" is the dominant dialect) and that of Rinderer and Beaman (1995) (they report "yellow" to be the dominant dialect). We also found that our black bees do not perform a distinct sickle dance, whereas the black bees used by Rinderer and Beaman (1995) did perform such a dance. However, our difference in dominance need not contradict the results of Rinderer and Beaman (1995), as there is no evidence that body color and dominance for dance dialect are linked.

  7. The Effects of Age of Grafted Larvae and of Supplemental Feeding on Performance of Iranian Honey Bee Colonies (Apis Mellifera Meda

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    Mahbobi Ali

    2014-06-01

    Full Text Available The performance of bee colonies greatly depends on the quality of the queens. The current research was conducted at the apiary of the Faculty of Agriculture, Zanjan University, in Zanjan, Iran. Together, 24 rearing colonies were assigned to 4 grafting larvae age groups: 1-day-old, 2-day-old, 3-day-old, and emergency queens. Two feeding groups, fed and not fed, were created. The effects of reared queens on biological characteristics and performance of honeybee colonies (Apis mellifera meda headed by those queens were measured. Age of grafted larvae significantly influenced the results. The performance ratios of the most efficient colonies headed by queens reared from 1-day-old larvae compared with the least-efficient queens reared from 3-day-old larvae were 118% in brood production, 140% in bee population, and 154% in honey production. However, the age of grafted larvae did not affect colony defense behavior. Supplemental feeding of rearing colonies increased brood production to 111%, bee population to 116%, and honey production to 115%. A combination of the effect of age of larvae and supplemental feeding resulted in twice as much honey (12 kg produced by colonies with queens reared from 1-day-old larvae in fed rearing colonies compared to those with queens raised from 3-day-old larvae in unfed rearing colonies.

  8. Response Ant Colony Optimization of End Milling Surface Roughness

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    Ahmed N. Abd Alla

    2010-03-01

    Full Text Available Metal cutting processes are important due to increased consumer demands for quality metal cutting related products (more precise tolerances and better product surface roughness that has driven the metal cutting industry to continuously improve quality control of metal cutting processes. This paper presents optimum surface roughness by using milling mould aluminium alloys (AA6061-T6 with Response Ant Colony Optimization (RACO. The approach is based on Response Surface Method (RSM and Ant Colony Optimization (ACO. The main objectives to find the optimized parameters and the most dominant variables (cutting speed, feedrate, axial depth and radial depth. The first order model indicates that the feedrate is the most significant factor affecting surface roughness.

  9. A comparison of genetic algorithm and artificial bee colony approaches in solving blocking hybrid flowshop scheduling problem with sequence dependent setup/changeover times

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    Pongpan Nakkaew

    2016-06-01

    Full Text Available In manufacturing process where efficiency is crucial in order to remain competitive, flowshop is a common configuration in which machines are arranged in series and products are produced through the stages one by one. In certain production processes, the machines are frequently configured in the way that each production stage may contain multiple processing units in parallel or hybrid. Moreover, along with precedent conditions, the sequence dependent setup times may exist. Finally, in case there is no buffer, a machine is said to be blocked if the next stage to handle its output is being occupied. Such NP-Hard problem, referred as Blocking Hybrid Flowshop Scheduling Problem with Sequence Dependent Setup/Changeover Times, is usually not possible to find the best exact solution to satisfy optimization objectives such as minimization of the overall production time. Thus, it is usually solved by approximate algorithms such as metaheuristics. In this paper, we investigate comparatively the effectiveness of the two approaches: a Genetic Algorithm (GA and an Artificial Bee Colony (ABC algorithm. GA is inspired by the process of natural selection. ABC, in the same manner, resembles the way types of bees perform specific functions and work collectively to find their foods by means of division of labor. Additionally, we apply an algorithm to improve the GA and ABC algorithms so that they can take advantage of parallel processing resources of modern multiple core processors while eliminate the need for screening the optimal parameters of both algorithms in advance.

  10. E-β-ocimene, a volatile brood pheromone involved in social regulation in the honey bee colony (Apis mellifera.

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    Alban Maisonnasse

    Full Text Available BACKGROUND: In honey bee colony, the brood is able to manipulate and chemically control the workers in order to sustain their own development. A brood ester pheromone produced primarily by old larvae (4 and 5 days old larvae was first identified as acting as a contact pheromone with specific effects on nurses in the colony. More recently a new volatile brood pheromone has been identified: E-β-ocimene, which partially inhibits ovary development in workers. METHODOLOGY AND PRINCIPAL FINDING: Our analysis of E-β-ocimene production revealed that young brood (newly hatched to 3 days old produce the highest quantity of E-β-ocimene relative to their body weight. By testing the potential action of this molecule as a non-specific larval signal, due to its high volatility in the colony, we demonstrated that in the presence of E-β-ocimene nest workers start to forage earlier in life, as seen in the presence of real brood. CONCLUSIONS/SIGNIFICANCE: In this way, young larvae are able to assign precedence to the task of foraging by workers in order to increase food stores for their own development. Thus, in the complexity of honey bee chemical communication, E-β-ocimene, a pheromone of young larvae, provides the brood with the means to express their nutritional needs to the workers.

  11. Honey Bee Inhibitory Signaling Is Tuned to Threat Severity and Can Act as a Colony Alarm Signal.

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    Ken Tan

    2016-03-01

    Full Text Available Alarm communication is a key adaptation that helps social groups resist predation and rally defenses. In Asia, the world's largest hornet, Vespa mandarinia, and the smaller hornet, Vespa velutina, prey upon foragers and nests of the Asian honey bee, Apis cerana. We attacked foragers and colony nest entrances with these predators and provide the first evidence, in social insects, of an alarm signal that encodes graded danger and attack context. We show that, like Apis mellifera, A. cerana possesses a vibrational "stop signal," which can be triggered by predator attacks upon foragers and inhibits waggle dancing. Large hornet attacks were more dangerous and resulted in higher bee mortality. Per attack at the colony level, large hornets elicited more stop signals than small hornets. Unexpectedly, stop signals elicited by large hornets (SS large hornet had a significantly higher vibrational fundamental frequency than those elicited by small hornets (SS small hornet and were more effective at inhibiting waggle dancing. Stop signals resulting from attacks upon the nest entrance (SS nest were produced by foragers and guards and were significantly longer in pulse duration than stop signals elicited by attacks upon foragers (SS forager. Unlike SS forager, SS nest were targeted at dancing and non-dancing foragers and had the common effect, tuned to hornet threat level, of inhibiting bee departures from the safe interior of the nest. Meanwhile, nest defenders were triggered by the bee alarm pheromone and live hornet presence to heat-ball the hornet. In A. cerana, sophisticated recruitment communication that encodes food location, the waggle dance, is therefore matched with an inhibitory/alarm signal that encodes information about the context of danger and its threat level.

  12. Honey Bee Inhibitory Signaling Is Tuned to Threat Severity and Can Act as a Colony Alarm Signal.

    Science.gov (United States)

    Tan, Ken; Dong, Shihao; Li, Xinyu; Liu, Xiwen; Wang, Chao; Li, Jianjun; Nieh, James C

    2016-03-01

    Alarm communication is a key adaptation that helps social groups resist predation and rally defenses. In Asia, the world's largest hornet, Vespa mandarinia, and the smaller hornet, Vespa velutina, prey upon foragers and nests of the Asian honey bee, Apis cerana. We attacked foragers and colony nest entrances with these predators and provide the first evidence, in social insects, of an alarm signal that encodes graded danger and attack context. We show that, like Apis mellifera, A. cerana possesses a vibrational "stop signal," which can be triggered by predator attacks upon foragers and inhibits waggle dancing. Large hornet attacks were more dangerous and resulted in higher bee mortality. Per attack at the colony level, large hornets elicited more stop signals than small hornets. Unexpectedly, stop signals elicited by large hornets (SS large hornet) had a significantly higher vibrational fundamental frequency than those elicited by small hornets (SS small hornet) and were more effective at inhibiting waggle dancing. Stop signals resulting from attacks upon the nest entrance (SS nest) were produced by foragers and guards and were significantly longer in pulse duration than stop signals elicited by attacks upon foragers (SS forager). Unlike SS forager, SS nest were targeted at dancing and non-dancing foragers and had the common effect, tuned to hornet threat level, of inhibiting bee departures from the safe interior of the nest. Meanwhile, nest defenders were triggered by the bee alarm pheromone and live hornet presence to heat-ball the hornet. In A. cerana, sophisticated recruitment communication that encodes food location, the waggle dance, is therefore matched with an inhibitory/alarm signal that encodes information about the context of danger and its threat level.

  13. Evaluation of pollen collected by honey bee, Apis mellifera L. colonies at Fayoum Governorate, Egypt. Part 1: Botanical origin

    Directory of Open Access Journals (Sweden)

    Abdel-Halim M. Ismail

    2013-06-01

    Full Text Available The present work is the 1st part of 3-part study carried out at Fayoum Governorate, Egypt to evaluate the pollen species collected by honey bee, Apis mellifera L., colonies during two successive years, 2009 and 2010. Obtained results showed that, in 2009, total amount of trapped pollen (fresh weight was 2354.89 g/colony/year (mean 588.72 g/colony/season, with peaks in summer and spring, while declined in autumn and winter. Correlation between mean maximum and minimum temperatures and weekly pollen weights was highly positive, while it was insignificant for relative humidity. In 2010, total amount of trapped pollen decreased to 1635.36 g/colony/year (mean 408.84 g/colony/season. The largest amounts were collected in summer followed by winter then spring, while least ones were in autumn. Correlation was highly positive between weekly mean of pollen weights and maximum temperature, while it was insignificant for minimum temperature or relative humidity. There were 24 plant species of 16 botanical families from which bees collected pollen. These sources were ranked according to their predominant quantities in the 1st and 2nd years by two numbers, respectively as the following: sesame 1 and 1, maize 2 and 2, clover 3 and 7, sunflower 4 and 8, wild mustard 5 and 3, casuarina 6 and 13, olive 7 and 11, eucalyptus 8 and 4, pumpkin 9 and 9, cocklebur 10 and 5, date palm 11 and 10, chamomile 12 and 12, field bindweed 13 and 6, pepper 14 and 20, coriander 15 and 16, acacia 16 and 24, citrus 17 and 0, marigold 18 and 0, common red 19 and 17, Christ’s thorn 20 and 22, tooth pick 21 and 21, brood bean 22 and 15, belladonna 23 and 23, pea 0 and 14, marjoram 0 and 18 and fennel 0 and 19. The 1st five plants seem to be the main pollen sources for honey bee colonies and consequently pollen producing during the whole year in the tested region. These sources represented 75.61% and 66.95% of the total annual yield in the two surveyed years, respectively.

  14. Risk factors associated with the presence of Varroa destructor in honey bee colonies from east-central Argentina.

    Science.gov (United States)

    Giacobino, A; Bulacio Cagnolo, N; Merke, J; Orellano, E; Bertozzi, E; Masciangelo, G; Pietronave, H; Salto, C; Signorini, M

    2014-08-01

    Varroa destructor is considered one of the major threats for worldwide apiculture. Damage caused by varroa mite includes body weight loss, malformation and weakening of the bees. It was also suggested as the main cause associated with colony winter mortality and as an important vector for several honey bee viruses. Little is known about multiple factors and their interaction affecting V. destructor prevalence in apiaries from South America. The aim of this study was to identify risk factors associated with V. destructor prevalence in east-central Argentina. Parasitic mite infestation level and colony strength measures were evaluated in 63 apiaries distributed in 4 different regions in east-central Argentina in a cross sectional study. Data regarding management practices in each apiary were collected by means of a questionnaire. A mixed-effects logistic regression model was constructed to associate management variables with the risk of achieving mite infestation higher than 3%. Colonies owned by beekeepers who indicated that they did not monitor colonies after mite treatment (OR=2.305; 95% CI: 0.944-5.629) nor disinfect hives woodenware material (OR=2.722; 95% CI: 1.380-5.565) were associated with an increased risk of presenting high intensity infestation with V. destructor (>3%). On the other hand, beekeepers who reported replacing more than 50% of the queens in their operation (OR=0.305; 95% CI: 0.107-0.872), feeding colonies protein substitute containing natural pollen (OR=0.348; 95% CI: 0.129-0.941) and feeding colonies High Fructose Corn Syrup (HFCS) (OR=0.108; 95% CI: 0.032-0.364), had colonies that were less likely to have V. destructor infestations above 3%, than beekeepers who did not report using these management practices. Further research should be conducted considering that certain management practices were associated to mite infestation level in order to improve the sanitary condition in the colonies. Epidemiological studies provide key information to

  15. Honey Bee Antiviral Immune Barriers as Affected by Multiple Stress Factors: A Novel Paradigm to Interpret Colony Health Decline and Collapse

    Directory of Open Access Journals (Sweden)

    Francesco Nazzi

    2018-03-01

    Full Text Available Any attempt to outline a logical framework in which to interpret the honey bee health decline and its contribution to elevated colony losses should recognize the importance of the multifactorial nature of the responsible syndrome and provide a functional model as a basis for defining and testing working hypotheses. We propose that covert infections by deformed wing virus (DWV represent a sword of Damocles permanently threatening the survival of honey bee colonies and suggest that any factor affecting the honey bee’s antiviral defenses can turn this pathogen into a killer. Here we discuss the available experimental evidence in the framework of a model based on honey bee immune competence as affected by multiple stress factors that is proposed as a conceptual tool for analyzing bee mortality and its underlying mechanisms.

  16. An Enhanced Discrete Artificial Bee Colony Algorithm to Minimize the Total Flow Time in Permutation Flow Shop Scheduling with Limited Buffers

    Directory of Open Access Journals (Sweden)

    Guanlong Deng

    2016-01-01

    Full Text Available This paper presents an enhanced discrete artificial bee colony algorithm for minimizing the total flow time in the flow shop scheduling problem with buffer capacity. First, the solution in the algorithm is represented as discrete job permutation to directly convert to active schedule. Then, we present a simple and effective scheme called best insertion for the employed bee and onlooker bee and introduce a combined local search exploring both insertion and swap neighborhood. To validate the performance of the presented algorithm, a computational campaign is carried out on the Taillard benchmark instances, and computations and comparisons show that the proposed algorithm is not only capable of solving the benchmark set better than the existing discrete differential evolution algorithm and iterated greedy algorithm, but also capable of performing better than two recently proposed discrete artificial bee colony algorithms.

  17. Runtime analysis of the 1-ANT ant colony optimizer

    DEFF Research Database (Denmark)

    Doerr, Benjamin; Neumann, Frank; Sudholt, Dirk

    2011-01-01

    The runtime analysis of randomized search heuristics is a growing field where, in the last two decades, many rigorous results have been obtained. First runtime analyses of ant colony optimization (ACO) have been conducted only recently. In these studies simple ACO algorithms such as the 1-ANT...... that give us a more detailed impression of the 1-ANT’s performance. Furthermore, the experiments also deal with the question whether using many ant solutions in one iteration can decrease the total runtime....

  18. DATA MINING UNTUK KLASIFIKASI PELANGGAN DENGAN ANT COLONY OPTIMIZATION

    Directory of Open Access Journals (Sweden)

    Maulani Kapiudin

    2007-01-01

    Full Text Available In this research the system for potentially customer classification is designed by extracting rule based classification from raw data with certain criteria. The searching process uses customer database from a bank with data mining technic by using ant colony optimization. A test based on min_case_per_rule variety and phenomene updating were done on a certain period of time. The result are group of customer class which base on rules built by ant and by modifying the pheromone updating, the area of the case is getting bigger. Prototype of the software is coded with C++ 6 version. The customer database master is created by using Microsoft Access. This paper gives information about potential customer of bank that can be classified by prototype of the software. Abstract in Bahasa Indonesia : Pada penelitian untuk sistem klasifikasi potensial customer ini didesain dengan melakukan ekstrak rule berdasarkan klasifikasi dari data mentah dengan kriteria tertentu. Proses pencarian menggunakan database pelanggan dari suatu bank dengan teknik data mining dengan ant colony optimization. Dilakukan percobaan dengan min_case_per_rule variety dan phenomene updating pada periode waktu tertentu. Hasilnya adalah sekelompok class pelanggan yang didasarkan dari rules yang dibangun dengan ant dan dengan dimodifikasi dengan pheromone updating, area permasalahan menjadi lebih melebar. Prototype dari software ini menggunakan C++ versi 6. Database pelanggan dibangun dengan Microsoft Access. Paper ini memberikan informasi mengenai potensi pelanggan dari bank, sehingga dapat diklasifikasikan dengan prototype dari software. Kata kunci: ant colony optimization, classification, min_case_per_rule, term, pheromone updating

  19. A seismic fault recognition method based on ant colony optimization

    Science.gov (United States)

    Chen, Lei; Xiao, Chuangbai; Li, Xueliang; Wang, Zhenli; Huo, Shoudong

    2018-05-01

    Fault recognition is an important section in seismic interpretation and there are many methods for this technology, but no one can recognize fault exactly enough. For this problem, we proposed a new fault recognition method based on ant colony optimization which can locate fault precisely and extract fault from the seismic section. Firstly, seismic horizons are extracted by the connected component labeling algorithm; secondly, the fault location are decided according to the horizontal endpoints of each horizon; thirdly, the whole seismic section is divided into several rectangular blocks and the top and bottom endpoints of each rectangular block are considered as the nest and food respectively for the ant colony optimization algorithm. Besides that, the positive section is taken as an actual three dimensional terrain by using the seismic amplitude as a height. After that, the optimal route from nest to food calculated by the ant colony in each block is judged as a fault. Finally, extensive comparative tests were performed on the real seismic data. Availability and advancement of the proposed method were validated by the experimental results.

  20. Improved Ant Colony Optimization for Seafood Product Delivery Routing Problem

    Directory of Open Access Journals (Sweden)

    Baozhen Yao

    2014-02-01

    Full Text Available This paper deals with a real-life vehicle delivery routing problem, which is a seafood product delivery routing problem. Considering the features of the seafood product delivery routing problem, this paper formulated this problem as a multi-depot open vehicle routing problem. Since the multi-depot open vehicle routing problem is a very complex problem, a method is used to reduce the complexity of the problem by changing the multi-depot open vehicle routing problem into an open vehicle routing problem with a dummy central depot in this paper. Then, ant colony optimization is used to solve the problem. To improve the performance of the algorithm, crossover operation and some adaptive strategies are used. Finally, the computational results for the benchmark problems of the multi-depot vehicle routing problem indicate that the proposed ant colony optimization is an effective method to solve the multi-depot vehicle routing problem. Furthermore, the computation results of the seafood product delivery problem from Dalian, China also suggest that the proposed ant colony optimization is feasible to solve the seafood product delivery routing problem.

  1. Nosema ceranae is an old resident of honey bee (Apis mellifera) colonies in Mexico, causing infection levels of one million spores per bee or higher during summer and fall.

    Science.gov (United States)

    Guerrero-Molina, Cristina; Correa-Benítez, Adriana; Hamiduzzaman, Mollah Md; Guzman-Novoa, Ernesto

    2016-11-01

    This study was conducted to identify Nosema spp. and to determine their infection levels in honey bee (Apis mellifera) samples collected in Mexico in 1995-1996. Samples of historical surveys from different countries are of particular interest to support or challenge the hypothesis that the microsporidium Nosema ceranae is a new parasite of A. mellifera that has recently dispersed across the world. We demonstrate that N. ceranae has parasitized honey bees in Mexico since at least 1995 and that the infection levels of this parasite during summer and fall, exceed the threshold at which treatment of honey bee colonies is recommended. Copyright © 2016 Elsevier Inc. All rights reserved.

  2. A Bio-Economic Case Study of Canadian Honey Bee (Hymenoptera: Apidae) Colonies: Marker-Assisted Selection (MAS) in Queen Breeding Affects Beekeeper Profits

    Science.gov (United States)

    Baylis, Kathy; Hoover, Shelley E.; Currie, Rob W.; Melathopoulos, Andony P.; Pernal, Stephen F.; Foster, Leonard J.; Guarna, M. Marta

    2017-01-01

    Abstract Over the past decade in North America and Europe, winter losses of honey bee (Hymenoptera: Apidae) colonies have increased dramatically. Scientific consensus attributes these losses to multifactorial causes including altered parasite and pathogen profiles, lack of proper nutrition due to agricultural monocultures, exposure to pesticides, management, and weather. One method to reduce colony loss and increase productivity is through selective breeding of queens to produce disease-, pathogen-, and mite-resistant stock. Historically, the only method for identifying desirable traits in honey bees to improve breeding was through observation of bee behavior. A team of Canadian scientists have recently identified markers in bee antennae that correspond to behavioral traits in bees and can be tested for in a laboratory. These scientists have demonstrated that this marker-assisted selection (MAS) can be used to produce hygienic, pathogen-resistant honey bee colonies. Based on this research, we present a beekeeping case study where a beekeeper’s profit function is used to evaluate the economic impact of adopting colonies selected for hygienic behavior using MAS into an apiary. Our results show a net profit gain from an MAS colony of between 2% and 5% when Varroa mites are effectively treated. In the case of ineffective treatment, MAS generates a net profit benefit of between 9% and 96% depending on the Varroa load. When a Varroa mite population has developed some treatment resistance, we show that MAS colonies generate a net profit gain of between 8% and 112% depending on the Varroa load and degree of treatment resistance. PMID:28334400

  3. A Bio-Economic Case Study of Canadian Honey Bee (Hymenoptera: Apidae) Colonies: Marker-Assisted Selection (MAS) in Queen Breeding Affects Beekeeper Profits.

    Science.gov (United States)

    Bixby, Miriam; Baylis, Kathy; Hoover, Shelley E; Currie, Rob W; Melathopoulos, Andony P; Pernal, Stephen F; Foster, Leonard J; Guarna, M Marta

    2017-06-01

    Over the past decade in North America and Europe, winter losses of honey bee (Hymenoptera: Apidae) colonies have increased dramatically. Scientific consensus attributes these losses to multifactorial causes including altered parasite and pathogen profiles, lack of proper nutrition due to agricultural monocultures, exposure to pesticides, management, and weather. One method to reduce colony loss and increase productivity is through selective breeding of queens to produce disease-, pathogen-, and mite-resistant stock. Historically, the only method for identifying desirable traits in honey bees to improve breeding was through observation of bee behavior. A team of Canadian scientists have recently identified markers in bee antennae that correspond to behavioral traits in bees and can be tested for in a laboratory. These scientists have demonstrated that this marker-assisted selection (MAS) can be used to produce hygienic, pathogen-resistant honey bee colonies. Based on this research, we present a beekeeping case study where a beekeeper's profit function is used to evaluate the economic impact of adopting colonies selected for hygienic behavior using MAS into an apiary. Our results show a net profit gain from an MAS colony of between 2% and 5% when Varroa mites are effectively treated. In the case of ineffective treatment, MAS generates a net profit benefit of between 9% and 96% depending on the Varroa load. When a Varroa mite population has developed some treatment resistance, we show that MAS colonies generate a net profit gain of between 8% and 112% depending on the Varroa load and degree of treatment resistance. © The Authors 2017. Published by Oxford University Press on behalf of Entomological Society of America.

  4. Incremental artificial bee colony with local search to economic dispatch problem with ramp rate limits and prohibited operating zones

    International Nuclear Information System (INIS)

    Özyön, Serdar; Aydin, Doğan

    2013-01-01

    Highlights: ► Prohibited operating zone economic dispatch problem has been solved by IABC-LS. ► The losses used in the solution of the problem have been computed by B-loss matrix. ► IABC-LS method has been applied to three test systems in literature. ► The values obtained by IABC and IABC-LS are better than the results in literature. - Abstract: In this study, prohibited operating zone economic power dispatch problem which considers ramp rate limit, has been solved by incremental artificial bee colony algorithm (IABC) and incremental artificial bee colony algorithm with local search (IABC-LS) methods. The transmission line losses used in the solution of the problem have been computed by B-loss matrix. IABC, IABC-LS methods have been applied to three different test systems in literature which consist of 6, 15 and 40 generators. The attained optimum solution values have been compared with the optimum results in literature and have been discussed.

  5. Identification of cultivated land using remote sensing images based on object-oriented artificial bee colony algorithm

    Science.gov (United States)

    Li, Nan; Zhu, Xiufang

    2017-04-01

    Cultivated land resources is the key to ensure food security. Timely and accurate access to cultivated land information is conducive to a scientific planning of food production and management policies. The GaoFen 1 (GF-1) images have high spatial resolution and abundant texture information and thus can be used to identify fragmentized cultivated land. In this paper, an object-oriented artificial bee colony algorithm was proposed for extracting cultivated land from GF-1 images. Firstly, the GF-1 image was segmented by eCognition software and some samples from the segments were manually identified into 2 types (cultivated land and non-cultivated land). Secondly, the artificial bee colony (ABC) algorithm was used to search for classification rules based on the spectral and texture information extracted from the image objects. Finally, the extracted classification rules were used to identify the cultivated land area on the image. The experiment was carried out in Hongze area, Jiangsu Province using wide field-of-view sensor on the GF-1 satellite image. The total precision of classification result was 94.95%, and the precision of cultivated land was 92.85%. The results show that the object-oriented ABC algorithm can overcome the defect of insufficient spectral information in GF-1 images and obtain high precision in cultivated identification.

  6. Application of Artificial Bee Colony Algorithm and Finite Element Analysis for Optimum Design of Brushless Permanent Magnet Motor

    Directory of Open Access Journals (Sweden)

    Reza Ilka

    2012-04-01

    Full Text Available ABSTRACT: This paper develops a mathematical relationship for the purpose of designing and selecting the optimum dimensions of a brushless permanent magnet motor. The design is optimised by the use of artificial bee colony algorithm with the goal of maximizing the power density and efficiency of the motor. The required dimensions of the brushless motor are calculated based on the optimum power density and efficiency requirements. Finally, the predicted results of the optimisation are validated using a 2-D numerical program based on finite element analysis.ABSTRAK: Kajian ini mencadangkan persamaan yang menghubungkan rekabentuk dan dimensi magnet motor kekal tanpa berus. Rekabentuk optima berdasarkan algorisma koloni lebah tiruan dengan tujuan meningkatkan ketumpatan kuasa dan keberkesanan dibentangkan dalam kajian ini. Dimensi magnet motor kekal tanpa berus dihitung dengan ketumpatan kuasa optima dan keberkesanan. Akhirnya, keputusan telah disahkan dengan menggunakan program berangka 2-D berdasarkan analisis elemen finit.KEYWORDS: brushless; permanent magnet motor; power density; artificial bee colony; algorithm; finite element analysis

  7. Neonicotinoid insecticides in pollen, honey and adult bees in colonies of the European honey bee (Apis mellifera L.) in Egypt.

    Science.gov (United States)

    Codling, Garry; Naggar, Yahya Al; Giesy, John P; Robertson, Albert J

    2018-03-01

    Honeybee losses have been attributed to multiple stressors and factors including the neonicotinoid insecticides (NIs). Much of the study of hive contamination has been focused upon temperate regions such as Europe, Canada and the United States. This study looks for the first time at honey, pollen and bees collected from across the Nile Delta in Egypt in both the spring and summer planting season of 2013. There is limited information upon the frequency of use of NIs in Egypt but the ratio of positive identification and concentrations of NIs are comparable to other regions. Metabolites of NIs were also monitored but given the low detection frequency, no link between matrices was possible in the study. Using a simple hazard assessment based upon published LD 50 values for individual neonicotinoids upon the foraging and brood workers it was found that there was a potential risk to brood workers if the lowest reported LD 50 was compared to the sum of the maximum NI concentrations. For non-lethal exposure there was significant risk at the worst case to brood bees but actual exposure effects are dependant upon the genetics and conditions of the Egyptian honeybee subspecies that remain to be determined.

  8. A large-scale field study examining effects of exposure to clothianidin seed-treated canola on honey bee colony health, development, and overwintering success

    Directory of Open Access Journals (Sweden)

    G. Christopher Cutler

    2014-10-01

    Full Text Available In summer 2012, we initiated a large-scale field experiment in southern Ontario, Canada, to determine whether exposure to clothianidin seed-treated canola (oil seed rape has any adverse impacts on honey bees. Colonies were placed in clothianidin seed-treated or control canola fields during bloom, and thereafter were moved to an apiary with no surrounding crops grown from seeds treated with neonicotinoids. Colony weight gain, honey production, pest incidence, bee mortality, number of adults, and amount of sealed brood were assessed in each colony throughout summer and autumn. Samples of honey, beeswax, pollen, and nectar were regularly collected, and samples were analyzed for clothianidin residues. Several of these endpoints were also measured in spring 2013. Overall, colonies were vigorous during and after the exposure period, and we found no effects of exposure to clothianidin seed-treated canola on any endpoint measures. Bees foraged heavily on the test fields during peak bloom and residue analysis indicated that honey bees were exposed to low levels (0.5–2 ppb of clothianidin in pollen. Low levels of clothianidin were detected in a few pollen samples collected toward the end of the bloom from control hives, illustrating the difficulty of conducting a perfectly controlled field study with free-ranging honey bees in agricultural landscapes. Overwintering success did not differ significantly between treatment and control hives, and was similar to overwintering colony loss rates reported for the winter of 2012–2013 for beekeepers in Ontario and Canada. Our results suggest that exposure to canola grown from seed treated with clothianidin poses low risk to honey bees.

  9. A large-scale field study examining effects of exposure to clothianidin seed-treated canola on honey bee colony health, development, and overwintering success.

    Science.gov (United States)

    Cutler, G Christopher; Scott-Dupree, Cynthia D; Sultan, Maryam; McFarlane, Andrew D; Brewer, Larry

    2014-01-01

    In summer 2012, we initiated a large-scale field experiment in southern Ontario, Canada, to determine whether exposure to clothianidin seed-treated canola (oil seed rape) has any adverse impacts on honey bees. Colonies were placed in clothianidin seed-treated or control canola fields during bloom, and thereafter were moved to an apiary with no surrounding crops grown from seeds treated with neonicotinoids. Colony weight gain, honey production, pest incidence, bee mortality, number of adults, and amount of sealed brood were assessed in each colony throughout summer and autumn. Samples of honey, beeswax, pollen, and nectar were regularly collected, and samples were analyzed for clothianidin residues. Several of these endpoints were also measured in spring 2013. Overall, colonies were vigorous during and after the exposure period, and we found no effects of exposure to clothianidin seed-treated canola on any endpoint measures. Bees foraged heavily on the test fields during peak bloom and residue analysis indicated that honey bees were exposed to low levels (0.5-2 ppb) of clothianidin in pollen. Low levels of clothianidin were detected in a few pollen samples collected toward the end of the bloom from control hives, illustrating the difficulty of conducting a perfectly controlled field study with free-ranging honey bees in agricultural landscapes. Overwintering success did not differ significantly between treatment and control hives, and was similar to overwintering colony loss rates reported for the winter of 2012-2013 for beekeepers in Ontario and Canada. Our results suggest that exposure to canola grown from seed treated with clothianidin poses low risk to honey bees.

  10. Bee Swarm Optimization for Medical Web Information Foraging.

    Science.gov (United States)

    Drias, Yassine; Kechid, Samir; Pasi, Gabriella

    2016-02-01

    The present work is related to Web intelligence and more precisely to medical information foraging. We present here a novel approach based on agents technology for information foraging. An architecture is proposed, in which we distinguish two important phases. The first one is a learning process for localizing the most relevant pages that might interest the user. This is performed on a fixed instance of the Web. The second takes into account the openness and the dynamicity of the Web. It consists on an incremental learning starting from the result of the first phase and reshaping the outcomes taking into account the changes that undergoes the Web. The whole system offers a tool to help the user undertaking information foraging. We implemented the system using a group of cooperative reactive agents and more precisely a colony of artificial bees. In order to validate our proposal, experiments were conducted on MedlinePlus, a benchmark dedicated for research in the domain of Health. The results are promising either for those related to Web regularities and for the response time, which is very short and hence complies the real time constraint.

  11. Ant Colony Optimization for Markowitz Mean-Variance Portfolio Model

    Science.gov (United States)

    Deng, Guang-Feng; Lin, Woo-Tsong

    This work presents Ant Colony Optimization (ACO), which was initially developed to be a meta-heuristic for combinatorial optimization, for solving the cardinality constraints Markowitz mean-variance portfolio model (nonlinear mixed quadratic programming problem). To our knowledge, an efficient algorithmic solution for this problem has not been proposed until now. Using heuristic algorithms in this case is imperative. Numerical solutions are obtained for five analyses of weekly price data for the following indices for the period March, 1992 to September, 1997: Hang Seng 31 in Hong Kong, DAX 100 in Germany, FTSE 100 in UK, S&P 100 in USA and Nikkei 225 in Japan. The test results indicate that the ACO is much more robust and effective than Particle swarm optimization (PSO), especially for low-risk investment portfolios.

  12. Dynamic Vehicle Routing Problems with Enhanced Ant Colony Optimization

    Directory of Open Access Journals (Sweden)

    Haitao Xu

    2018-01-01

    Full Text Available As we all know, there are a great number of optimization problems in the world. One of the relatively complicated and high-level problems is the vehicle routing problem (VRP. Dynamic vehicle routing problem (DVRP is a major variant of VRP, and it is closer to real logistic scene. In DVRP, the customers’ demands appear with time, and the unserved customers’ points must be updated and rearranged while carrying out the programming paths. Owing to the complexity and significance of the problem, DVRP applications have grabbed the attention of researchers in the past two decades. In this paper, we have two main contributions to solving DVRP. Firstly, DVRP is solved with enhanced Ant Colony Optimization (E-ACO, which is the traditional Ant Colony Optimization (ACO fusing improved K-means and crossover operation. K-means can divide the region with the most reasonable distance, while ACO using crossover is applied to extend search space and avoid falling into local optimum prematurely. Secondly, several new evaluation benchmarks are proposed, which can objectively and comprehensively estimate the proposed method. In the experiment, the results for different scale problems are compared to those of previously published papers. Experimental results show that the algorithm is feasible and efficient.

  13. Calibration of Water Supply Systems Based on Ant Colony Optimization

    Directory of Open Access Journals (Sweden)

    Mahmoud Faghfoor Maghrebi

    2013-03-01

    Full Text Available Leakage is one of the main problems in the water supply systems and due to the limitations in water supply and its costly process, reduction of leak in water distribution networks can be considered as one of the main goals of the water supply authorities. One of the leak detection techniques in water distribution system is the usage of the recorded node pressures at some locations to calibrate the whole system node pressures. Calibration process is accomplished by the optimization of a constrained objective function. Therefore, in addition to performing a hydraulic analysis of the network, application of an optimization technique is needed. In the current paper, a comparsion between the ant colony and genetic algorithm methodes, in calibration of the node pressures and leak detections was investigated. To examine the workability and the way of leak detection, analysis of the network with an assumed leak was carried out. The results showed that the effectiveness of the ant colony optimization in the detection of the position and magnitude of leak in a water network.

  14. The effect of drone comb on a honey bee colony's production of honey

    OpenAIRE

    Seeley , Thomas

    2002-01-01

    International audience; This study examined the impact on a colony's honey production of providing it with a natural amount (20%) of drone comb. Over 3 summers, for the period mid May to late August, I measured the weight gains of 10 colonies, 5 with drone comb and 5 without it. Colonies with drone comb gained only 25.2 $\\pm$ 16.0 kg whereas those without drone comb gained 48.8 $\\pm$ 14.8 kg. Colonies with drone comb also had a higher mean rate of drone flights and a lower incidence of drone ...

  15. The impact of insecticides to local honey bee colony Apis cerana indica in laboratory condition

    Science.gov (United States)

    Putra, Ramadhani E.; Permana, Agus D.; Nuriyah, Syayidah

    2014-03-01

    Heavy use of insecticides considered as one of common practice at local farming systems. Even though many Indonesian researchers had stated the possible detrimental effect of insecticide on agriculture environment and biodiversity, researches on this subject had been neglected. Therefore, our purpose in this research is observing the impact of insecticides usage by farmer to non target organisme like local honey bee (Apis cerana indica), which commonly kept in area near agriculture system. This research consisted of field observations out at Ciburial, Dago Pakar, Bandung and laboratory tests at School of Life Sciences and Technology, Institut Teknologi Bandung. The field observations recorded visited agriculture corps and types of pollen carried by bees to the nest while laboratory test recorderd the effect of common insecticide to mortality and behavior of honey bees. Three types of insecticides used in this research were insecticides A with active agent Chlorantraniliprol 50 g/l, insecticide B with active agent Profenofos 500 g/l, and insecticides C with active agent Chlorantraniliprol 100 g/l and λ-cyhalotrin 50g/l. The results show that during one week visit, wild flower, Wedelia montana, visited by most honey bees with average visit 60 honey bees followed by corn, Zea mays, with 21 honey bees. The most pollen carried by foragers was Wedelia montana, Calliandra callothyrsus, and Zea mays. Preference test show that honeybees tend move to flowers without insecticides as the preference to insecticides A was 12.5%, insecticides B was 0%, and insecticides was C 4.2%. Mortality test showed that insecticides A has LD50 value 0.01 μg/μl, insecticide B 0.31 μg/μl, and insecticides C 0.09 μg/μl which much lower than suggested dosage recommended by insecticides producer. This research conclude that the use of insecticide could lower the pollination service provide by honey bee due to low visitation rate to flowers and mortality of foraging bees.

  16. Fog computing job scheduling optimization based on bees swarm

    Science.gov (United States)

    Bitam, Salim; Zeadally, Sherali; Mellouk, Abdelhamid

    2018-04-01

    Fog computing is a new computing architecture, composed of a set of near-user edge devices called fog nodes, which collaborate together in order to perform computational services such as running applications, storing an important amount of data, and transmitting messages. Fog computing extends cloud computing by deploying digital resources at the premise of mobile users. In this new paradigm, management and operating functions, such as job scheduling aim at providing high-performance, cost-effective services requested by mobile users and executed by fog nodes. We propose a new bio-inspired optimization approach called Bees Life Algorithm (BLA) aimed at addressing the job scheduling problem in the fog computing environment. Our proposed approach is based on the optimized distribution of a set of tasks among all the fog computing nodes. The objective is to find an optimal tradeoff between CPU execution time and allocated memory required by fog computing services established by mobile users. Our empirical performance evaluation results demonstrate that the proposal outperforms the traditional particle swarm optimization and genetic algorithm in terms of CPU execution time and allocated memory.

  17. Brood removal or queen caging combined with oxalic acid treatment to control varroa mites (Varroa destructor) in honey bee colonies (Apis mellifera)

    Science.gov (United States)

    Few studies of honey bee colonies exist where varroa mite control is achieved by integrating broodless conditions, through either total brood removal or queen caging, in combination with oxalic acid (OA) applications. We observed significant varroa mortality after applications of OA in obtaining bro...

  18. Using an adverse outcome pathway network to describe the weight of evidence linking nicotinic acetylcholine receptor activation to honey bee colony failure

    Science.gov (United States)

    Significant and unsustainable losses of managed honey bee (Apis mellifera) colonies have been documented over recent years, which have led to scientific investigation to determine the contributing factors. Evidence suggests that both chemical and non-chemical stressors play a rol...

  19. Weight of evidence evaluation of a network of adverse outcome pathways linking activation of the nicotinic acetylcholine receptor in honey bees to colony death

    Science.gov (United States)

    Ongoing honey bee colony losses are of significant international concern because of the essential role these insects play in pollinating many high nutrient crops, such as fruits, vegetables, and nuts. Both chemical and non-chemical stressors have been implicated as possible cont...

  20. Development of a user-friendly delivery method for the fungus Metarhizium anisopliae to control the ectoparasitic mite Varroa destructor in honey bee, Apis mellifera, colonies.

    Science.gov (United States)

    Kanga, Lambert H B; Adamczyk, John; Patt, Joseph; Gracia, Carlos; Cascino, John

    2010-12-01

    A user-friendly method to deliver Metarhizium spores to honey bee colonies for control of Varroa mites was developed and tested. Patty blend formulations protected the fungal spores at brood nest temperatures and served as an improved delivery system of the fungus to bee hives. Field trials conducted in 2006 in Texas using freshly harvested spores indicated that patty blend formulations of 10 g of conidia per hive (applied twice) significantly reduced the numbers of mites per adult bee, mites in sealed brood cells, and residual mites at the end of the 47-day experimental period. Colony development in terms of adult bee populations and brood production also improved. Field trials conducted in 2007 in Florida using less virulent spores produced mixed results. Patty blends of 10 g of conidia per hive (applied twice) were less successful in significantly reducing the number of mites per adult bee. However, hive survivorship and colony strength were improved, and the numbers of residual mites were significantly reduced at the end of the 42-day experimental period. The overall results from 2003 to 2008 field trials indicated that it was critical to have fungal spores with good germination, pathogenicity and virulence. We determined that fungal spores (1 × 10(10) viable spores per gram) with 98% germination and high pathogenicity (95% mite mortality at day 7) provided successful control of mite populations in established honey bee colonies at 10 g of conidia per hive (applied twice). Overall, microbial control of Varroa mite with M. anisopliae is feasible and could be a useful component of an integrated pest management program.

  1. Hybrid Pareto artificial bee colony algorithm for multi-objective single machine group scheduling problem with sequence-dependent setup times and learning effects.

    Science.gov (United States)

    Yue, Lei; Guan, Zailin; Saif, Ullah; Zhang, Fei; Wang, Hao

    2016-01-01

    Group scheduling is significant for efficient and cost effective production system. However, there exist setup times between the groups, which require to decrease it by sequencing groups in an efficient way. Current research is focused on a sequence dependent group scheduling problem with an aim to minimize the makespan in addition to minimize the total weighted tardiness simultaneously. In most of the production scheduling problems, the processing time of jobs is assumed as fixed. However, the actual processing time of jobs may be reduced due to "learning effect". The integration of sequence dependent group scheduling problem with learning effects has been rarely considered in literature. Therefore, current research considers a single machine group scheduling problem with sequence dependent setup times and learning effects simultaneously. A novel hybrid Pareto artificial bee colony algorithm (HPABC) with some steps of genetic algorithm is proposed for current problem to get Pareto solutions. Furthermore, five different sizes of test problems (small, small medium, medium, large medium, large) are tested using proposed HPABC. Taguchi method is used to tune the effective parameters of the proposed HPABC for each problem category. The performance of HPABC is compared with three famous multi objective optimization algorithms, improved strength Pareto evolutionary algorithm (SPEA2), non-dominated sorting genetic algorithm II (NSGAII) and particle swarm optimization algorithm (PSO). Results indicate that HPABC outperforms SPEA2, NSGAII and PSO and gives better Pareto optimal solutions in terms of diversity and quality for almost all the instances of the different sizes of problems.

  2. Cross infectivity of Nosema bombi, transmission and impact on bumble bee colonies (Bombus terrestris)

    NARCIS (Netherlands)

    Steen, van der J.J.M.

    2005-01-01

    The project "Biodiversity, impact and control of microsporidia in bumble bee (bombus spp.) pollinators" (acronim "Pollinator parasites") within Key Action 5 of the Fifth framework R&D Programme Quality of LIfe and Management of Living Resources was initiated January 1, 2003 and terminates

  3. A Novel Spectrum Scheduling Scheme with Ant Colony Optimization Algorithm

    Directory of Open Access Journals (Sweden)

    Liping Liu

    2018-01-01

    Full Text Available Cognitive radio is a promising technology for improving spectrum utilization, which allows cognitive users access to the licensed spectrum while primary users are absent. In this paper, we design a resource allocation framework based on graph theory for spectrum assignment in cognitive radio networks. The framework takes into account the constraints that interference for primary users and possible collision among cognitive users. Based on the proposed model, we formulate a system utility function to maximize the system benefit. Based on the proposed model and objective problem, we design an improved ant colony optimization algorithm (IACO from two aspects: first, we introduce differential evolution (DE process to accelerate convergence speed by monitoring mechanism; then we design a variable neighborhood search (VNS process to avoid the algorithm falling into the local optimal. Simulation results demonstrate that the improved algorithm achieves better performance.

  4. A morphologically specialized soldier caste improves colony defense in a neotropical eusocial bee.

    Science.gov (United States)

    Grüter, Christoph; Menezes, Cristiano; Imperatriz-Fonseca, Vera L; Ratnieks, Francis L W

    2012-01-24

    Division of labor among workers is common in insect societies and is thought to be important in their ecological success. In most species, division of labor is based on age (temporal castes), but workers in some ants and termites show morphological specialization for particular tasks (physical castes). Large-headed soldier ants and termites are well-known examples of this specialization. However, until now there has been no equivalent example of physical worker subcastes in social bees or wasps. Here we provide evidence for a physical soldier subcaste in a bee. In the neotropical stingless bee Tetragonisca angustula, nest defense is performed by two groups of guards, one hovering near the nest entrance and the other standing on the wax entrance tube. We show that both types of guards are 30% heavier than foragers and of different shape; foragers have relatively larger heads, whereas guards have larger legs. Low variation within each subcaste results in negligible size overlap between guards and foragers, further indicating that they are distinct physical castes. In addition, workers that remove garbage from the nest are of intermediate size, suggesting that they might represent another unrecognized caste. Guards or soldiers are reared in low but sufficient numbers (1-2% of emerging workers), considering that bee Lestrimelitta limao, an important natural enemy, larger workers were able to fight for longer before being defeated by the much larger robber. This discovery opens up opportunities for the comparative study of physical castes in social insects, including the question of why soldiers appear to be so much rarer in bees than in ants or termites.

  5. Optimization of diesel engine performance by the Bees Algorithm

    Science.gov (United States)

    Azfanizam Ahmad, Siti; Sunthiram, Devaraj

    2018-03-01

    Biodiesel recently has been receiving a great attention in the world market due to the depletion of the existing fossil fuels. Biodiesel also becomes an alternative for diesel No. 2 fuel which possesses characteristics such as biodegradable and oxygenated. However, there are facts suggested that biodiesel does not have the equivalent features as diesel No. 2 fuel as it has been claimed that the usage of biodiesel giving increment in the brake specific fuel consumption (BSFC). The objective of this study is to find the maximum brake power and brake torque as well as the minimum BSFC to optimize the condition of diesel engine when using the biodiesel fuel. This optimization was conducted using the Bees Algorithm (BA) under specific biodiesel percentage in fuel mixture, engine speed and engine load. The result showed that 58.33kW of brake power, 310.33 N.m of brake torque and 200.29/(kW.h) of BSFC were the optimum value. Comparing to the ones obtained by other algorithm, the BA produced a fine brake power and a better brake torque and BSFC. This finding proved that the BA can be used to optimize the performance of diesel engine based on the optimum value of the brake power, brake torque and BSFC.

  6. Feature Selection Method Based on Artificial Bee Colony Algorithm and Support Vector Machines for Medical Datasets Classification

    Directory of Open Access Journals (Sweden)

    Mustafa Serter Uzer

    2013-01-01

    Full Text Available This paper offers a hybrid approach that uses the artificial bee colony (ABC algorithm for feature selection and support vector machines for classification. The purpose of this paper is to test the effect of elimination of the unimportant and obsolete features of the datasets on the success of the classification, using the SVM classifier. The developed approach conventionally used in liver diseases and diabetes diagnostics, which are commonly observed and reduce the quality of life, is developed. For the diagnosis of these diseases, hepatitis, liver disorders and diabetes datasets from the UCI database were used, and the proposed system reached a classification accuracies of 94.92%, 74.81%, and 79.29%, respectively. For these datasets, the classification accuracies were obtained by the help of the 10-fold cross-validation method. The results show that the performance of the method is highly successful compared to other results attained and seems very promising for pattern recognition applications.

  7. Metal contaminant accumulation in the hive: Consequences for whole-colony health and brood production in the honey bee (Apis mellifera L.).

    Science.gov (United States)

    Hladun, Kristen R; Di, Ning; Liu, Tong-Xian; Trumble, John T

    2016-02-01

    Metal pollution has been increasing rapidly over the past century, and at the same time, the human population has continued to rise and produce contaminants that may negatively impact pollinators. Honey bees (Apis mellifera L.) forage over large areas and can collect contaminants from the environment. The primary objective of the present study was to determine whether the metal contaminants cadmium (Cd), copper (Cu), lead (Pb), and selenium (Se) can have a detrimental effect on whole-colony health in the managed pollinator A. mellifera. The authors isolated small nucleus colonies under large cages and fed them an exclusive diet of sugar syrup and pollen patty spiked with Cd, Cu, Pb, and Se or a control (no additional metal). Treatment levels were based on concentrations in honey and pollen from contaminated hives around the world. They measured whole-colony health including wax, honey, and brood production; colony weight; brood survival; and metal accumulation in various life stages. Colonies treated with Cd or Cu contained more dead pupae within capped cells compared with control, and Se-treated colonies had lower total worker weights compared to control. Lead had a minimal effect on colony performance, although many members of the hive accumulated significant quantities of the metal. By examining the honey bee as a social organism through whole-colony assessments of toxicity, the authors found that the distribution of toxicants throughout the colony varied from metal to metal, some caste members were more susceptible to certain metals, and the colony's ability to grow over time may have been reduced in the presence of Se. Apiaries residing near metal-contaminated areas may be at risk and can suffer changes in colony dynamics and survival. © 2015 SETAC.

  8. A four-year field program investigating long-term effects of repeated exposure of honey bee colonies to flowering crops treated with thiamethoxam.

    Directory of Open Access Journals (Sweden)

    Edward Pilling

    Full Text Available Neonicotinoid residues in nectar and pollen from crop plants have been implicated as one of the potential factors causing the declines of honey bee populations. Median residues of thiamethoxam in pollen collected from honey bees after foraging on flowering seed treated maize were found to be between 1 and 7 µg/kg, median residues of the metabolite CGA322704 (clothianidin in the pollen were between 1 and 4 µg/kg. In oilseed rape, median residues of thiamethoxam found in pollen collected from bees were between <1 and 3.5 µg/kg and in nectar from foraging bees were between 0.65 and 2.4 µg/kg. Median residues of CGA322704 in pollen and nectar in the oilseed rape trials were all below the limit of quantification (1 µg/kg. Residues in the hive were even lower in both the maize and oilseed rape trials, being at or below the level of detection of 1 µg/kg for bee bread in the hive and at or below the level of detection of 0.5 µg/kg for hive nectar, honey and royal jelly samples. The long-term risk to honey bee colonies in the field was also investigated, including the sensitive overwintering stage, from four years consecutive single treatment crop exposures to flowering maize and oilseed rape grown from thiamethoxam treated seeds at rates recommended for insect control. Throughout the study, mortality, foraging behavior, colony strength, colony weight, brood development and food storage levels were similar between treatment and control colonies. Detailed examination of brood development throughout the year demonstrated that colonies exposed to the treated crop were able to successfully overwinter and had a similar health status to the control colonies in the following spring. We conclude that these data demonstrate there is a low risk to honey bees from systemic residues in nectar and pollen following the use of thiamethoxam as a seed treatment on oilseed rape and maize.

  9. A Four-Year Field Program Investigating Long-Term Effects of Repeated Exposure of Honey Bee Colonies to Flowering Crops Treated with Thiamethoxam

    Science.gov (United States)

    Pilling, Edward; Campbell, Peter; Coulson, Mike; Ruddle, Natalie; Tornier, Ingo

    2013-01-01

    Neonicotinoid residues in nectar and pollen from crop plants have been implicated as one of the potential factors causing the declines of honey bee populations. Median residues of thiamethoxam in pollen collected from honey bees after foraging on flowering seed treated maize were found to be between 1 and 7 µg/kg, median residues of the metabolite CGA322704 (clothianidin) in the pollen were between 1 and 4 µg/kg. In oilseed rape, median residues of thiamethoxam found in pollen collected from bees were between <1 and 3.5 µg/kg and in nectar from foraging bees were between 0.65 and 2.4 µg/kg. Median residues of CGA322704 in pollen and nectar in the oilseed rape trials were all below the limit of quantification (1 µg/kg). Residues in the hive were even lower in both the maize and oilseed rape trials, being at or below the level of detection of 1 µg/kg for bee bread in the hive and at or below the level of detection of 0.5 µg/kg for hive nectar, honey and royal jelly samples. The long-term risk to honey bee colonies in the field was also investigated, including the sensitive overwintering stage, from four years consecutive single treatment crop exposures to flowering maize and oilseed rape grown from thiamethoxam treated seeds at rates recommended for insect control. Throughout the study, mortality, foraging behavior, colony strength, colony weight, brood development and food storage levels were similar between treatment and control colonies. Detailed examination of brood development throughout the year demonstrated that colonies exposed to the treated crop were able to successfully overwinter and had a similar health status to the control colonies in the following spring. We conclude that these data demonstrate there is a low risk to honey bees from systemic residues in nectar and pollen following the use of thiamethoxam as a seed treatment on oilseed rape and maize. PMID:24194871

  10. Efficient distribution of toy products using ant colony optimization algorithm

    Science.gov (United States)

    Hidayat, S.; Nurpraja, C. A.

    2017-12-01

    CV Atham Toys (CVAT) produces wooden toys and furniture, comprises 13 small and medium industries. CVAT always attempt to deliver customer orders on time but delivery costs are high. This is because of inadequate infrastructure such that delivery routes are long, car maintenance costs are high, while fuel subsidy by the government is still temporary. This study seeks to minimize the cost of product distribution based on the shortest route using one of five Ant Colony Optimization (ACO) algorithms to solve the Vehicle Routing Problem (VRP). This study concludes that the best of the five is the Ant Colony System (ACS) algorithm. The best route in 1st week gave a total distance of 124.11 km at a cost of Rp 66,703.75. The 2nd week route gave a total distance of 132.27 km at a cost of Rp 71,095.13. The 3rd week best route gave a total distance of 122.70 km with a cost of Rp 65,951.25. While the 4th week gave a total distance of 132.27 km at a cost of Rp 74,083.63. Prior to this study there was no effort to calculate these figures.

  11. Developing a Reading Concentration Monitoring System by Applying an Artificial Bee Colony Algorithm to E-Books in an Intelligent Classroom

    Directory of Open Access Journals (Sweden)

    Yueh-Min Huang

    2012-10-01

    Full Text Available A growing number of educational studies apply sensors to improve student learning in real classroom settings. However, how can sensors be integrated into classrooms to help instructors find out students’ reading concentration rates and thus better increase learning effectiveness? The aim of the current study was to develop a reading concentration monitoring system for use with e-books in an intelligent classroom and to help instructors find out the students’ reading concentration rates. The proposed system uses three types of sensor technologies, namely a webcam, heartbeat sensor, and blood oxygen sensor to detect the learning behaviors of students by capturing various physiological signals. An artificial bee colony (ABC optimization approach is applied to the data gathered from these sensors to help instructors understand their students’ reading concentration rates in a classroom learning environment. The results show that the use of the ABC algorithm in the proposed system can effectively obtain near-optimal solutions. The system has a user-friendly graphical interface, making it easy for instructors to clearly understand the reading status of their students.

  12. Developing a reading concentration monitoring system by applying an artificial bee colony algorithm to e-books in an intelligent classroom.

    Science.gov (United States)

    Hsu, Chia-Cheng; Chen, Hsin-Chin; Su, Yen-Ning; Huang, Kuo-Kuang; Huang, Yueh-Min

    2012-10-22

    A growing number of educational studies apply sensors to improve student learning in real classroom settings. However, how can sensors be integrated into classrooms to help instructors find out students' reading concentration rates and thus better increase learning effectiveness? The aim of the current study was to develop a reading concentration monitoring system for use with e-books in an intelligent classroom and to help instructors find out the students' reading concentration rates. The proposed system uses three types of sensor technologies, namely a webcam, heartbeat sensor, and blood oxygen sensor to detect the learning behaviors of students by capturing various physiological signals. An artificial bee colony (ABC) optimization approach is applied to the data gathered from these sensors to help instructors understand their students' reading concentration rates in a classroom learning environment. The results show that the use of the ABC algorithm in the proposed system can effectively obtain near-optimal solutions. The system has a user-friendly graphical interface, making it easy for instructors to clearly understand the reading status of their students.

  13. An exposure study to assess the potential impact of fipronil in treated sunflower seeds on honey bee colony losses in Spain.

    Science.gov (United States)

    Bernal, José; Martin-Hernandez, Raquel; Diego, Juan C; Nozal, María J; Gozalez-Porto, Amelia V; Bernal, José L; Higes, Mariano

    2011-10-01

    There is great concern about the high losses and strong depopulation of honey bee colonies in some areas of Spain. Some beekeepers have suggested that sunflower seeds treated with the insecticide fipronil could be an important factor in causing those losses. Therefore, an in-depth field study has been carried out in two regions of Spain where sunflower production is intense (Cuenca and Andalucía) and where, for some crops and varieties, fipronil has been used as seed insecticide. Samples of adult bees and pollen were analysed for bee pathogens and pesticide residues respectively. Neither fipronil residues nor its metabolites were detected in any of the samples analysed, indicating that short-term or chronic exposure of bees to fipronil and/or its metabolites can be ruled out in the apiaries surveyed. Varroa destructor and Nosema ceranae were found to be very prevalent. The combination of the two pathogens could augment the risk of colony death in infected colonies, without fipronil residues exerting a significant effect in the given field conditions. Indeed, in this study the losses observed in apiaries located close to sunflower crops were similar to those in apiaries situated in forested areas with wild vegetation. Copyright © 2011 Society of Chemical Industry.

  14. Pesticide use within a pollinator-dependent crop has negative effects on the abundance and species richness of sweat bees, Lasioglossum spp., and on bumble bee colony growth.

    Science.gov (United States)

    Pesticides are implicated in current bee declines. Wild bees that nest or forage within agroecosystems may be exposed to numerous pesticides applied throughout their life cycles, with potential additive or synergistic effects. In pollinator-dependent crops, pesticides may reduce bee populations, cre...

  15. Reliability optimization using multiobjective ant colony system approaches

    International Nuclear Information System (INIS)

    Zhao Jianhua; Liu Zhaoheng; Dao, M.-T.

    2007-01-01

    The multiobjective ant colony system (ACS) meta-heuristic has been developed to provide solutions for the reliability optimization problem of series-parallel systems. This type of problems involves selection of components with multiple choices and redundancy levels that produce maximum benefits, and is subject to the cost and weight constraints at the system level. These are very common and realistic problems encountered in conceptual design of many engineering systems. It is becoming increasingly important to develop efficient solutions to these problems because many mechanical and electrical systems are becoming more complex, even as development schedules get shorter and reliability requirements become very stringent. The multiobjective ACS algorithm offers distinct advantages to these problems compared with alternative optimization methods, and can be applied to a more diverse problem domain with respect to the type or size of the problems. Through the combination of probabilistic search, multiobjective formulation of local moves and the dynamic penalty method, the multiobjective ACSRAP, allows us to obtain an optimal design solution very frequently and more quickly than with some other heuristic approaches. The proposed algorithm was successfully applied to an engineering design problem of gearbox with multiple stages

  16. ADAPTIVE ANT COLONY OPTIMIZATION BASED GRADIENT FOR EDGE DETECTION

    Directory of Open Access Journals (Sweden)

    Febri Liantoni

    2014-08-01

    Full Text Available Ant Colony Optimization (ACO is a nature-inspired optimization algorithm which is motivated by ants foraging behavior. Due to its favorable advantages, ACO has been widely used to solve several NP-hard problems, including edge detection. Since ACO initially distributes ants at random, it may cause imbalance ant distribution which later affects path discovery process. In this paper an adaptive ACO is proposed to optimize edge detection by adaptively distributing ant according to gradient analysis. Ants are adaptively distributed according to gradient ratio of each image regions. Region which has bigger gradient ratio, will have bigger number of ant distribution. Experiments are conducted using images from various datasets. Precision and recall are used to quantitatively evaluate performance of the proposed algorithm. Precision and recall of adaptive ACO reaches 76.98 % and 96.8 %. Whereas highest precision and recall for standard ACO are 69.74 % and 74.85 %. Experimental results show that the adaptive ACO outperforms standard ACO which randomly distributes ants.

  17. Are Dispersal Mechanisms Changing the Host-Parasite Relationship and Increasing the Virulence of Varroa destructor (Mesostigmata: Varroidae) in Managed Honey Bee (Hymenoptera: Apidae) Colonies?

    Science.gov (United States)

    DeGrandi-Hoffman, Gloria; Ahumada, Fabiana; Graham, Henry

    2017-08-01

    Varroa (Varroa destructor Anderson and Trueman) are a serious pest of European honey bees (Apis mellifera L.), and difficult to control in managed colonies. In our 11-mo longitudinal study, we applied multiple miticide treatments, yet mite numbers remained high and colony losses exceeded 55%. High mortality from varroa in managed apiaries is a departure from the effects of the mite in feral colonies where bees and varroa can coexist. Differences in mite survival strategies and dispersal mechanisms may be contributing factors. In feral colonies, mites can disperse through swarming. In managed apiaries, where swarming is reduced, mites disperse on foragers robbing or drifting from infested hives. Using a honey bee-varroa population model, we show that yearly swarming curtails varroa population growth, enabling colony survival for >5 yr. Without swarming, colonies collapsed by the third year. To disperse, varroa must attach to foragers that then enter other hives. We hypothesize that stress from parasitism and virus infection combined with effects that viruses have on cognitive function may contribute to forager drift and mite and virus dispersal. We also hypothesize that drifting foragers with mites can measurably increase mite populations. Simulations initialized with field data indicate that low levels of drifting foragers with mites can create sharp increases in mite populations in the fall and heavily infested colonies in the spring. We suggest new research directions to investigate factors leading to mite dispersal on foragers, and mite management strategies with consideration of varroa as a migratory pest. Published by Oxford University Press on behalf of Entomological Society of America 2017. This work is written by US Government employees and is in the public domain in the US.

  18. Thermosonication and optimization of stingless bee honey processing.

    Science.gov (United States)

    Chong, K Y; Chin, N L; Yusof, Y A

    2017-10-01

    The effects of thermosonication on the quality of a stingless bee honey, the Kelulut, were studied using processing temperature from 45 to 90 ℃ and processing time from 30 to 120 minutes. Physicochemical properties including water activity, moisture content, color intensity, viscosity, hydroxymethylfurfural content, total phenolic content, and radical scavenging activity were determined. Thermosonication reduced the water activity and moisture content by 7.9% and 16.6%, respectively, compared to 3.5% and 6.9% for conventional heating. For thermosonicated honey, color intensity increased by 68.2%, viscosity increased by 275.0%, total phenolic content increased by 58.1%, and radical scavenging activity increased by 63.0% when compared to its raw form. The increase of hydroxymethylfurfural to 62.46 mg/kg was still within the limits of international standards. Optimized thermosonication conditions using response surface methodology were predicted at 90 ℃ for 111 minutes. Thermosonication was revealed as an effective alternative technique for honey processing.

  19. Study on ant colony optimization for fuel loading pattern problem

    International Nuclear Information System (INIS)

    Kishi, Hironori; Kitada, Takanori

    2013-01-01

    Modified ant colony optimization (ACO) was applied to the in-core fuel loading pattern (LP) optimization problem to minimize the power peaking factor (PPF) in the modeled 1/4 symmetry PWR core. Loading order was found to be important in ACO. Three different loading orders with and without the adjacent effect between fuel assemblies (FAs) were compared, and it was found that the loading order from the central core is preferable because many selections of FAs to be inserted are available in the core center region. LPs were determined from pheromone trail and heuristic information, which is a priori knowledge based on the feature of the problem. Three types of heuristic information were compared to obtain the desirable performance of searching LPs with low PPF. Moreover, mutation operation, such as the genetic algorithm (GA), was introduced into the ACO algorithm to avoid searching similar LPs because heuristic information used in ACO tends to localize the searching space in the LP problem. The performance of ACO with some improvement was compared with those of simulated annealing and GA. In conclusion, good performance can be achieved by setting proper heuristic information and mutation operation parameter in ACO. (author)

  20. Applying Data Clustering Feature to Speed Up Ant Colony Optimization

    Directory of Open Access Journals (Sweden)

    Chao-Yang Pang

    2014-01-01

    Full Text Available Ant colony optimization (ACO is often used to solve optimization problems, such as traveling salesman problem (TSP. When it is applied to TSP, its runtime is proportional to the squared size of problem N so as to look less efficient. The following statistical feature is observed during the authors’ long-term gene data analysis using ACO: when the data size N becomes big, local clustering appears frequently. That is, some data cluster tightly in a small area and form a class, and the correlation between different classes is weak. And this feature makes the idea of divide and rule feasible for the estimate of solution of TSP. In this paper an improved ACO algorithm is presented, which firstly divided all data into local clusters and calculated small TSP routes and then assembled a big TSP route with them. Simulation shows that the presented method improves the running speed of ACO by 200 factors under the condition that data set holds feature of local clustering.

  1. An epidemiological model of viral infections in a Varroa-infested bee colony: the case of a bee-dependent mite population size

    OpenAIRE

    Bernardi, Sara; Venturino, Ezio

    2016-01-01

    In recent years the spread of the ectoparasitic mite Varroa destructor has become the most serious threat to worldwide apiculture. In the model presented here we extend the bee population dynamics with mite viral epidemiology examined in an earlier paper by allowing a bee-dependent mite population size. The results of the analysis match field observations well and give a clear explanation of how Varroa affects the epidemiology of certain naturally occurring bee viruses, causing considerable d...

  2. Enhanced ant colony optimization for inventory routing problem

    Science.gov (United States)

    Wong, Lily; Moin, Noor Hasnah

    2015-10-01

    The inventory routing problem (IRP) integrates and coordinates two important components of supply chain management which are transportation and inventory management. We consider a one-to-many IRP network for a finite planning horizon. The demand for each product is deterministic and time varying as well as a fleet of capacitated homogeneous vehicles, housed at a depot/warehouse, delivers the products from the warehouse to meet the demand specified by the customers in each period. The inventory holding cost is product specific and is incurred at the customer sites. The objective is to determine the amount of inventory and to construct a delivery routing that minimizes both the total transportation and inventory holding cost while ensuring each customer's demand is met over the planning horizon. The problem is formulated as a mixed integer programming problem and is solved using CPLEX 12.4 to get the lower and upper bound (best integer) for each instance considered. We propose an enhanced ant colony optimization (ACO) to solve the problem and the built route is improved by using local search. The computational experiments demonstrating the effectiveness of our approach is presented.

  3. Software Piracy Detection Model Using Ant Colony Optimization Algorithm

    Science.gov (United States)

    Astiqah Omar, Nor; Zakuan, Zeti Zuryani Mohd; Saian, Rizauddin

    2017-06-01

    Internet enables information to be accessible anytime and anywhere. This scenario creates an environment whereby information can be easily copied. Easy access to the internet is one of the factors which contribute towards piracy in Malaysia as well as the rest of the world. According to a survey conducted by Compliance Gap BSA Global Software Survey in 2013 on software piracy, found out that 43 percent of the software installed on PCs around the world was not properly licensed, the commercial value of the unlicensed installations worldwide was reported to be 62.7 billion. Piracy can happen anywhere including universities. Malaysia as well as other countries in the world is faced with issues of piracy committed by the students in universities. Piracy in universities concern about acts of stealing intellectual property. It can be in the form of software piracy, music piracy, movies piracy and piracy of intellectual materials such as books, articles and journals. This scenario affected the owner of intellectual property as their property is in jeopardy. This study has developed a classification model for detecting software piracy. The model was developed using a swarm intelligence algorithm called the Ant Colony Optimization algorithm. The data for training was collected by a study conducted in Universiti Teknologi MARA (Perlis). Experimental results show that the model detection accuracy rate is better as compared to J48 algorithm.

  4. Local behavioral rules sustain the cell allocation pattern in the combs of honey bee colonies (Apis mellifera).

    Science.gov (United States)

    Montovan, Kathryn J; Karst, Nathaniel; Jones, Laura E; Seeley, Thomas D

    2013-11-07

    In the beeswax combs of honey bees, the cells of brood, pollen, and honey have a consistent spatial pattern that is sustained throughout the life of a colony. This spatial pattern is believed to emerge from simple behavioral rules that specify how the queen moves, where foragers deposit honey/pollen and how honey/pollen is consumed from cells. Prior work has shown that a set of such rules can explain the formation of the allocation pattern starting from an empty comb. We show that these rules cannot maintain the pattern once the brood start to vacate their cells, and we propose new, biologically realistic rules that better sustain the observed allocation pattern. We analyze the three resulting models by performing hundreds of simulation runs over many gestational periods and a wide range of parameter values. We develop new metrics for pattern assessment and employ them in analyzing pattern retention over each simulation run. Applied to our simulation results, these metrics show alteration of an accepted model for honey/pollen consumption based on local information can stabilize the cell allocation pattern over time. We also show that adding global information, by biasing the queen's movements towards the center of the comb, expands the parameter regime over which pattern retention occurs. © 2013 Published by Elsevier Ltd. All rights reserved.

  5. Land-use change reduces habitat suitability for supporting managed honey bee colonies in the Northern Great Plains

    Science.gov (United States)

    Otto, Clint R.; Roth, Cali; Carlson, Benjamin; Smart, Matthew

    2016-01-01

    Human reliance on insect pollination services continues to increase even as pollinator populations exhibit global declines. Increased commodity crop prices and federal subsidies for biofuel crops, such as corn and soybeans, have contributed to rapid land-use change in the US Northern Great Plains (NGP), changes that may jeopardize habitat for honey bees in a part of the country that supports >40% of the US colony stock. We investigated changes in biofuel crop production and grassland land covers surrounding ∼18,000 registered commercial apiaries in North and South Dakota from 2006 to 2014. We then developed habitat selection models to identify remotely sensed land-cover and land-use features that influence apiary site selection by Dakota beekeepers. Our study demonstrates a continual increase in biofuel crops, totaling 1.2 Mha, around registered apiary locations in North and South Dakota. Such crops were avoided by commercial beekeepers when selecting apiary sites in this region. Furthermore, our analysis reveals how grasslands that beekeepers target when selecting commercial apiary locations are becoming less common in eastern North and South Dakota, changes that may have lasting impact on pollinator conservation efforts. Our study highlights how land-use change in the NGP is altering the landscape in ways that are seemingly less conducive to beekeeping. Our models can be used to guide future conservation efforts highlighted in the US national pollinator health strategy by identifying areas that support high densities of commercial apiaries and that have exhibited significant land-use changes.

  6. A hybrid artificial bee colony algorithm and pattern search method for inversion of particle size distribution from spectral extinction data

    Science.gov (United States)

    Wang, Li; Li, Feng; Xing, Jian

    2017-10-01

    In this paper, a hybrid artificial bee colony (ABC) algorithm and pattern search (PS) method is proposed and applied for recovery of particle size distribution (PSD) from spectral extinction data. To be more useful and practical, size distribution function is modelled as the general Johnson's ? function that can overcome the difficulty of not knowing the exact type beforehand encountered in many real circumstances. The proposed hybrid algorithm is evaluated through simulated examples involving unimodal, bimodal and trimodal PSDs with different widths and mean particle diameters. For comparison, all examples are additionally validated by the single ABC algorithm. In addition, the performance of the proposed algorithm is further tested by actual extinction measurements with real standard polystyrene samples immersed in water. Simulation and experimental results illustrate that the hybrid algorithm can be used as an effective technique to retrieve the PSDs with high reliability and accuracy. Compared with the single ABC algorithm, our proposed algorithm can produce more accurate and robust inversion results while taking almost comparative CPU time over ABC algorithm alone. The superiority of ABC and PS hybridization strategy in terms of reaching a better balance of estimation accuracy and computation effort increases its potentials as an excellent inversion technique for reliable and efficient actual measurement of PSD.

  7. Comparison Performance of Genetic Algorithm and Ant Colony Optimization in Course Scheduling Optimizing

    Directory of Open Access Journals (Sweden)

    Imam Ahmad Ashari

    2016-11-01

    Full Text Available Scheduling problems at the university is a complex type of scheduling problems. The scheduling process should be carried out at every turn of the semester's. The core of the problem of scheduling courses at the university is that the number of components that need to be considered in making the schedule, some of the components was made up of students, lecturers, time and a room with due regard to the limits and certain conditions so that no collision in the schedule such as mashed room, mashed lecturer and others. To resolve a scheduling problem most appropriate technique used is the technique of optimization. Optimization techniques can give the best results desired. Metaheuristic algorithm is an algorithm that has a lot of ways to solve the problems to the very limit the optimal solution. In this paper, we use a genetic algorithm and ant colony optimization algorithm is an algorithm metaheuristic to solve the problem of course scheduling. The two algorithm will be tested and compared to get performance is the best. The algorithm was tested using data schedule courses of the university in Semarang. From the experimental results we conclude that the genetic algorithm has better performance than the ant colony optimization  algorithm in solving the case of course scheduling.

  8. An experimental analysis of design choices of multi-objective ant colony optimization algorithms

    OpenAIRE

    Lopez-Ibanez, Manuel; Stutzle, Thomas

    2012-01-01

    There have been several proposals on how to apply the ant colony optimization (ACO) metaheuristic to multi-objective combinatorial optimization problems (MOCOPs). This paper proposes a new formulation of these multi-objective ant colony optimization (MOACO) algorithms. This formulation is based on adding specific algorithm components for tackling multiple objectives to the basic ACO metaheuristic. Examples of these components are how to represent multiple objectives using pheromone and heuris...

  9. Population Growth of Varroa destructor (Acari: Varroidae) in Colonies of Russian and Unselected Honey Bee (Hymenoptera: Apidae) Stocks as Related to Numbers of Foragers With Mites.

    Science.gov (United States)

    DeGrandi-Hoffman, Gloria; Ahumada, Fabiana; Danka, Robert; Chambers, Mona; DeJong, Emily Watkins; Hidalgo, Geoff

    2017-06-01

    Varroa (Varroa destructor Anderson and Trueman) is an external parasite of honey bees (Apis mellifera L.) and a leading cause of colony losses worldwide. Varroa populations can be controlled with miticides, but mite-resistant stocks such as the Russian honey bee (RHB) also are available. Russian honey bee and other mite-resistant stocks limit Varroa population growth by affecting factors that contribute to mite reproduction. However, mite population growth is not entirely due to reproduction. Numbers of foragers with mites (FWM) entering and leaving hives also affect the growth of mite populations. If FWM significantly contribute to Varroa population growth, mite numbers in RHB colonies might not differ from unselected lines (USL). Foragers with mites were monitored at the entrances of RHB and USL hives from August to November, 2015, at two apiary sites. At site 1, RHB colonies had fewer FWM than USL and smaller phoretic mite populations. Russian honey bee also had fewer infested brood cells and lower percentages with Varroa offspring than USL. At site 2, FWM did not differ between RHB and USL, and phoretic mite populations were not significantly different. At both sites, there were sharp increases in phoretic mite populations from September to November that corresponded with increasing numbers of FWM. Under conditions where FWM populations are similar between RHB and USL, attributes that contribute to mite resistance in RHB may not keep Varroa population levels below that of USL. Published by Oxford University Press on behalf of Entomological Society of America 2017. This work is written by US Government employees and is in the public domain in the US.

  10. Varroa destructor (Mesostigmata: Varroidae in Costa Rica: population dynamics and its influence on the colony condition of Africanized honey bees (Hymenoptera: Apidae

    Directory of Open Access Journals (Sweden)

    Rafael A Calderón

    2008-12-01

    Full Text Available The development of Varroa destructor Anderson & Trueman (Mesostigmata: Varroidae population dynamics in Africanized honey bees, Apis mellifera L. (Hymenoptera: Apidae colonies was monitored from February to July 2004 in Atenas, Costa Rica. A correlation between the mite infestation level and the colony condition was evaluated. For each colony, infestation of varroa in adult bees was measured twice a month. Sticky boards were placed on the bottom boards of each colony to collect fallen mites. The condition of the colonies was evaluated by measuring the amount of brood and adult bees. Our results consistently showed that mite infestation on adult bees increased significantly in the experimental colonies, rising to 10.0% by the end of the experiment. In addition, the mean mite fall increased significantly over the course of the study in the treated (R= 0.72, PLa dinámica poblacional del ácaro Varroa destructor Anderson & Trueman (Mesostigmata: Varroidae en abejas africanizadas, Apis mellifera L. (Hymenoptera: Apidae fue monitoreada de febrero a julio 2004, en Atenas, Costa Rica. Asimismo, se analizó la relación entre el nivel de infestación de varroa y la condición de la colmena. La infestación del ácaro V. destructor fue evaluada en abejas adultas dos veces al mes. Además, se colocaron trampas adhesivas en el fondo de la colmena para recoger los ácaros que caen naturalmente. La condición de la colmena fue determinada midiendo la cantidad de cría y la población de abejas adultas. La infestación del ácaro V. destructor en abejas adultas aumentó significativamente durante el estudio hasta alcanzar un 10.0%. Igualmente, la caída natural de ácaros se incrementó, tanto en colmenas que fueron tratadas previa-mente con un acaricida químico (R= 0.72, P<0.05 como en colmenas sin tratamiento (R= 0.74, P<0.05, hasta llegar a 63.8 y 73.5 ácaros por día, respectivamente. El aumento de la infestación en las colmenas coincidió con una

  11. New Paenibacillus larvae bacterial isolates from honey bee colonies infected with American foulbrood disease in Egypt.

    Science.gov (United States)

    Masry, Saad Hamdy Daif; Kabeil, Sanaa Soliman; Hafez, Elsayed Elsayed

    2014-03-04

    The American foulbrood disease is widely distributed all over the world and causes a serious problem for the honeybee industry. Different infected larvae were collected from different apiaries, ground in phosphate saline buffer (PSB) and bacterial isolation was carried out on nutrient agar medium. Different colonies were observed and were characterized biologically. Two bacterial isolates (SH11 and SH33) were subjected to molecular identification using 16S rRNA gene and the sequence analysis revealed that the two isolates are Paenibacillus larvae with identity not exceeding 83%. The DNA sequence alignment between the other P. larvae bacterial strains and the two identified bacterial isolates showed that all the examined bacterial strains have the same ancestor, i.e. they have the same origin. The SH33 isolate was closely related to the P. larvae isolated from Germany, whereas the isolate SH11 was close to the P. larvae isolated from India. The phylogenetic tree constructed for 20 different Bacillus sp. and the two isolates SH11 and SH33 demonstrated that the two isolates are Bacillus sp. and they are new isolates. The bacterial isolates will be subjected to more tests for more confirmations.

  12. Consumption of tyrosine in royal jelly increases brain levels of dopamine and tyramine and promotes transition from normal to reproductive workers in queenless honey bee colonies.

    Science.gov (United States)

    Matsuyama, Syuhei; Nagao, Takashi; Sasaki, Ken

    2015-01-15

    Dopamine (DA) and tyramine (TA) have neurohormonal roles in the production of reproductive workers in queenless colonies of honey bees, but the regulation of these biogenic amines in the brain are still largely unclear. Nutrition is an important factor in promoting reproduction and might be involved in the regulation of these biogenic amines in the brain. To test this hypothesis, we examined the effect of oral treatments of tyrosine (Tyr; a common precursor of DA, TA and octopamine, and a component of royal jelly) in queenless workers and quantified the resulting production of biogenic amines. Tyrosine treatments enhanced the levels of DA, TA and their metabolites in the brain. Workers fed royal jelly had significantly larger brain levels of Tyr, DA, TA and the metabolites in the brains compared with those bees fed honey or sucrose (control). Treatment with Tyr also inhibited the behavior of workers outside of the hive and promoted ovarian development. These results suggest that there is a link between nutrition and the regulation of DA and TA in the brain to promote the production of reproductive workers in queenless honey bee colonies. Copyright © 2014 Elsevier Inc. All rights reserved.

  13. Differences Among Commonly Sprayed Orchard Fungicides in Targeting the Beneficial Fungi Associated with Honey Bee Colony and Bee Bread Provisions (In Vitro)

    Science.gov (United States)

    Our studies evaluated the effects of representative fungicides, boscalid and pyraclostrobin, propiconazole, and chlorothalonil, alone and in combination, on 12 fungi species isolated from bee bread. Chlorothalonil was fungicidal (slowed growth without killing) and was least effective on Aspergillus...

  14. Organochlorine Pesticides in Honey and Pollen Samples from Managed Colonies of the Honey Bee Apis mellifera Linnaeus and the Stingless Bee Scaptotrigona mexicana Guérin from Southern, Mexico.

    Science.gov (United States)

    Ruiz-Toledo, Jovani; Vandame, Rémy; Castro-Chan, Ricardo Alberto; Penilla-Navarro, Rosa Patricia; Gómez, Jaime; Sánchez, Daniel

    2018-05-10

    In this paper, we show the results of investigating the presence of organochlorine pesticides in honey and pollen samples from managed colonies of the honey bee, Apis mellifera L. and of the stingless bee Scaptotrigona mexicana Guérin. Three colonies of each species were moved into each of two sites. Three samples of pollen and three samples of honey were collected from each colony: the first collection occurred at the beginning of the study and the following ones at every six months during a year. Thus the total number of samples collected was 36 for honey (18 for A. mellifera and 18 for S. mexicana ) and 36 for pollen (18 for A. mellifera and 18 for S. mexicana ). We found that 88.44% and 93.33% of honey samples, and 22.22% and 100% of pollen samples of S. mexicana and A. mellifera , respectively, resulted positive to at least one organochlorine. The most abundant pesticides were Heptaclor (44% of the samples), γ-HCH (36%), DDT (19%), Endrin (18%) and DDE (11%). Despite the short foraging range of S. mexicana , the number of pesticides quantified in the honey samples was similar to that of A. mellifera . Paradoxically we found a small number of organochlorines in pollen samples of S. mexicana in comparison to A. mellifera , perhaps indicating a low abundance of pollen sources within the foraging range of this species.

  15. Organochlorine Pesticides in Honey and Pollen Samples from Managed Colonies of the Honey Bee Apis mellifera Linnaeus and the Stingless Bee Scaptotrigona mexicana Guérin from Southern, Mexico

    Directory of Open Access Journals (Sweden)

    Jovani Ruiz-Toledo

    2018-05-01

    Full Text Available In this paper, we show the results of investigating the presence of organochlorine pesticides in honey and pollen samples from managed colonies of the honey bee, Apis mellifera L. and of the stingless bee Scaptotrigona mexicana Guérin. Three colonies of each species were moved into each of two sites. Three samples of pollen and three samples of honey were collected from each colony: the first collection occurred at the beginning of the study and the following ones at every six months during a year. Thus the total number of samples collected was 36 for honey (18 for A. mellifera and 18 for S. mexicana and 36 for pollen (18 for A. mellifera and 18 for S. mexicana. We found that 88.44% and 93.33% of honey samples, and 22.22% and 100% of pollen samples of S. mexicana and A. mellifera, respectively, resulted positive to at least one organochlorine. The most abundant pesticides were Heptaclor (44% of the samples, γ-HCH (36%, DDT (19%, Endrin (18% and DDE (11%. Despite the short foraging range of S. mexicana, the number of pesticides quantified in the honey samples was similar to that of A. mellifera. Paradoxically we found a small number of organochlorines in pollen samples of S. mexicana in comparison to A. mellifera, perhaps indicating a low abundance of pollen sources within the foraging range of this species.

  16. A Modified Active Appearance Model Based on an Adaptive Artificial Bee Colony

    Science.gov (United States)

    Othman, Zulaiha Ali

    2014-01-01

    Active appearance model (AAM) is one of the most popular model-based approaches that have been extensively used to extract features by highly accurate modeling of human faces under various physical and environmental circumstances. However, in such active appearance model, fitting the model with original image is a challenging task. State of the art shows that optimization method is applicable to resolve this problem. However, another common problem is applying optimization. Hence, in this paper we propose an AAM based face recognition technique, which is capable of resolving the fitting problem of AAM by introducing a new adaptive ABC algorithm. The adaptation increases the efficiency of fitting as against the conventional ABC algorithm. We have used three datasets: CASIA dataset, property 2.5D face dataset, and UBIRIS v1 images dataset in our experiments. The results have revealed that the proposed face recognition technique has performed effectively, in terms of accuracy of face recognition. PMID:25165748

  17. Longevity extension of worker honey bees (Apis mellifera) by royal jelly: optimal dose and active ingredient

    OpenAIRE

    Yang, Wenchao; Tian, Yuanyuan; Han, Mingfeng; Miao, Xiaoqing

    2017-01-01

    In the Western honey bee, Apis mellifera, queens and workers have different longevity although they share the same genome. Queens consume royal jelly (RJ) as the main food throughout their life, including as adults, but workers only eat worker jelly when they are larvae less than 3 days old. In order to explore the effect of RJ and the components affecting longevity of worker honey bees, we first determined the optimal dose for prolonging longevity of workers as 4% RJ in 50% sucrose solution,...

  18. Nosema ceranae Winter Control: Study of the Effectiveness of Different Fumagillin Treatments and Consequences on the Strength of Honey Bee (Hymenoptera: Apidae) Colonies.

    Science.gov (United States)

    Mendoza, Y; Diaz-Cetti, S; Ramallo, G; Santos, E; Porrini, M; Invernizzi, C

    2017-02-01

    In Uruguay, colonies of honey bees moving to Eucalyptus grandis plantation in autumn habitually become infected with the microsporidian Nosema ceranae , a parasite that attacks the digestive system of bees. Beekeepers attributed to N. ceranae depopulation of the colonies that often occurs at the end of the blooming period, and many use the antibiotic fumagillin to reduce the level of infection. The aim of this study was to compare the effectiveness of four different fumagillin treatments and determine how this antibiotic affects the strength of the colonies during the winter season. The colonies treated with fumagillin in July showed less spore load at the end of applications, being the most effective the following treatments: the four applications sprayed over bees of 30 mg of fumagillin in 100 ml of sugar syrup 1:1, and four applications of 90 mg of fumagillin in 250 ml of sugar syrup 1:1 using a feeder. However, 2 month after the treatment applications, the colonies treated with fumagillin were the same size as the untreated colonies. In September, the colonies treated and not treated with fumagillin did not differ in colony strength (adult bee population and brood area) or spores abundance. Our study demonstrates that fumagillin treatment temporarily decreased the spore load of N. ceranae , but this was not reflected in either the size of the colonies or the probability of surviving the winter regardless of the dose or the administration strategy applied. Given the results obtained, we suggest to not perform the pharmacological treatment under the conditions described in the experiment. En Uruguay las colonias de abejas melíferas que se trasladan a las forestaciones de Eucalyptus grandis en otoño indefectiblemente se infectan con el microsporido Nosema ceranae , parásito que ataca el sistema digestivo de las abejas. Los apicultores atribuyen a N. ceranae el despoblamiento de las colonias que ocurre con frecuencia al terminar el periodo de floraci

  19. Bees do not use nearest-neighbour rules for optimization of multi-location routes.

    Science.gov (United States)

    Lihoreau, Mathieu; Chittka, Lars; Le Comber, Steven C; Raine, Nigel E

    2012-02-23

    Animals collecting patchily distributed resources are faced with complex multi-location routing problems. Rather than comparing all possible routes, they often find reasonably short solutions by simply moving to the nearest unvisited resources when foraging. Here, we report the travel optimization performance of bumble-bees (Bombus terrestris) foraging in a flight cage containing six artificial flowers arranged such that movements between nearest-neighbour locations would lead to a long suboptimal route. After extensive training (80 foraging bouts and at least 640 flower visits), bees reduced their flight distances and prioritized shortest possible routes, while almost never following nearest-neighbour solutions. We discuss possible strategies used during the establishment of stable multi-location routes (or traplines), and how these could allow bees and other animals to solve complex routing problems through experience, without necessarily requiring a sophisticated cognitive representation of space.

  20. Honey bee (Apis mellifera) nurses do not consume pollens based on their nutritional quality

    Science.gov (United States)

    Honey bees (Apis mellifera) consume a variety of pollens to meet the majority of their requirements for protein and lipids. Recent work indicates that at both the colony and individual levels, honey bees prefer diets that reflect the proper ratio of nutrients necessary for optimal survival and homeo...

  1. Bee health

    DEFF Research Database (Denmark)

    Lecocq, Antoine

    and descriptive work at the colony, smaller social group and individual levels as well as in a greater pollinator context. Its aim is to confirm and deepen our understanding of the biology and life-history of the Western honey bee, Apis mellifera. In an ever-changing landscape of flower patches and increase...... long term data based on the daily weight of colonies spread around Denmark, we showed that colonies in urban landscapes, surrounded by parks and private gardens are more productive than their counterparts in agricultural landscapes, surrounded by large monocultures and virtual foraging deserts for much...... pathogens to other pollinators. The threat of inter-specific pathogen transmission appears to be real, and testing the infectivity of honey bee pathogens on other bee pollinators, represents a logical step following on from the recent detection of those pathogens using molecular methods. The preliminary...

  2. Seasonal flight and resource collection patterns of colonies of the stingless bee Melipona bicolor schencki Gribodo (Apidae, Meliponini in an Araucaria forest area in southern Brazil

    Directory of Open Access Journals (Sweden)

    Ney Telles Ferreira Junior

    2010-01-01

    Full Text Available Melipona bicolor schencki occurs in southern Brazil and at high elevations in southeastern Brazil. It has potential for use in meliponiculture but this stingless bee species is vulnerable to extinction and we have little knowledge about its ecology. In order to gather essential information for species conservation and management, we made a study of seasonal flight activities in its natural environment. We sampled bees entering the nests with pollen, nectar/water and resin/mud, in five colonies during each season. In parallel, we analyzed the influence of hour of the day and meteorological factors on flight activity. Flights were most intense during spring and summer, with daily mean estimates of 2,100 and 2,333 flights respectively, while in fall and winter the daily flight estimate was reduced to 612 and 1,104 flights, respectively. Nectar and water were the most frequently-collected resources, followed by pollen and building materials. This preference occurred in all seasons, but with variations in intensity. During spring, daily flight activity lasted over 14 hours; this period was reduced in the other seasons, reaching eight hours in winter. Meteorological factors were associated with 40.2% of the variation in flight and resource collection activity. Apparently, other factors that we did not measure, such as colony needs and availability of floral resources, also strongly influence the intensity of resource collection.

  3. Winter honey bee colony losses, Varroa destructor control strategies, and the role of weather conditions: Results from a survey among beekeepers.

    Science.gov (United States)

    Beyer, Marco; Junk, Jürgen; Eickermann, Michael; Clermont, Antoine; Kraus, François; Georges, Carlo; Reichart, Andreas; Hoffmann, Lucien

    2018-06-01

    Sets of treatments that were applied against varroa mites in the Luxembourgish beekeeper community were surveyed annually with a questionnaire between the winters 2010/11 and 2014/15. The average temperature and the precipitation sum of the month, when the respective varroa control method was applied were considered as co-variables when evaluating the efficacy of varroa control regimes. Success or failure of control regimes was evaluated based on the percentage of colonies lost per apiary in the winter following the treatment(s). Neither a positive nor a negative effect of formic acid (concentration 60%, w/v) on the colony losses could be found, irrespective of the weather conditions around the time of application. The higher concentration of 85% formic acid was linked with reduced colony losses when applications were done in August. Colony losses were reduced when Thymovar was applied in July or August, but applications in September were associated with increased losses compared with apiaries not treated with Thymovar during the same period. Apilife application in July as well as Apivar applications between July and September were associated with reduced colony losses. The removal of the drone brood and trickled oxalic acid application had beneficial effects when being done in April and December, respectively. Relatively warm (3.0±1.3°C) and wet (507.0±38.6mm/2months) conditions during the winter months December and January and relatively cool (17.2±1.4°C average monthly temperature) and wet (110.8±55.5mm/month) conditions in July were associated with elevated honey bee colony losses. Copyright © 2018 Elsevier Ltd. All rights reserved.

  4. ABC Assay: Method Development and Application to Quantify the Role of Three DWV Master Variants in Overwinter Colony Losses of European Honey Bees

    Directory of Open Access Journals (Sweden)

    Jessica L. Kevill

    2017-10-01

    Full Text Available Deformed wing virus (DWV is one of the most prevalent honey bee viral pathogens in the world. Typical of many RNA viruses, DWV is a quasi-species, which is comprised of a large number of different variants, currently consisting of three master variants: Type A, B, and C. Little is known about the impact of each variant or combinations of variants upon the biology of individual hosts. Therefore, we have developed a new set of master variant-specific DWV primers and a set of standards that allow for the quantification of each of the master variants. Competitive reverse transcriptase polymerase chain reaction (RT-PCR experimental design confirms that each new DWV primer set is specific to the retrospective master variant. The sensitivity of the ABC assay is dependent on whether DNA or RNA is used as the template and whether other master variants are present in the sample. Comparison of the overall proportions of each master variant within a sample of known diversity, as confirmed by next-generation sequence (NGS data, validates the efficiency of the ABC assay. The ABC assay was used on archived material from a Devon overwintering colony loss (OCL 2006–2007 study; further implicating DWV type A and, for the first time, possibly C in the untimely collapse of honey bee colonies. Moreover, in this study DWV type B was not associated with OCL. The use of the ABC assay will allow researchers to quickly and cost effectively pre-screen for the presence of DWV master variants in honey bees.

  5. Stakeholder Conference on Bee Health

    Science.gov (United States)

    USDA and EPA released a comprehensive scientific report on honey bee health in May 2013. The report points to multiple factors playing a role in honey bee colony declines, including parasites and disease, genetics, poor nutrition, and pesticide exposure.

  6. Scalable Clustering of High-Dimensional Data Technique Using SPCM with Ant Colony Optimization Intelligence

    Directory of Open Access Journals (Sweden)

    Thenmozhi Srinivasan

    2015-01-01

    Full Text Available Clusters of high-dimensional data techniques are emerging, according to data noisy and poor quality challenges. This paper has been developed to cluster data using high-dimensional similarity based PCM (SPCM, with ant colony optimization intelligence which is effective in clustering nonspatial data without getting knowledge about cluster number from the user. The PCM becomes similarity based by using mountain method with it. Though this is efficient clustering, it is checked for optimization using ant colony algorithm with swarm intelligence. Thus the scalable clustering technique is obtained and the evaluation results are checked with synthetic datasets.

  7. Topology optimum design of compliant mechanisms using modified ant colony optimization

    Energy Technology Data Exchange (ETDEWEB)

    Yoo, Kwang Seon; Han, Seog Young [Hanyang University, Seoul (Korea, Republic of)

    2015-08-15

    A Modified ant colony optimization (MACO) algorithm was suggested for topology optimal design of compliant mechanisms since standard ACO cannot provide an appropriate optimal topology. In order to improve computational efficiency and suitability of standard ACO algorithm in topology optimization for compliant mechanisms, a continuous variable, called the 'Element contribution significance (ECS),'is employed, which serves to replace the positions of ants in the standard ACO algorithm, and assess the importance of each element in the optimization process. MACO algorithm was applied to topology optimizations of both linear and geometrically nonlinear compliant mechanisms using three kinds of objective functions, and optimized topologies were compared each other. From the comparisons, it was concluded that MACO algorithm can effectively be applied to topology optimizations of linear and geometrically nonlinear compliant mechanisms, and the ratio of Mutual potential energy (MPE) to Strain energy (SE) type of objective function is the best for topology optimal design of compliant mechanisms.

  8. Bearing Fault Detection Based on Maximum Likelihood Estimation and Optimized ANN Using the Bees Algorithm

    Directory of Open Access Journals (Sweden)

    Behrooz Attaran

    2015-01-01

    Full Text Available Rotating machinery is the most common machinery in industry. The root of the faults in rotating machinery is often faulty rolling element bearings. This paper presents a technique using optimized artificial neural network by the Bees Algorithm for automated diagnosis of localized faults in rolling element bearings. The inputs of this technique are a number of features (maximum likelihood estimation values, which are derived from the vibration signals of test data. The results shows that the performance of the proposed optimized system is better than most previous studies, even though it uses only two features. Effectiveness of the above method is illustrated using obtained bearing vibration data.

  9. Image Watermarking Algorithm Based on Multiobjective Ant Colony Optimization and Singular Value Decomposition in Wavelet Domain

    Directory of Open Access Journals (Sweden)

    Khaled Loukhaoukha

    2013-01-01

    Full Text Available We present a new optimal watermarking scheme based on discrete wavelet transform (DWT and singular value decomposition (SVD using multiobjective ant colony optimization (MOACO. A binary watermark is decomposed using a singular value decomposition. Then, the singular values are embedded in a detailed subband of host image. The trade-off between watermark transparency and robustness is controlled by multiple scaling factors (MSFs instead of a single scaling factor (SSF. Determining the optimal values of the multiple scaling factors (MSFs is a difficult problem. However, a multiobjective ant colony optimization is used to determine these values. Experimental results show much improved performances of the proposed scheme in terms of transparency and robustness compared to other watermarking schemes. Furthermore, it does not suffer from the problem of high probability of false positive detection of the watermarks.

  10. Survey on Recent Research and Implementation of Ant Colony Optimization in Various Engineering Applications

    Directory of Open Access Journals (Sweden)

    Mohan B. Chandra

    2011-08-01

    Full Text Available Ant colony optimization (ACO takes inspiration from the foraging behaviour of real ant species. This ACO exploits a similar mechanism for solving optimization problems for the various engineering field of study. Many successful implementations using ACO are now available in many applications. This paper reviewing varies systematic approach on recent research and implementation of ACO. Finally it presents the experimental result of ACO which is applied for routing problem and compared with existing algorithms.

  11. Single Allocation Hub-and-spoke Networks Design Based on Ant Colony Optimization Algorithm

    Directory of Open Access Journals (Sweden)

    Yang Pingle

    2014-10-01

    Full Text Available Capacitated single allocation hub-and-spoke networks can be abstracted as a mixed integer linear programming model equation with three variables. Introducing an improved ant colony algorithm, which has six local search operators. Meanwhile, introducing the "Solution Pair" concept to decompose and optimize the composition of the problem, the problem can become more specific and effectively meet the premise and advantages of using ant colony algorithm. Finally, location simulation experiment is made according to Australia Post data to demonstrate this algorithm has good efficiency and stability for solving this problem.

  12. Comprehensive preference optimization of an irreversible thermal engine using pareto based mutable smart bee algorithm and generalized regression neural network

    DEFF Research Database (Denmark)

    Mozaffari, Ahmad; Gorji-Bandpy, Mofid; Samadian, Pendar

    2013-01-01

    Optimizing and controlling of complex engineering systems is a phenomenon that has attracted an incremental interest of numerous scientists. Until now, a variety of intelligent optimizing and controlling techniques such as neural networks, fuzzy logic, game theory, support vector machines...... and stochastic algorithms were proposed to facilitate controlling of the engineering systems. In this study, an extended version of mutable smart bee algorithm (MSBA) called Pareto based mutable smart bee (PBMSB) is inspired to cope with multi-objective problems. Besides, a set of benchmark problems and four...... well-known Pareto based optimizing algorithms i.e. multi-objective bee algorithm (MOBA), multi-objective particle swarm optimization (MOPSO) algorithm, non-dominated sorting genetic algorithm (NSGA-II), and strength Pareto evolutionary algorithm (SPEA 2) are utilized to confirm the acceptable...

  13. Differential Bees Flux Balance Analysis with OptKnock for in silico microbial strains optimization.

    Directory of Open Access Journals (Sweden)

    Yee Wen Choon

    Full Text Available Microbial strains optimization for the overproduction of desired phenotype has been a popular topic in recent years. The strains can be optimized through several techniques in the field of genetic engineering. Gene knockout is a genetic engineering technique that can engineer the metabolism of microbial cells with the objective to obtain desirable phenotypes. However, the complexities of the metabolic networks have made the process to identify the effects of genetic modification on the desirable phenotypes challenging. Furthermore, a vast number of reactions in cellular metabolism often lead to the combinatorial problem in obtaining optimal gene deletion strategy. Basically, the size of a genome-scale metabolic model is usually large. As the size of the problem increases, the computation time increases exponentially. In this paper, we propose Differential Bees Flux Balance Analysis (DBFBA with OptKnock to identify optimal gene knockout strategies for maximizing the production yield of desired phenotypes while sustaining the growth rate. This proposed method functions by improving the performance of a hybrid of Bees Algorithm and Flux Balance Analysis (BAFBA by hybridizing Differential Evolution (DE algorithm into neighborhood searching strategy of BAFBA. In addition, DBFBA is integrated with OptKnock to validate the results for improving the reliability the work. Through several experiments conducted on Escherichia coli, Bacillus subtilis, and Clostridium thermocellum as the model organisms, DBFBA has shown a better performance in terms of computational time, stability, growth rate, and production yield of desired phenotypes compared to the methods used in previous works.

  14. Clarifying Cutting and Sewing Processes with Due Windows Using an Effective Ant Colony Optimization

    Directory of Open Access Journals (Sweden)

    Rong-Hwa Huang

    2013-01-01

    Full Text Available The cutting and sewing process is a traditional flow shop scheduling problem in the real world. This two-stage flexible flow shop is often commonly associated with manufacturing in the fashion and textiles industry. Many investigations have demonstrated that the ant colony optimization (ACO algorithm is effective and efficient for solving scheduling problems. This work applies a novel effective ant colony optimization (EACO algorithm to solve two-stage flexible flow shop scheduling problems and thereby minimize earliness, tardiness, and makespan. Computational results reveal that for both small and large problems, EACO is more effective and robust than both the particle swarm optimization (PSO algorithm and the ACO algorithm. Importantly, this work demonstrates that EACO can solve complex scheduling problems in an acceptable period of time.

  15. ACOustic: A Nature-Inspired Exploration Indicator for Ant Colony Optimization

    Directory of Open Access Journals (Sweden)

    Rafid Sagban

    2015-01-01

    Full Text Available A statistical machine learning indicator, ACOustic, is proposed to evaluate the exploration behavior in the iterations of ant colony optimization algorithms. This idea is inspired by the behavior of some parasites in their mimicry to the queens’ acoustics of their ant hosts. The parasites’ reaction results from their ability to indicate the state of penetration. The proposed indicator solves the problem of robustness that results from the difference of magnitudes in the distance’s matrix, especially when combinatorial optimization problems with rugged fitness landscape are applied. The performance of the proposed indicator is evaluated against the existing indicators in six variants of ant colony optimization algorithms. Instances for travelling salesman problem and quadratic assignment problem are used in the experimental evaluation. The analytical results showed that the proposed indicator is more informative and more robust.

  16. Ant Colony Optimization ACO For The Traveling Salesman Problem TSP Using Partitioning

    Directory of Open Access Journals (Sweden)

    Alok Bajpai

    2015-08-01

    Full Text Available Abstract An ant colony optimization is a technique which was introduced in 1990s and which can be applied to a variety of discrete combinatorial optimization problem and to continuous optimization. The ACO algorithm is simulated with the foraging behavior of the real ants to find the incremental solution constructions and to realize a pheromone laying-and-following mechanism. This pheromone is the indirect communication among the ants. In this paper we introduces the partitioning technique based on the divide and conquer strategy for the traveling salesman problem which is one of the most important combinatorial problem in which the original problem is partitioned into the group of sub problems. And then we apply the ant colony algorithm using candidate list strategy for each smaller sub problems. After that by applying the local optimization and combining the sub problems to find the good solution for the original problem by improving the exploration efficiency of the ants. At the end of this paper we have also be presented the comparison of result with the normal ant colony system for finding the optimal solution to the traveling salesman problem.

  17. Optimization of travel salesman problem using the ant colony system and Greedy search

    International Nuclear Information System (INIS)

    Esquivel E, J.; Ordonez A, A.; Ortiz S, J. J.

    2008-01-01

    In this paper we present some results obtained during the development of optimization systems that can be used to design refueling and patterns of control rods in a BWR. These systems use ant colonies and Greedy search. The first phase of this project is to be familiar with these optimization techniques applied to the problem of travel salesman problem (TSP). The utility of TSP study is that, like the refueling design and pattern design of control rods are problems of combinative optimization. Even, the similarity with the problem of the refueling design is remarkable. It is presented some results for the TSP with the 32 state capitals of Mexico country. (Author)

  18. Multi-view 3D scene reconstruction using ant colony optimization techniques

    International Nuclear Information System (INIS)

    Chrysostomou, Dimitrios; Gasteratos, Antonios; Nalpantidis, Lazaros; Sirakoulis, Georgios C

    2012-01-01

    This paper presents a new method performing high-quality 3D object reconstruction of complex shapes derived from multiple, calibrated photographs of the same scene. The novelty of this research is found in two basic elements, namely: (i) a novel voxel dissimilarity measure, which accommodates the elimination of the lighting variations of the models and (ii) the use of an ant colony approach for further refinement of the final 3D models. The proposed reconstruction procedure employs a volumetric method based on a novel projection test for the production of a visual hull. While the presented algorithm shares certain aspects with the space carving algorithm, it is, nevertheless, first enhanced with the lightness compensating image comparison method, and then refined using ant colony optimization. The algorithm is fast, computationally simple and results in accurate representations of the input scenes. In addition, compared to previous publications, the particular nature of the proposed algorithm allows accurate 3D volumetric measurements under demanding lighting environmental conditions, due to the fact that it can cope with uneven light scenes, resulting from the characteristics of the voxel dissimilarity measure applied. Besides, the intelligent behavior of the ant colony framework provides the opportunity to formulate the process as a combinatorial optimization problem, which can then be solved by means of a colony of cooperating artificial ants, resulting in very promising results. The method is validated with several real datasets, along with qualitative comparisons with other state-of-the-art 3D reconstruction techniques, following the Middlebury benchmark. (paper)

  19. Flexible Job Shop Scheduling Problem Using an Improved Ant Colony Optimization

    Directory of Open Access Journals (Sweden)

    Lei Wang

    2017-01-01

    Full Text Available As an extension of the classical job shop scheduling problem, the flexible job shop scheduling problem (FJSP plays an important role in real production systems. In FJSP, an operation is allowed to be processed on more than one alternative machine. It has been proven to be a strongly NP-hard problem. Ant colony optimization (ACO has been proven to be an efficient approach for dealing with FJSP. However, the basic ACO has two main disadvantages including low computational efficiency and local optimum. In order to overcome these two disadvantages, an improved ant colony optimization (IACO is proposed to optimize the makespan for FJSP. The following aspects are done on our improved ant colony optimization algorithm: select machine rule problems, initialize uniform distributed mechanism for ants, change pheromone’s guiding mechanism, select node method, and update pheromone’s mechanism. An actual production instance and two sets of well-known benchmark instances are tested and comparisons with some other approaches verify the effectiveness of the proposed IACO. The results reveal that our proposed IACO can provide better solution in a reasonable computational time.

  20. Multiobjective optimal placement of switches and protective devices in electric power distribution systems using ant colony optimization

    Energy Technology Data Exchange (ETDEWEB)

    Tippachon, Wiwat; Rerkpreedapong, Dulpichet [Department of Electrical Engineering, Kasetsart University, 50 Phaholyothin Rd., Ladyao, Jatujak, Bangkok 10900 (Thailand)

    2009-07-15

    This paper presents a multiobjective optimization methodology to optimally place switches and protective devices in electric power distribution networks. Identifying the type and location of them is a combinatorial optimization problem described by a nonlinear and nondifferential function. The multiobjective ant colony optimization (MACO) has been applied to this problem to minimize the total cost while simultaneously minimize two distribution network reliability indices including system average interruption frequency index (SAIFI) and system interruption duration index (SAIDI). Actual distribution feeders are used in the tests, and test results have shown that the algorithm can determine the set of optimal nondominated solutions. It allows the utility to obtain the optimal type and location of devices to achieve the best system reliability with the lowest cost. (author)

  1. A nuclear reactor core fuel reload optimization using Artificial-Ant-Colony Connective Networks

    International Nuclear Information System (INIS)

    Lima, Alan M.M. de; Schirru, Roberto

    2005-01-01

    A Pressurized Water Reactor core must be reloaded every time the fuel burnup reaches a level when it is not possible to sustain nominal power operation. The nuclear core fuel reload optimization consists in finding a burned-up and fresh-fuel-assembly pattern that maximizes the number of full operational days. This problem is NP-hard, meaning that complexity grows exponentially with the number of fuel assemblies in the core. Besides that, the problem is non-linear and its search space is highly discontinual and multimodal. In this work a parallel computational system based on Ant Colony System (ACS) called Artificial-Ant-Colony Networks is introduced to solve the nuclear reactor core fuel reload optimization problem. ACS is a system based on artificial agents that uses the reinforcement learning technique and was originally developed to solve the Traveling Salesman Problem, which is conceptually similar to the nuclear fuel reload problem. (author)

  2. Analysis of parameter estimation and optimization application of ant colony algorithm in vehicle routing problem

    Science.gov (United States)

    Xu, Quan-Li; Cao, Yu-Wei; Yang, Kun

    2018-03-01

    Ant Colony Optimization (ACO) is the most widely used artificial intelligence algorithm at present. This study introduced the principle and mathematical model of ACO algorithm in solving Vehicle Routing Problem (VRP), and designed a vehicle routing optimization model based on ACO, then the vehicle routing optimization simulation system was developed by using c ++ programming language, and the sensitivity analyses, estimations and improvements of the three key parameters of ACO were carried out. The results indicated that the ACO algorithm designed in this paper can efficiently solve rational planning and optimization of VRP, and the different values of the key parameters have significant influence on the performance and optimization effects of the algorithm, and the improved algorithm is not easy to locally converge prematurely and has good robustness.

  3. Optimization of China Crude Oil Transportation Network with Genetic Ant Colony Algorithm

    Directory of Open Access Journals (Sweden)

    Yao Wang

    2015-08-01

    Full Text Available Taking into consideration both shipping and pipeline transport, this paper first analysed the risk factors for different modes of crude oil import transportation. Then, based on the minimum of both transportation cost and overall risk, a multi-objective programming model was established to optimize the transportation network of crude oil import, and the genetic algorithm and ant colony algorithm were employed to solve the problem. The optimized result shows that VLCC (Very Large Crude Carrier is superior in long distance sea transportation, whereas pipeline transport is more secure than sea transport. Finally, this paper provides related safeguard suggestions on crude oil import transportation.

  4. Ant colony optimization techniques for the hamiltonian p-median problem

    Directory of Open Access Journals (Sweden)

    M. Zohrehbandian

    2010-12-01

    Full Text Available Location-Routing problems involve locating a number of facilitiesamong candidate sites and establishing delivery routes to a set of users in such a way that the total system cost is minimized. A special case of these problems is Hamiltonian p-Median problem (HpMP. This research applies the metaheuristic method of ant colony optimization (ACO to solve the HpMP. Modifications are made to the ACO algorithm used to solve the traditional vehicle routing problem (VRP in order to allow the search of the optimal solution of the HpMP. Regarding this metaheuristic algorithm a computational experiment is reported as well.

  5. Local Contrast Enhancement Using Intuitionistic Fuzzy Sets Optimized By Artificial Bee Colony Algorithm

    Directory of Open Access Journals (Sweden)

    Daniel M. Wonohadidjojo

    2017-03-01

    was applied. The results of local contrast enhancement using both methods were compared with the results using histogram equalization method. The tests were conducted using two MDCK cell images. The results of local contrast enhancement using both methods were evaluated by observing the enhanced images and IEM values. The results show that the methods outperform the histogram equalization method. Furthermore, the method using IFSABC is better than the IFS method.

  6. A new honey bee mating optimization algorithm for non-smooth economic dispatch

    International Nuclear Information System (INIS)

    Niknam, Taher; Mojarrad, Hasan Doagou; Meymand, Hamed Zeinoddini; Firouzi, Bahman Bahmani

    2011-01-01

    The non-storage characteristics of electricity and the increasing fuel costs worldwide call for the need to operate the systems more economically. Economic dispatch (ED) is one of the most important optimization problems in power systems. ED has the objective of dividing the power demand among the online generators economically while satisfying various constraints. The importance of economic dispatch is to get maximum usable power using minimum resources. To solve the static ED problem, honey bee mating algorithm (HBMO) can be used. The basic disadvantage of the original HBMO algorithm is the fact that it may miss the optimum and provide a near optimum solution in a limited runtime period. In order to avoid this shortcoming, we propose a new method that improves the mating process of HBMO and also, combines the improved HBMO with a Chaotic Local Search (CLS) called Chaotic Improved Honey Bee Mating Optimization (CIHBMO). The proposed algorithm is used to solve ED problems taking into account the nonlinear generator characteristics such as prohibited operation zones, multi-fuel and valve-point loading effects. The CIHBMO algorithm is tested on three test systems and compared with other methods in the literature. Results have shown that the proposed method is efficient and fast for ED problems with non-smooth and non-continuous fuel cost functions. Moreover, the optimal power dispatch obtained by the algorithm is superior to previous reported results. -- Research highlights: →Economic dispatch. →Reducing electrical energy loss. →Saving electrical energy. →Optimal operation.

  7. To bee or not to bee—comments on “Discrete optimum design of truss structures using artificial bee colony algorithm”

    DEFF Research Database (Denmark)

    Stolpe, Mathias

    2011-01-01

    to three alternative heuristics on the same benchmark examples. The most advanced heuristics presented herein find equally good, or better, designs compared to those presented in Sonmez (Struct Multidisc Optim 43:85–97, 2011). However, for the largest benchmark example, we use four orders of magnitude...

  8. Simulation optimization based ant colony algorithm for the uncertain quay crane scheduling problem

    Directory of Open Access Journals (Sweden)

    Naoufal Rouky

    2019-01-01

    Full Text Available This work is devoted to the study of the Uncertain Quay Crane Scheduling Problem (QCSP, where the loading /unloading times of containers and travel time of quay cranes are considered uncertain. The problem is solved with a Simulation Optimization approach which takes advantage of the great possibilities offered by the simulation to model the real details of the problem and the capacity of the optimization to find solutions with good quality. An Ant Colony Optimization (ACO meta-heuristic hybridized with a Variable Neighborhood Descent (VND local search is proposed to determine the assignments of tasks to quay cranes and the sequences of executions of tasks on each crane. Simulation is used inside the optimization algorithm to generate scenarios in agreement with the probabilities of the distributions of the uncertain parameters, thus, we carry out stochastic evaluations of the solutions found by each ant. The proposed optimization algorithm is tested first for the deterministic case on several well-known benchmark instances. Then, in the stochastic case, since no other work studied exactly the same problem with the same assumptions, the Simulation Optimization approach is compared with the deterministic version. The experimental results show that the optimization algorithm is competitive as compared to the existing methods and that the solutions found by the Simulation Optimization approach are more robust than those found by the optimization algorithm.

  9. The Patch-Levy-Based Bees Algorithm Applied to Dynamic Optimization Problems

    Directory of Open Access Journals (Sweden)

    Wasim A. Hussein

    2017-01-01

    Full Text Available Many real-world optimization problems are actually of dynamic nature. These problems change over time in terms of the objective function, decision variables, constraints, and so forth. Therefore, it is very important to study the performance of a metaheuristic algorithm in dynamic environments to assess the robustness of the algorithm to deal with real-word problems. In addition, it is important to adapt the existing metaheuristic algorithms to perform well in dynamic environments. This paper investigates a recently proposed version of Bees Algorithm, which is called Patch-Levy-based Bees Algorithm (PLBA, on solving dynamic problems, and adapts it to deal with such problems. The performance of the PLBA is compared with other BA versions and other state-of-the-art algorithms on a set of dynamic multimodal benchmark problems of different degrees of difficulties. The results of the experiments show that PLBA achieves better results than the other BA variants. The obtained results also indicate that PLBA significantly outperforms some of the other state-of-the-art algorithms and is competitive with others.

  10. An ant colony optimization algorithm for phylogenetic estimation under the minimum evolution principle

    Directory of Open Access Journals (Sweden)

    Milinkovitch Michel C

    2007-11-01

    Full Text Available Abstract Background Distance matrix methods constitute a major family of phylogenetic estimation methods, and the minimum evolution (ME principle (aiming at recovering the phylogeny with shortest length is one of the most commonly used optimality criteria for estimating phylogenetic trees. The major difficulty for its application is that the number of possible phylogenies grows exponentially with the number of taxa analyzed and the minimum evolution principle is known to belong to the NP MathType@MTEF@5@5@+=feaafiart1ev1aaatCvAUfKttLearuWrP9MDH5MBPbIqV92AaeXatLxBI9gBaebbnrfifHhDYfgasaacPC6xNi=xH8viVGI8Gi=hEeeu0xXdbba9frFj0xb9qqpG0dXdb9aspeI8k8fiI+fsY=rqGqVepae9pg0db9vqaiVgFr0xfr=xfr=xc9adbaqaaeGacaGaaiaabeqaaeqabiWaaaGcbaWenfgDOvwBHrxAJfwnHbqeg0uy0HwzTfgDPnwy1aaceaGae8xdX7Kaeeiuaafaaa@3888@-hard class of problems. Results In this paper, we introduce an Ant Colony Optimization (ACO algorithm to estimate phylogenies under the minimum evolution principle. ACO is an optimization technique inspired from the foraging behavior of real ant colonies. This behavior is exploited in artificial ant colonies for the search of approximate solutions to discrete optimization problems. Conclusion We show that the ACO algorithm is potentially competitive in comparison with state-of-the-art algorithms for the minimum evolution principle. This is the first application of an ACO algorithm to the phylogenetic estimation problem.

  11. Optimization of fuel reloads for a BWR using the ant colony system

    International Nuclear Information System (INIS)

    Esquivel E, J.; Ortiz S, J. J.

    2009-10-01

    In this work some results obtained during the development of optimization systems are presented, which are employees for the fuel reload design in a BWR. The systems use the ant colony optimization technique. As first instance, a system is developed that was adapted at travel salesman problem applied for the 32 state capitals of Mexican Republic. The purpose of this implementation is that a similarity exists with the design of fuel reload, since the two problems are of combinatorial optimization with decision variables that have similarity between both. The system was coupled to simulator SIMULATE-3, obtaining good results when being applied to an operation cycle in equilibrium for reactors of nuclear power plant of Laguna Verde. (Author)

  12. Solving Multi-Resource Constrained Project Scheduling Problem using Ant Colony Optimization

    Directory of Open Access Journals (Sweden)

    Hsiang-Hsi Huang

    2015-01-01

    Full Text Available This paper applied Ant Colony Optimization (ACO to develop a resource constraints scheduling model to achieve the resource allocation optimization and the shortest completion time of a project under resource constraints and the activities precedence requirement for projects. Resource leveling is also discussed and has to be achieved under the resource allocation optimization in this research. Testing cases and examples adopted from the international test bank were studied for verifying the effectiveness of the proposed model. The results showed that the solutions of different cases all have a better performance within a reasonable time. These can be obtained through ACO algorithm under the same constrained conditions. A program was written for the proposed model that is able to automatically produce the project resource requirement figure after the project duration is solved.

  13. A nuclear reactor core fuel reload optimization using artificial ant colony connective networks

    International Nuclear Information System (INIS)

    Lima, Alan M.M. de; Schirru, Roberto; Carvalho da Silva, Fernando; Medeiros, Jose Antonio Carlos Canedo

    2008-01-01

    The core of a nuclear Pressurized Water Reactor (PWR) may be reloaded every time the fuel burn-up is such that it is not more possible to maintain the reactor operating at nominal power. The nuclear core fuel reload optimization problem consists in finding a pattern of burned-up and fresh-fuel assemblies that maximize the number of full operational days. This is an NP-Hard problem, meaning that complexity grows exponentially with the number of fuel assemblies in the core. Moreover, the problem is non-linear and its search space is highly discontinuous and multi-modal. Ant Colony System (ACS) is an optimization algorithm based on artificial ants that uses the reinforcement learning technique. The ACS was originally developed to solve the Traveling Salesman Problem (TSP), which is conceptually similar to the nuclear core fuel reload problem. In this work a parallel computational system based on the ACS, called Artificial Ant Colony Networks is introduced to solve the core fuel reload optimization problem

  14. An improved ant colony optimization algorithm with fault tolerance for job scheduling in grid computing systems.

    Directory of Open Access Journals (Sweden)

    Hajara Idris

    Full Text Available The Grid scheduler, schedules user jobs on the best available resource in terms of resource characteristics by optimizing job execution time. Resource failure in Grid is no longer an exception but a regular occurring event as resources are increasingly being used by the scientific community to solve computationally intensive problems which typically run for days or even months. It is therefore absolutely essential that these long-running applications are able to tolerate failures and avoid re-computations from scratch after resource failure has occurred, to satisfy the user's Quality of Service (QoS requirement. Job Scheduling with Fault Tolerance in Grid Computing using Ant Colony Optimization is proposed to ensure that jobs are executed successfully even when resource failure has occurred. The technique employed in this paper, is the use of resource failure rate, as well as checkpoint-based roll back recovery strategy. Check-pointing aims at reducing the amount of work that is lost upon failure of the system by immediately saving the state of the system. A comparison of the proposed approach with an existing Ant Colony Optimization (ACO algorithm is discussed. The experimental results of the implemented Fault Tolerance scheduling algorithm show that there is an improvement in the user's QoS requirement over the existing ACO algorithm, which has no fault tolerance integrated in it. The performance evaluation of the two algorithms was measured in terms of the three main scheduling performance metrics: makespan, throughput and average turnaround time.

  15. Scalable unit commitment by memory-bounded ant colony optimization with A{sup *} local search

    Energy Technology Data Exchange (ETDEWEB)

    Saber, Ahmed Yousuf; Alshareef, Abdulaziz Mohammed [Department of Electrical and Computer Engineering, King Abdulaziz University, P.O. Box 80204, Jeddah 21589 (Saudi Arabia)

    2008-07-15

    Ant colony optimization (ACO) is successfully applied in optimization problems. Performance of the basic ACO for small problems with moderate dimension and searching space is satisfactory. As the searching space grows exponentially in the large-scale unit commitment problem, the basic ACO is not applicable for the vast size of pheromone matrix of ACO in practical time and physical computer-memory limit. However, memory-bounded methods prune the least-promising nodes to fit the system in computer memory. Therefore, the authors propose memory-bounded ant colony optimization (MACO) in this paper for the scalable (no restriction for system size) unit commitment problem. This MACO intelligently solves the limitation of computer memory, and does not permit the system to grow beyond a bound on memory. In the memory-bounded ACO implementation, A{sup *} heuristic is introduced to increase local searching ability and probabilistic nearest neighbor method is applied to estimate pheromone intensity for the forgotten value. Finally, the benchmark data sets and existing methods are used to show the effectiveness of the proposed method. (author)

  16. A nuclear reactor core fuel reload optimization using artificial ant colony connective networks

    Energy Technology Data Exchange (ETDEWEB)

    Lima, Alan M.M. de [Universidade Federal do Rio de Janeiro, PEN/COPPE - UFRJ, Ilha do Fundao s/n, CEP 21945-970 Rio de Janeiro (Brazil)], E-mail: alanmmlima@yahoo.com.br; Schirru, Roberto [Universidade Federal do Rio de Janeiro, PEN/COPPE - UFRJ, Ilha do Fundao s/n, CEP 21945-970 Rio de Janeiro (Brazil)], E-mail: schirru@lmp.ufrj.br; Carvalho da Silva, Fernando [Universidade Federal do Rio de Janeiro, PEN/COPPE - UFRJ, Ilha do Fundao s/n, CEP 21945-970 Rio de Janeiro (Brazil)], E-mail: fernando@con.ufrj.br; Medeiros, Jose Antonio Carlos Canedo [Universidade Federal do Rio de Janeiro, PEN/COPPE - UFRJ, Ilha do Fundao s/n, CEP 21945-970 Rio de Janeiro (Brazil)], E-mail: canedo@lmp.ufrj.br

    2008-09-15

    The core of a nuclear Pressurized Water Reactor (PWR) may be reloaded every time the fuel burn-up is such that it is not more possible to maintain the reactor operating at nominal power. The nuclear core fuel reload optimization problem consists in finding a pattern of burned-up and fresh-fuel assemblies that maximize the number of full operational days. This is an NP-Hard problem, meaning that complexity grows exponentially with the number of fuel assemblies in the core. Moreover, the problem is non-linear and its search space is highly discontinuous and multi-modal. Ant Colony System (ACS) is an optimization algorithm based on artificial ants that uses the reinforcement learning technique. The ACS was originally developed to solve the Traveling Salesman Problem (TSP), which is conceptually similar to the nuclear core fuel reload problem. In this work a parallel computational system based on the ACS, called Artificial Ant Colony Networks is introduced to solve the core fuel reload optimization problem.

  17. GIS-BASED ROUTE FINDING USING ANT COLONY OPTIMIZATION AND URBAN TRAFFIC DATA FROM DIFFERENT SOURCES

    Directory of Open Access Journals (Sweden)

    M. Davoodi

    2015-12-01

    Full Text Available Nowadays traffic data is obtained from multiple sources including GPS, Video Vehicle Detectors (VVD, Automatic Number Plate Recognition (ANPR, Floating Car Data (FCD, VANETs, etc. All such data can be used for route finding. This paper proposes a model for finding the optimum route based on the integration of traffic data from different sources. Ant Colony Optimization is applied in this paper because the concept of this method (movement of ants in a network is similar to urban road network and movements of cars. The results indicate that this model is capable of incorporating data from different sources, which may even be inconsistent.

  18. Gis-Based Route Finding Using ANT Colony Optimization and Urban Traffic Data from Different Sources

    Science.gov (United States)

    Davoodi, M.; Mesgari, M. S.

    2015-12-01

    Nowadays traffic data is obtained from multiple sources including GPS, Video Vehicle Detectors (VVD), Automatic Number Plate Recognition (ANPR), Floating Car Data (FCD), VANETs, etc. All such data can be used for route finding. This paper proposes a model for finding the optimum route based on the integration of traffic data from different sources. Ant Colony Optimization is applied in this paper because the concept of this method (movement of ants in a network) is similar to urban road network and movements of cars. The results indicate that this model is capable of incorporating data from different sources, which may even be inconsistent.

  19. Apiculture and Bee Health in Central Sweden

    OpenAIRE

    Larne, Olof

    2014-01-01

    Pollination necessary for the agricultural crop production affects the functions of the ecosystems on earth. In landscapes where wild pollinators are decreasing, honey bees promote the maintenance of plant species, therefore honey bee losses are of great concern. Current honey bee colony losses (Apis mellifera) worldwide are caused by Colony collapse disorder, the mite Varroa destructor and pesticides. This results in the honey bees weakened immune defenses making them susceptible to differen...

  20. A Honey Bee Foraging approach for optimal location of a biomass power plant

    Energy Technology Data Exchange (ETDEWEB)

    Vera, David; Jurado, Francisco [Dept. of Electrical Engineering, University of Jaen, 23700 EPS Linares, Jaen (Spain); Carabias, Julio; Ruiz-Reyes, Nicolas [Dept. of Telecommunication Engineering, University of Jaen, 23700 EPS Linares, Jaen (Spain)

    2010-07-15

    Over eight million hectares of olive trees are cultivated worldwide, especially in Mediterranean countries, where more than 97% of the world's olive oil is produced. The three major olive oil producers worldwide are Spain, Italy, and Greece. Olive tree pruning residues are an autochthonous and important renewable source that, in most of cases, farmers burn through an uncontrolled manner. Besides, industrial uses have not yet been developed. The aim of this paper consists of a new calculation tool based on particles swarm (Binary Honey Bee Foraging, BHBF). Effectively, this approach will make possible to determine the optimal location, biomass supply area and power plant size that offer the best profitability for investor. Moreover, it prevents the accurate method (not feasible from computational viewpoint). In this work, Profitability Index (PI) is set as the fitness function for the BHBF approach. Results are compared with other evolutionary optimization algorithms such as Binary Particle Swarm Optimization (BPSO), and Genetic Algorithms (GA). All the experiments have shown that the optimal plant size is 2 MW, PI = 3.3122, the best location corresponds to coordinate: X = 49, Y = 97 and biomass supply area is 161.33 km{sup 2}. The simulation times have been reduced to the ninth of time than the greedy (accurate) solution. Matlab registered is used to run all simulations. (author)

  1. A universal optimization strategy for ant colony optimization algorithms based on the Physarum-inspired mathematical model

    International Nuclear Information System (INIS)

    Zhang, Zili; Gao, Chao; Liu, Yuxin; Qian, Tao

    2014-01-01

    Ant colony optimization (ACO) algorithms often fall into the local optimal solution and have lower search efficiency for solving the travelling salesman problem (TSP). According to these shortcomings, this paper proposes a universal optimization strategy for updating the pheromone matrix in the ACO algorithms. The new optimization strategy takes advantages of the unique feature of critical paths reserved in the process of evolving adaptive networks of the Physarum-inspired mathematical model (PMM). The optimized algorithms, denoted as PMACO algorithms, can enhance the amount of pheromone in the critical paths and promote the exploitation of the optimal solution. Experimental results in synthetic and real networks show that the PMACO algorithms are more efficient and robust than the traditional ACO algorithms, which are adaptable to solve the TSP with single or multiple objectives. Meanwhile, we further analyse the influence of parameters on the performance of the PMACO algorithms. Based on these analyses, the best values of these parameters are worked out for the TSP. (paper)

  2. Advanced Harmony Search with Ant Colony Optimization for Solving the Traveling Salesman Problem

    Directory of Open Access Journals (Sweden)

    Ho-Yoeng Yun

    2013-01-01

    Full Text Available We propose a novel heuristic algorithm based on the methods of advanced Harmony Search and Ant Colony Optimization (AHS-ACO to effectively solve the Traveling Salesman Problem (TSP. The TSP, in general, is well known as an NP-complete problem, whose computational complexity increases exponentially by increasing the number of cities. In our algorithm, Ant Colony Optimization (ACO is used to search the local optimum in the solution space, followed by the use of the Harmony Search to escape the local optimum determined by the ACO and to move towards a global optimum. Experiments were performed to validate the efficiency of our algorithm through a comparison with other algorithms and the optimum solutions presented in the TSPLIB. The results indicate that our algorithm is capable of generating the optimum solution for most instances in the TSPLIB; moreover, our algorithm found better solutions in two cases (kroB100 and pr144 when compared with the optimum solution presented in the TSPLIB.

  3. Geometry optimization of five-phase permanent magnet synchronous motors using Bees algorithm

    Directory of Open Access Journals (Sweden)

    R Ilka

    2015-12-01

    Full Text Available Among all types of electrical motors, permanent magnet synchronous motors (PMSMs are reliable and efficient motors in industrial applications. Because of their superiority over other kinds of motors, they are replacing conventional electric motors. On the other hand, high-phase PMSMs are good candidates to be used in certain industrial and military projects such as electric vehicles, spacecrafts, naval systems and etc. In these cases, the motor has to be designed with minimum volume and high torque and efficiency. Design optimization can improve their features noticeably, thus reduce volume and enhance performance of motors. In this paper, a new method for optimum design of a five-phase surface-mounted permanent magnet synchronous motor is presented to achieve minimum permanent magnets (PMs volume with an increased torque and efficiency. Design optimization is performed in search for optimum dimensions of the motor and its permanent magnets using Bees Algorithm (BA. The design optimization results in a motor with great improvement regarding the original motor which is compared with two well-known evolutionary algorithms i.e. GA and PSO. Finally, finite element method simulation is utilized to validate the accuracy of the design.

  4. Distributed Bees Algorithm Parameters Optimization for a Cost Efficient Target Allocation in Swarms of Robots

    Directory of Open Access Journals (Sweden)

    Álvaro Gutiérrez

    2011-11-01

    Full Text Available Swarms of robots can use their sensing abilities to explore unknown environments and deploy on sites of interest. In this task, a large number of robots is more effective than a single unit because of their ability to quickly cover the area. However, the coordination of large teams of robots is not an easy problem, especially when the resources for the deployment are limited. In this paper, the Distributed Bees Algorithm (DBA, previously proposed by the authors, is optimized and applied to distributed target allocation in swarms of robots. Improved target allocation in terms of deployment cost efficiency is achieved through optimization of the DBA’s control parameters by means of a Genetic Algorithm. Experimental results show that with the optimized set of parameters, the deployment cost measured as the average distance traveled by the robots is reduced. The cost-efficient deployment is in some cases achieved at the expense of increased robots’ distribution error. Nevertheless, the proposed approach allows the swarm to adapt to the operating conditions when available resources are scarce.

  5. Ant colony optimization and neural networks applied to nuclear power plant monitoring

    Energy Technology Data Exchange (ETDEWEB)

    Santos, Gean Ribeiro dos; Andrade, Delvonei Alves de; Pereira, Iraci Martinez, E-mail: gean@usp.br, E-mail: delvonei@ipen.br, E-mail: martinez@ipen.br [Instituto de Pesquisas Energeticas e Nucleares (IPEN/CNEN-SP), Sao Paulo, SP (Brazil)

    2015-07-01

    A recurring challenge in production processes is the development of monitoring and diagnosis systems. Those systems help on detecting unexpected changes and interruptions, preventing losses and mitigating risks. Artificial Neural Networks (ANNs) have been extensively used in creating monitoring systems. Usually the ANNs created to solve this kind of problem are created by taking into account only parameters as the number of inputs, outputs, and hidden layers. The result networks are generally fully connected and have no improvements in its topology. This work intends to use an Ant Colony Optimization (ACO) algorithm to create a tuned neural network. The ACO search algorithm will use Back Error Propagation (BP) to optimize the network topology by suggesting the best neuron connections. The result ANN will be applied to monitoring the IEA-R1 research reactor at IPEN. (author)

  6. Ant colony optimization and neural networks applied to nuclear power plant monitoring

    International Nuclear Information System (INIS)

    Santos, Gean Ribeiro dos; Andrade, Delvonei Alves de; Pereira, Iraci Martinez

    2015-01-01

    A recurring challenge in production processes is the development of monitoring and diagnosis systems. Those systems help on detecting unexpected changes and interruptions, preventing losses and mitigating risks. Artificial Neural Networks (ANNs) have been extensively used in creating monitoring systems. Usually the ANNs created to solve this kind of problem are created by taking into account only parameters as the number of inputs, outputs, and hidden layers. The result networks are generally fully connected and have no improvements in its topology. This work intends to use an Ant Colony Optimization (ACO) algorithm to create a tuned neural network. The ACO search algorithm will use Back Error Propagation (BP) to optimize the network topology by suggesting the best neuron connections. The result ANN will be applied to monitoring the IEA-R1 research reactor at IPEN. (author)

  7. Transfer function fitting using a continuous Ant Colony Optimization (ACO algorithm

    Directory of Open Access Journals (Sweden)

    A. Reineix

    2015-03-01

    Full Text Available An original approach is proposed in order to achieve the  fitting of ultra-wideband complex frequency functions, such  as the complex impedances, by using the so-called ACO  (Ant Colony Optimization methods. First, we present the  optimization principle of ACO, which originally was  dedicated to the combinatorial problems. Further on, the  extension to the continuous and mixed problems is  explained in more details. The interest in this approach is  proved by its ability to define practical constraints and  objectives, such as minimizing the number of filters used in  the model with respect to a fixed relative error. Finally, the  establishment of the model for the first and second order  filter types illustrates the power of the method and its  interest for the time-domain electromagnetic computation.

  8. Cloud computing task scheduling strategy based on differential evolution and ant colony optimization

    Science.gov (United States)

    Ge, Junwei; Cai, Yu; Fang, Yiqiu

    2018-05-01

    This paper proposes a task scheduling strategy DEACO based on the combination of Differential Evolution (DE) and Ant Colony Optimization (ACO), aiming at the single problem of optimization objective in cloud computing task scheduling, this paper combines the shortest task completion time, cost and load balancing. DEACO uses the solution of the DE to initialize the initial pheromone of ACO, reduces the time of collecting the pheromone in ACO in the early, and improves the pheromone updating rule through the load factor. The proposed algorithm is simulated on cloudsim, and compared with the min-min and ACO. The experimental results show that DEACO is more superior in terms of time, cost, and load.

  9. An Effective Hybrid Routing Algorithm in WSN: Ant Colony Optimization in combination with Hop Count Minimization

    Directory of Open Access Journals (Sweden)

    Ailian Jiang

    2018-03-01

    Full Text Available Low cost, high reliability and easy maintenance are key criteria in the design of routing protocols for wireless sensor networks (WSNs. This paper investigates the existing ant colony optimization (ACO-based WSN routing algorithms and the minimum hop count WSN routing algorithms by reviewing their strengths and weaknesses. We also consider the critical factors of WSNs, such as energy constraint of sensor nodes, network load balancing and dynamic network topology. Then we propose a hybrid routing algorithm that integrates ACO and a minimum hop count scheme. The proposed algorithm is able to find the optimal routing path with minimal total energy consumption and balanced energy consumption on each node. The algorithm has unique superiority in terms of searching for the optimal path, balancing the network load and the network topology maintenance. The WSN model and the proposed algorithm have been implemented using C++. Extensive simulation experimental results have shown that our algorithm outperforms several other WSN routing algorithms on such aspects that include the rate of convergence, the success rate in searching for global optimal solution, and the network lifetime.

  10. A Hybrid Ant Colony Optimization Algorithm for the Extended Capacitated Arc Routing Problem.

    Science.gov (United States)

    Li-Ning Xing; Rohlfshagen, P; Ying-Wu Chen; Xin Yao

    2011-08-01

    The capacitated arc routing problem (CARP) is representative of numerous practical applications, and in order to widen its scope, we consider an extended version of this problem that entails both total service time and fixed investment costs. We subsequently propose a hybrid ant colony optimization (ACO) algorithm (HACOA) to solve instances of the extended CARP. This approach is characterized by the exploitation of heuristic information, adaptive parameters, and local optimization techniques: Two kinds of heuristic information, arc cluster information and arc priority information, are obtained continuously from the solutions sampled to guide the subsequent optimization process. The adaptive parameters ease the burden of choosing initial values and facilitate improved and more robust results. Finally, local optimization, based on the two-opt heuristic, is employed to improve the overall performance of the proposed algorithm. The resulting HACOA is tested on four sets of benchmark problems containing a total of 87 instances with up to 140 nodes and 380 arcs. In order to evaluate the effectiveness of the proposed method, some existing capacitated arc routing heuristics are extended to cope with the extended version of this problem; the experimental results indicate that the proposed ACO method outperforms these heuristics.

  11. Research of Ant Colony Optimized Adaptive Control Strategy for Hybrid Electric Vehicle

    Directory of Open Access Journals (Sweden)

    Linhui Li

    2014-01-01

    Full Text Available Energy management control strategy of hybrid electric vehicle has a great influence on the vehicle fuel consumption with electric motors adding to the traditional vehicle power system. As vehicle real driving cycles seem to be uncertain, the dynamic driving cycles will have an impact on control strategy’s energy-saving effect. In order to better adapt the dynamic driving cycles, control strategy should have the ability to recognize the real-time driving cycle and adaptively adjust to the corresponding off-line optimal control parameters. In this paper, four types of representative driving cycles are constructed based on the actual vehicle operating data, and a fuzzy driving cycle recognition algorithm is proposed for online recognizing the type of actual driving cycle. Then, based on the equivalent fuel consumption minimization strategy, an ant colony optimization algorithm is utilized to search the optimal control parameters “charge and discharge equivalent factors” for each type of representative driving cycle. At last, the simulation experiments are conducted to verify the accuracy of the proposed fuzzy recognition algorithm and the validity of the designed control strategy optimization method.

  12. A Stochastic Inversion Method for Potential Field Data: Ant Colony Optimization

    Science.gov (United States)

    Liu, Shuang; Hu, Xiangyun; Liu, Tianyou

    2014-07-01

    Simulating natural ants' foraging behavior, the ant colony optimization (ACO) algorithm performs excellently in combinational optimization problems, for example the traveling salesman problem and the quadratic assignment problem. However, the ACO is seldom used to inverted for gravitational and magnetic data. On the basis of the continuous and multi-dimensional objective function for potential field data optimization inversion, we present the node partition strategy ACO (NP-ACO) algorithm for inversion of model variables of fixed shape and recovery of physical property distributions of complicated shape models. We divide the continuous variables into discrete nodes and ants directionally tour the nodes by use of transition probabilities. We update the pheromone trails by use of Gaussian mapping between the objective function value and the quantity of pheromone. It can analyze the search results in real time and promote the rate of convergence and precision of inversion. Traditional mapping, including the ant-cycle system, weaken the differences between ant individuals and lead to premature convergence. We tested our method by use of synthetic data and real data from scenarios involving gravity and magnetic anomalies. The inverted model variables and recovered physical property distributions were in good agreement with the true values. The ACO algorithm for binary representation imaging and full imaging can recover sharper physical property distributions than traditional linear inversion methods. The ACO has good optimization capability and some excellent characteristics, for example robustness, parallel implementation, and portability, compared with other stochastic metaheuristics.

  13. Design of the smart home system based on the optimal routing algorithm and ZigBee network.

    Directory of Open Access Journals (Sweden)

    Dengying Jiang

    Full Text Available To improve the traditional smart home system, its electric wiring, networking technology, information transmission and facility control are studied. In this paper, we study the electric wiring, networking technology, information transmission and facility control to improve the traditional smart home system. First, ZigBee is used to replace the traditional electric wiring. Second, a network is built to connect lots of wireless sensors and facilities, thanks to the capability of ZigBee self-organized network and Genetic Algorithm-Particle Swarm Optimization Algorithm (GA-PSOA to search for the optimal route. Finally, when the smart home system is connected to the internet based on the remote server technology, home environment and facilities could be remote real-time controlled. The experiments show that the GA-PSOA reduce the system delay and decrease the energy consumption of the wireless system.

  14. Design of the smart home system based on the optimal routing algorithm and ZigBee network.

    Science.gov (United States)

    Jiang, Dengying; Yu, Ling; Wang, Fei; Xie, Xiaoxia; Yu, Yongsheng

    2017-01-01

    To improve the traditional smart home system, its electric wiring, networking technology, information transmission and facility control are studied. In this paper, we study the electric wiring, networking technology, information transmission and facility control to improve the traditional smart home system. First, ZigBee is used to replace the traditional electric wiring. Second, a network is built to connect lots of wireless sensors and facilities, thanks to the capability of ZigBee self-organized network and Genetic Algorithm-Particle Swarm Optimization Algorithm (GA-PSOA) to search for the optimal route. Finally, when the smart home system is connected to the internet based on the remote server technology, home environment and facilities could be remote real-time controlled. The experiments show that the GA-PSOA reduce the system delay and decrease the energy consumption of the wireless system.

  15. AUTOMATA PROGRAMS CONSTRUCTION FROM SPECIFICATION WITH AN ANT COLONY OPTIMIZATION ALGORITHM BASED ON MUTATION GRAPH

    Directory of Open Access Journals (Sweden)

    Daniil S. Chivilikhin

    2014-11-01

    Full Text Available The procedure of testing traditionally used in software engineering cannot guarantee program correctness; therefore verification is used at the excess requirements to programs reliability. Verification makes it possible to check certain properties of programs in all possible computational states; however, this process is very complex. In the model checking method a model of the program is built (often, manually and requirements in terms of temporal logic are formulated. Such temporal properties of the model can be checked automatically. The main issue in this framework is the gap between the program and its model. Automata-based programming paradigm gives the possibility to overcome this limitation. In this paradigm, program logic is represented using finite-state machines. The advantage of finite-state machines is that their models can be constructed automatically. The paper deals with the application of mutation-based ant colony optimization algorithm to the problem of finite-state machine construction from their specification, defined by test scenarios and temporal properties. The presented approach has been tested on the elevator doors control problem as well as on randomly generated data. Obtained results show the ant colony algorithm is two-three times faster than the previously used genetic algorithm. The proposed approach can be recommended for inferring control programs for critical systems.

  16. Distributed optimization using ant colony optimization in a concrete delivery supply chain

    NARCIS (Netherlands)

    Faria, J.M.; Silva, C.A.; Costa Sousa, da J.M.; Surico, M.; Kaymak, U.

    2006-01-01

    The timely production and distribution of rapidly perishable goods such as ready-mixed concrete is a complex combinatorial optimization problem in the context of supply chain management. The problem involves several tightly interrelated scheduling and routing problems that have to be solved

  17. A modified artificial bee colony based on chaos theory for solving non-convex emission/economic dispatch

    International Nuclear Information System (INIS)

    Shayeghi, H.; Ghasemi, A.

    2014-01-01

    Highlights: • This paper presents a developed multi objective CIABC based on CLS theory for solving EED problem. • The EED problem is formulated as a non-convex multi objective optimization problem. • Considered three test systems to demonstrate its efficiency including practical constrains. • The significant improvement in the results comparing the reported literature. - Abstract: In this paper, a modified ABC based on chaos theory namely CIABC is comprehensively enhanced and effectively applied for solving a multi-objective EED problem to minimize three conflicting objective functions with non-smooth and non-convex generator fuel cost characteristics while satisfying the operation constraints. The proposed method uses a Chaotic Local Search (CLS) to enhance the self searching ability of the original ABC algorithm for finding feasible optimal solutions of the EED problem. Also, many linear and nonlinear constraints, such as generation limits, transmission line loss, security constraints and non-smooth cost functions are considered as dynamic operational constraints. Moreover, a method based on fuzzy set theory is employed to extract one of the Pareto-optimal solutions as the best compromise one. The proposed multi objective evolutionary method has been applied to the standard IEEE 30 bus six generators, fourteen generators and 40 thermal generating units, respectively, as small, medium and large test power system. The numerical results obtained with the proposed method based on tables and figures compared with other evolutionary algorithm of scientific literatures. The results regards that the proposed CIABC algorithm surpasses the other available methods in terms of computational efficiency and solution quality

  18. An approach using quantum ant colony optimization applied to the problem of nuclear reactors reload

    International Nuclear Information System (INIS)

    Silva, Marcio H.; Lima, Alan M.M. de; Schirru, Roberto; Medeiros, J.A.C.C.

    2009-01-01

    The basic concept behind the nuclear reactor fuel reloading problem is to find a configuration of new and used fuel elements, to keep the plant working at full power by the largest possible duration, within the safety restrictions. The main restriction is the power peaking factor, which is the limit value for the preservation of the fuel assembly. The QACO A lfa algorithm is a modified version of Quantum Ant Colony Optimization (QACO) proposed by Wang et al, which uses a new actualization method and a pseudo evaporation step. We examined the QACO A lfa behavior associated to physics of reactors code RECNOD when applied to this problem. Although the QACO have been developed for continuous functions, the binary model used in this work allows applying it to discrete problems, such as the mentioned above. (author)

  19. The Lobe Fissure Tracking by the Modified Ant Colony Optimization Framework in CT Images

    Directory of Open Access Journals (Sweden)

    Chii-Jen Chen

    2014-11-01

    Full Text Available Chest computed tomography (CT is the most commonly used technique for the inspection of lung lesions. However, the lobe fissures in lung CT is still difficult to observe owing to its imaging structure. Therefore, in this paper, we aimed to develop an efficient tracking framework to extract the lobe fissures by the proposed modified ant colony optimization (ACO algorithm. We used the method of increasing the consistency of pheromone on lobe fissure to improve the accuracy of path tracking. In order to validate the proposed system, we had tested our method in a database from 15 lung patients. In the experiment, the quantitative assessment shows that the proposed ACO method achieved the average F-measures of 80.9% and 82.84% in left and right lungs, respectively. The experiments indicate our method results more satisfied performance, and can help investigators detect lung lesion for further examination.

  20. Intelligent Hypothermia Care System using Ant ‎Colony Optimization for Rules Prediction

    Directory of Open Access Journals (Sweden)

    Hayder Naser Khraibet

    2017-12-01

    Full Text Available Intelligent Hypothermia Care System (IHCS is an intelligence system uses set of methodologies, algorithms, architectures and processes to determine where patients in a postoperative recovery area must be sent. Hypothermia is a significant concern after surgery. This paper utilizes the classification task in data mining to propose an intelligent technique to predict where to send a patient after surgery: intensive care unit, general floor or home. To achieve this goal, this paper evaluates the performance of decision tree algorithm, exemplifying the deterministic approach, against the AntMiner algorithm, exemplifying the heuristic approach, to choose the best approach in detecting the patient’s status. Results show the outperformance of the heuristic approach. The implication of this proposal will be twofold: in hypothermia treatment and in the application of ant colony optimization

  1. Solving optimum operation of single pump unit problem with ant colony optimization (ACO) algorithm

    International Nuclear Information System (INIS)

    Yuan, Y; Liu, C

    2012-01-01

    For pumping stations, the effective scheduling of daily pump operations from solutions to the optimum design operation problem is one of the greatest potential areas for energy cost-savings, there are some difficulties in solving this problem with traditional optimization methods due to the multimodality of the solution region. In this case, an ACO model for optimum operation of pumping unit is proposed and the solution method by ants searching is presented by rationally setting the object function and constrained conditions. A weighted directed graph was constructed and feasible solutions may be found by iteratively searching of artificial ants, and then the optimal solution can be obtained by applying the rule of state transition and the pheromone updating. An example calculation was conducted and the minimum cost was found as 4.9979. The result of ant colony algorithm was compared with the result from dynamic programming or evolutionary solving method in commercial software under the same discrete condition. The result of ACO is better and the computing time is shorter which indicates that ACO algorithm can provide a high application value to the field of optimal operation of pumping stations and related fields.

  2. Meta-Heuristics in Short Scale Construction: Ant Colony Optimization and Genetic Algorithm.

    Science.gov (United States)

    Schroeders, Ulrich; Wilhelm, Oliver; Olaru, Gabriel

    2016-01-01

    The advent of large-scale assessment, but also the more frequent use of longitudinal and multivariate approaches to measurement in psychological, educational, and sociological research, caused an increased demand for psychometrically sound short scales. Shortening scales economizes on valuable administration time, but might result in inadequate measures because reducing an item set could: a) change the internal structure of the measure, b) result in poorer reliability and measurement precision, c) deliver measures that cannot effectively discriminate between persons on the intended ability spectrum, and d) reduce test-criterion relations. Different approaches to abbreviate measures fare differently with respect to the above-mentioned problems. Therefore, we compare the quality and efficiency of three item selection strategies to derive short scales from an existing long version: a Stepwise COnfirmatory Factor Analytical approach (SCOFA) that maximizes factor loadings and two metaheuristics, specifically an Ant Colony Optimization (ACO) with a tailored user-defined optimization function and a Genetic Algorithm (GA) with an unspecific cost-reduction function. SCOFA compiled short versions were highly reliable, but had poor validity. In contrast, both metaheuristics outperformed SCOFA and produced efficient and psychometrically sound short versions (unidimensional, reliable, sensitive, and valid). We discuss under which circumstances ACO and GA produce equivalent results and provide recommendations for conditions in which it is advisable to use a metaheuristic with an unspecific out-of-the-box optimization function.

  3. An approach using quantum ant colony optimization applied to the problem of identification of nuclear power plant transients

    International Nuclear Information System (INIS)

    Silva, Marcio H.; Schirru, Roberto; Medeiros, Jose A.C.C.

    2009-01-01

    Using concepts and principles of the quantum computation, as the quantum bit and superposition of states, coupled with the biological metaphor of a colony of ants, used in the Ant Colony Optimization algorithm (ACO), Wang et al developed the Quantum Ant Colony Optimization (QACO). In this paper we present a modification of the algorithm proposed by Wang et al. While the original QACO was used just for simple benchmarks functions with, at the most, two dimensions, QACO A lfa was developed for application where the original QACO, due to its tendency to converge prematurely, does not obtain good results, as in complex multidimensional functions. Furthermore, to evaluate its behavior, both algorithms are applied to the real problem of identification of accidents in PWR nuclear power plants. (author)

  4. Fuzzy Random λ-Mean SAD Portfolio Selection Problem: An Ant Colony Optimization Approach

    Science.gov (United States)

    Thakur, Gour Sundar Mitra; Bhattacharyya, Rupak; Mitra, Swapan Kumar

    2010-10-01

    To reach the investment goal, one has to select a combination of securities among different portfolios containing large number of securities. Only the past records of each security do not guarantee the future return. As there are many uncertain factors which directly or indirectly influence the stock market and there are also some newer stock markets which do not have enough historical data, experts' expectation and experience must be combined with the past records to generate an effective portfolio selection model. In this paper the return of security is assumed to be Fuzzy Random Variable Set (FRVS), where returns are set of random numbers which are in turn fuzzy numbers. A new λ-Mean Semi Absolute Deviation (λ-MSAD) portfolio selection model is developed. The subjective opinions of the investors to the rate of returns of each security are taken into consideration by introducing a pessimistic-optimistic parameter vector λ. λ-Mean Semi Absolute Deviation (λ-MSAD) model is preferred as it follows absolute deviation of the rate of returns of a portfolio instead of the variance as the measure of the risk. As this model can be reduced to Linear Programming Problem (LPP) it can be solved much faster than quadratic programming problems. Ant Colony Optimization (ACO) is used for solving the portfolio selection problem. ACO is a paradigm for designing meta-heuristic algorithms for combinatorial optimization problem. Data from BSE is used for illustration.

  5. Integrating geological uncertainty in long-term open pit mine production planning by ant colony optimization

    Science.gov (United States)

    Gilani, Seyed-Omid; Sattarvand, Javad

    2016-02-01

    Meeting production targets in terms of ore quantity and quality is critical for a successful mining operation. In-situ grade uncertainty causes both deviations from production targets and general financial deficits. A new stochastic optimization algorithm based on ant colony optimization (ACO) approach is developed herein to integrate geological uncertainty described through a series of the simulated ore bodies. Two different strategies were developed based on a single predefined probability value (Prob) and multiple probability values (Pro bnt) , respectively in order to improve the initial solutions that created by deterministic ACO procedure. Application at the Sungun copper mine in the northwest of Iran demonstrate the abilities of the stochastic approach to create a single schedule and control the risk of deviating from production targets over time and also increase the project value. A comparison between two strategies and traditional approach illustrates that the multiple probability strategy is able to produce better schedules, however, the single predefined probability is more practical in projects requiring of high flexibility degree.

  6. The density of feral honey bee (Apis mellifera) colonies in South East Australia is greater in undisturbed than in disturbed habitats

    OpenAIRE

    Hinson , Eloise M.; Duncan , Michael; Lim , Julianne; Arundel , Jonathan; Oldroyd , Benjamin P.

    2015-01-01

    International audience; AbstractApis mellifera is an important pollinator but is sometimes associated with adverse effects on natural ecosystems. We surveyed pairs of disturbed and undisturbed sites across three biomes in South East Australia. We used pheromone lures to trap drones, genotyped the drones to infer the number of colonies within flight range and then estimated colony densities using synthetic sampling distributions. Estimated colony densities ranged from 0.1 to 1.5 colonies km−2 ...

  7. The modification of hybrid method of ant colony optimization, particle swarm optimization and 3-OPT algorithm in traveling salesman problem

    Science.gov (United States)

    Hertono, G. F.; Ubadah; Handari, B. D.

    2018-03-01

    The traveling salesman problem (TSP) is a famous problem in finding the shortest tour to visit every vertex exactly once, except the first vertex, given a set of vertices. This paper discusses three modification methods to solve TSP by combining Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO) and 3-Opt Algorithm. The ACO is used to find the solution of TSP, in which the PSO is implemented to find the best value of parameters α and β that are used in ACO.In order to reduce the total of tour length from the feasible solution obtained by ACO, then the 3-Opt will be used. In the first modification, the 3-Opt is used to reduce the total tour length from the feasible solutions obtained at each iteration, meanwhile, as the second modification, 3-Opt is used to reduce the total tour length from the entire solution obtained at every iteration. In the third modification, 3-Opt is used to reduce the total tour length from different solutions obtained at each iteration. Results are tested using 6 benchmark problems taken from TSPLIB by calculating the relative error to the best known solution as well as the running time. Among those modifications, only the second and third modification give satisfactory results except the second one needs more execution time compare to the third modifications.

  8. Bee Hive management and colonisation: a practical approach ...

    African Journals Online (AJOL)

    The managerial issues include the method of approaching the bees and hives, feeding of the bees and prevention of predators. Exploitation of the colony for bee products is usually done with special tools that ensure no disturbance of the inhabitants while also protecting the harvester. The market for bee products varies ...

  9. Assessing grooming behavior of Russian honey bees toward Varroa destructor.

    Science.gov (United States)

    The grooming behavior of Russian bees was compared to Italian bees. Overall, Russian bees had significantly lower numbers of mites than the Italian bees with a mean of 1,937 ± 366 and 5,088 ± 733 mites, respectively. This low mite population in the Russian colonies was probably due to the increased ...

  10. Estimating the net electricity energy generation and demand using the ant colony optimization approach. Case of Turkey

    International Nuclear Information System (INIS)

    Toksari, M. Duran

    2009-01-01

    This paper presents Turkey's net electricity energy generation and demand based on economic indicators. Forecasting model for electricity energy generation and demand is first proposed by the ant colony optimization (ACO) approach. It is multi-agent system in which the behavior of each ant is inspired by the foraging behavior of real ants to solve optimization problem. Ant colony optimization electricity energy estimation (ACOEEE) model is developed using population, gross domestic product (GDP), import and export. All equations proposed here are linear electricity energy generation and demand (linear A COEEGE and linear ACOEEDE) and quadratic energy generation and demand (quadratic A COEEGE and quadratic ACOEEDE). Quadratic models for both generation and demand provided better fit solution due to the fluctuations of the economic indicators. The ACOEEGE and ACOEEDE models indicate Turkey's net electricity energy generation and demand until 2025 according to three scenarios. (author)

  11. A Multipath Routing Protocol Based on Clustering and Ant Colony Optimization for Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Jing Yang

    2010-05-01

    Full Text Available For monitoring burst events in a kind of reactive wireless sensor networks (WSNs, a multipath routing protocol (MRP based on dynamic clustering and ant colony optimization (ACO is proposed.. Such an approach can maximize the network lifetime and reduce the energy consumption. An important attribute of WSNs is their limited power supply, and therefore some metrics (such as energy consumption of communication among nodes, residual energy, path length were considered as very important criteria while designing routing in the MRP. Firstly, a cluster head (CH is selected among nodes located in the event area according to some parameters, such as residual energy. Secondly, an improved ACO algorithm is applied in the search for multiple paths between the CH and sink node. Finally, the CH dynamically chooses a route to transmit data with a probability that depends on many path metrics, such as energy consumption. The simulation results show that MRP can prolong the network lifetime, as well as balance of energy consumption among nodes and reduce the average energy consumption effectively.

  12. Offensive Strategy in the 2D Soccer Simulation League Using Multi-Group Ant Colony Optimization

    Directory of Open Access Journals (Sweden)

    Shengbing Chen

    2016-02-01

    Full Text Available The 2D soccer simulation league is one of the best test beds for the research of artificial intelligence (AI. It has achieved great successes in the domain of multi-agent cooperation and machine learning. However, the problem of integral offensive strategy has not been solved because of the dynamic and unpredictable nature of the environment. In this paper, we present a novel offensive strategy based on multi-group ant colony optimization (MACO-OS. The strategy uses the pheromone evaporation mechanism to count the preference value of each attack action in different environments, and saves the values of success rate and preference in an attack information tree in the background. The decision module of the attacker then selects the best attack action according to the preference value. The MACO-OS approach has been successfully implemented in our 2D soccer simulation team in RoboCup competitions. The experimental results have indicated that the agents developed with this strategy, along with related techniques, delivered outstanding performances.

  13. Ant Colony Optimization Algorithm for Centralized Dynamic Channel Allocation in Multi-Cell OFDMA Systems

    Science.gov (United States)

    Kim, Hyo-Su; Kim, Dong-Hoi

    The dynamic channel allocation (DCA) scheme in multi-cell systems causes serious inter-cell interference (ICI) problem to some existing calls when channels for new calls are allocated. Such a problem can be addressed by advanced centralized DCA design that is able to minimize ICI. Thus, in this paper, a centralized DCA is developed for the downlink of multi-cell orthogonal frequency division multiple access (OFDMA) systems with full spectral reuse. However, in practice, as the search space of channel assignment for centralized DCA scheme in multi-cell systems grows exponentially with the increase of the number of required calls, channels, and cells, it becomes an NP-hard problem and is currently too complicated to find an optimum channel allocation. In this paper, we propose an ant colony optimization (ACO) based DCA scheme using a low-complexity ACO algorithm which is a kind of heuristic algorithm in order to solve the aforementioned problem. Simulation results demonstrate significant performance improvements compared to the existing schemes in terms of the grade of service (GoS) performance and the forced termination probability of existing calls without degrading the system performance of the average throughput.

  14. Automatic boiling water reactor loading pattern design using ant colony optimization algorithm

    Energy Technology Data Exchange (ETDEWEB)

    Wang, C.-D. [Department of Engineering and System Science, National Tsing Hua University, 101, Section 2 Kuang Fu Road, Hsinchu 30013, Taiwan (China); Nuclear Engineering Division, Institute of Nuclear Energy Research, No. 1000, Wenhua Rd., Jiaan Village, Longtan Township, Taoyuan County 32546, Taiwan (China)], E-mail: jdwang@iner.gov.tw; Lin Chaung [Department of Engineering and System Science, National Tsing Hua University, 101, Section 2 Kuang Fu Road, Hsinchu 30013, Taiwan (China)

    2009-08-15

    An automatic boiling water reactor (BWR) loading pattern (LP) design methodology was developed using the rank-based ant system (RAS), which is a variant of the ant colony optimization (ACO) algorithm. To reduce design complexity, only the fuel assemblies (FAs) of one eight-core positions were determined using the RAS algorithm, and then the corresponding FAs were loaded into the other parts of the core. Heuristic information was adopted to exclude the selection of the inappropriate FAs which will reduce search space, and thus, the computation time. When the LP was determined, Haling cycle length, beginning of cycle (BOC) shutdown margin (SDM), and Haling end of cycle (EOC) maximum fraction of limit for critical power ratio (MFLCPR) were calculated using SIMULATE-3 code, which were used to evaluate the LP for updating pheromone of RAS. The developed design methodology was demonstrated using FAs of a reference cycle of the BWR6 nuclear power plant. The results show that, the designed LP can be obtained within reasonable computation time, and has a longer cycle length than that of the original design.

  15. An ant colony optimization based feature selection for web page classification.

    Science.gov (United States)

    Saraç, Esra; Özel, Selma Ayşe

    2014-01-01

    The increased popularity of the web has caused the inclusion of huge amount of information to the web, and as a result of this explosive information growth, automated web page classification systems are needed to improve search engines' performance. Web pages have a large number of features such as HTML/XML tags, URLs, hyperlinks, and text contents that should be considered during an automated classification process. The aim of this study is to reduce the number of features to be used to improve runtime and accuracy of the classification of web pages. In this study, we used an ant colony optimization (ACO) algorithm to select the best features, and then we applied the well-known C4.5, naive Bayes, and k nearest neighbor classifiers to assign class labels to web pages. We used the WebKB and Conference datasets in our experiments, and we showed that using the ACO for feature selection improves both accuracy and runtime performance of classification. We also showed that the proposed ACO based algorithm can select better features with respect to the well-known information gain and chi square feature selection methods.

  16. Proposed algorithm to improve job shop production scheduling using ant colony optimization method

    Science.gov (United States)

    Pakpahan, Eka KA; Kristina, Sonna; Setiawan, Ari

    2017-12-01

    This paper deals with the determination of job shop production schedule on an automatic environment. On this particular environment, machines and material handling system are integrated and controlled by a computer center where schedule were created and then used to dictate the movement of parts and the operations at each machine. This setting is usually designed to have an unmanned production process for a specified interval time. We consider here parts with various operations requirement. Each operation requires specific cutting tools. These parts are to be scheduled on machines each having identical capability, meaning that each machine is equipped with a similar set of cutting tools therefore is capable of processing any operation. The availability of a particular machine to process a particular operation is determined by the remaining life time of its cutting tools. We proposed an algorithm based on the ant colony optimization method and embedded them on matlab software to generate production schedule which minimize the total processing time of the parts (makespan). We test the algorithm on data provided by real industry and the process shows a very short computation time. This contributes a lot to the flexibility and timelines targeted on an automatic environment.

  17. Intelligent method for diagnosing structural faults of rotating machinery using ant colony optimization.

    Science.gov (United States)

    Li, Ke; Chen, Peng

    2011-01-01

    Structural faults, such as unbalance, misalignment and looseness, etc., often occur in the shafts of rotating machinery. These faults may cause serious machine accidents and lead to great production losses. This paper proposes an intelligent method for diagnosing structural faults of rotating machinery using ant colony optimization (ACO) and relative ratio symptom parameters (RRSPs) in order to detect faults and distinguish fault types at an early stage. New symptom parameters called "relative ratio symptom parameters" are defined for reflecting the features of vibration signals measured in each state. Synthetic detection index (SDI) using statistical theory has also been defined to evaluate the applicability of the RRSPs. The SDI can be used to indicate the fitness of a RRSP for ACO. Lastly, this paper also compares the proposed method with the conventional neural networks (NN) method. Practical examples of fault diagnosis for a centrifugal fan are provided to verify the effectiveness of the proposed method. The verification results show that the structural faults often occurring in the centrifugal fan, such as unbalance, misalignment and looseness states are effectively identified by the proposed method, while these faults are difficult to detect using conventional neural networks.

  18. A sequential fuzzy diagnosis method for rotating machinery using ant colony optimization and possibility theory

    Energy Technology Data Exchange (ETDEWEB)

    Sun, Hao; Ping, Xueliang; Cao, Yi; Lie, Ke [Jiangnan University, Wuxi (China); Chen, Peng [Mie University, Mie (Japan); Wang, Huaqing [Beijing University, Beijing (China)

    2014-04-15

    This study proposes a novel intelligent fault diagnosis method for rotating machinery using ant colony optimization (ACO) and possibility theory. The non-dimensional symptom parameters (NSPs) in the frequency domain are defined to reflect the features of the vibration signals measured in each state. A sensitive evaluation method for selecting good symptom parameters using principal component analysis (PCA) is proposed for detecting and distinguishing faults in rotating machinery. By using ACO clustering algorithm, the synthesizing symptom parameters (SSP) for condition diagnosis are obtained. A fuzzy diagnosis method using sequential inference and possibility theory is also proposed, by which the conditions of the machinery can be identified sequentially. Lastly, the proposed method is compared with a conventional neural networks (NN) method. Practical examples of diagnosis for a V-belt driving equipment used in a centrifugal fan are provided to verify the effectiveness of the proposed method. The results verify that the faults that often occur in V-belt driving equipment, such as a pulley defect state, a belt defect state and a belt looseness state, are effectively identified by the proposed method, while these faults are difficult to detect using conventional NN.

  19. Viral diseases in honey bee queens

    DEFF Research Database (Denmark)

    Francis, Roy Mathew

    Honey bees are important insects for human welfare, due to pollination as well as honey production. Viral diseases strongly impact honey bee health, especially since the spread of varroa mites. This dissertation deals with the interactions between honey bees, viruses and varroa mites. A new tool...... was developed to diagnose three viruses in honey bees. Quantitative PCR was used to investigate the distribution of two popular viruses in five different tissues of 86 honey bee queens. Seasonal variation of viral infection in honey bee workers and varroa mites were determined by sampling 23 colonies under...

  20. Ant colony optimization algorithm for interpretable Bayesian classifiers combination: application to medical predictions.

    Directory of Open Access Journals (Sweden)

    Salah Bouktif

    Full Text Available Prediction and classification techniques have been well studied by machine learning researchers and developed for several real-word problems. However, the level of acceptance and success of prediction models are still below expectation due to some difficulties such as the low performance of prediction models when they are applied in different environments. Such a problem has been addressed by many researchers, mainly from the machine learning community. A second problem, principally raised by model users in different communities, such as managers, economists, engineers, biologists, and medical practitioners, etc., is the prediction models' interpretability. The latter is the ability of a model to explain its predictions and exhibit the causality relationships between the inputs and the outputs. In the case of classification, a successful way to alleviate the low performance is to use ensemble classiers. It is an intuitive strategy to activate collaboration between different classifiers towards a better performance than individual classier. Unfortunately, ensemble classifiers method do not take into account the interpretability of the final classification outcome. It even worsens the original interpretability of the individual classifiers. In this paper we propose a novel implementation of classifiers combination approach that does not only promote the overall performance but also preserves the interpretability of the resulting model. We propose a solution based on Ant Colony Optimization and tailored for the case of Bayesian classifiers. We validate our proposed solution with case studies from medical domain namely, heart disease and Cardiotography-based predictions, problems where interpretability is critical to make appropriate clinical decisions.The datasets, Prediction Models and software tool together with supplementary materials are available at http://faculty.uaeu.ac.ae/salahb/ACO4BC.htm.

  1. Nest and colony characteristics of three stingless bee species in Vietnam with the first description of the nest of Lisotrigona carpenteri (Hymenoptera: Apidae: Meliponini)

    NARCIS (Netherlands)

    Chinh, T.X.; Sommeijer, M.J.; Boot, W.J.; Michener, C.D.

    2005-01-01

    In tropical primary forest and its buffer zones in North Vietnam, nests of three stingless bee species were studied: Lisotrigona carpenteri Engel, Trigona (Tetragonula) laeviceps Smith and Trigona (Lepidotrigona) ventralis Smith. We record nest architecture, adult population, the number of brood

  2. [Application of rational ant colony optimization to improve the reproducibility degree of laser three-dimensional copy].

    Science.gov (United States)

    Cui, Xiao-Yan; Huo, Zhong-Gang; Xin, Zhong-Hua; Tian, Xiao; Zhang, Xiao-Dong

    2013-07-01

    Three-dimensional (3D) copying of artificial ears and pistol printing are pushing laser three-dimensional copying technique to a new page. Laser three-dimensional scanning is a fresh field in laser application, and plays an irreplaceable part in three-dimensional copying. Its accuracy is the highest among all present copying techniques. Reproducibility degree marks the agreement of copied object with the original object on geometry, being the most important index property in laser three-dimensional copying technique. In the present paper, the error of laser three-dimensional copying was analyzed. The conclusion is that the data processing to the point cloud of laser scanning is the key technique to reduce the error and increase the reproducibility degree. The main innovation of this paper is as follows. On the basis of traditional ant colony optimization, rational ant colony optimization algorithm proposed by the author was applied to the laser three-dimensional copying as a new algorithm, and was put into practice. Compared with customary algorithm, rational ant colony optimization algorithm shows distinct advantages in data processing of laser three-dimensional copying, reducing the error and increasing the reproducibility degree of the copy.

  3. Current Pesticide Risk Assessment Protocols Do Not Adequately Address Differences Between Honey Bees (Apis mellifera and Bumble Bees (Bombus spp.

    Directory of Open Access Journals (Sweden)

    Kimberly Stoner

    2016-12-01

    Full Text Available Recent research has demonstrated colony-level sublethal effects of imidacloprid on bumble bees, affecting foraging and food consumption, and thus colony growth and reproduction, at lower pesticide concentrations than for honey bee colonies. However, these studies may not reflect the full effects of neonicotinoids on bumble bees because bumble bee life cycles are different from those of honey bees. Unlike honey bees, bumble bees live in colonies for only a few months each year. Assessing the sublethal effects of systemic insecticides only on the colony level is appropriate for honey bees, but for bumble bees, this approach addresses just part of their annual life cycle. Queens are solitary from the time they leave their home colonies in fall until they produce their first workers the following year. Queens forage for pollen and nectar, and are thus exposed to more risk of direct pesticide exposure than honey bee queens. Almost no research has been done on pesticide exposure to and effects on bumble bee queens. Additional research should focus on critical periods in a bumble bee queen’s life which have the greatest nutritional demands, foraging requirements, and potential for exposure to pesticides, particularly the period during and after nest establishment in the spring when the queen must forage for the nutritional needs of her brood and for her own needs while she maintains an elevated body temperature in order to incubate the brood.

  4. Assessing the Role of Environmental Conditions on Efficacy Rates of Heterorhabditis indica (Nematoda: Heterorhabditidae) for Controlling Aethina tumida (Coleoptera: Nitidulidae) in Honey Bee (Hymenoptera: Apidae) Colonies: a Citizen Science Approach.

    Science.gov (United States)

    Hill, Elizabeth S; Smythe, Ashleigh B; Delaney, Deborah A

    2016-02-01

    Certain species of entomopathogenic nematodes, such as Heterorhabditis indica Poinar, Karunakar & David, have the potential to be effective controls for Aethina tumida (Murray), or small hive beetles, when applied to the soil surrounding honey bee (Apis mellifera L.) hives. Despite the efficacy of H. indica, beekeepers have struggled to use them successfully as a biocontrol. It is believed that the sensitivity of H. indica to certain environmental conditions is the primary reason for this lack of success. Although research has been conducted to explore the impact of specific environmental conditions--such as soil moisture or soil temperature-on entomopathogenic nematode infectivity, no study to date has taken a comprehensive approach that considers the impact of multiple environmental conditions simultaneously. In exploring this, a multivariate logistic regression model was used to determine what environmental conditions resulted in reductions of A. tumida populations in honey bee colonies. To obtain the sample sizes necessary to run a multivariate logistic regression, this study utilized citizen scientist beekeepers and their hives from across the mid-Atlantic region of the United States. Results suggest that soil moisture, soil temperatures, sunlight exposure, and groundcover contribute to the efficacy of H. indica in reducing A. tumida populations in A. mellifera colonies. The results of this study offer direction for future research on the environmental preferences of H. indica and can be used to educate beekeepers about methods for better utilizing H. indica as a biological control. © The Authors 2015. Published by Oxford University Press on behalf of Entomological Society of America. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

  5. Special Issue: Honey Bee Viruses

    Directory of Open Access Journals (Sweden)

    Sebastian Gisder

    2015-10-01

    Full Text Available Pollination of flowering plants is an important ecosystem service provided by wild insect pollinators and managed honey bees. Hence, losses and declines of pollinating insect species threaten human food security and are of major concern not only for apiculture or agriculture but for human society in general. Honey bee colony losses and bumblebee declines have attracted intensive research interest over the last decade and although the problem is far from being solved we now know that viruses are among the key players of many of these bee losses and bumblebee declines. With this special issue on bee viruses we, therefore, aimed to collect high quality original papers reflecting the current state of bee virus research. To this end, we focused on newly discovered viruses (Lake Sinai viruses, bee macula-like virus, or a so far neglected virus species (Apis mellifera filamentous virus, and cutting edge technologies (mass spectrometry, RNAi approach applied in the field.

  6. Special Issue: Honey Bee Viruses

    Science.gov (United States)

    Gisder, Sebastian; Genersch, Elke

    2015-01-01

    Pollination of flowering plants is an important ecosystem service provided by wild insect pollinators and managed honey bees. Hence, losses and declines of pollinating insect species threaten human food security and are of major concern not only for apiculture or agriculture but for human society in general. Honey bee colony losses and bumblebee declines have attracted intensive research interest over the last decade and although the problem is far from being solved we now know that viruses are among the key players of many of these bee losses and bumblebee declines. With this special issue on bee viruses we, therefore, aimed to collect high quality original papers reflecting the current state of bee virus research. To this end, we focused on newly discovered viruses (Lake Sinai viruses, bee macula-like virus), or a so far neglected virus species (Apis mellifera filamentous virus), and cutting edge technologies (mass spectrometry, RNAi approach) applied in the field. PMID:26702462

  7. Hygienic behaviour in Brazilian stingless bees

    Science.gov (United States)

    Alves, Denise A.; Bento, José M. S.; Marchini, Luis C.; Ratnieks, Francis L. W.

    2016-01-01

    ABSTRACT Social insects have many defence mechanisms against pests and pathogens. One of these is hygienic behaviour, which has been studied in detail in the honey bee, Apis mellifera. Hygienic honey bee workers remove dead and diseased larvae and pupae from sealed brood cells, thereby reducing disease transfer within the colony. Stingless bees, Meliponini, also rear broods in sealed cells. We investigated hygienic behaviour in three species of Brazilian stingless bees (Melipona scutellaris, Scaptotrigona depilis, Tetragonisca angustula) in response to freeze-killed brood. All three species had high mean levels of freeze-killed brood removal after 48 h ∼99% in M. scutellaris, 80% in S. depilis and 62% in T. angustula (N=8 colonies per species; three trials per colony). These levels are greater than in unselected honey bee populations, ∼46%. In S. depilis there was also considerable intercolony variation, ranging from 27% to 100% removal after 2 days. Interestingly, in the S. depilis colony with the slowest removal of freeze-killed brood, 15% of the adult bees emerging from their cells had shrivelled wings indicating a disease or disorder, which is as yet unidentified. Although the gross symptoms resembled the effects of deformed wing virus in the honey bee, this virus was not detected in the samples. When brood comb from the diseased colony was introduced to the other S. depilis colonies, there was a significant negative correlation between freeze-killed brood removal and the emergence of deformed worker bees (P=0.001), and a positive correlation with the cleaning out of brood cells (P=0.0008). This shows that the more hygienic colonies were detecting and removing unhealthy brood prior to adult emergence. Our results indicate that hygienic behaviour may play an important role in colony health in stingless bees. The low levels of disease normally seen in stingless bees may be because they have effective mechanisms of disease management, not because they lack

  8. Hygienic behaviour in Brazilian stingless bees

    Directory of Open Access Journals (Sweden)

    Hasan Al Toufailia

    2016-11-01

    Full Text Available Social insects have many defence mechanisms against pests and pathogens. One of these is hygienic behaviour, which has been studied in detail in the honey bee, Apis mellifera. Hygienic honey bee workers remove dead and diseased larvae and pupae from sealed brood cells, thereby reducing disease transfer within the colony. Stingless bees, Meliponini, also rear broods in sealed cells. We investigated hygienic behaviour in three species of Brazilian stingless bees (Melipona scutellaris, Scaptotrigona depilis, Tetragonisca angustula in response to freeze-killed brood. All three species had high mean levels of freeze-killed brood removal after 48 h ∼99% in M. scutellaris, 80% in S. depilis and 62% in T. angustula (N=8 colonies per species; three trials per colony. These levels are greater than in unselected honey bee populations, ∼46%. In S. depilis there was also considerable intercolony variation, ranging from 27% to 100% removal after 2 days. Interestingly, in the S. depilis colony with the slowest removal of freeze-killed brood, 15% of the adult bees emerging from their cells had shrivelled wings indicating a disease or disorder, which is as yet unidentified. Although the gross symptoms resembled the effects of deformed wing virus in the honey bee, this virus was not detected in the samples. When brood comb from the diseased colony was introduced to the other S. depilis colonies, there was a significant negative correlation between freeze-killed brood removal and the emergence of deformed worker bees (P=0.001, and a positive correlation with the cleaning out of brood cells (P=0.0008. This shows that the more hygienic colonies were detecting and removing unhealthy brood prior to adult emergence. Our results indicate that hygienic behaviour may play an important role in colony health in stingless bees. The low levels of disease normally seen in stingless bees may be because they have effective mechanisms of disease management, not because

  9. Why does bee health matter? The science surrounding honey bee health concerns and what we can do about it

    Science.gov (United States)

    Spivak, Marla S; Browning, Zac; Goblirsch, Mike; Lee, Katie; Otto, Clint R.; Smart, Matthew; Wu-Smart, Judy

    2017-01-01

    A colony of honey bees is an amazing organism when it is healthy; it is a superorganism in many senses of the word. As with any organism, maintaining a state of health requires cohesiveness and interplay among cells and tissues and, in the case of a honey bee colony, the bees themselves. The individual bees that make up a honey bee colony deliver to the superorganism what it needs: pollen and nectar collected from flowering plants that contain nutrients necessary for growth and survival. Honey bees with access to better and more complete nutrition exhibit improved immune system function and behavioral defenses for fighting off effects of pathogens and pesticides (Evans and Spivak 2010; Mao, Schuler, and Berenbaum 2013; Wahl and Ulm 1983). Sadly, as this story is often told in the headlines, the focus is rarely about what it means for a honey bee colony to be healthy and is instead primarily focused on colony survival rates. Bee colonies are chronically exposed to parasitic mites, viruses, diseases, miticides, pesticides, and poor nutrition, which weaken and make innate defenses insufficient at overcoming these combined stressors. Colonies that are chronically weakened can be even more susceptible to infections and levels of pesticide exposure that might otherwise be innocuous, further promoting a downward spiral of health. Sick and weakened bees diminish the colony’s resiliency, ultimately leading to a breakdown in the social structure, production, efficiency, immunity, and reproduction of the colony, and eventual or sudden colony death.

  10. Honey bee pathology: current threats to honey bees and beekeeping.

    Science.gov (United States)

    Genersch, Elke

    2010-06-01

    Managed honey bees are the most important commercial pollinators of those crops which depend on animal pollination for reproduction and which account for 35% of the global food production. Hence, they are vital for an economic, sustainable agriculture and for food security. In addition, honey bees also pollinate a variety of wild flowers and, therefore, contribute to the biodiversity of many ecosystems. Honey and other hive products are, at least economically and ecologically rather, by-products of beekeeping. Due to this outstanding role of honey bees, severe and inexplicable honey bee colony losses, which have been reported recently to be steadily increasing, have attracted much attention and stimulated many research activities. Although the phenomenon "decline of honey bees" is far from being finally solved, consensus exists that pests and pathogens are the single most important cause of otherwise inexplicable colony losses. This review will focus on selected bee pathogens and parasites which have been demonstrated to be involved in colony losses in different regions of the world and which, therefore, are considered current threats to honey bees and beekeeping.

  11. Balancing emergency message dissemination and network lifetime in wireless body area network using ant colony optimization and Bayesian game formulation

    Directory of Open Access Journals (Sweden)

    R. Latha

    Full Text Available Nowadays, Wireless Body Area Network (WBAN is emerging very fast and so many new methods and algorithms are coming up for finding the optimal path for disseminating emergency messages. Ant Colony Optimization (ACO is one of the cultural algorithms for solving many hard problems such as Travelling Salesman Problem (TSP. ACO is a natural behaviour of ants, which work stochastically with the help of pheromone trails deposited in the shortest route to find their food. This optimization procedure involves adapting, positive feedback and inherent parallelism. Each ant will deposit certain amount of pheromone in the tour construction it makes searching for food. This type of communication is known as stigmetric communication. In addition, if a dense WBAN environment prevails, such as hospital, i.e. in the environment of overlapping WBAN, game formulation was introduced for analyzing the mixed strategy behaviour of WBAN. In this paper, the ant colony optimization approach to the travelling salesman problem was applied to the WBAN to determine the shortest route for sending emergency message to the doctor via sensor nodes; and also a static Bayesian game formulation with mixed strategy was analysed to enhance the network lifetime. Whenever the patient needs any critical care or any other medical issue arises, emergency messages will be created by the WBAN and sent to the doctor's destination. All the modes of communication were realized in a simulation environment using OMNet++. The authors investigated a balanced model of emergency message dissemination and network lifetime in WBAN using ACO and Bayesian game formulation. Keywords: Wireless body area network, Ant colony optimization, Bayesian game model, Sensor network, Message latency, Network lifetime

  12. Generalist Bee Species on Brazilian Bee-Plant Interaction Networks

    Directory of Open Access Journals (Sweden)

    Astrid de Matos Peixoto Kleinert

    2012-01-01

    Full Text Available Determining bee and plant interactions has an important role on understanding general biology of bee species as well as the potential pollinating relationship between them. Bee surveys have been conducted in Brazil since the end of the 1960s. Most of them applied standardized methods and had identified the plant species where the bees were collected. To analyze the most generalist bees on Brazilian surveys, we built a matrix of bee-plant interactions. We estimated the most generalist bees determining the three bee species of each surveyed locality that presented the highest number of interactions. We found 47 localities and 39 species of bees. Most of them belong to Apidae (31 species and Halictidae (6 families and to Meliponini (14 and Xylocopini (6 tribes. However, most of the surveys presented Apis mellifera and/or Trigona spinipes as the most generalist species. Apis mellifera is an exotic bee species and Trigona spinipes, a native species, is also widespread and presents broad diet breath and high number of individuals per colony.

  13. The habitat disruption induces immune-suppression and oxidative stress in honey bees

    OpenAIRE

    Morimoto, Tomomi; Kojima, Yuriko; Toki, Taku; Komeda, Yayoi; Yoshiyama, Mikio; Kimura, Kiyoshi; Nirasawa, Keijiro; Kadowaki, Tatsuhiko

    2011-01-01

    The honey bee is a major insect used for pollination of many commercial crops worldwide. Although the use of honey bees for pollination can disrupt the habitat, the effects on their physiology have never been determined. Recently, honey bee colonies have often collapsed when introduced in greenhouses for pollination in Japan. Thus, suppressing colony collapses and maintaining the number of worker bees in the colonies is essential for successful long-term pollination in greenhouses and recycli...

  14. Honey bee hemocyte profiling by flow cytometry.

    Science.gov (United States)

    Marringa, William J; Krueger, Michael J; Burritt, Nancy L; Burritt, James B

    2014-01-01

    Multiple stress factors in honey bees are causing loss of bee colonies worldwide. Several infectious agents of bees are believed to contribute to this problem. The mechanisms of honey bee immunity are not completely understood, in part due to limited information about the types and abundances of hemocytes that help bees resist disease. Our study utilized flow cytometry and microscopy to examine populations of hemolymph particulates in honey bees. We found bee hemolymph includes permeabilized cells, plasmatocytes, and acellular objects that resemble microparticles, listed in order of increasing abundance. The permeabilized cells and plasmatocytes showed unexpected differences with respect to properties of the plasma membrane and labeling with annexin V. Both permeabilized cells and plasmatocytes failed to show measurable mitochondrial membrane potential by flow cytometry using the JC-1 probe. Our results suggest hemolymph particulate populations are dynamic, revealing significant differences when comparing individual hive members, and when comparing colonies exposed to diverse conditions. Shifts in hemocyte populations in bees likely represent changing conditions or metabolic differences of colony members. A better understanding of hemocyte profiles may provide insight into physiological responses of honey bees to stress factors, some of which may be related to colony failure.

  15. A critical number of workers in a honeybee colony triggers investment in reproduction.

    Science.gov (United States)

    Smith, Michael L; Ostwald, Madeleine M; Loftus, J Carter; Seeley, Thomas D

    2014-10-01

    Social insect colonies, like individual organisms, must decide as they develop how to allocate optimally their resources among survival, growth, and reproduction. Only when colonies reach a certain state do they switch from investing purely in survival and growth to investing also in reproduction. But how do worker bees within a colony detect that their colony has reached the state where it is adaptive to begin investing in reproduction? Previous work has shown that larger honeybee colonies invest more in reproduction (i.e., the production of drones and queens), however, the term 'larger' encompasses multiple colony parameters including number of adult