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Sample records for bee colony optimization

  1. Enhanced Bee Colony Algorithm for Complex Optimization Problems

    Directory of Open Access Journals (Sweden)

    S.Suriya

    2012-01-01

    Full Text Available Optimization problems are considered to be one kind of NP hard problems. Usually heuristic approaches are found to provide solutions for NP hard problems. There are a plenty of heuristic algorithmsavailable to solve optimization problems namely: Ant Colony Optimization, Particle Swarm Optimization, Bee Colony Optimization, etc. The basic Bee Colony algorithm, a population based search algorithm, is analyzed to be a novel tool for complex optimization problems. The algorithm mimics the food foraging behavior of swarmsof honey bees. This paper deals with a modified fitness function of Bee Colony algorithm. The effect of problem dimensionality on the performance of the algorithms will be investigated. This enhanced Bee Colony Optimization will be evaluated based on the well-known benchmark problems. The testing functions like Rastrigin, Rosenbrock, Ackley, Griewank and Sphere are used to evaluavate the performance of the enhanced Bee Colony algorithm. The simulation will be developed on MATLAB.

  2. Artificial bee colony algorithm variants on constrained optimization

    National Research Council Canada - National Science Library

    Bahriye Akay; Dervis Karaboga

    2017-01-01

    .... In this study, the performance analysis of artificial bee colony algorithm (ABC), one of the intelligent optimization techniques, is examined on constrained problems and the effect of some modifications on the performance of the algorithm is examined...

  3. A Survey on the Applications of Bee Colony Optimization Techniques

    Directory of Open Access Journals (Sweden)

    Dr. Arvinder Kaur

    2011-08-01

    Full Text Available In this paper an overview of the areas where the Bee Colony Optimization (BCO and its variants are applied have been given. Bee System was identified by Sato and Hagiwara in 1997 and the Bee Colony Optimization (BCO was identified by Lucic and Teodorovic in 2001. BCO has emerged as a specialized class of Swarm Intelligence with bees as agents. It is an emerging field for researchers in the field of optimization problems because it provides immense problem solving scope for combinatorial and NP-hard problems. BCO is one of the benchmark systems portraying team work, collaborative work. BCO is a bottom-up approach of modeling where agents form global solution by optimizing the local solution.

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

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    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.

  5. Bee Colony Optimization - part II: The application survey

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    Teodorović Dušan

    2015-01-01

    Full Text Available Bee Colony Optimization (BCO is a meta-heuristic method based on foraging habits of honeybees. This technique was motivated by the analogy found between the natural behavior of bees searching for food and the behavior of optimization algorithms searching for an optimum in combinatorial optimization problems. BCO has been successfully applied to various hard combinatorial optimization problems, mostly in transportation, location and scheduling fields. There are some applications in the continuous optimization field that have appeared recently. The main purpose of this paper is to introduce the scientific community more closely with BCO by summarizing its existing successful applications. [Projekat Ministarstva nauke Republike Srbije, br. OI174010, OI174033, TR36002

  6. 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.

  7. Protein structure prediction using bee colony optimization metaheuristic

    DEFF Research Database (Denmark)

    Fonseca, Rasmus; Paluszewski, Martin; Winter, Pawel

    2010-01-01

    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 of the pr......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...... 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...... in the literature. We have devised a new variant that uni¿es the existing and is much more ¿exible with respect to replacing the various elements of the BCO. In particular this applies to the choice of the local search as well as the method for generating scout locations and performing the waggle dance. We apply...

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

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    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.

  9. Bee Colony Optimization - part I: The algorithm overview

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    Davidović Tatjana

    2015-01-01

    Full Text Available This paper is an extensive survey of the Bee Colony Optimization (BCO algorithm, proposed for the first time in 2001. BCO and its numerous variants belong to a class of nature-inspired meta-heuristic methods, based on the foraging habits of honeybees. Our main goal is to promote it among the wide operations research community. BCO is a simple, but efficient meta-heuristic technique that has been successfully applied to many optimization problems, mostly in transport, location and scheduling fields. Firstly, we shall give a brief overview of the other meta-heuristics inspired by bees’ foraging principles pointing out the differences between them. Then, we shall provide the detailed description of the BCO algorithm and its modifications, including the strategies for BCO parallelization, and giving the preliminary results regarding its convergence. The application survey is elaborated in Part II of our paper. [Projekat Ministarstva nauke Republike Srbije, br. OI174010, br. OI174033 i br. TR36002

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

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    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.

  11. Penerapan Bee Colony Optimization Algorithm untuk Penentuan Rute Terpendek (Studi Kasus : Objek Wisata Daerah Istimewa Yogyakarta

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    Danuri Danuri

    2013-01-01

    Abstract The shortest path determination is an optimization problem which often used as a case study for research. Distance is the most defining factor in performing the search paths to be passed. Path with the shortest distance would be chosen as a path selection. Bee colony optimization algorithm used in this study to complete problems shortest path determination. There are two main process es during search path that is forward and backward. Bee colony optimization algorithm works on the process forward. The value probability of a path is base intransition process and the duration of waggle dance track of every bee who had found the position of the goal will be a preferred route. The results obtained in this study is the bee colony optimization algorithm can be used to find shortest path. The number of bees are released greatly affects in finding routes that can be passed. The more the number of bees that removed the greater the chances of finding the shortest path.   Keyword— Shortest Path, Bee Colony Optimization Algorithm

  12. Particle Swarm and Bacterial Foraging Inspired Hybrid Artificial Bee Colony Algorithm for Numerical Function Optimization

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    Li Mao

    2016-01-01

    Full Text Available Artificial bee colony (ABC algorithm has good performance in discovering the optimal solutions to difficult optimization problems, but it has weak local search ability and easily plunges into local optimum. In this paper, we introduce the chemotactic behavior of Bacterial Foraging Optimization into employed bees and adopt the principle of moving the particles toward the best solutions in the particle swarm optimization to improve the global search ability of onlooker bees and gain a hybrid artificial bee colony (HABC algorithm. To obtain a global optimal solution efficiently, we make HABC algorithm converge rapidly in the early stages of the search process, and the search range contracts dynamically during the late stages. Our experimental results on 16 benchmark functions of CEC 2014 show that HABC achieves significant improvement at accuracy and convergence rate, compared with the standard ABC, best-so-far ABC, directed ABC, Gaussian ABC, improved ABC, and memetic ABC algorithms.

  13. 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.

  14. 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)

  15. A hybrid artificial bee colony optimization and quantum evolutionary algorithm for continuous optimization problems.

    Science.gov (United States)

    Duan, Hai-Bin; Xu, Chun-Fang; Xing, Zhi-Hui

    2010-02-01

    In this paper, a novel hybrid Artificial Bee Colony (ABC) and Quantum Evolutionary Algorithm (QEA) is proposed for solving continuous optimization problems. ABC is adopted to increase the local search capacity as well as the randomness of the populations. In this way, the improved QEA can jump out of the premature convergence and find the optimal value. To show the performance of our proposed hybrid QEA with ABC, a number of experiments are carried out on a set of well-known Benchmark continuous optimization problems and the related results are compared with two other QEAs: the QEA with classical crossover operation, and the QEA with 2-crossover strategy. The experimental comparison results demonstrate that the proposed hybrid ABC and QEA approach is feasible and effective in solving complex continuous optimization problems.

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

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    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.

  17. 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.

  18. 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.

  19. Optimal Multi-Level Thresholding Based on Maximum Tsallis Entropy via an Artificial Bee Colony Approach

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    Yudong Zhang

    2011-04-01

    Full Text Available This paper proposes a global multi-level thresholding method for image segmentation. As a criterion for this, the traditional method uses the Shannon entropy, originated from information theory, considering the gray level image histogram as a probability distribution, while we applied the Tsallis entropy as a general information theory entropy formalism. For the algorithm, we used the artificial bee colony approach since execution of an exhaustive algorithm would be too time-consuming. The experiments demonstrate that: 1 the Tsallis entropy is superior to traditional maximum entropy thresholding, maximum between class variance thresholding, and minimum cross entropy thresholding; 2 the artificial bee colony is more rapid than either genetic algorithm or particle swarm optimization. Therefore, our approach is effective and rapid.

  20. Parametric Optimization of Nd:YAG Laser Beam Machining Process Using Artificial Bee Colony Algorithm

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    Rajarshi Mukherjee

    2013-01-01

    Full Text Available Nd:YAG laser beam machining (LBM process has a great potential to manufacture intricate shaped microproducts with its unique characteristics. In practical applications, such as drilling, grooving, cutting, or scribing, the optimal combination of Nd:YAG LBM process parameters needs to be sought out to provide the desired machining performance. Several mathematical techniques, like Taguchi method, desirability function, grey relational analysis, and genetic algorithm, have already been applied for parametric optimization of Nd:YAG LBM processes, but in most of the cases, suboptimal or near optimal solutions have been reached. This paper focuses on the application of artificial bee colony (ABC algorithm to determine the optimal Nd:YAG LBM process parameters while considering both single and multiobjective optimization of the responses. A comparative study with other population-based algorithms, like genetic algorithm, particle swarm optimization, and ant colony optimization algorithm, proves the global applicability and acceptability of ABC algorithm for parametric optimization. In this algorithm, exchange of information amongst the onlooker bees minimizes the search iteration for the global optimal and avoids generation of suboptimal solutions. The results of two sample paired t-tests also demonstrate its superiority over the other optimization algorithms.

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

    Science.gov (United States)

    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. PMID:28473849

  2. Artificial bee colony algorithm with comprehensive search mechanism for numerical optimization

    Institute of Scientific and Technical Information of China (English)

    Mudong Li; Hui Zhao; Xingwei Weng; Hanqiao Huang

    2015-01-01

    The artificial bee colony (ABC) algorithm is a sim-ple and effective global optimization algorithm which has been successful y applied in practical optimization problems of various fields. However, the algorithm is stil insufficient in balancing ex-ploration and exploitation. To solve this problem, we put forward an improved algorithm with a comprehensive search mechanism. The search mechanism contains three main strategies. Firstly, the heuristic Gaussian search strategy composed of three different search equations is proposed for the employed bees, which ful y utilizes and balances the exploration and exploitation of the three different search equations by introducing the selectivity probability Ps. Secondly, in order to improve the search accuracy, we propose the Gbest-guided neighborhood search strategy for onlooker bees to improve the exploitation performance of ABC. Thirdly, the self-adaptive population perturbation strategy for the current colony is used by random perturbation or Gaussian perturbation to en-hance the diversity of the population. In addition, to improve the quality of the initial population, we introduce the chaotic opposition-based learning method for initialization. The experimental results and Wilcoxon signed ranks test based on 27 benchmark func-tions show that the proposed algorithm, especial y for solving high dimensional and complex function optimization problems, has a higher convergence speed and search precision than ABC and three other current ABC-based algorithms.

  3. 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.

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

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    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.

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

    Science.gov (United States)

    Prasanth, Rajanampalli Seshasai Srinivasa; Hans Raj, Kandikonda

    2016-06-01

    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.

  6. 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.

  7. Optimization of solar air collector using genetic algorithm and artificial bee colony algorithm

    Energy Technology Data Exchange (ETDEWEB)

    Sencan Sahin, Arzu [Sueleyman Demirel University, Technology Faculty, Isparta (Turkey)

    2012-11-15

    Thermal performance of solar air collector depends on many parameters as inlet air temperature, air velocity, collector slope and properties related to collector. In this study, the effect of the different parameters which affect the performance of the solar air collector are investigated. In order to maximize the thermal performance of a solar air collector genetic algorithm (GA) and artificial bee colony algorithm (ABC) have been used. The results obtained indicate that GA and ABC algorithms can be applied successfully for the optimization of the thermal performance of solar air collector. (orig.)

  8. Hybridizing artificial bee colony with biogeography-based optimization for constrained mechanical design problems

    Institute of Scientific and Technical Information of China (English)

    蔡绍洪; 龙文; 焦建军

    2015-01-01

    A novel hybrid algorithm named ABC-BBO, which integrates artificial bee colony (ABC) algorithm with biogeography-based optimization (BBO) algorithm, is proposed to solve constrained mechanical design problems. ABC-BBO combined the exploration of ABC algorithm with the exploitation of BBO algorithm effectively, and hence it can generate the promising candidate individuals. The proposed hybrid algorithm speeds up the convergence and improves the algorithm’s performance. Several benchmark test functions and mechanical design problems are applied to verifying the effects of these improvements and it is demonstrated that the performance of this proposed ABC-BBO is superior to or at least highly competitive with other population-based optimization approaches.

  9. Optimization of mechanical draft counter flow wet-cooling tower using artificial bee colony algorithm

    Energy Technology Data Exchange (ETDEWEB)

    Rao, R.V., E-mail: ravipudirao@gmail.co [S.V. National Institute of Technology, Surat, Gujarat State 395 007 (India); Patel, V.K. [S.V. National Institute of Technology, Surat, Gujarat State 395 007 (India)

    2011-07-15

    Research highlights: {yields} ABC algorithm is used for optimization of counter flow wet-cooling tower. {yields} Minimizing the total annual cost for specific heat duty is the objective function. {yields} Six examples are presented to demonstrate the effectiveness of the proposed algorithm. {yields} The results are compared with the results of GAMS optimization package. {yields} The ABC algorithm can be modified to suit optimization of other thermal systems. -- Abstract: This study explores the use of artificial bee colony (ABC) algorithm for design optimization of mechanical draft counter flow wet-cooling tower. Minimizing the total annual cost for specific heat duty requirement is considered as objective function. Three design variables such as water to air mass ratio, mass velocity of water and mass velocity of air are considered for optimization. Evaluations of the cooling tower geometry and performances are based on an adaptive version of Merkel's method. Temperature and enthalpy constraints are included in the optimization procedure. Six examples are presented to demonstrate the effectiveness and accuracy of the proposed algorithm. The results of optimization using ABC are validated by comparing with those obtained by using GAMS optimization package. The effect of variation of ABC parameters on the convergence and optimum value of the objective function has also been presented.

  10. Application of PSO, Artificial Bee Colony and Bacterial Foraging Optimization algorithms to economic load dispatch: An analysis

    CERN Document Server

    Baijal, Anant; Jayabarathi, T

    2011-01-01

    This paper illustrates successful implementation of three evolutionary algorithms, namely- Particle Swarm Optimization(PSO), Artificial Bee Colony (ABC) and Bacterial Foraging Optimization (BFO) algorithms to economic load dispatch problem (ELD). Power output of each generating unit and optimum fuel cost obtained using all three algorithms have been compared. The results obtained show that ABC and BFO algorithms converge to optimal fuel cost with reduced computational time when compared to PSO for the two example problems considered.

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

    OpenAIRE

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

    2014-01-01

    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...

  12. 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.

  13. Optimization of Spherical Roller Bearing Design Using Artificial Bee Colony Algorithm and Grid Search Method

    Science.gov (United States)

    Tiwari, Rajiv; Waghole, Vikas

    2015-07-01

    Bearing standards impose restrictions on the internal geometry of spherical roller bearings. Geometrical and strength constraints conditions have been formulated for the optimization of bearing design. The long fatigue life is one of the most important criteria in the optimum design of bearing. The life is directly proportional to the dynamic capacity; hence, the objective function has been chosen as the maximization of dynamic capacity. The effect of speed and static loads acting on the bearing are also taken into account. Design variables for the bearing include five geometrical parameters: the roller diameter, the roller length, the bearing pitch diameter, the number of rollers, and the contact angle. There are a few design constraint parameters which are also included in the optimization, the bounds of which are obtained by initial runs of the optimization. The optimization program is made to run for different values of these design constraint parameters and a range of the parameters is obtained for which the objective function has a higher value. The artificial bee colony algorithm (ABCA) has been used to solve the constrained optimized problem and the optimum design is compared with the one obtained from the grid search method (GSM), both operating independently. Both the ABCA and the GSM have been finally combined together to reach the global optimum point. A constraint violation study has also been carried out to give priority to the constraint having greater possibility of violations. Optimized bearing designs show a better performance parameter with those specified in bearing catalogs. The sensitivity analysis of bearing parameters has also been carried out to see the effect of manufacturing tolerance on the objective function.

  14. 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.

  15. 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.

  16. Analysis of six stages supply chain management in inventory optimization for warehouse with artificial bee colony algorithm using genetic algorithm

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    Ajay Singh Yadav

    2017-09-01

    Full Text Available The purpose of the proposed study is to give a new dimension on warehouse with Artificial bee colony algorithm using genetic algorithm processes in six stages - 11 member supply chain in inventory optimization to describe the certain and uncertain market demand which is based on supply reliability and to develop more realistic and more flexible models. we hope that the proposed study has a great potential to solve various practical tribulations related to the warehouse using genetic algorithm processes in six stages - 11 member supply chain in inventory optimization and also provide a general review for the application of soft computing techniques like genetic algorithms to use for improve the effectiveness and efficiency for various aspect of warehouse with Artificial bee colony algorithm using genetic algorithm.

  17. 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

    neural network combined with a Markov chain (ANN-MC) approach is used to predict nondispatchable power generation and load demand considering uncertainties. Furthermore, other capabilities such as extendibility, reliability, and flexibility are examined about the proposed approach......., the DR magnitude, the duration, and the minimum cost of energy. In this paper, a multiperiod artificial bee colony optimization algorithm is implemented for economic dispatch considering generation, storage, and responsive load offers. The better performance of the proposed algorithm is shown...

  18. Comparative Analysis of Improved Cuckoo Search(ICS Algorithm and Artificial Bee Colony (ABC Algorithm on Continuous Optimization Problems

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    Shariba Islam Tusiy

    2015-02-01

    Full Text Available This work is related on two well-known algorithm, Improved Cuckoo Search and Artificial Bee Colony Algorithm which are inspired from nature. Improved Cuckoo Search (ICS algorithm is based on Lévy flight and behavior of some birds and fruit flies and they have some assumptions and each assumption is highly observed to maintain their characteristics. Besides Artificial Bee Colony (ABC algorithm is based on swarm intelligence, which is based on bee colony with the way the bees maintain their life in that colony. Bees’ characteristics are the main part of this algorithm. This is a theoretical result of this topic and a quantitative research paper.

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

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    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.

  20. Coordination Between the Sexes Constrains the Optimization of Reproductive Timing in Honey Bee Colonies.

    Science.gov (United States)

    Lemanski, Natalie J; Fefferman, Nina H

    2017-06-01

    Honeybees are an excellent model system for examining how trade-offs shape reproductive timing in organisms with seasonal environments. Honeybee colonies reproduce two ways: producing swarms comprising a queen and thousands of workers or producing males (drones). There is an energetic trade-off between producing workers, which contribute to colony growth, and drones, which contribute only to reproduction. The timing of drone production therefore determines both the drones' likelihood of mating and when colonies reach sufficient size to swarm. Using a linear programming model, we ask when a colony should produce drones and swarms to maximize reproductive success. We find the optimal behavior for each colony is to produce all drones prior to swarming, an impossible solution on a population scale because queens and drones would never co-occur. Reproductive timing is therefore not solely determined by energetic trade-offs but by the game theoretic problem of coordinating the production of reproductives among colonies.

  1. 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

  2. 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.

  3. 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.

  4. An Experimental Approach for Optimizing Coating Parameters of Electroless Ni-P-Cu Coating Using Artificial Bee Colony Algorithm.

    Science.gov (United States)

    Roy, Supriyo; Sahoo, Prasanta

    2014-01-01

    This paper aims to present an experimental investigation for optimum tribological behavior (wear depth and coefficient of friction) of electroless Ni-P-Cu coatings based on four process parameters using artificial bee colony algorithm. Experiments are carried out by utilizing the combination of three coating process parameters, namely, nickel sulphate, sodium hypophosphite, and copper sulphate, and the fourth parameter is postdeposition heat treatment temperature. The design of experiment is based on the Taguchi L27 experimental design. After coating, measurement of wear and coefficient of friction of each heat-treated sample is done using a multitribotester apparatus with block-on-roller arrangement. Both friction and wear are found to increase with increase of source of nickel concentration and decrease with increase of source of copper concentration. Artificial bee colony algorithm is successfully employed to optimize the multiresponse objective function for both wear depth and coefficient of friction. It is found that, within the operating range, a lower value of nickel concentration, medium value of hypophosphite concentration, higher value of copper concentration, and higher value of heat treatment temperature are suitable for having minimum wear and coefficient of friction. The surface morphology, phase transformation behavior, and composition of coatings are also studied with the help of scanning electron microscopy, X-ray diffraction analysis, and energy dispersed X-ray analysis, respectively.

  5. Optimal Censoring Scheme Selection Based on Artificial Bee Colony Optimization (ABC Algorithm

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    K. Kalaivani

    2015-07-01

    Full Text Available Life testing plans are more vital for carrying out researches on reliability and survival analysis. The inadequacy in the number of testing units or the timing limitations prevents the experiment from being continued until all the failures are detected. Hence, censoring grows to be an inheritably important and well-organized methodology for estimating the model parameters of underlying distributions. Type I and II censoring schemes are the most widely employed censoring schemes. The chief problem associated with the designing of life testing experiments practically is the determination of optimum censoring scheme. Hence, this study attempts to determine the optimum censoring through the minimization of total cost spent for the experiment, consuming less termination time and reasonable number of failures. The ABC algorithm is being employed in this study for obtaining the optimal censoring schemes. Entropy and variance serves as the optimal criterion. The proposed method utilizes Risk analysis to evaluate the efficiency or reliability of the optimal censoring scheme that is being determined. Optimum censoring scheme indicates the process of determining the best scheme from among the entire censoring schemes possible, in accordance to a specific optimality criterion.

  6. 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.

  7. 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.

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

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    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

  9. Predictive markers of honey bee colony collapse.

    Directory of Open Access Journals (Sweden)

    Benjamin Dainat

    Full Text Available Across the Northern hemisphere, managed honey bee colonies, Apis mellifera, are currently affected by abrupt depopulation during winter and many factors are suspected to be involved, either alone or in combination. Parasites and pathogens are considered as principal actors, in particular the ectoparasitic mite Varroa destructor, associated viruses and the microsporidian Nosema ceranae. Here we used long term monitoring of colonies and screening for eleven disease agents and genes involved in bee immunity and physiology to identify predictive markers of honeybee colony losses during winter. The data show that DWV, Nosema ceranae, Varroa destructor and Vitellogenin can be predictive markers for winter colony losses, but their predictive power strongly depends on the season. In particular, the data support that V. destructor is a key player for losses, arguably in line with its specific impact on the health of individual bees and colonies.

  10. Pathogen webs in collapsing honey bee colonies.

    Directory of Open Access Journals (Sweden)

    R Scott Cornman

    Full Text Available Recent losses in honey bee colonies are unusual in their severity, geographical distribution, and, in some cases, failure to present recognized characteristics of known disease. Domesticated honey bees face numerous pests and pathogens, tempting hypotheses that colony collapses arise from exposure to new or resurgent pathogens. Here we explore the incidence and abundance of currently known honey bee pathogens in colonies suffering from Colony Collapse Disorder (CCD, otherwise weak colonies, and strong colonies from across the United States. Although pathogen identities differed between the eastern and western United States, there was a greater incidence and abundance of pathogens in CCD colonies. Pathogen loads were highly covariant in CCD but not control hives, suggesting that CCD colonies rapidly become susceptible to a diverse set of pathogens, or that co-infections can act synergistically to produce the rapid depletion of workers that characterizes the disorder. We also tested workers from a CCD-free apiary to confirm that significant positive correlations among pathogen loads can develop at the level of individual bees and not merely as a secondary effect of CCD. This observation and other recent data highlight pathogen interactions as important components of bee disease. Finally, we used deep RNA sequencing to further characterize microbial diversity in CCD and non-CCD hives. We identified novel strains of the recently described Lake Sinai viruses (LSV and found evidence of a shift in gut bacterial composition that may be a biomarker of CCD. The results are discussed with respect to host-parasite interactions and other environmental stressors of honey bees.

  11. Pathogen webs in collapsing honey bee colonies.

    Science.gov (United States)

    Cornman, R Scott; Tarpy, David R; Chen, Yanping; Jeffreys, Lacey; Lopez, Dawn; Pettis, Jeffery S; vanEngelsdorp, Dennis; Evans, Jay D

    2012-01-01

    Recent losses in honey bee colonies are unusual in their severity, geographical distribution, and, in some cases, failure to present recognized characteristics of known disease. Domesticated honey bees face numerous pests and pathogens, tempting hypotheses that colony collapses arise from exposure to new or resurgent pathogens. Here we explore the incidence and abundance of currently known honey bee pathogens in colonies suffering from Colony Collapse Disorder (CCD), otherwise weak colonies, and strong colonies from across the United States. Although pathogen identities differed between the eastern and western United States, there was a greater incidence and abundance of pathogens in CCD colonies. Pathogen loads were highly covariant in CCD but not control hives, suggesting that CCD colonies rapidly become susceptible to a diverse set of pathogens, or that co-infections can act synergistically to produce the rapid depletion of workers that characterizes the disorder. We also tested workers from a CCD-free apiary to confirm that significant positive correlations among pathogen loads can develop at the level of individual bees and not merely as a secondary effect of CCD. This observation and other recent data highlight pathogen interactions as important components of bee disease. Finally, we used deep RNA sequencing to further characterize microbial diversity in CCD and non-CCD hives. We identified novel strains of the recently described Lake Sinai viruses (LSV) and found evidence of a shift in gut bacterial composition that may be a biomarker of CCD. The results are discussed with respect to host-parasite interactions and other environmental stressors of honey bees.

  12. 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.

  13. Allee effects and colony collapse disorder in honey bees

    Science.gov (United States)

    We propose a mathematical model to quantify the hypothesis that a major ultimate cause of Colony Collapse Disorder (CCD) in honey bees is the presence of an Allee effect in the growth dynamics of honey bee colonies. In the model, both recruitment of adult bees as well as mortality of adult bees have...

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

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    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.

  15. 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.

  16. Classification of Medical Datasets Using SVMs with Hybrid Evolutionary Algorithms Based on Endocrine-Based Particle Swarm Optimization and Artificial Bee Colony Algorithms.

    Science.gov (United States)

    Lin, Kuan-Cheng; Hsieh, Yi-Hsiu

    2015-10-01

    The classification and analysis of data is an important issue in today's research. Selecting a suitable set of features makes it possible to classify an enormous quantity of data quickly and efficiently. Feature selection is generally viewed as a problem of feature subset selection, such as combination optimization problems. Evolutionary algorithms using random search methods have proven highly effective in obtaining solutions to problems of optimization in a diversity of applications. In this study, we developed a hybrid evolutionary algorithm based on endocrine-based particle swarm optimization (EPSO) and artificial bee colony (ABC) algorithms in conjunction with a support vector machine (SVM) for the selection of optimal feature subsets for the classification of datasets. The results of experiments using specific UCI medical datasets demonstrate that the accuracy of the proposed hybrid evolutionary algorithm is superior to that of basic PSO, EPSO and ABC algorithms, with regard to classification accuracy using subsets with a reduced number of features.

  17. 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.

  18. Metatranscriptomic analyses of honey bee colonies

    Directory of Open Access Journals (Sweden)

    Cansu Ozge Tozkar

    2015-03-01

    Full Text Available 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 5 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. 60 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, Apis filamentous 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.

  19. Recent Honey Bee Colony Declines

    Science.gov (United States)

    2007-06-20

    production ([http://www.nass.usda.gov/QuickStats/]). Melon production is based on reported 2002 harvested acreage. c. Apricots, avocados , blueberries...crops are almost totally (90%-100%) dependent on honey bee pollination, including almonds, apples, avocados , blueberries, cranberries, cherries, kiwi...is regularly posted to the website of the Mid-Atlantic Apiculture Research and Extension Consortium (MAAREC), which represents beekeeping

  20. COMPARISON BETWEEN ARTIFICIAL BEE COLONY ALGORITHM, SHUFFLED FROG LEAPING ALGORITHM AND NERO-FUZZY SYSTEM IN DESIGN OF OPTIMAL PID CONTROLLERS

    Directory of Open Access Journals (Sweden)

    Fatemeh Masoudnia

    2013-11-01

    Full Text Available In this paper three optimum approaches to design PID controller for a Gryphon Robot are presented. The three applied approaches are Artificial Bee Colony, Shuffled Frog Leaping algorithms and nero-fuzzy system. The design goal is to minimize the integral absolute error and reduce transient response by minimizing overshoot, settling time and rise time of step response. An Objective function of these indexes is defined and minimized applying Shuffled Frog Leaping (SFL algorithm, Artificial Bee Colony (ABC algorithm and Nero-Fuzzy System (FNN. After optimization of the objective function, the optimal parameters for the PID controller are adjusted. Simulation results show that FNN has a remarkable effect on decreasing the amount of settling time and rise-time and eliminating of steady-state error while the SFL algorithm performs better on steady-state error and the ABC algorithm is better on decreasing of overshoot. In steady state manner, all of the methods react robustly to the disturbance, but FNN shows more stability in transient response.

  1. 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.

  2. 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.

  3. 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.

  4. 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...

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

    Directory of Open Access Journals (Sweden)

    Jerry J Bromenshenk

    Full Text Available BACKGROUND: 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. METHODOLOGY/PRINCIPAL FINDINGS: 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. CONCLUSIONS/SIGNIFICANCE: 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

  6. 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.

  7. Independent component analysis based on adaptive artificial bee colony

    National Research Council Canada - National Science Library

    Shi Zhang; Chao-Wei Bao; Hai-Bin Shen

    2016-01-01

    .... An independent component analysis method based on adaptive artificial bee colony algorithm is proposed in this paper, aiming at the problems of slow convergence and low computational precision...

  8. 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

    An Artificial Bee Colony algorithm was presented by Sonmez (StructMultidisc Optim 43:85–97, 2011) for solving discrete truss design problems. It was numerically tested on four benchmark examples and concluded to be robust and efficient. We compare the Artificial Bee Colony algorithm numerically...

  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. A balance-evolution artificial bee colony algorithm for protein structure optimization based on a three-dimensional AB off-lattice model.

    Science.gov (United States)

    Li, Bai; Chiong, Raymond; Lin, Mu

    2015-02-01

    Protein structure prediction is a fundamental issue in the field of computational molecular biology. In this paper, the AB off-lattice model is adopted to transform the original protein structure prediction scheme into a numerical optimization problem. We present a balance-evolution artificial bee colony (BE-ABC) algorithm to address the problem, with the aim of finding the structure for a given protein sequence with the minimal free-energy value. This is achieved through the use of convergence information during the optimization process to adaptively manipulate the search intensity. Besides that, an overall degradation procedure is introduced as part of the BE-ABC algorithm to prevent premature convergence. Comprehensive simulation experiments based on the well-known artificial Fibonacci sequence set and several real sequences from the database of Protein Data Bank have been carried out to compare the performance of BE-ABC against other algorithms. Our numerical results show that the BE-ABC algorithm is able to outperform many state-of-the-art approaches and can be effectively employed for protein structure optimization.

  11. 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

  12. Optimal energy management system in grid connected Microgrid integrated with distributed generation by using the multi-period artificial bee colony

    Directory of Open Access Journals (Sweden)

    Fatemeh Azarinejadian

    2014-10-01

    Full Text Available Presenting proper and efficient energy management system (EMS in the Microgrids (MG is an important issue for gaining assurance to optimize energy usage based on price preference and system technical constraints. Nowadays by utilizing renewable energy resources and energy storage systems in MG, in addition to reducing the pollution caused by fossil fuels, the safety and power system reliability can also be improved. The proposed algorithm to implement an EMS shall increase the MG ability in both islanded and grid connected operating mode to supply non-responsive load demands from the economic points of view. Implementing EMS over the MG of Institute de Recerca on Energia de Catalunya (IREC based on artificial bee colony (ABC optimization approach to economic dispatch between various generation units, considering their offer price is main novelty of this paper. The obtained results from the proposed algorithm when compared with the EMS algorithm based on mixed integer non-linear programming (MINLP demonstrate the reduction of about 18% of the total generation; and it also improves the efficiency of the demand/ generation side management.

  13. Fuzzy Sets in Dynamic Adaptation of Parameters of a Bee Colony Optimization for Controlling the Trajectory of an Autonomous Mobile Robot.

    Science.gov (United States)

    Amador-Angulo, Leticia; Mendoza, Olivia; Castro, Juan R; Rodríguez-Díaz, Antonio; Melin, Patricia; Castillo, Oscar

    2016-09-09

    A hybrid approach composed by different types of fuzzy systems, such as the Type-1 Fuzzy Logic System (T1FLS), Interval Type-2 Fuzzy Logic System (IT2FLS) and Generalized Type-2 Fuzzy Logic System (GT2FLS) for the dynamic adaptation of the alpha and beta parameters of a Bee Colony Optimization (BCO) algorithm is presented. The objective of the work is to focus on the BCO technique to find the optimal distribution of the membership functions in the design of fuzzy controllers. We use BCO specifically for tuning membership functions of the fuzzy controller for trajectory stability in an autonomous mobile robot. We add two types of perturbations in the model for the Generalized Type-2 Fuzzy Logic System to better analyze its behavior under uncertainty and this shows better results when compared to the original BCO. We implemented various performance indices; ITAE, IAE, ISE, ITSE, RMSE and MSE to measure the performance of the controller. The experimental results show better performances using GT2FLS then by IT2FLS and T1FLS in the dynamic adaptation the parameters for the BCO algorithm.

  14. Training a Feed-Forward Neural Network with Artificial Bee Colony based Backpropagation Method

    Directory of Open Access Journals (Sweden)

    Sudarshan Nandy

    2012-09-01

    Full Text Available Back-propagation algorithm is one of the most widely used and popular techniques to optimize the feedforward neural network training. Nature inspired meta-heuristic algorithms also provide derivative-freesolution to optimize complex problem. Artificial bee colony algorithm is a nature inspired meta-heuristicalgorithm, mimicking the foraging or food source searching behaviour of bees in a bee colony and thisalgorithm is implemented in several applications for an improved optimized outcome. The proposedmethod in this paper includes an improved artificial bee colony algorithm based back-propagation neuralnetwork training method for fast and improved convergence rate of the hybrid neural network learningmethod. The result is analysed with the genetic algorithm based back-propagation method, and it isanother hybridized procedure of its kind. Analysis is performed over standard data sets, reflecting the lightof efficiency of proposed method in terms of convergence speed and rate.

  15. A fingerprint minutiae matching algorithm based on chaotic bee colony optimization%基于混沌蜂群优化的指纹匹配算法

    Institute of Scientific and Technical Information of China (English)

    史骏鹏; 吴一全

    2016-01-01

    In order to further improve the operational speed and the recognition efficiency of fingerprint matching al⁃gorithms, a fingerprint matching algorithm based on chaotic bee colony activity and a variable boundary box was proposed. Firstly, by combining the advantages of artificial bee colony optimization including fast convergence times, fewer control parameters, and the lack of local optima, with the features of a chaos strategy including its random⁃like property and ergodicity, the chaotic bee colony activity was introduced into point pattern matching for fingerprint images. A corresponding fitness function incorporating both matching accuracy and operational time was then designed. The corresponding fitness function was then used to estimate the geometric transformation parameters for fingerprint rough matching. Finally, a variable boundary box can be used for fine matching, because it avoids any influences relating to local deformation of the fingerprint images. A large number of experimental results show that, compared with two alternative fingerprint matching algorithms ( based on local features and genetic algorithm optimization, respectively) the proposed algorithm has a shorter operational time and has higher matching accuracy.%为了进一步加快指纹匹配算法的运算速度、提高识别效率,提出了一种基于混沌蜂群优化和可变界限盒的指纹匹配算法。首先,结合人工蜂群优化算法收敛速度快、控制参数少、能够避免局部最优等优点以及混沌策略的类随机性、高遍历性等特点,在指纹点匹配中引入混沌蜂群优化算法,并设计兼顾了匹配精度和运算时间的适应度函数;然后利用适应度函数估计出指纹特征匹配的几何变换参数并进行指纹点特征的粗匹配;最后,利用可变界限盒进行精匹配,避免指纹图像局部形变带来的影响。大量实验结果表明,与基于局部特征的指纹匹配算法

  16. Solving the Scheduling Problem in Computational Grid using Artificial Bee Colony Algorithm

    Directory of Open Access Journals (Sweden)

    Seyyed Mohsen Hashemi

    2013-07-01

    Full Text Available Scheduling tasks on computational grids is known as NP-complete problem. Scheduling tasks in Grid computing, means assigning tasks to resources such that the time termination and average waiting time criteria and the number of required machines are optimized. Based on heuristic or meta-heuristic search have been proposed to obtain optimal solutions. The presented method tries to optimize all of the mentioned criteria with artificial bee colony system with consideration to precedence of tasks. Bee colony optimization is one of algorithms which categorized in swarm intelligence that can be used in optimization problems. This algorithm is based on the intelligent behavior of honey bees in foraging process. The result shows using bees for solving scheduling problem in computational grid makes better finish time and average waiting time.

  17. Rapid behavioral maturation accelerates failure of stressed honey bee colonies.

    Science.gov (United States)

    Perry, Clint J; Søvik, Eirik; Myerscough, Mary R; Barron, Andrew B

    2015-03-17

    Many complex factors have been linked to the recent marked increase in honey bee colony failure, including pests and pathogens, agrochemicals, and nutritional stressors. It remains unclear, however, why colonies frequently react to stressors by losing almost their entire adult bee population in a short time, resulting in a colony population collapse. Here we examine the social dynamics underlying such dramatic colony failure. Bees respond to many stressors by foraging earlier in life. We manipulated the demography of experimental colonies to induce precocious foraging in bees and used radio tag tracking to examine the consequences of precocious foraging for their performance. Precocious foragers completed far fewer foraging trips in their life, and had a higher risk of death in their first flights. We constructed a demographic model to explore how this individual reaction of bees to stress might impact colony performance. In the model, when forager death rates were chronically elevated, an increasingly younger forager force caused a positive feedback that dramatically accelerated terminal population decline in the colony. This resulted in a breakdown in division of labor and loss of the adult population, leaving only brood, food, and few adults in the hive. This study explains the social processes that drive rapid depopulation of a colony, and we explore possible strategies to prevent colony failure. Understanding the process of colony failure helps identify the most effective strategies to improve colony resilience.

  18. Application of Bees Algorithm in Multi-Join Query Optimization

    Directory of Open Access Journals (Sweden)

    Mohammad Alamery

    2012-09-01

    Full Text Available Multi-join query optimization is an important technique for designing and implementing database management system. It is a crucial factor that affects the capability of database. This paper proposes a Bees algorithm that simulates the foraging behavior of honey bee swarm to solve Multi-join query optimization problem. The performance of the Bees algorithm and Ant Colony Optimization algorithm are compared with respect to computational time and the simulation result indicates that Bees algorithm is more effective and efficient.

  19. Performance Comparison of Constrained Artificial Bee Colony Algorithm

    Directory of Open Access Journals (Sweden)

    Soudeh Babaeizadeh

    2015-06-01

    Full Text Available This study is aimed to evaluate, analyze and compare the performances of available constrained Artificial Bee Colony (ABC algorithms in the literature. In recent decades, many different variants of the ABC algorithms have been suggested to solve Constrained Optimization Problems (COPs. However, to the best of the authors' knowledge, there rarely are comparative studies on the numerical performance of those algorithms. This study is considering a set of well-known benchmark problems from test problems of Congress of Evolutionary Computation 2006 (CEC2006.

  20. Artificial Bee Colony with Different Mutation Schemes: A comparative study

    Directory of Open Access Journals (Sweden)

    Iyad Abu Doush

    2014-03-01

    Full Text Available Artificial Bee Colony (ABC is a swarm-based metaheuristic for continuous optimization. Recent work hybridized this algorithm with other metaheuristics in order to improve performance. The work in this paper, experimentally evaluates the use of different mutation operators with the ABC algorithm. The introduced operator is activated according to a determined probability called mutation rate (MR. The results on standard benchmark function suggest that the use of this operator improves performance in terms of convergence speed and quality of final obtained solution. It shows that Power and Polynomial mutations give best results. The fastest convergence was for the mutation rate value (MR=0.2.

  1. 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%.

  2. Honey Bee Colonies Remote Monitoring System

    Science.gov (United States)

    Gil-Lebrero, Sergio; Quiles-Latorre, Francisco Javier; Ortiz-López, Manuel; Sánchez-Ruiz, Víctor; Gámiz-López, Victoria; Luna-Rodríguez, Juan Jesús

    2016-01-01

    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. PMID:28036061

  3. 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.

  4. Polygonal Approximation Using an Artificial Bee Colony Algorithm

    Directory of Open Access Journals (Sweden)

    Shu-Chien Huang

    2015-01-01

    Full Text Available A polygonal approximation method based on the new artificial bee colony (NABC algorithm is proposed in this paper. In the present method, a solution is represented by a vector, and the objective function is defined as the integral square error between the given curve and its corresponding polygon. The search process, including the employed bee stage, the onlooker bee stage, and the scout bee stage, has been constructed for this specific problem. Most experiments show that the present method when compared with the DE-based method can obtain superior approximation results with less error norm with respect to the original curves.

  5. 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...

  6. Winter survival of individual honey bees and honey bee colonies depends on level of Varroa destructor infestation.

    Directory of Open Access Journals (Sweden)

    Coby van Dooremalen

    Full Text Available 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.

  7. The Importance of Microbes in Nutrition and Health of Honey Bee Colonies Part-2: Factors Affecting the Microbial Community in Honey Bee Colonies

    Science.gov (United States)

    Honey bee colonies have innumerable symbiotic bacteria and fungi that are essential to the health of the colony. In the first part of this series, we discussed the importance of microbes in maintaining the health of honey bee colonies. The bacteria, yeasts and molds that live in a healthy colony a...

  8. Bacterial Colony Optimization

    Directory of Open Access Journals (Sweden)

    Ben Niu

    2012-01-01

    Full Text Available This paper investigates the behaviors at different developmental stages in Escherichia coli (E. coli lifecycle and developing a new biologically inspired optimization algorithm named bacterial colony optimization (BCO. BCO is based on a lifecycle model that simulates some typical behaviors of E. coli bacteria during their whole lifecycle, including chemotaxis, communication, elimination, reproduction, and migration. A newly created chemotaxis strategy combined with communication mechanism is developed to simplify the bacterial optimization, which is spread over the whole optimization process. However, the other behaviors such as elimination, reproduction, and migration are implemented only when the given conditions are satisfied. Two types of interactive communication schemas: individuals exchange schema and group exchange schema are designed to improve the optimization efficiency. In the simulation studies, a set of 12 benchmark functions belonging to three classes (unimodal, multimodal, and rotated problems are performed, and the performances of the proposed algorithms are compared with five recent evolutionary algorithms to demonstrate the superiority of BCO.

  9. 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.

  10. 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

  11. 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 s

  12. Colony collapse disorder (CCD) and bee age impact honey bee pathophysiology

    Science.gov (United States)

    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 dysfunction with symptoms is an important step in understanding the mechanisms of disease. ...

  13. 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.

  14. A multiuser detector based on artificial bee colony algorithm for DS-UWB systems.

    Science.gov (United States)

    Yin, Zhendong; Liu, Xiaohui; Wu, Zhilu

    2013-01-01

    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.

  15. 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.

  16. Artificial Bee Colony optimization algorithm studying climb process of Monkey Algorithm%学习猴群爬过程的人工蜂群优化算法

    Institute of Scientific and Technical Information of China (English)

    贾瑞民; 何登旭; 石绍堂

    2012-01-01

    针对人工蜂群算法迭代后期容易陷入局部的缺点,将猴群算法的爬过程引入到采蜜蜂采蜜的阶段,加强局部搜索.通过仿真实验测试,与参考文献中的改进算法进行比较,可以得到提出的改进算法比原人工蜂群算法及现有的部分改进算法性能优良,能够在一定程度上跳出局部最优,得到的近似解也更加接近测试函数理论最优解.%An improved Artificial Bee Colony (ABC) algorithm is proposed to overcome the flaw of standard ABC by introducing the climb process of Monkey Algorithm to update food source in order to enhance the local search ability at the stage of employ bees. Simulation results show that the proposed algorithm has better performance than ABC and the other kinds of improved algorithms put forward literally. It can jump out of local optima in a certain extent and get approximate solutions which are much closer to the theoretical solutions of the test functions.

  17. Optimal design of planet transmission mechanism in agricultural machinery based on bee colony and invasive weed optimization%基于蜂群-杂草算法的农机行星传动优化设计

    Institute of Scientific and Technical Information of China (English)

    龚亚星; 王联国

    2015-01-01

    A hybrid algorithm of bee colony and invasive weed optimization (BCIWO) is proposed by introducing the optimization mechanism of the artificial bee colony (ABC) algorithm into the invasive weed optimization (IWO) algo-rithm and applied into the optimal design of star gear planet transmission mechanism in agricultural machinery .This new algorithm (BCIWO) is compared with GA and IWO .The results showed that : ① The minimal design volume (1 .4725 × 107 mm3 ) of the planet transmission mechanism is obtained by BCIWO ,which is smaller than GA and IWO ; ② The so-lution fluctuation of BCIWO is also smaller than other two algorithms ,which illustrates it has a good stability ; ③ The calculation speed of BCIWO is faster .So we can conclude that BCIWO is worth further promoting in mechanism design field .%将人工蜂群算法的寻优机制引入到入侵性杂草优化算法中,提出了一种混合蜂群杂草算法(BCIWO),将其应用于农业机械中星齿行星传动机构的优化设计。选取一个实例,将新算法与遗传算法(GA)及基本杂草算法(IWO)的实验结果进行对比,结果表明:①采用 BCIWO 算法所得的行星传动结构的设计体积为1.4725×107 mm3,小于 GA 与 IWO 所得;② BCIWO 多次求解的波动较小,求解稳定性较高;③ BCIWO 的计算速度较快。从而推知, BCIWO 在机械优化设计领域更具推广性。

  18. Optimization Design of Artificial Bee Colony Algorithm in Automobile Structure Based on Sequential Response Surface Method%基于序列响应面法的汽车结构件蜂群优化设计

    Institute of Scientific and Technical Information of China (English)

    陈黎明; 陈文亮

    2013-01-01

    The artificial bee colony (ABC) algorithm, a relatively recent bio—inspired approach mimicking the behavior of real bee colony, was applied to deal with the optimization problems of automobile structure. The metamodel of objective and constrains were gotten through combination of design of experiment and sequential response surface method. Then,the optimum design was obtained by the modified artificial bee colony algorithm. It can reduce the computing cost by the metamodeling techniques. Finally.a typical example was selected to proof this method. The comparison results between the simulated and experimental values show that this method has enough precision and satisfies the engineering practical demands.%将蜂群算法应用于汽车结构件的优化问题.先由试验设计和序列响应面法构建目标函数及约束条件的代理模型,再应用改进的蜂群算法求解最优设计.在优化过程中调用的是代理模型,显著减少了有限元计算次数,提高了优化效率.最后,选取典型实例对该算法进行验证,比较预期值与实际值的结果表明,该算法具备了足够的求解精度,能够满足工程实际要求.

  19. A non-policing honey bee colony (Apis mellifera capensis)

    Science.gov (United States)

    Beekman, Madeleine; Good, Gregory; Allsopp, Mike; Radloff, Sarah; Pirk, Chris; Ratnieks, Francis

    2002-09-01

    In the Cape honey bee Apis mellifera capensis, workers lay female eggs without mating by thelytokous parthenogenesis. As a result, workers are as related to worker-laid eggs as they are to queen-laid eggs and therefore worker policing is expected to be lower, or even absent. This was tested by transferring worker- and queen-laid eggs into three queenright A. m. capensis discriminator colonies and monitoring their removal. Our results show that worker policing is variable in A. m. capensis and that in one colony worker-laid eggs were not removed. This is the first report of a non-policing queenright honey bee colony. DNA microsatellite and morphometric analysis suggests that the racial composition of the three discriminator colonies was different. The variation in policing rates could be explained by differences in degrees of hybridisation between A. m. capensis and A. m. scutellata, although a larger survey is needed to confirm this.

  20. 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...

  1. 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.

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

    Science.gov (United States)

    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. PMID:27583438

  3. 人工蜂群算法在渠道断面优化设计中的应用%Application of artificial bee colony algorithm to optimization of channel section

    Institute of Scientific and Technical Information of China (English)

    钱坤; 苏国韶

    2011-01-01

    With regard to the problems of huge tentative calculation, high accumulative error and low precision in the conventional optimal design for channel section, the artificial bee colony algorithm with good robust, high convergence speed and outstanding performance on solving global optimization problems was applied to the optimal design of channel section. Through establishing the multi-objective optimization function considering the hydraulic conditions and economic factors, the global optimal solutions to the problems of channel section were searched by means of the new method based on the artificial bee colony algorithm. The results show that the proposed method is feasible and has the advantages of high efficiency, high precision and being easy to implement.%针对常规渠道断面优化设计中存在的试算工作量大、累积误差大、计算精度不高的问题,将鲁棒性强、收敛速度快且全局寻优性能优异的人工蜂群算法引入渠道断面优化设计中,通过建立包含水力要素与经济要素的多目标优化函数,采用人工蜂群算法在全局空间下搜索渠道断面优化问题的全局最优解.研究结果表明,该方法是可行的,具有高效快速、计算精度高和易于实现等优点.

  4. A metagenomic survey of microbes in honey bee colony collapse disorder

    Science.gov (United States)

    In Colony Collapse Disorder (CCD), honey bee colonies inexplicably lose all of their workers. CCD has resulted in a loss of 50-90% of colonies in beekeeping operations across the United States. The observation that irradiated combs from affected colonies can be repopulated with naïve bees suggests a...

  5. 基于梯度蜂群混合算法的电力系统最优潮流计算%Hybrid Artificial bee Colony Algorithm Based on Gradient Method for Optimal Power Flower Calculation System

    Institute of Scientific and Technical Information of China (English)

    杨琳; 孔峰

    2011-01-01

    Aiming at optimal power flow calculation problem in power system, this paper presents a new method of hybrid artificial bee colony algorithm based on gradient method GABC. Firstly, the new algorithm used quickness searching of gradient method to obtain a local minimum. then by utilizing the abilities of global searching of artificial bee colony algorithm, it escaped from trapping this local minimum. At last, the global minimum was achieved through iterative computation. Simulation experiments of IEEE5 system show that the improved algorithm can be better dealt with optimal flow constraints in dealing with the issue of optimal power flow. This method can also find preferable results and its correctness and validity is proven by a series of tests and computation, and that the algorithms can be widely applied to the areas of power system planning and operation.%针对电力系统最优潮流计算的问题提出一种基于梯度蜂群混合算法GABC.利用梯度算法的快速寻优特性得到某一局部极值,然后采用蜂群算法的全局寻优能力跳出该局部极值,并经过反复交替迭代最终找到问题的最优解.通过对IEEE5节点系统的计算结果表明改进后的人工蜂群算法可较好的处理最优潮流约束条件,有效提高基本蜂群算法的全局寻优能力和收敛精度.在处理最优潮流问题上具有一定的有效性和优越性.

  6. An Artificial Bee Colony Algorithm for the Capacitated Vehicle Routing Problem

    DEFF Research Database (Denmark)

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

    This paper introduces an artificial bee colony heuristic for 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. The performance of the heuristic is evaluated on two sets of benchmark ins...

  7. 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.

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

    OpenAIRE

    Chunsheng Hou; Hadassah Rivkin; Yossi Slabezki; Nor Chejanovsky

    2014-01-01

    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 ...

  9. Cooperative Traffic Control based on the Artificial Bee Colony

    Directory of Open Access Journals (Sweden)

    Jinjian Li

    2016-12-01

    Full Text Available This paper studies the traffic control problem in an isolated intersection without traffic lights and phase, because the right-of-way is distributed to each vehicle individually based on connection of the Vehicle-to-Infrastructure (V2I, and the compatible streams are dynamically combined according to the arrival vehicles in each traffic flows. The control objective in the proposed algorithm is to minimize the time delay, which is defined as the difference between the travel time in real state and that in free flow state. In order to realize this target, a cooperative control structure with a two-way communications is proposed. First of all, once the vehicle enters the communication zone, it sends its information to the intersection. Then the passing sequence is optimized in the intersection with the heuristic algorithm of the Artificial Bee Colony, based on the arrival interval of the vehicles. At last, each vehicle plans its speed profile to meet the received passing sequence by V2I. The simulation results show that each vehicle can finish the entire travel trip with a near free flow speed in the proposed method.

  10. 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

  11. 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

  12. Modeling of higher order systems using artificial bee colony algorithm

    Directory of Open Access Journals (Sweden)

    Aytekin Bağış

    2016-05-01

    Full Text Available In this work, modeling of the higher order systems based on the use of the artificial bee colony (ABC algorithm were examined. Proposed model parameters for the sample systems in the literature were obtained by using the algorithm, and its performance was presented comparatively with the other methods. Simulation results show that the ABC algorithm based system modeling approach can be used as an efficient and powerful method for higher order systems.

  13. Probabilistic dynamic deployment of wireless sensor networks by artificial bee colony algorithm.

    Science.gov (United States)

    Ozturk, Celal; Karaboga, Dervis; Gorkemli, Beyza

    2011-01-01

    As the usage and development of wireless sensor networks are increasing, the problems related to these networks are being realized. Dynamic deployment is one of the main topics that directly affect the performance of the wireless sensor networks. In this paper, the artificial bee colony algorithm is applied to the dynamic deployment of stationary and mobile sensor networks to achieve better performance by trying to increase the coverage area of the network. A probabilistic detection model is considered to obtain more realistic results while computing the effectively covered area. Performance of the algorithm is compared with that of the particle swarm optimization algorithm, which is also a swarm based optimization technique and formerly used in wireless sensor network deployment. Results show artificial bee colony algorithm can be preferable in the dynamic deployment of wireless sensor networks.

  14. 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.

  15. Improvement of Gregory’s Formula Using Artificial Bee Colony Algorithm

    Directory of Open Access Journals (Sweden)

    S. AICHOUCHE

    2016-04-01

    Full Text Available Solving numerical integration is an important question in scientific calculations and engineering. Gregory’s method is among the very first quadrature formulas ever described in the literature, dating back to James Gregory (1638- 1675. In this article we prove that the Gregory Formula (G can be optimized by minimizing some of their coefficients in the remainder term by Artificial Bee Colony (ABC Algorithm. Experimental tests prove that obtained Formula can be rendered a powerful formula for library use.

  16. 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)

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

    OpenAIRE

    Giménez Bonillo, Sara

    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

  18. 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.

  19. 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.

  20. 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.

  1. 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.

  2. 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.

  3. 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.

  4. Bee colony optimization algorithm for split delivery vehicle routing problem%需求可拆分车辆路径问题的蜂群优化算法

    Institute of Scientific and Technical Information of China (English)

    汪婷婷; 倪郁东; 何文玲

    2014-01-01

    In this paper ,the split delivery vehicle routing problem is researched and the mathematical optimization model w hich seeks the shortest distance to meet the delivery requirements is constructed . T he model releases the constraint that only one vehicle can be used to meet the delivery requirement for one customer .A new bee colony optimization(BCO) algorithm is proposed based on the improve-ment of the reaction threshold and the value of stimulatory signal .The experimental result proves the feasibility of the algorithm ,and also highlights the strong optimization ability of BCO algorithm by comparison with other typical algorithms .%文章研究了需求可拆分的车辆路径问题,通过解除传统车辆路径问题中每个任务点需求只能由1辆车满足的约束,建立了寻求满足配送要求最短行驶距离的数学优化模型,在改进反应阈值和刺激信号值的基础上提出了一种新型蜂群优化算法。仿真实验结果验证了算法的可行性,并通过与其他典型算法对比凸显了该算法较强的寻优能力。

  5. 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

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

    Science.gov (United States)

    Jack, Cameron J; Lucas, Hannah M; Webster, Thomas C; Sagili, Ramesh R

    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

  7. 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.

  8. 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.

  9. Artificial Bee Colony Algorithm with Time-Varying Strategy

    Directory of Open Access Journals (Sweden)

    Quande Qin

    2015-01-01

    Full Text Available Artificial bee colony (ABC is one of the newest additions to the class of swarm intelligence. ABC algorithm has been shown to be competitive with some other population-based algorithms. However, there is still an insufficiency that ABC is good at exploration but poor at exploitation. To make a proper balance between these two conflictive factors, this paper proposed a novel ABC variant with a time-varying strategy where the ratio between the number of employed bees and the number of onlooker bees varies with time. The linear and nonlinear time-varying strategies can be incorporated into the basic ABC algorithm, yielding ABC-LTVS and ABC-NTVS algorithms, respectively. The effects of the added parameters in the two new ABC algorithms are also studied through solving some representative benchmark functions. The proposed ABC algorithm is a simple and easy modification to the structure of the basic ABC algorithm. Moreover, the proposed approach is general and can be incorporated in other ABC variants. A set of 21 benchmark functions in 30 and 50 dimensions are utilized in the experimental studies. The experimental results show the effectiveness of the proposed time-varying strategy.

  10. 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.

  11. 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.

  12. A metagenomic survey of microbes in honey bee colony collapse disorder.

    Science.gov (United States)

    Cox-Foster, Diana L; Conlan, Sean; Holmes, Edward C; Palacios, Gustavo; Evans, Jay D; Moran, Nancy A; Quan, Phenix-Lan; Briese, Thomas; Hornig, Mady; Geiser, David M; Martinson, Vince; vanEngelsdorp, Dennis; Kalkstein, Abby L; Drysdale, Andrew; Hui, Jeffrey; Zhai, Junhui; Cui, Liwang; Hutchison, Stephen K; Simons, Jan Fredrik; Egholm, Michael; Pettis, Jeffery S; Lipkin, W Ian

    2007-10-12

    In colony collapse disorder (CCD), honey bee colonies inexplicably lose their workers. CCD has resulted in a loss of 50 to 90% of colonies in beekeeping operations across the United States. The observation that irradiated combs from affected colonies can be repopulated with naive bees suggests that infection may contribute to CCD. We used an unbiased metagenomic approach to survey microflora in CCD hives, normal hives, and imported royal jelly. Candidate pathogens were screened for significance of association with CCD by the examination of samples collected from several sites over a period of 3 years. One organism, Israeli acute paralysis virus of bees, was strongly correlated with CCD.

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

    OpenAIRE

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

    2012-01-01

    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...

  14. Evaluating the Effect of Environmental Chemicals on Honey Bee Development from the Individual to Colony Level.

    Science.gov (United States)

    Ko, Chong-Yu; Chen, Yue-Wen; Nai, Yu-Shin

    2017-04-01

    The presence of pesticides in the beekeeping environment is one of the most serious problems that impacts the life of a honey bee. Pesticides can be brought back to the beehive after the bees have foraged on flowers that have been sprayed with pesticides. Pesticide contaminated food can be exchanged between workers which then feed larvae and therefore can potentially affect the development of honey bees. Thus, residual pesticides in the environment can become a chronic damaging factor to honey bee populations and gradually lead to colony collapse. In the presented protocol, honey bee feeding methods are described and applied to either an individual honey bee or to a colony. Here, the insect growth regulator (IGR) pyriproxyfen (PPN), which is widely used to control pest insects and is harmful to the development of honey bee larvae and pupae, is used as the pesticide. The presenting procedure can be applied to other potentially harmful chemicals or honeybee pathogens for further studies.

  15. 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.

  16. 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.

  17. An Effective Hybrid Artificial Bee Colony Algorithm for Nonnegative Linear Least Squares Problems

    Directory of Open Access Journals (Sweden)

    Xiangyu Kong

    2014-07-01

    Full Text Available An effective hybrid artificial bee colony algorithm is proposed in this paper for nonnegative linear least squares problems. To further improve the performance of algorithm, orthogonal initialization method is employed to generate the initial swarm. Furthermore, to balance the exploration and exploitation abilities, a new search mechanism is designed. The performance of this algorithm is verified by using 27 benchmark functions and 5 nonnegative linear least squares test problems. And the comparison analyses are given between the proposed algorithm and other swarm intelligence algorithms. Numerical results demonstrate that the proposed algorithm displays a high performance compared with other algorithms for global optimization problems and nonnegative linear least squares problems.

  18. Classification of remote sensed data using Artificial Bee Colony algorithm

    Directory of Open Access Journals (Sweden)

    J. Jayanth

    2015-06-01

    Full Text Available The present study employs the traditional swarm intelligence technique in the classification of satellite data since the traditional statistical classification technique shows limited success in classifying remote sensing data. The traditional statistical classifiers examine only the spectral variance ignoring the spatial distribution of the pixels corresponding to the land cover classes and correlation between various bands. The Artificial Bee Colony (ABC algorithm based upon swarm intelligence which is used to characterise spatial variations within imagery as a means of extracting information forms the basis of object recognition and classification in several domains avoiding the issues related to band correlation. The results indicate that ABC algorithm shows an improvement of 5% overall classification accuracy at 6 classes over the traditional Maximum Likelihood Classifier (MLC and Artificial Neural Network (ANN and 3% against support vector machine.

  19. 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.

  20. 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.

  1. Automatic software fault localization based on ar tificial bee colony

    Institute of Scientific and Technical Information of China (English)

    Linzhi Huang∗; Jun Ai

    2015-01-01

    Software debugging accounts for a vast majority of the financial and time costs in software developing and maintenance. Thus, approaches of software fault localization that can help au-tomate the debugging process have become a hot topic in the field of software engineering. Given the great demand for software fault localization, an approach based on the artificial bee colony (ABC) algorithm is proposed to be integrated with other related techniques. In this process, the source program is initial y instru-mented after analyzing the dependence information. The test case sets are then compiled and run on the instrumented program, and execution results are input to the ABC algorithm. The algorithm can determine the largest fitness value and best food source by calculating the average fitness of the employed bees in the iter-ative process. The program unit with the highest suspicion score corresponding to the best test case set is regarded as the final fault localization. Experiments are conducted with the TCAS program in the Siemens suite. Results demonstrate that the proposed fault localization method is effective and efficient. The ABC algorithm can efficiently avoid the local optimum, and ensure the validity of the fault location to a larger extent.

  2. 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.

  3. 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.

  4. Ant colony optimization in continuous problem

    Institute of Scientific and Technical Information of China (English)

    YU Ling; LIU Kang; LI Kaishi

    2007-01-01

    Based on the analysis of the basic ant colony optimization and optimum problem in a continuous space,an ant colony optimization (ACO) for continuous problem is constructed and discussed. The algorithm is efficient and beneficial to the study of the ant colony optimization in a continuous space.

  5. Enhanced probability-selection artificial bee colony algorithm for economic load dispatch: A comprehensive analysis

    Science.gov (United States)

    Ghani Abro, Abdul; Mohamad-Saleh, Junita

    2014-10-01

    The prime motive of economic load dispatch (ELD) is to optimize the production cost of electrical power generation through appropriate division of load demand among online generating units. Bio-inspired optimization algorithms have outperformed classical techniques for optimizing the production cost. Probability-selection artificial bee colony (PS-ABC) algorithm is a recently proposed variant of ABC optimization algorithm. PS-ABC generates optimal solutions using three different mutation equations simultaneously. The results show improved performance of PS-ABC over the ABC algorithm. Nevertheless, all the mutation equations of PS-ABC are excessively self-reinforced and, hence, PS-ABC is prone to premature convergence. Therefore, this research work has replaced the mutation equations and has improved the scout-bee stage of PS-ABC for enhancing the algorithm's performance. The proposed algorithm has been compared with many ABC variants and numerous other optimization algorithms on benchmark functions and ELD test cases. The adapted ELD test cases comprise of transmission losses, multiple-fuel effect, valve-point effect and toxic gases emission constraints. The results reveal that the proposed algorithm has the best capability to yield the optimal solution for the problem among the compared algorithms.

  6. 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: (1

  7. An artificial bee colony algorith m with the feasibility rule for portfolio investment optimizations%投资组合优化的可行性规则人工蜂群算法

    Institute of Scientific and Technical Information of China (English)

    刘永波

    2014-01-01

    给出含交易费用和投资者风险偏好的最佳证券投资组合约束优化模型,并应用人工蜂群算法( ABC)求解该问题。应用可行性规则处理优化问题的约束条件,形成可行性规则人工蜂群算法( FRABC)。应用Markov链理论证明FRABC算法为全局收敛算法。给出了证券投资组合优化仿真实例。实验结果表明,FRABC算法可行有效,且寻优结果优于自适应遗传算法。在相同计算开销的条件下,FRABC算法的各项性能指标也明显好于遗传算法、粒子群算法及基本人工蜂群算法等对比算法。%This current work was carried out to approach the portfolio investment optimization problem by using an artificial bee colony (ABC) algorithm, in order to provide references for related researches .A constrained optimiza-tion model was constructed to formulate the portfolio investment optimization problem concerning securities subject to transaction fees and risk preferences of investors .This study employs feasibility rules to handle the constrained conditions of the optimization problem and forms an ABC algorithm with the feasibility rule ( FRABC) .It has been concluded by means of the Markov chain theory that the developed FRABC algorithm is globally convergent .A real-istic case of the portfolio investment optimization is given to show that this method is valid and feasible , and the re-sults are better than the ones obtained by the adaptive genetic algorithm ( AGA) .The proposed FRABC algorithm performs better , in terms of the final results , than the compared algorithms such as the genetic algorithm , particle swarm optimization algorithm and the basic ABC algorithm with the feasibility rule , under the assumed condition that the computational costs for the two algorithms are the same .

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

    Science.gov (United States)

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

    2017-08-01

    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.

  9. Task Scheduling Based On Load Balancing Using Artificial Bee Colony In Cloud Computing Environment

    Directory of Open Access Journals (Sweden)

    Fatemeh Rastkhadiv

    2016-12-01

    Full Text Available Cloud computing is a development of distributed computing, parallel computing and grid computing. The aim of cloud computing is providing dynamic leasing of server capabilities as scalable, virtualized services to end users. Resource management as an important issue of cloud computing.Load balancing is a problem of resource management. Recently, Cloud schedulers based on bio-inspired and metaheuristic techniques have been proposed. A good task scheduler should adapt its scheduling strategy to the dynamic environment. Load balancing of nonpreemptive independent tasks on virtual machines is an important aspect of task scheduling in clouds.In this paper, wehave proposeda new cloud scheduler based on load balancing using by Artificial Bee Colonyalgorithm, one of the most popular bio-inspired technique. Artificial bee colony algorithm is an optimization metaheuristic algorithm based on a particular intelligent behavior of honey bee swarms. Our scheduler is designed to achieve well balanced load across virtual machines for maximizing the throughput and deliver to theminimum makespan. The experimental results simulatedusing Cloudsim shows its effectiveness to optimize load balancing and task scheduling compared with both of FCFS and ACO. Our proposed approach allows for more agile task handling while reducing task completion time. Our proposed approach reduces makespan and degree of imbalance.It distributes tasks and makes load balancing between virtual machines. Consequently the proposed algorithm increases performance and resource efficiency.

  10. 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.

  11. Dynamics of the presence of israeli acute paralysis virus in honey bee colonies with colony collapse disorder.

    Science.gov (United States)

    Hou, Chunsheng; Rivkin, Hadassah; Slabezki, Yossi; Chejanovsky, Nor

    2014-05-05

    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.

  12. 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.

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

    Science.gov (United States)

    Shao, Jing; Yang, Lina; Peng, Ling; Chi, Tianhe; Wang, Xiaomeng

    2015-01-01

    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.

  14. A mathematical model for the interplay of Nosema infection and forager losses in honey bee colonies.

    Science.gov (United States)

    Petric, Alex; Guzman-Novoa, Ernesto; Eberl, Hermann J

    2016-10-05

    We present a mathematical model (a) for the infection of a honey bee colony with Nosema ceranae. This is a system of five ordinary differential equations for the dependent variables healthy and infected worker bees in the hive, healthy and infected forager bees, and disease potential deposited in the hive. The model is then (b) extended to account for increased forager losses, e.g. caused by exposure to external stressors. The model is non-autonomous with periodic coefficient functions. Algebraic complexity prevents a rigorous mathematical analysis. Therefore, we resort to computer simulations in addition to some analytical results in the constant coefficient case. We investigate each of the two stressors (a) and (b) individually and jointly. Our results indicate that the combined effect of two stressors, both of which can be tolerated by the colony individually, might lead to colony failure, suggesting multi-factorial causes behind losses of honey bee colonies.

  15. 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...

  16. 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.

  17. 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.

  18. Effects of queen ages on Varroa (Varroa destructor infestation level in honey bee (Apis mellifera caucasica colonies and colony performance

    Directory of Open Access Journals (Sweden)

    Duran Özkök

    2010-01-01

    Full Text Available This study was conducted to determine the effects of queen age on varroa population levels in hives and performance of honey bee (A. mellifera caucasica colonies. Levels of varroa infestation and performances of the colonies which had 0, 1- and 2-year-old queens were compared in mild climate conditions. Varroa numbers on adults and drone brood, number of frames covered with bees and brood areas were determined every month between 10 May and 10 October 2004. Overall average (± S.E. % infestation levels of varroa were found to be 5.96 ± 1.42, 11.58 ± 1.46 and 15.87 ± 1.39% on adult bees and 21.55 ± 1.43, 31.96 ± 1.44 and 37.55 ± 1.45% in drone brood cells for 0, 1- and 2-year-old queen colonies, respectively. The colonies which had 0, 1- and 2-year-old queens produced 2673.58 ± 39.69, 2711.75 ± 39.68, and 1815.08 ± 39.70 cm2 overall average (± S.E. sealed brood and 10.35 ± 0.24, 10.43 ± 0.26 and 7.51 ± 0.21 numbers of frame adult bees, respectively. Honey harvested from 0, 1- and 2-year-old queen colonies averaged 21.60 ± 5.25, 22.20 ± 6.55, and 14.70 ± 2.50 kg/colony, respectively. The colonies headed by young queens had a lower level of varroa infestation, a greater brood area, longer worker bee population and greater honey yield in comparison to colonies headed by old queens.

  19. An effective discrete artificial bee colony algorithm for flow shop scheduling problem with intermediate buffers

    Institute of Scientific and Technical Information of China (English)

    张素君; 顾幸生

    2015-01-01

    An effective discrete artificial bee colony(DABC) algorithm is proposed for the flow shop scheduling problem with intermediate buffers (IBFSP) in order to minimize the maximum completion time (i.e makespan). The effective combination of the insertion and swap operator is applied to producing neighborhood individual at the employed bee phase. The tournament selection is adopted to avoid falling into local optima, while, the optimized insert operator embeds in onlooker bee phase for further searching the neighborhood solution to enhance the local search ability of algorithm. The tournament selection with size 2 is again applied and a better selected solution will be performed destruction and construction of iterated greedy (IG) algorithm, and then the result replaces the worse one. Simulation results show that our algorithm has a better performance compared with the HDDE and CHS which were proposed recently. It provides the better known solutions for the makespan criterion to flow shop scheduling problem with limited buffers for the Car benchmark by Carlier and Rec benchmark by Reeves. The convergence curves show that the algorithm not only has faster convergence speed but also has better convergence value.

  20. Multilevel Minimum Cross Entropy Image Thresholding using Artificial Bee Colony Algorithm

    Directory of Open Access Journals (Sweden)

    Ming-Huwi Horng

    2013-09-01

    Full Text Available The minimum cross entropy thresholding (MCET has been widely applied in image processing.   In this paper, a new multilevel MCET algorithm based on the artificial bee colony (ABC algorithm is proposed.  The proposed thresholding algorithm is called ABC-based MCET algorithm. Four different methods including the exhaustive search, the honey bee mating optimization (HBMO, the particle swarm optimization (PSO and the quantum particle swarm optimization (QPSO methods are also implemented for comparison with the results of the proposed method. The experimental results demonstrate that the proposed ABC-based MCET algorithm can efficiently search for multiple thresholds that are very close to the optimal ones selected by using the exhaustive search method. Compared with the other three thresholding methods, the segmentation results using the ABC-based MCET algorithm is the best.  It is promising to encourage further research for applying the HBMO algorithm to complex problems of image processing and pattern recognition.  

  1. 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.

  2. Application of Artificial Bee Colony Algorithm to Maximum Likelihood DOA Estimation

    Institute of Scientific and Technical Information of China (English)

    Zhicheng Zhang; Jun Lin; Yaowu Shi

    2013-01-01

    Maximum Likelihood (ML) method has an excellent performance for Direction-Of-Arrival (DOA) estimation,but a multidimensional nonlinear solution search is required which complicates the computation and prevents the method from practical use.To reduce the high computational burden of ML method and make it more suitable to engineering applications,we apply the Artificial Bee Colony (ABC) algorithm to maximize the likelihood function for DOA estimation.As a recently proposed bio-inspired computing algorithm,ABC algorithm is originally used to optimize multivariable functions by imitating the behavior of bee colony finding excellent nectar sources in the nature environment.It offers an excellent alternative to the conventional methods in ML-DOA estimation.The performance of ABC-based ML and other popular meta-heuristic-based ML methods for DOA estimation are compared for various scenarios of convergence,Signal-to-Noise Ratio (SNR),and number of iterations.The computation loads of ABC-based ML and the conventional ML methods for DOA estimation are also investigated.Simulation results demonstrate that the proposed ABC based method is more efficient in computation and statistical performance than other ML-based DOA estimation methods.

  3. Changes in transcript abundance relating to colony collapse disorder in honey bees (Apis mellifera).

    Science.gov (United States)

    Johnson, Reed M; Evans, Jay D; Robinson, Gene E; Berenbaum, May R

    2009-09-01

    Colony collapse disorder (CCD) is a mysterious disappearance of honey bees that has beset beekeepers in the United States since late 2006. Pathogens and other environmental stresses, including pesticides, have been linked to CCD, but a causal relationship has not yet been demonstrated. Because the gut acts as a primary interface between the honey bee and its environment as a site of entry for pathogens and toxins, we used whole-genome microarrays to compare gene expression between guts of bees from CCD colonies originating on both the east and west coasts of the United States and guts of bees from healthy colonies sampled before the emergence of CCD. Considerable variation in gene expression was associated with the geographical origin of bees, but a consensus list of 65 transcripts was identified as potential markers for CCD status. Overall, elevated expression of pesticide response genes was not observed. Genes involved in immune response showed no clear trend in expression pattern despite the increased prevalence of viruses and other pathogens in CCD colonies. Microarray analysis revealed unusual ribosomal RNA fragments that were conspicuously more abundant in the guts of CCD bees. The presence of these fragments may be a possible consequence of picorna-like viral infection, including deformed wing virus and Israeli acute paralysis virus, and may be related to arrested translation. Ribosomal fragment abundance and presence of multiple viruses may prove to be useful diagnostic markers for colonies afflicted with CCD.

  4. First analysis of risk factors associated with bee colony collapse disorder by classification and regression trees

    Science.gov (United States)

    Sudden losses of managed honey bee (Apis mellifera L.) colonies are considered an important problem worldwide but the underlying cause or causes of these losses are currently unknown. In the United States, this syndrome was termed Colony Collapse Disorder (CCD), since the defining trait was a rapid ...

  5. 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.

  6. 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...

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

    DEFF Research Database (Denmark)

    Forfert, Nadège; Natsopoulou, Myrsini E.; Paxton, Robert J.;

    2016-01-01

    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....

  8. 蜂群优化的二维非对称 Tsallis 交叉熵图像阈值选取%Two-dimensional asymmte ric tsallis cross entropy image threshold selection using bee colony optimization

    Institute of Scientific and Technical Information of China (English)

    吴一全; 王凯; 曹鹏祥

    2015-01-01

    Cross entropy can measure the difference between the original image and its segmentation result .Comparedwith Shannon cross entropy , Tsallis cross entropy, in which a parameter q is introduced, provides flexibilityand universality for the segmentation of image threshold .The asymmetric Tsallis cross entropy has more concise expressionform.Therefore, a method of threshold selection is proposed based on the two -dimensional asymmetric Tsalliscross entropy using bee colony optimization.Firstly, the asymmetric Tsallis cross entropy is introduced and thethreshold selection formulae based on the two -dimensional asymmetric Tsallis cross entropy are derived .Recursivealgorithms are used to calculate the intermediate variables involved in criterion function for threshold selection and alookup table is built to eliminate the redundant operations .The optimal two-dimensional threshold is searched by thebee colony algorithm.A large number of experiment results showed that the proposed method is greatly improved interms of subjective visual effect and inter-regional contrast evaluation indicators compared to the relevant methods ,such as the two-dimensional maximum Shannon entropy method , the two-dimensional Shannon cross entropy method,the two-dimensional Tsallis entropy method, and the two-dimensional symmetrical Tsallis cross entropy method .It can segment objects more accurately and has a faster running speed .%交叉熵能够度量图像分割前后的差异,与Shannon交叉熵相比,引入参数q的Tsallis交叉熵则为图像阈值分割提供了灵活性和普适性,而非对称Tsallis交叉熵的表达形式更加简洁。由此,提出了蜂群优化的二维非对称Tsal-lis交叉熵图像阈值选取方法。首先引出了非对称Tsallis交叉熵,导出了二维非对称Tsallis交叉熵阈值选取公式,并利用递推方式计算阈值选取准则函数涉及的中间变量,建立查找表,消除冗余运算;然后采用蜂群算法搜寻最

  9. Performance of Bee Colonies Headed by Queens Instrumentally Inseminated with Semen of Drones Who Come from a Single Colony or Many Colonies

    Directory of Open Access Journals (Sweden)

    Gerula Dariusz

    2014-12-01

    Full Text Available The aim of the study was to determine the effect of honey bee worker diversity within the colony on: development, honey productivity, and wintering. Two different levels of diversity within the colony were tested. The appropriate levels of diversity within the colony were obtained by selecting drones for inseminating the queens. Lower genetic diversity was obtained in the colonies headed by a queen inseminated with semen collected from drones originating from a single colony. Higher genetic diversity was obtained in the colonies with queens inseminated with semen from drones of 30 different colonies. Colonies with a higher genetic variation of workers in the colonies had greater levels of functional characteristics. However, apart from the number of dead bees in winter, the genetic diversity level of the workers on the colony development and honey production, did not have a significant influence. There was an averaging effect observed concerning that male component in the colonies with a higher genetic variation of workers - on honey yield, when compared to the non-additive effect of the best drones.

  10. Evaluation of lysozyme-HCl for the treatment of chalkbrood disease in honey bee colonies.

    Science.gov (United States)

    Van Haga, A; Keddie, B A; Pernal, S F

    2012-12-01

    Chalkbrood, caused by Ascosphaera apis (Maassen and Claussen) Olive and Spiltor, is a cosmopolitan fungal disease of honey bee larvae (Apis mellifera L.) for which there is no chemotherapeutic control. We evaluated the efficacy of lysozyme-HCl, an inexpensive food-grade antimicrobial extracted from hen egg white, for the treatment of chalkbrood disease in honey bee colonies. Our study compared three doses of lysozyme-HCl in sugar syrup (600, 3,000, and 6,000 mg) administered weekly for 3 wk among chalkbrood-inoculated colonies, colonies that were inoculated but remained untreated, and colonies neither inoculated or treated. Lysozyme-HCl at the highest dose evaluated was found to suppress development of chalkbrood disease in inoculated colonies to levels observed in uninoculated, untreated colonies, and did not adversely affect adult bee survival or brood production. Honey production was significantly negatively correlated with increased disease severity but there were no significant differences in winter survival among treatment groups. Based on our results, lysozyme-HCl appears to be a promising, safe therapeutic agent for the control of chalkbrood in honey bee colonies.

  11. Individual precocity, temporal persistence, and task-specialization of hygienic bees from selected colonies of Apis mellifera

    Directory of Open Access Journals (Sweden)

    Scannapieco Alejandra C.

    2016-06-01

    Full Text Available Hygienic behaviour is a complex trait that gives Apis mellifera L. resistance against brood diseases. Variability in the expression of hygienic behaviour is evidenced at the colony-level and is explained by the proportion and propensity of individual worker bees that engage in hygienic activities. We investigated the temporal performance and the dynamics of task-specialisation of individual bees over time, both in selected hygienic (H and non-hygienic (NH colonies. Then we evaluated the impact of these behavioural aspects on the colony performance. Bees that perform hygienic behaviour (hygienic bees in our H colonies were more persistent in the hygienic activities throughout the days of the investigation. Such bees were more efficient in the removal of pin-killed brood than hygienic bees in the NH colonies. Hygienic bees in the H colonies were also specialist in the sub-tasks involved in the detection of odour stimulus from dead brood and continued to perform these activities throughout the days of the investigation (temporal persistence. Age-distribution of hygienic bees in the H colonies was asymmetrical, with a larger proportion of these bees performing hygienic activities early in life. At a colony-level, H showed higher efficiency compared to the NH colonies. The present results highlight the fact that individual behaviour may influence the collective dynamics of the hygienic behaviour in honeybee colonies. The results also note that the selection for highly hygienic colonies would result in changes in individual bees that improve the performance of the behaviour at the colony level. The relevance of task-partitioning and age-specialisation of hygienic bees on social immunity is discussed.

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

    Science.gov (United States)

    Samson-Robert, Olivier; Labrie, Geneviève; Chagnon, Madeleine; Fournier, Valérie

    2017-01-01

    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.

  13. 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.

  14. Fire Evacuation using Ant Colony Optimization Algorithm

    National Research Council Canada - National Science Library

    Kanika Singhal; Shashank Sahu

    2016-01-01

    ... planning.The objective of the algorithm is to minimizes the entire rescue time of all evacuees.The ant colony optimization algorithm is used to solve the complications of shortest route planning. Presented paper gives a comparative overview of various emergency scenarios using ant colony optimization algorithm.

  15. 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.

  16. Application of Artificial Bee Colony Algorithm to Portfolio Adjustment Problem with Transaction Costs

    Directory of Open Access Journals (Sweden)

    Wei Chen

    2014-01-01

    Full Text Available Compared with the conventional probabilistic mean-variance methodology, fuzzy number can better describe an uncertain environment with vagueness and ambiguity. In this paper, we discuss a portfolio adjusting problem under the assumption that the returns of risky assets are fuzzy numbers and there exist general transaction costs in portfolio adjusting process. In the proposed model, we take the first possibilistic moment about zero of a portfolio as the investment return and the second possibilistic moment about the possibilistic mean value of the portfolio as the investment risk. To solve the proposed model, a modified artificial bee colony (ABC algorithm is developed for calculating the optimal portfolio adjusting strategy. Finally, a numerical example is given to illustrate the effectiveness of the proposed model and approach.

  17. Flame Image Segmentation Based on the Bee Colony Algorithm with Characteristics of Levy Flights

    Directory of Open Access Journals (Sweden)

    Xiaolin Zhang

    2015-01-01

    Full Text Available The real-time processing of the image segmentation method with accuracy is very important in the application of the flame image detection system. This paper considers a novel method for flame image segmentation. It is the bee colony algorithm with characteristics enhancement of Levy flights against the problems of the algorithm during segmentation, including long calculation time and poor stability. By introducing the idea of Levy flights, this method designs a new local search strategy. By setting the current optimal value and based on the collaboration between the populations, it reinforces the overall convergence speed. By adopting the new fitness evaluation method and combining it with the two-dimensional entropy multithreshold segmentation principle, this paper develops a threshold segmentation test of the flame image. Test results show that this method has some advantages in terms of accuracy of threshold selection and calculation time. The robustness of the algorithm meets the actual demands in the engineering application.

  18. 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.

  19. Optimizing ZigBee Security using Stochastic Model Checking

    OpenAIRE

    Yuksel, Ender; Nielson, Hanne Riis; Nielson, Flemming; Fruth, Matthias; Kwiatkowska, Marta

    2012-01-01

    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, 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 checki...

  20. 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.

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

    Science.gov (United States)

    Freire Roberto, Guilherme; Castilho Maschi, Luis Fernando; Pigatto, Daniel Fernando; Jaquie Castelo Branco, Kalinka Regina Lucas; Alves Neves, Leandro; Montez, Carlos; Sandro Roschildt Pinto, Alex

    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).

  2. Combined pesticide exposure severely affects individual- and colony-level traits in bees.

    Science.gov (United States)

    Gill, Richard J; Ramos-Rodriguez, Oscar; Raine, Nigel E

    2012-11-01

    Reported widespread declines of wild and managed insect pollinators have serious consequences for global ecosystem services and agricultural production. Bees contribute approximately 80% of insect pollination, so it is important to understand and mitigate the causes of current declines in bee populations . Recent studies have implicated the role of pesticides in these declines, as exposure to these chemicals has been associated with changes in bee behaviour and reductions in colony queen production. However, the key link between changes in individual behaviour and the consequent impact at the colony level has not been shown. Social bee colonies depend on the collective performance of many individual workers. Thus, although field-level pesticide concentrations can have subtle or sublethal effects at the individual level, it is not known whether bee societies can buffer such effects or whether it results in a severe cumulative effect at the colony level. Furthermore, widespread agricultural intensification means that bees are exposed to numerous pesticides when foraging, yet the possible combinatorial effects of pesticide exposure have rarely been investigated. Here we show that chronic exposure of bumblebees to two pesticides (neonicotinoid and pyrethroid) at concentrations that could approximate field-level exposure impairs natural foraging behaviour and increases worker mortality leading to significant reductions in brood development and colony success. We found that worker foraging performance, particularly pollen collecting efficiency, was significantly reduced with observed knock-on effects for forager recruitment, worker losses and overall worker productivity. Moreover, we provide evidence that combinatorial exposure to pesticides increases the propensity of colonies to fail.

  3. 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.

  4. Israeli acute paralysis virus: epidemiology, pathogenesis and implications for honey bee health and Colony Collapse Disorder (CCD)

    Science.gov (United States)

    Israeli acute paralysis virus (IAPV) is a widespread RNA virus that was linked with honey bee Colony Collapse Disorder (CCD), the sudden and massive die-off of honey bee colonies in the U.S. in 2006-2007. Here we describe the transmission, prevalence and genetic diversity of IAPV, host transcripti...

  5. Characterization of viral siRNA populations in honey bee colony collapse disorder.

    Science.gov (United States)

    Chejanovsky, Nor; Ophir, Ron; Schwager, Michal Sharabi; Slabezki, Yossi; Grossman, Smadar; Cox-Foster, Diana

    2014-04-01

    Colony Collapse Disorder (CCD), a special case of collapse of honey bee colonies, has resulted in significant losses for beekeepers. CCD-colonies show abundance of pathogens which suggests that they have a weakened immune system. Since honey bee viruses are major players in colony collapse and given the important role of viral RNA interference (RNAi) in combating viral infections we investigated if CCD-colonies elicit an RNAi response. Deep-sequencing analysis of samples from CCD-colonies from US and Israel revealed abundant small interfering RNAs (siRNA) of 21-22 nucleotides perfectly matching the Israeli acute paralysis virus (IAPV), Kashmir virus and Deformed wing virus genomes. Israeli colonies showed high titers of IAPV and a conserved RNAi-pattern of matching the viral genome. That was also observed in sample analysis from colonies experimentally infected with IAPV. Our results suggest that CCD-colonies set out a siRNA response that is specific against predominant viruses associated with colony losses. Copyright © 2014 Elsevier Inc. All rights reserved.

  6. 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.

  7. Superinfection exclusion and the long-term survival of honey bees in Varroa-infested colonies

    Science.gov (United States)

    Mordecai, Gideon J; Brettell, Laura E; Martin, Stephen J; Dixon, David; Jones, Ian M; Schroeder, Declan C

    2016-01-01

    Over the past 50 years, many millions of European honey bee (Apis mellifera) colonies have died as the ectoparasitic mite, Varroa destructor, has spread around the world. Subsequent studies have indicated that the mite's association with a group of RNA viral pathogens (Deformed Wing Virus, DWV) correlates with colony death. Here, we propose a phenomenon known as superinfection exclusion that provides an explanation of how certain A. mellifera populations have survived, despite Varroa infestation and high DWV loads. Next-generation sequencing has shown that a non-lethal DWV variant ‘type B' has become established in these colonies and that the lethal ‘type A' DWV variant fails to persist in the bee population. We propose that this novel stable host-pathogen relationship prevents the accumulation of lethal variants, suggesting that this interaction could be exploited for the development of an effective treatment that minimises colony losses in the future. PMID:26505829

  8. Population growth of Varroa destructor (Acari: Varroidae) in commercial honey bee colonies treated with beta plant acids.

    Science.gov (United States)

    DeGrandi-Hoffman, Gloria; Ahumada, Fabiana; Curry, Robert; Probasco, Gene; Schantz, Lloyd

    2014-10-01

    Varroa (Varroa destuctor Anderson and Trueman) populations in honey bee (Apis mellifera L.) colonies might be kept at low levels by well-timed miticide applications. HopGuard(®) (HG) that contains beta plant acids as the active ingredient was used to reduce mite populations. Schedules for applications of the miticide that could maintain low mite levels were tested in hives started from either package bees or splits of larger colonies. The schedules were developed based on defined parameters for efficacy of the miticide and predictions of varroa population growth generated from a mathematical model of honey bee colony-varroa population dynamics. Colonies started from package bees and treated with HG in the package only or with subsequent HG treatments in the summer had 1.2-2.1 mites per 100 bees in August. Untreated controls averaged significantly more mites than treated colonies (3.3 mites per 100 bees). By October, mite populations ranged from 6.3 to 15.0 mites per 100 bees with the lowest mite numbers in colonies treated with HG in August. HG applications in colonies started from splits in April reduced mite populations to 0.12 mites per 100 bees. In September, the treated colonies had significantly fewer mites than the untreated controls. Subsequent HG applications in September that lasted for 3 weeks reduced mite populations to levels in November that were significantly lower than in colonies that were untreated or had an HG treatment that lasted for 1 week. The model accurately predicted colony population growth and varroa levels until the fall when varroa populations measured in colonies established from package bees or splits were much greater than predicted. Possible explanations for the differences between actual and predicted mite populations are discussed.

  9. 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.

  10. Characterization of the Active Microbiotas Associated with Honey Bees Reveals Healthier and Broader Communities when Colonies are Genetically Diverse

    Science.gov (United States)

    Mattila, Heather R.; Rios, Daniela; Walker-Sperling, Victoria E.; Roeselers, Guus; Newton, Irene 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 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. PMID:22427917

  11. 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.

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

    Science.gov (United States)

    Mattila, Heather R; Rios, Daniela; Walker-Sperling, Victoria E; Roeselers, Guus; Newton, Irene 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 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.

  13. Influence of Honey Bee Genotype and Wintering Method on Wintering Performance of Varroa destructor (Parasitiformes: Varroidae)-Infected Honey Bee (Hymenoptera: Apidae) Colonies in a Northern Climate.

    Science.gov (United States)

    Bahreini, Rassol; Currie, Robert W

    2015-08-01

    The objective of this study was to assess the effectiveness of a cooperative breeding program designed to enhance winter survival of honey bees (Apis mellifera L.) when exposed to high levels of varroa (Varroa destructor Anderson and Trueman) in outdoor-wintered and indoor-wintered colonies. Half of the colonies from selected and unselected stocks were randomly assigned to be treated with late autumn oxalic acid treatment or to be left untreated. Colonies were then randomly assigned to be wintered either indoors (n = 37) or outdoors (n = 40). Late autumn treatment with oxalic acid did not improve wintering performance. However, genotype of bees affected colony survival and the proportion of commercially viable colonies in spring, as indicated by greater rates of colony survival and commercially viable colonies for selected stock (43% survived and 33% were viable) in comparison to unselected stock (19% survived and 9% were viable) across all treatment groups. Indoor wintering improved spring bee population score, proportion of colonies surviving, and proportion of commercially viable colonies relative to outdoor wintering (73% of selected stock and 41% of unselected stock survived during indoor wintering). Selected stock showed better "tolerance" to varroa as the selected stock also maintained higher bee populations relative to unselected stock. However, there was no evidence of "resistance" in selected colonies (reduced mite densities). Collectively, this experiment showed that breeding can improve tolerance to varroa and this can help minimize colony loss through winter and improve colony wintering performance. Overall, colony wintering success of both genotypes of bees was better when colonies were wintered indoors than when colonies were wintered outdoors.

  14. Susceptibility of Bee Larvae to Chalkbrood in Relation to Hygienic Behaviour of Worker Bees in Colonies of Chosen Races of Honeybee (Apis Mellifera

    Directory of Open Access Journals (Sweden)

    Panasiuk Beata

    2014-06-01

    Full Text Available The susceptibility of bee larvae to Ascosphaera apis infestation and the hygienic behaviour of worker bees in relation to A. apis infected and freeze-killed brood were evaluated in three races of bees: Apis mellifera carnica, Apis mellifera caucasica, and Apis mellifera mellifera. Experimental bee colonies were evaluated in field conditions during the three beekeeping seasons. The lowest percentage of infected larvae was observed in car GR1 and mel A colonies (8.5% and 15%, respectively and the highest in car Mr and cau P colonies (21% and 24.3%, respectively. Bees in the car GR1 and mel A colonies removed mummified brood in a shorter period of time (6.5 and 7.1 days on average, respectively than car Mr and cau P colonies (above 8 days. Bees in the mel A and car GR1 colonies cleaned significantly more cells with freeze-killed brood within 24 and 48 hours (above 70% and 80% on average, respectively than car Mr and cau P colonies (on average 10 - 20% lower cleaning rate. A low correlation coefficient was found for the susceptibility of larvae to A. apis infection and hygienic behaviour.

  15. 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.

  16. Fuzzy Artificial Bee Colony System with Cooling Schedule for the Segmentation of Medical Images by Using of Spatial Information

    Directory of Open Access Journals (Sweden)

    Jzau-sheng Lin

    2013-02-01

    Full Text Available In this study, segmentation of medical images using a fuzzy artificial bee colony algorithm with a cooling schedule is created. In this study, we embedded fuzzy inference strategy into the artificial bee colony system to construct a segmentation system named Fuzzy Artificial Bee Colony System (FABCS. A conventional FCM algorithm did not utilize the spatial information in the image. We set a local circular area with a variable radius by using a cooling schedule for each bee to search suitable cluster centers with the FCM algorithm in an image. The cluster centers can be calculated by each bee with the membership states in the FABCS and then updated iteratively for all bees in order to find near-global solution in MR image segmentation. The proposed FABCS found the cluster centers with local spatial information instead of global pixels’ intensities. In the simulation and real medical-image segmentation results, the proposed FABCS network can reserve the segmentation performance.

  17. 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 ...

  18. 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 ei

  19. Changes in Gene Expression Relating to Colony Collapse Disorder in honey bees, Apis mellifera

    Science.gov (United States)

    Colony collapse disorder (CCD) is a mysterious disappearance of honey bees that has beset beekeepers in the United States since late in 2006. Pathogens and other environmental stresses, including pesticides, have been linked to CCD, but a causal relationship has not yet been demonstrated. The gut,...

  20. 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...

  1. 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.

  2. 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

  3. 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

  4. Varroa-Virus Interaction in Collapsing Honey Bee Colonies

    DEFF Research Database (Denmark)

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

    2013-01-01

    to be carried over with the bees into the next season. In general, AKI and DWV titres did not show any notable response to the treatment and steadily increased over the season from April to October. In the untreated control group, titres increased most dramatically. Viral copies were correlated to number...

  5. 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.

  6. 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.

  7. Research on Vehicle Routing Optimization Problem with Stochastic Demands Based on Artificial Bee Colony Algorithm%基于蜂群算法的随机需求车辆路径优化问题研究

    Institute of Scientific and Technical Information of China (English)

    王连稳; 蔡延光

    2013-01-01

      研究了随机需求车辆路径优化问题(VRPSD),在只允许路由失败一次和允许部分服务的情况下,给出了应用人工蜂群算法(ABC)用于求解该问题的具体方案。并针对需求为二项分布的VRPSD问题做了Matlab仿真试验,结果验证了该算法解决VRPSD的可行性。%This paper studied the vehicle routing problem with stochastic demands(VRPSD),and give specific methods with the application of Artificial Bee Colony algorithm(ABC) to solve the problem occurring in the situation that only one time routing failure and partial service are al owed.Matlab simulation has been done for a type of vehicle routing problem with stochastic demand which fol ows binomial distribution,demonstrating the feasibility of the algorithm to solve VRPSD.

  8. 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.

  9. Distance Between Honey Bee Apis mellifera Colonies Regulates Populations of Varroa destructor at a Landscape Scale

    Science.gov (United States)

    Nolan, Maxcy P.; Delaplane, Keith S.

    2016-01-01

    Inter-colony distance of Apis mellifera significantly affects colony numbers of the parasitic mite Varroa destructor. We set up 15 apiaries, each consisting of two colonies. Each apiary pair was assigned an inter-colony distance of 0, 10, or 100 m. Colonies were rendered nearly mite-free, then one colony in each pair was seeded with 300 female mites (mite-donor colony), while the other remained uninoculated (mite-recipient colony). After four months of monitoring, a whole model analysis showed that apiaries in which colonies were spaced 100 m apart contained lower average mite numbers than 0 m or 10 m apiaries. There were interactions among colony type, distance, and sampling date; however, when there were significant differences mite numbers were always lower in 100 m apiaries than 10 m apiaries. These findings pose the possibility that Varroa populations are resource regulated at a landscape scale: near-neighbor colonies constitute reproductive resource for mites in the form of additional bee brood. PMID:27812228

  10. Distance Between Honey Bee Apis mellifera Colonies Regulates Populations of Varroa destructor at a Landscape Scale.

    Science.gov (United States)

    Nolan, Maxcy P; Delaplane, Keith S

    2016-01-01

    Inter-colony distance of Apis mellifera significantly affects colony numbers of the parasitic mite Varroa destructor. We set up 15 apiaries, each consisting of two colonies. Each apiary pair was assigned an inter-colony distance of 0, 10, or 100 m. Colonies were rendered nearly mite-free, then one colony in each pair was seeded with 300 female mites (mite-donor colony), while the other remained uninoculated (mite-recipient colony). After four months of monitoring, a whole model analysis showed that apiaries in which colonies were spaced 100 m apart contained lower average mite numbers than 0 m or 10 m apiaries. There were interactions among colony type, distance, and sampling date; however, when there were significant differences mite numbers were always lower in 100 m apiaries than 10 m apiaries. These findings pose the possibility that Varroa populations are resource regulated at a landscape scale: near-neighbor colonies constitute reproductive resource for mites in the form of additional bee brood.

  11. Propolis envelope in Apis mellifera colonies supports honey bees against the pathogen, Paenibacillus larvae.

    Science.gov (United States)

    Borba, Renata S; Spivak, Marla

    2017-09-12

    Honey bees have immune defenses both as individuals and as a colony (e.g., individual and social immunity). One form of honey bee social immunity is the collection of antimicrobial plant resins and the deposition of the resins as a propolis envelope within the nest. In this study, we tested the effects of the propolis envelope as a natural defense against Paenibacillus larvae, the causative agent of American foulbrood (AFB) disease. Using colonies with and without a propolis envelope, we quantified: 1) the antimicrobial activity of larval food fed to 1-2 day old larvae; and 2) clinical signs of AFB. Our results show that the antimicrobial activity of larval food was significantly higher when challenged colonies had a propolis envelope compared to colonies without the envelope. In addition, colonies with a propolis envelope had significantly reduced levels of AFB clinical signs two months following challenge. Our results indicate that the propolis envelope serves as an antimicrobial layer around the colony that helps protect the brood from bacterial pathogen infection, resulting in a lower colony-level infection load.

  12. Colony failure linked to low sperm viability in honey bee (Apis mellifera) queens and an exploration of potential causative factors

    Science.gov (United States)

    Queen health is closely linked to colony performance in honey bees as a single queen is normally responsible for all egg laying and brood production within the colony. In the U. S. in recent years, queens have been failing at a high rate; with 50% or greater of queens replaced in colonies within 6 m...

  13. An efficient artificial bee colony algorithm with application to nonlinear predictive control

    Science.gov (United States)

    Ait Sahed, Oussama; Kara, Kamel; Benyoucef, Abousoufyane; Laid Hadjili, Mohamed

    2016-05-01

    In this paper a constrained nonlinear predictive control algorithm, that uses the artificial bee colony (ABC) algorithm to solve the optimization problem, is proposed. The main objective is to derive a simple and efficient control algorithm that can solve the nonlinear constrained optimization problem with minimal computational time. Indeed, a modified version, enhancing the exploring and the exploitation capabilities, of the ABC algorithm is proposed and used to design a nonlinear constrained predictive controller. This version allows addressing the premature and the slow convergence drawbacks of the standard ABC algorithm, using a modified search equation, a well-known organized distribution mechanism for the initial population and a new equation for the limit parameter. A convergence statistical analysis of the proposed algorithm, using some well-known benchmark functions is presented and compared with several other variants of the ABC algorithm. To demonstrate the efficiency of the proposed algorithm in solving engineering problems, the constrained nonlinear predictive control of the model of a Multi-Input Multi-Output industrial boiler is considered. The control performances of the proposed ABC algorithm-based controller are also compared to those obtained using some variants of the ABC algorithms.

  14. 基于人工蜂群算法的电网故障诊断%Fault Diagnosis of Power Network Based on Artificial Bee Colony Algorithm

    Institute of Scientific and Technical Information of China (English)

    韦晓广; 陈奎

    2012-01-01

    In order to solve 0-1 programming problem in fault diagnosis of power network, the paper proposed optimization methods of artificial bee colony algorithm from aspects of algebra and geometry. The simulation results show that the artificial bee colony algorithm is feasible and reasonable, and the overall performance is significantly superior to traditional genetic algorithms; artificial bee colony algorithm based on geometric has better stability and search capabilities than the algorithm based on algebraic, and is more suitable for occasions with high stability and accuracy requirements. Fault diagnosis of power network, artificial bee colony algorithm, algebra method,%针对电网故障诊断中的0-1规划问题,从代数和几何角度优化了人工蜂群算法.仿真结果表明,人工蜂群算法具有可行性和合理性,并且综合性能显著优于传统的遗传算法 ;在两种人工蜂群算法中,基于几何思想的人工蜂群算法具有更好的稳定性和搜索能力,更加适用于对稳定性和精准度要求很高的场合.

  15. 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.

  16. 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

  17. 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.

  18. 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.

  19. Weighing risk factors associated with bee colony collapse disorder by classification and regression tree analysis.

    Science.gov (United States)

    VanEngelsdorp, Dennis; Speybroeck, Niko; Evans, Jay D; Nguyen, Bach Kim; Mullin, Chris; Frazier, Maryann; Frazier, Jim; Cox-Foster, Diana; Chen, Yanping; Tarpy, David R; Haubruge, Eric; Pettis, Jeffrey S; Saegerman, Claude

    2010-10-01

    Colony collapse disorder (CCD), a syndrome whose defining trait is the rapid loss of adult worker honey bees, Apis mellifera L., is thought to be responsible for a minority of the large overwintering losses experienced by U.S. beekeepers since the winter 2006-2007. Using the same data set developed to perform a monofactorial analysis (PloS ONE 4: e6481, 2009), we conducted a classification and regression tree (CART) analysis in an attempt to better understand the relative importance and interrelations among different risk variables in explaining CCD. Fifty-five exploratory variables were used to construct two CART models: one model with and one model without a cost of misclassifying a CCD-diagnosed colony as a non-CCD colony. The resulting model tree that permitted for misclassification had a sensitivity and specificity of 85 and 74%, respectively. Although factors measuring colony stress (e.g., adult bee physiological measures, such as fluctuating asymmetry or mass of head) were important discriminating values, six of the 19 variables having the greatest discriminatory value were pesticide levels in different hive matrices. Notably, coumaphos levels in brood (a miticide commonly used by beekeepers) had the highest discriminatory value and were highest in control (healthy) colonies. Our CART analysis provides evidence that CCD is probably the result of several factors acting in concert, making afflicted colonies more susceptible to disease. This analysis highlights several areas that warrant further attention, including the effect of sublethal pesticide exposure on pathogen prevalence and the role of variability in bee tolerance to pesticides on colony survivorship.

  20. Learning and memory in workers reared by nutritionally stressed honey bee (Apis mellifera L.) colonies.

    Science.gov (United States)

    Mattila, Heather R; Smith, Brian H

    2008-12-15

    Chronic nutritional stress can have a negative impact on an individual's learning ability and memory. However, in social animals that share food among group members, such as the honey bee (Apis mellifera L.), it is unknown whether group-level nutritional stress is manifested in the learning performance of individuals. Accordingly, we examined learning and memory in honey bee workers reared by colonies exposed to varying degrees of long-term pollen stress. Pollen provides honey bee workers with almost all of the proteins, lipids, vitamins, and minerals that they require as larvae and adults. Colonies were created that were either chronically pollen poor or pollen rich, or were intermediate in pollen supply; treatments altered colonies' pollen stores and brood-rearing capacity. Workers from these colonies were put through a series of olfactory-conditioning assays using proboscis-extension response (PER). PER thresholds were determined, then workers learned in olfactory-conditioning trials to associate two floral odors (one novel and the other presented previously without reward) with stimulation with sucrose and a sucrose reward. The strength of the memory that was formed for the odor/sucrose association was tested after olfactory-conditioning assays ended. Colony-level nutritional status had no effect on worker learning or memory (response threshold of workers to sucrose, acquisition of the odor/sucrose association, occurrence of latent inhibition, or memory retention over 72 h). We conclude that potential effects of chronic, colony-wide nutrient deprivation on learning and memory are not found in workers, probably because colonies use brood-rearing capacity to buffer nutrient stress at the level of the individual.

  1. Sub-lethal effects of dietary neonicotinoid insecticide exposure on honey bee queen fecundity and colony development

    Science.gov (United States)

    Wu-Smart, Judy; Spivak, Marla

    2016-08-01

    Many factors can negatively affect honey bee (Apis mellifera L.) health including the pervasive use of systemic neonicotinoid insecticides. Through direct consumption of contaminated nectar and pollen from treated plants, neonicotinoids can affect foraging, learning, and memory in worker bees. Less well studied are the potential effects of neonicotinoids on queen bees, which may be exposed indirectly through trophallaxis, or food-sharing. To assess effects on queen productivity, small colonies of different sizes (1500, 3000, and 7000 bees) were fed imidacloprid (0, 10, 20, 50, and 100 ppb) in syrup for three weeks. We found adverse effects of imidacloprid on queens (egg-laying and locomotor activity), worker bees (foraging and hygienic activities), and colony development (brood production and pollen stores) in all treated colonies. Some effects were less evident as colony size increased, suggesting that larger colony populations may act as a buffer to pesticide exposure. This study is the first to show adverse effects of imidacloprid on queen bee fecundity and behavior and improves our understanding of how neonicotinoids may impair short-term colony functioning. These data indicate that risk-mitigation efforts should focus on reducing neonicotinoid exposure in the early spring when colonies are smallest and queens are most vulnerable to exposure.

  2. Sub-lethal effects of dietary neonicotinoid insecticide exposure on honey bee queen fecundity and colony development.

    Science.gov (United States)

    Wu-Smart, Judy; Spivak, Marla

    2016-08-26

    Many factors can negatively affect honey bee (Apis mellifera L.) health including the pervasive use of systemic neonicotinoid insecticides. Through direct consumption of contaminated nectar and pollen from treated plants, neonicotinoids can affect foraging, learning, and memory in worker bees. Less well studied are the potential effects of neonicotinoids on queen bees, which may be exposed indirectly through trophallaxis, or food-sharing. To assess effects on queen productivity, small colonies of different sizes (1500, 3000, and 7000 bees) were fed imidacloprid (0, 10, 20, 50, and 100 ppb) in syrup for three weeks. We found adverse effects of imidacloprid on queens (egg-laying and locomotor activity), worker bees (foraging and hygienic activities), and colony development (brood production and pollen stores) in all treated colonies. Some effects were less evident as colony size increased, suggesting that larger colony populations may act as a buffer to pesticide exposure. This study is the first to show adverse effects of imidacloprid on queen bee fecundity and behavior and improves our understanding of how neonicotinoids may impair short-term colony functioning. These data indicate that risk-mitigation efforts should focus on reducing neonicotinoid exposure in the early spring when colonies are smallest and queens are most vulnerable to exposure.

  3. Optimizing ZigBee Security using Stochastic Model Checking

    DEFF Research Database (Denmark)

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

    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......, 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...

  4. Optimizing ZigBee Security using Stochastic Model Checking

    CERN Document Server

    Yüksel, Ender; Nielson, Flemming; Fruth, Matthias; Kwiatkowska, Marta

    2012-01-01

    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, 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 application scenarios.

  5. 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 tes...

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

    Science.gov (United States)

    Currie, Robert W.

    2016-01-01

    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 higher DWV was

  7. 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

  8. 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

  9. 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.

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

    Directory of Open Access Journals (Sweden)

    Galen P Dively

    Full Text Available 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.

  11. 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.

  12. 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 poll

  13. Phenotypic and genetic analyses of the Varroa Sensitive Hygienic trait in Russian Honey Bee (Hymenoptera: Apidae) colonies

    Science.gov (United States)

    Varroa destructor continues to threaten colonies of European honey bees. General hygiene and more specific VarroaVarroa Sensitive Hygiene (VSH) provide resistance toward the Varroa mite in a number of stocks. In this study, Russian (RHB) and Italian honey bees were assessed for the VSH trait. Two...

  14. 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 ...

  15. 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). ...

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

    African Journals Online (AJOL)

    PROF. OLIVER OSUAGWA

    Genetic Algorithm (MO-GA) for dynamic job scheduling ... selection of a data centre. 2.2 Load ... An artificial ant colony, that was capable of .... Scheduling in Hybrid Cloud,” International Journal of Engineering and Technology Volume 2. No.

  17. The Potential of Bee-Generated Carbon Dioxide for Control of Varroa Mite (Mesostigmata: Varroidae) in Indoor Overwintering Honey bee (Hymenoptera: Apidae) Colonies.

    Science.gov (United States)

    Bahreini, Rassol; Currie, Robert W

    2015-10-01

    The objective of this study was to manipulate ventilation rate to characterize interactions between stocks of honey bees (Apis mellifera L.) and ventilation setting on varroa mite (Varroa destructor Anderson and Trueman) mortality in honey bee colonies kept indoors over winter. The first experiment used colonies established from stock selected locally for wintering performance under exposure to varroa (n = 6) and unselected bees (n = 6) to assess mite and bee mortality and levels of carbon dioxide (CO2) and oxygen (O2) in the bee cluster when kept under a simulated winter condition at 5°C. The second experiment, used colonies from selected bees (n = 10) and unselected bees (n = 12) that were exposed to either standard ventilation (14.4 liter/min per hive) or restricted ventilation (0.24 liter/min per hive, in a Plexiglas ventilation chamber) during a 16-d treatment period to assess the influence of restricted air flow on winter mortality rates of varroa mites and honey bees. Experiment 2 was repeated in early, mid-, and late winter. The first experiment showed that under unrestricted ventilation with CO2 concentrations averaging level (3.82 ± 0.31%, range 0.43-8.44%) increased by 200% relative to standard ventilation (1.29 ± 0.31%; range 0.09-5.26%) within the 16-d treatment period. The overall mite mortality rates and the reduction in mean abundance of varroa mite over time was greater under restricted ventilation (37 ± 4.2%) than under standard ventilation (23 ± 4.2%) but not affected by stock of bees during the treatment period. Selected bees showed overall greater mite mortality relative to unselected bees in both experiments. Restricting ventilation increased mite mortality, but did not affect worker bee mortality relative to that for colonies under standard ventilation. Restricted ventilation did not affect the overall level of Nosema compared with the control. However, there was an interaction between stock, season, and

  18. An improved marriage in honey bees optimization algorithm for single objective unconstrained optimization.

    Science.gov (United States)

    Celik, Yuksel; Ulker, Erkan

    2013-01-01

    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.

  19. 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.

  20. 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.

  1. A Novel Hybrid Data Clustering Algorithm Based on Artificial Bee Colony Algorithm and K-Means

    Institute of Scientific and Technical Information of China (English)

    TRAN Dang Cong; WU Zhijian; WANG Zelin; DENG Changshou

    2015-01-01

    To improve the performance of K-means clustering algorithm, this paper presents a new hybrid ap-proach of Enhanced artificial bee colony algorithm and K-means (EABCK). In EABCK, the original artificial bee colony algorithm (called ABC) is enhanced by a new mu-tation operation and guided by the global best solution (called EABC). Then, the best solution is updated by K-means in each iteration for data clustering. In the experi-ments, a set of benchmark functions was used to evaluate the performance of EABC with other comparative ABC variants. To evaluate the performance of EABCK on data clustering, eleven benchmark datasets were utilized. The experimental results show that EABC and EABCK out-perform other comparative ABC variants and data clus-tering algorithms, respectively.

  2. 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.

  3. 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.

  4. Risk analysis of dam based on artificial bee colony algorithm with fuzzy c-means clustering

    Energy Technology Data Exchange (ETDEWEB)

    Li, Haojin; Li, Junjie; Kang, Fei

    2011-05-15

    Risk analysis is a method which has been incorporated into infrastructure engineering. Fuzzy c-means clustering (FCM) is a simple and fast method utilized most of the time, but it can induce errors as it is sensitive to initialization. The aim of this paper was to propose a new method for risk analysis using an artificial bee colony algorithm (ABC) with FCM. This new technique is first explained and then applied on three experiments. Results demonstrated that the combination of artificial bee colony algorithm fuzzy c-means clustering (ABCFCM) is overcoming the FCM issue since it is not initialization sensitive and experiments showed that this algorithm is more accurate and than FCM. This paper provides a new tool for risk analysis which can be used for risk prioritizing and reinforcing dangerous dams in a more scientific way.

  5. Comparison of within hive sampling and seasonal activity of Nosema ceranae in honey bee colonies.

    Science.gov (United States)

    Traver, Brenna E; Williams, Matthew R; Fell, Richard D

    2012-02-01

    Nosema ceranae is a microsporidian parasite of the European honey bee, Apis mellifera, that is found worldwide and in multiple Apis spp.; however, little is known about the effects of N. ceranae on A. mellifera. Previous studies using spore counts suggest that there is no longer a seasonal cycle for N. ceranae and that it is found year round with little variation in infection intensity among months. Our goal was to determine whether infection levels differ in bees collected from different areas of the hive and if there may be seasonal differences in N. ceranae infections. A multiplex species-specific real-time PCR assay was used for the detection and quantification of N. ceranae. Colonies were sampled monthly from September 2009-2010 by collecting workers from honey supers, the fringe of the brood nest, and the brood nest. We found that all bees sampled were infected with N. ceranae and that there was no significant difference in infection levels among the different groups of bees sampled (P=0.74). However, significant differences in colony infection levels were found at different times of the year (Pbeekeepers on N. ceranae management.

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

    OpenAIRE

    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 either with formic acid (2004) or Thymovar (2005). Both the oxalic acid and the formic acid were very effective, resulting in an average efficacy of 97% and 96%, respectively. There was some worker b...

  7. Oxalic acid for the control of varroosis in honey bee colonies - a review

    OpenAIRE

    RADEMACHER, Eva; Harz, Marika

    2006-01-01

    International audience; The review summarizes research results on the use of oxalic acid as an acaricide in honey bee colonies. Three different treatment techniques (i.e. trickling, evaporation and spraying) have been developed for the application of oxalic acid. Detailed information is given on the efficacy against Varroa destructor, tolerability by Apis mellifera, protective procedures for the user, residue situation and consumer safety, as well as recommendations for use.

  8. 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

  9. 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.

  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. Optimum Design of Fractional Order PID Controller for an AVR System Using an Improved Artificial Bee Colony Algorithm

    Institute of Scientific and Technical Information of China (English)

    ZHANG Dong-Li; TANG Ying-Gan; GUAN Xin-Ping

    2014-01-01

    Fractional order proportional-integral-derivative (FOPID) controller generalizes the standard PID controller. Compared to PID controller, FOPID controller has more pa-rameters and the tuning of parameters is more complex. In this paper, an improved artificial bee colony algorithm, which com-bines cyclic exchange neighborhood with chaos (CNC-ABC), is proposed for the sake of tuning the parameters of FOPID con-troller. The characteristic of the proposed CNC-ABC exists in two folds: one is that it enlarges the search scope of the solution by utilizing cyclic exchange neighborhood techniques, speeds up the convergence of artificial bee colony algorithm (ABC). The other is that it has potential to get out of local optima by exploit-ing the ergodicity of chaos. The proposed CNC-ABC algorithm is used to optimize the parameters of the FOPID controller for an automatic voltage regulator (AVR) system. Numerical sim-ulations show that the CNC-ABC FOPID controller has better performance than other FOPID and PID controllers.

  12. Pteridine levels and head weights are correlated with age and colony task in the honey bee, Apis mellifera.

    Science.gov (United States)

    Rinkevich, Frank D; Margotta, Joseph W; Pittman, Jean M; Ottea, James A; Healy, Kristen B

    2016-01-01

    Background. The age of an insect strongly influences many aspects of behavior and reproduction. The interaction of age and behavior is epitomized in the temporal polyethism of honey bees in which young adult bees perform nurse and maintenance duties within the colony, while older bees forage for nectar and pollen. Task transition is dynamic and driven by colony needs. However, an abundance of precocious foragers or overage nurses may have detrimental effects on the colony. Additionally, honey bee age affects insecticide sensitivity. Therefore, determining the age of a set of individual honey bees would be an important measurement of colony health. Pteridines are purine-based pigment molecules found in many insect body parts. Pteridine levels correlate well with age, and wild caught insects may be accurately aged by measuring pteridine levels. The relationship between pteridines and age varies with a number of internal and external factors among many species. Thus far, no studies have investigated the relationship of pteridines with age in honey bees. Methods. We established single-cohort colonies to obtain age-matched nurse and forager bees. Bees of known ages were also sampled from colonies with normal demographics. Nurses and foragers were collected every 3-5 days for up to 42 days. Heads were removed and weighed before pteridines were purified and analyzed using previously established fluorometric methods. Results. Our analysis showed that pteridine levels significantly increased with age in a linear manner in both single cohort colonies and colonies with normal demography. Pteridine levels were higher in foragers than nurses of the same age in bees from single cohort colonies. Head weight significantly increased with age until approximately 28-days of age and then declined for both nurse and forager bees in single cohort colonies. A similar pattern of head weight in bees from colonies with normal demography was observed but head weight was highest in 8-day old

  13. A Discrete Artificial Bee Colony Algorithm for Minimizing the Total Flow Time in the Blocking Flow Shop Scheduling

    Institute of Scientific and Technical Information of China (English)

    邓冠龙; 徐震浩; 顾幸生

    2012-01-01

    A discrete artificial bee colony algorithm is proposed for solving the blocking flow shop scheduling problem with total flow time criterion. Firstly, the solution in the algorithm is represented as job permutation. Secondly, an initialization scheme based on a variant of the NEH (Nawaz-Enscore-Ham) heuristic and a local search is designed to construct the initial population with both quality and diversity. Thirdly, based on the idea of iterated greedy algorithm, some newly designed schemes for employed bee, onlooker bee and scout bee are presented. The performance of the proposed algorithm is tested on the well-known Taillard benchmark set, and the computational results demonstrate the effectiveness of the discrete artificial bee colony algorithm. In addition, the best known solutions of the benchmark set are provided for the blocking flow shop scheduling problem with total flow time criterion.

  14. Acaricide treatment affects viral dynamics in Varroa destructor-infested honey bee colonies via both host physiology and mite control.

    Science.gov (United States)

    Locke, Barbara; Forsgren, Eva; Fries, Ingemar; de Miranda, Joachim R

    2012-01-01

    Honey bee (Apis mellifera) colonies are declining, and a number of stressors have been identified that affect, alone or in combination, the health of honey bees. The ectoparasitic mite Varroa destructor, honey bee viruses that are often closely associated with the mite, and pesticides used to control the mite population form a complex system of stressors that may affect honey bee health in different ways. During an acaricide treatment using Apistan (plastic strips coated with tau-fluvalinate), we analyzed the infection dynamics of deformed wing virus (DWV), sacbrood virus (SBV), and black queen cell virus (BQCV) in adult bees, mite-infested pupae, their associated Varroa mites, and uninfested pupae, comparing these to similar samples from untreated control colonies. Titers of DWV increased initially with the onset of the acaricide application and then slightly decreased progressively coinciding with the removal of the Varroa mite infestation. This initial increase in DWV titers suggests a physiological effect of tau-fluvalinate on the host's susceptibility to viral infection. DWV titers in adult bees and uninfested pupae remained higher in treated colonies than in untreated colonies. The titers of SBV and BQCV did not show any direct relationship with mite infestation and showed a variety of possible effects of the acaricide treatment. The results indicate that other factors besides Varroa mite infestation may be important to the development and maintenance of damaging DWV titers in colonies. Possible biochemical explanations for the observed synergistic effects between tau-fluvalinate and virus infections are discussed.

  15. 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.

  16. 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.

  17. Nosema spp. infection and its negative effects on honey bees (Apis mellifera iberiensis) at the colony level.

    Science.gov (United States)

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

    2013-04-10

    Nosemosis caused by the microsporidia Nosema apis and Nosema ceranae are among the most common pathologies affecting adult honey bees. N. apis infection has been associated with a reduced lifespan of infected bees and increased winter mortality, and its negative impact on colony strength and productivity has been described in several studies. By contrast, when the effects of nosemosis type C, caused by N. ceranae infection, have been analysed at the colony level, these studies have largely focused on collapse as a response to infection without addressing the potential sub-clinical effects on colony strength and productivity. Given the spread and prevalence of N. ceranae worldwide, we set out here to characterize the sub-clinical and clinical signs of N. ceranae infection on colony strength and productivity. We evaluated the evolution of 50 honey bee colonies naturally infected by Nosema (mainly N. ceranae) over a one year period. Under our experimental conditions, N. ceranae infection was highly pathogenic for honey bee colonies, producing significant reductions in colony size, brood rearing and honey production. These deleterious effects at the colony level may affect beekeeping profitability and have serious consequences on pollination. Further research is necessary to identify possible treatments or beekeeping techniques that will limit the rapid spread of this dangerous emerging disease.

  18. Assessing the health of colonies and individual honey bees (Apis mellifera L.) in a commercial beekeeping operation

    Science.gov (United States)

    Metrics of honey bee health were assessed every six weeks over three years in colonies owned by a migratory beekeeper. The colonies were located in six apiaries during the summer months in North Dakota and were transported to California for almond pollination every winter. We previously characteri...

  19. 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

  20. CONGESTION MANAGEMENT BY OPTIMAL ALLOCATION OF FACTS CONTROLLERS USING HYBRID FISH BEE OPTIMIZATION

    Directory of Open Access Journals (Sweden)

    S. Thangalakshmi

    2014-01-01

    Full Text Available The role of Independent System Operator (ISO in the restructured power industry includes system control, capacity planning, transmission tariff and congestion management; the challenging task being minimizing the congestion. One of the popular techniques used to alleviate congestion is using Flexible AC Transmission Systems (FACTS devices. The power system generally operates near its rated capacity in deregulated market because of intensive usage of transmission grids. So, the major issues that need to be addressed are improving the voltage profile and reducing the power loss in the electrical network. Motivation: The location of FACTS devices can improve the power flow in the line, maintain the bus profile and reduce the losses. However locating the ideal location is a NP problem. This study presents a novel heuristic method to determine the types of FACTS devices and its optimal location in a power system without violating the thermal and voltage limits. Power flow sensitivity index to find the optimal location of UPFC is suggested in this study. A hybrid fish bee swarm optimization is proposed which is based on Artificial Bee Colony (ABC and Fish School Search (FSS methods. This proposed algorithm is tested based on IEEE 30 bus system and line performances are studied.

  1. Ant Colony Optimization Algorithm for Continuous Domains Based on Position Distribution Model of Ant Colony Foraging

    OpenAIRE

    Liqiang Liu; Yuntao Dai; Jinyu Gao

    2014-01-01

    Ant colony optimization algorithm for continuous domains is a major research direction for ant colony optimization algorithm. In this paper, we propose a distribution model of ant colony foraging, through analysis of the relationship between the position distribution and food source in the process of ant colony foraging. We design a continuous domain optimization algorithm based on the model and give the form of solution for the algorithm, the distribution model of pheromone, the update rules...

  2. 求解旅行商问题的人工蜂群算法%Artificial Bee Colony Algorithm for Solving Traveling Salesman Problem

    Institute of Scientific and Technical Information of China (English)

    黄秋菀; 王志刚; 夏慧明

    2013-01-01

    The article uses artificial bee colony algorithm to solve traveling salesman problems, gives the specific solutions of artificial bee colony algorithm for solving traveling salesman problem, and makes simulation experiment for the different traveling salesman. The results show that the algorithm can efficiently and quickly find optimal solutions to small problems.%  采用人工蜂群算法对旅行商问题进行求解,给出了人工蜂群算法求解该问题的具体方案,对不同的旅行商问题算例进行了仿真实验。结果表明,算法可以有效、快速地找到较小规模问题的最优解。

  3. [Chaotic artificial bee colony algorithm: a new approach to the problem of minimization of energy of the 3D protein structure].

    Science.gov (United States)

    Wang, Y; Guo, G D; Chen, L F

    2013-01-01

    Frediction of the three-dimensional structure of a protein from its amino acid sequence can be considered as a global optimization problem. In this paper, the Chaotic Artificial Bee Colony (CABC) algorithm was introduced and applied to 3D protein structure prediction. Based on the 3D off-lattice AB model, the CABC algorithm combines global search and local search of the Artificial Bee Colony (ABC) algorithm with the Chaotic search algorithm to avoid the problem of premature convergence and easily trapping the local optimum solution. The experiments carried out with the popular Fibonacci sequences demonstrate that the proposed algorithm provides an effective and high-performance method for protein structure prediction.

  4. On the Effects of Artificial Feeding on Bee Colony Dynamics: A Mathematical Model

    Science.gov (United States)

    Paiva, Juliana Pereira Lisboa Mohallem; Paiva, Henrique Mohallem; Esposito, Elisa; Morais, Michelle Manfrini

    2016-01-01

    This paper proposes a new mathematical model to evaluate the effects of artificial feeding on bee colony population dynamics. The proposed model is based on a classical framework and contains differential equations that describe the changes in the number of hive bees, forager bees, and brood cells, as a function of amounts of natural and artificial food. The model includes the following elements to characterize the artificial feeding scenario: a function to model the preference of the bees for natural food over artificial food; parameters to quantify the quality and palatability of artificial diets; a function to account for the efficiency of the foragers in gathering food under different environmental conditions; and a function to represent different approaches used by the beekeeper to feed the hive with artificial food. Simulated results are presented to illustrate the main characteristics of the model and its behavior under different scenarios. The model results are validated with experimental data from the literature involving four different artificial diets. A good match between simulated and experimental results was achieved. PMID:27875589

  5. 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.

  6. Stress-mediated Allee effects can cause the sudden collapse of honey bee colonies.

    Science.gov (United States)

    Booton, Ross D; Iwasa, Yoh; Marshall, James A R; Childs, Dylan Z

    2017-05-07

    The recent rapid decline in global honey bee populations could have significant implications for ecological systems, economics and food security. No single cause of honey bee collapse has yet to be identified, although pesticides, mites and other pathogens have all been shown to have a sublethal effect. We present a model of a functioning bee hive and introduce external stress to investigate the impact on the regulatory processes of recruitment to the forager class, social inhibition and the laying rate of the queen. The model predicts that constant density-dependent stress acting through an Allee effect on the hive can result in sudden catastrophic switches in dynamical behaviour and the eventual collapse of the hive. The model proposes that around a critical point the hive undergoes a saddle-node bifurcation, and that a small increase in model parameters can have irreversible consequences for the entire hive. We predict that increased stress levels can be counteracted by a higher laying rate of the queen, lower levels of forager recruitment or lower levels of natural mortality of foragers, and that increasing social inhibition can not maintain the colony under high levels of stress. We lay the theoretical foundation for sudden honey bee collapse in order to facilitate further experimental and theoretical consideration. Copyright © 2017 Elsevier Ltd. All rights reserved.

  7. Ant Colony Optimization and Hypergraph Covering Problems

    CERN Document Server

    Pat, Ankit

    2011-01-01

    Ant Colony Optimization (ACO) is a very popular metaheuristic for solving computationally hard combinatorial optimization problems. Runtime analysis of ACO with respect to various pseudo-boolean functions and different graph based combinatorial optimization problems has been taken up in recent years. In this paper, we investigate the runtime behavior of an MMAS*(Max-Min Ant System) ACO algorithm on some well known hypergraph covering problems that are NP-Hard. In particular, we have addressed the Minimum Edge Cover problem, the Minimum Vertex Cover problem and the Maximum Weak- Independent Set problem. The influence of pheromone values and heuristic information on the running time is analysed. The results indicate that the heuristic information has greater impact towards improving the expected optimization time as compared to pheromone values. For certain instances of hypergraphs, we show that the MMAS* algorithm gives a constant order expected optimization time when the dominance of heuristic information is ...

  8. Using Artificial Bee Colony Algorithm for MLP Training on Earthquake Time Series Data Prediction

    CERN Document Server

    Shah, Habib; Nawi, Nazri Mohd

    2011-01-01

    Nowadays, computer scientists have shown the interest in the study of social insect's behaviour in neural networks area for solving different combinatorial and statistical problems. Chief among these is the Artificial Bee Colony (ABC) algorithm. This paper investigates the use of ABC algorithm that simulates the intelligent foraging behaviour of a honey bee swarm. Multilayer Perceptron (MLP) trained with the standard back propagation algorithm normally utilises computationally intensive training algorithms. One of the crucial problems with the backpropagation (BP) algorithm is that it can sometimes yield the networks with suboptimal weights because of the presence of many local optima in the solution space. To overcome ABC algorithm used in this work to train MLP learning the complex behaviour of earthquake time series data trained by BP, the performance of MLP-ABC is benchmarked against MLP training with the standard BP. The experimental result shows that MLP-ABC performance is better than MLP-BP for time se...

  9. 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.

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

    Science.gov (United States)

    Tan, Ken; Hu, Zongwen; Chen, Weiwen; Wang, Zhengwei; Wang, Yuchong; Nieh, James C

    2013-01-01

    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.

  11. 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

  12. 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.

  13. Dancing Bees Improve Colony Foraging Success as Long-Term Benefits Outweigh Short-Term Costs

    Science.gov (United States)

    Schürch, Roger; Grüter, Christoph

    2014-01-01

    Waggle dancing bees provide nestmates with spatial information about high quality resources. Surprisingly, attempts to quantify the benefits of this encoded spatial information have failed to find positive effects on colony foraging success under many ecological circumstances. Experimental designs have often involved measuring the foraging success of colonies that were repeatedly switched between oriented dances versus disoriented dances (i.e. communicating vectors versus not communicating vectors). However, if recruited bees continue to visit profitable food sources for more than one day, this procedure would lead to confounded results because of the long-term effects of successful recruitment events. Using agent-based simulations, we found that spatial information was beneficial in almost all ecological situations. Contrary to common belief, the benefits of recruitment increased with environmental stability because benefits can accumulate over time to outweigh the short-term costs of recruitment. Furthermore, we found that in simulations mimicking previous experiments, the benefits of communication were considerably underestimated (at low food density) or not detected at all (at medium and high densities). Our results suggest that the benefits of waggle dance communication are currently underestimated and that different experimental designs, which account for potential long-term benefits, are needed to measure empirically how spatial information affects colony foraging success. PMID:25141306

  14. A Detailed Study about Foraging Behavior of Artificial Bee Colony (ABC and its Extensions

    Directory of Open Access Journals (Sweden)

    S.Santhosh Kumar

    2013-04-01

    Full Text Available Swam intelligence is an emerging field in Artificial Intelligence. The living nature and life style of animals, birds and other living organisms can be inherited and applied to solve many real worldproblems. ABC is a recently developed swam intelligence algorithm developed by Dervis Karaboga in the year 2005.In ABC, foraging is one of the behavior of honey bees to search, collect food from its foodresources. Many research works has undergone about foraging behavior and it is applied to solve variety of optimization problems. This paper discusses the detailed study of different types of extensions offoraging behavior of honey bees.

  15. Daily number of bee louse (Braula coeca) in honey bee (Apis mellifera camica and A. m. syriaca) colonies maintained under semi-arid conditions

    Institute of Scientific and Technical Information of China (English)

    Shahera Zaitoun; Abd AI-Majeed AI-Ghzawi

    2008-01-01

    Experimental work was conducted at two apiaries located in Irbid district and in Shuna North, Jordan, during the years 2004-2006. The aims of these investigations were to estimate the seasonal changes in the infestation rates of the bee louse (Braula sp.) and to develop an easy and rapid method of estimating the infestation rate on workers with bee Braula. Two major honey bee subspecies are reared in Jordan; Apis mellifera carnica and Apis mellifera syriaca were used in this study. The results showed that the infestation rate began to increase rapidly in May, reaching the season's maximum rate of 16.2%, 15.8% and 17.4% forA. ra. carnica and 22.6%, 23.9% and 22.9% forA. m. syr/aca in December of 2004,2005 and 2006, respectively. The maximum adult numbers of bees were found in April and June, whereas the minimum for the year was in January in both honey bee subspecies colonies during the study period. The actual population of the bee louse could be estimated by counting the daily dropped lice and multiplying by a factor of 158. This factor is valid for the experimental colonies of both subspecies kept for 3 years under semi-arid Mediterranean conditions.

  16. Secondary biomarkers of insecticide-induced stress of honey bee colonies and their relevance for overwintering strength.

    Science.gov (United States)

    Wegener, Jakob; Ruhnke, Haike; Milchreit, Kathrin; Kleebaum, Katharina; Franke, Monique; Mispagel, Sebastian; Bischoff, Gabriela; Kamp, Günter; Bienefeld, Kaspar

    2016-10-01

    The evaluation of pesticide side-effects on honeybees is hampered by a lack of colony-level bioassays that not only are sensitive to physiological changes, but also allow predictions about the consequences of exposure for longer-term colony productivity and survival. Here we measured 28 biometrical, biochemical and behavioural indicators in a field study with 63 colonies and 3 apiaries. Colonies were stressed in early summer by feeding them for five days with either the carbamate growth regulator fenoxycarb or the neurotoxic neonicotinoid imidacloprid, or left untreated. Candidate stress indicators were measured 8-64 days later. We determined which of the indicators were influenced by the treatments, and which could be used as predictors in regression analyses of overwintering strength. Among the indicators influenced by fenoxycarb were the amount of brood in colonies as well as the learning performance and 24h-memory of bees, and the concentration of the brood food component 10HDA in head extracts. Imidacloprid significantly affected honey production, total number of bees and activity of the immune-related enzyme phenoloxidase in forager bee extracts. Indicators predictive of overwintering strength but unrelated to insecticide feeding included vitellogenin titer and glucose oxidase-activity in haemolymph/whole body-extracts of hive bees. Apart from variables that were themselves components of colony strength (numbers of bees/brood cells), the only indicator that was both influenced by an insecticide and predictive of overwintering strength was the concentration of 10HDA in worker bee heads. Our results show that physiological and biochemical bioassays can be used to study effects of insecticides at the colony level and assess the vitality of bee colonies. At the same time, most bioassays evaluated here appear of limited use for predicting pesticide effects on colony overwintering strength, because those that were sensitive to the insecticides were not identical

  17. 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

  18. 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.

  19. 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.

  20. 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.

  1. Multi-level Threshold Image Segmentation Based on PSNR using Artificial Bee Colony Algorithm

    Directory of Open Access Journals (Sweden)

    Cao Yun-Fei

    2012-01-01

    Full Text Available Image segmentation is still a crucial problem in image processing. It hasn yet been solved very well. In this study, we propose a novel multi-level thresholding image segmentation method based on PSNR using artificial bee colony algorithm (ABCA. PSNR is considered as an objective function of ABCA. The multi-level thresholds (t*1, t*2 ,...., t*n-1, t*n are those maximizing the PSNR. We compare entropy and PSNR in segmenting gray-level images. The experiments results demonstrate proposed method is effective and efficient.

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

    Science.gov (United States)

    van Engelsdorp, Dennis; Hayes, Jerry; Underwood, Robyn M; Pettis, Jeffery

    2008-01-01

    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. 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. 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 across the continent, serving as a reference for comparison with future losses as well as

  3. 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

  4. 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.

  5. 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.

  6. 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.

  7. 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.

  8. 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.

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

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    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.

  10. Aethina tumida (Coleoptera: Nitidulidae) and Oplostomus haroldi (Coleoptera: Scarabaeidae): Occurrence in Kenya, Distribution within Honey Bee Colonies, and Response to Host Odors

    Science.gov (United States)

    Aethina tumida Murray is considered a minor parasitic pest of African honey bee colonies, but little information is available on other coleopteran pests. We surveyed for A. tumida and other beetles in honey bee colonies at four major beekeeping locations: Watamu, Chawia-Taita, Matuu, and Nairobi in...

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

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    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.

  12. An Endmember Extraction Method Based on Artificial Bee Colony Algorithms for Hyperspectral Remote Sensing Images

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    Xu Sun

    2015-12-01

    Full Text Available Mixed pixels are common in hyperspectral remote sensing images. Endmember extraction is a key step in spectral unmixing. The linear spectral mixture model (LSMM constitutes a geometric approach that is commonly used for this purpose. This paper introduces the use of artificial bee colony (ABC algorithms for spectral unmixing. First, the objective function of the external minimum volume model is improved to enhance the robustness of the results, and then, the ABC-based endmember extraction process is presented. Depending on the characteristics of the objective function, two algorithms, Artificial Bee Colony Endmember Extraction-RMSE (ABCEE-R and ABCEE-Volume (ABCEE-V are proposed. Finally, two sets of experiment using synthetic data and one set of experiments using a real hyperspectral image are reported. Comparative experiments reveal that ABCEE-R and ABCEE-V can achieve better endmember extraction results than other algorithms when processing data with a low signal-to-noise ratio (SNR. ABCEE-R does not require high accuracy in the number of endmembers, and it can always obtain the result with the best root mean square error (RMSE; when the number of endmembers extracted and the true number of endmembers does not match, the RMSE of the ABCEE-V results is usually not as good as that of ABCEE-R, but the endmembers extracted using the former algorithm are closer to the true endmembers.

  13. Pesticide residues in beeswax samples collected from honey bee colonies (Apis mellifera L.) in France.

    Science.gov (United States)

    Chauzat, Marie-Pierre; Faucon, Jean-Paul

    2007-11-01

    In 2002 a field survey was initiated in French apiaries in order to monitor the health of honey bee colonies (Apis mellifera L.). Studied apiaries were evenly distributed across five sites located in continental France. Beeswax samples were collected once a year over 2 years from a total of 125 honey bee colonies. Multiresidue analyses were performed on these samples in order to identify residues of 16 insecticides and acaricides and two fungicides. Residues of 14 of the searched-for compounds were found in samples. Tau-fluvalinate, coumaphos and endosulfan residues were the most frequently occurring residues (61.9, 52.2 and 23.4% of samples respectively). Coumaphos was found in the highest average quantities (792.6 microg kg(-1)). Residues of cypermethrin, lindane and deltamethrin were found in 21.9, 4.3 and 2.4% of samples respectively. Statistical tests showed no difference between years of sampling, with the exception of the frequency of pyrethroid residues. Beeswax contamination was the result of both in-hive acaricide treatments and, to a much lesser extent, environmental pollution.

  14. 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)

  15. Comparison of productivity of colonies of honey bees, Apis mellifera, supplemented with sucrose or high fructose corn syrup.

    Science.gov (United States)

    Sammataro, Diana; Weiss, Milagra

    2013-01-01

    Honey bee colony feeding trials were conducted to determine whether differential effects of carbohydrate feeding (sucrose syrup (SS) vs. high fructose corn syrup, or HFCS) could be measured between colonies fed exclusively on these syrups. In one experiment, there was a significant difference in mean wax production between the treatment groups and a significant interaction between time and treatment for the colonies confined in a flight arena. On average, the colonies supplied with SS built 7916.7 cm(2) ± 1015.25 cm(2) honeycomb, while the colonies supplied with HFCS built 4571.63 cm(2) ± 786.45 cm(2). The mean mass of bees supplied with HFCS was 4.65 kg (± 0.97 kg), while those supplied with sucrose had a mean of 8.27 kg (± 1.26). There was no significant difference between treatment groups in terms of brood rearing. Differences in brood production were complicated due to possible nutritional deficiencies experienced by both treatment groups. In the second experiment, colonies supplemented with SS through the winter months at a remote field site exhibited increased spring brood production when compared to colonies fed with HFCS. The differences in adult bee populations were significant, having an overall average of 10.0 ± 1.3 frames of bees fed the sucrose syrup between November 2008 and April 2009, compared to 7.5 ± 1.6 frames of bees fed exclusively on HFCS. For commercial queen beekeepers, feeding the right supplementary carbohydrates could be especially important, given the findings of this study.

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

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    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.

  17. Environment or beekeeping management: What explains better the prevalence of honey bee colonies with high levels of Varroa destructor?

    Science.gov (United States)

    Giacobino, Agostina; Pacini, Adriana; Molineri, Ana; Bulacio Cagnolo, N; Merke, J; Orellano, E; Bertozzi, E; Masciangelo, G; Pietronave, H; Signorini, M

    2017-01-06

    Varroa destructor is one of the major threats to honey bee colonies. The mite abundance in the colonies is affected by environmental conditions as well as by beekeeping management. The aim of this study was to recognize the main drivers associated with autumn V. destructor infestation in honey bee colonies when different regions from Argentina are compared. A total of 361 colonies distributed in five Argentinean eco-regions were examined to evaluate Varroa mite infestation rate during autumn and Nosema sp. presence. Regions were different regarding annual temperature, precipitation and especially vegetation landscape. In addition, beekeeping management practices were obtained from a checklist questionnaire answered by the beekeepers. The prevalence of colonies with high infestation level was lower in semi-arid Chaco followed by humid and transition Chaco regions. Also, colonies that were positive for Nosema sp. showed a higher Varroa infestation rate. The "environmental" effect was stronger compared with the influence of secondary drivers associated with beekeeping activities. As well, a significant association between V. destructor infestation rates and Nosema presence was identified. Under contrasting natural conditions, environment seems a predominant driver on Varroa destructor infestation level in honey bee colonies.

  18. 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

  19. 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.

  20. 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

  1. 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.

  2. 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.

  3. Application of Artificial Bee Colony Algorithm for Solving Traveling Salesman Problem%应用人工蜂群算法求解旅行商问题

    Institute of Scientific and Technical Information of China (English)

    魏超

    2014-01-01

    The traveling salesman problem (TSP) is one of the classical combinatorial optimization problems. Artificial bee colony algorithm is a new heuristic algorithm proposed in recent years. According to the model of the traveling salesman problem, artificial bee colony algorithm was designed to simulate an example solution. Compare artificial colony algorithm and genetic algorithm at the same time, the results show that the artificial colony algorithm can effectively solve the traveling salesman problem and has a certain advantage in terms of speed of convergence, computational efficiency and stability than genetic algorithm.%旅行商问题(TSP)是经典的组合优化问题之一。人工蜂群算法是近年来被提出的一种新的智能启发式算法。根据旅行商问题的模型特点,设计人工蜂群算法对算例进行仿真求解。同时将人工蜂群算法与遗传算法进行对比,结果表明:人工蜂群算法可以有效的求解旅行商问题,在收敛速度、计算效率、稳定性方面相对遗传算法具有一定的优势。

  4. 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.

  5. 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.

  6. 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.

    Directory of Open Access Journals (Sweden)

    Berna Emsen

    Full Text Available 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.

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

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    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.

  8. Strong Combination of Ant Colony Optimization with Constraint Programming Optimization

    Science.gov (United States)

    Khichane, Madjid; Albert, Patrick; Solnon, Christine

    We introduce an approach which combines ACO (Ant Colony Optimization) and IBM ILOG CP Optimizer for solving COPs (Combinatorial Optimization Problems). The problem is modeled using the CP Optimizer modeling API. Then, it is solved in a generic way by a two-phase algorithm. The first phase aims at creating a hot start for the second: it samples the solution space and applies reinforcement learning techniques as implemented in ACO to create pheromone trails. During the second phase, CP Optimizer performs a complete tree search guided by the pheromone trails previously accumulated. The first experimental results on knapsack, quadratic assignment and maximum independent set problems show that this new algorithm enhances the performance of CP Optimizer alone.

  9. Sublethal effects of Imidacloprid on honey bee colony growth and activity at three sites in the U.S.

    Science.gov (United States)

    Field experiments in southern Arizona, central Arkansas and southern Mississippi were conducted to evaluate the effects of sublethal concentrations (0, 5, 20 and 100 ppb) of imidacloprid in sugar syrup on honey bee colony growth and activity. Response variables included discrete data from hive inspe...

  10. Swarming, defensive and hygienic behaviour in honey bee colonies of different genetic origin in a pan-European experiment

    DEFF Research Database (Denmark)

    Uzunov, Aleksandar; Costa, Cecilia; Panasiuk, Beata;

    2014-01-01

    Honey bee colonies exhibit a wide range of variation in their behaviour, depending on their genetic origin and environmental factors. The COLOSS Genotype-Environment Interactions Experiment gave us the opportunity to investigate the phenotypic expression of the swarming, defensive and hygienic be...

  11. 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...

  12. Individual responsiveness to shock and colony-level aggression in honey bees: evidence for a genetic component.

    Science.gov (United States)

    Avalos, Arian; Rodríguez-Cruz, Yoselyn; Giray, Tugrul

    2014-05-01

    The phenotype of the social group is related to phenotypes of individuals that form that society. We examined how honey bee colony aggressiveness relates to individual response of male drones and foraging workers. Although the natural focus in colony aggression has been on the worker caste, the sterile females engaged in colony maintenance and defense, males carry the same genes. We measured aggressiveness scores of colonies and examined components of individual aggressive behavior in workers and haploid sons of workers from the same colony. We describe for the first time, that males, although they have no stinger, do bend their abdomen (abdominal flexion) in a posture similar to stinging behavior of workers in response to electric shock. Individual worker sting response and movement rates in response to shock were significantly correlated with colony scores. In the case of drones, sons of workers from the same colonies, abdominal flexion significantly correlated but their movement rates did not correlate with colony aggressiveness. Furthermore, the number of workers responding at increasing levels of voltage exhibits a threshold-like response, whereas the drones respond in increasing proportion to shock. We conclude that there are common and caste-specific components to aggressive behavior in honey bees. We discuss implications of these results on social and behavioral regulation and genetics of aggressive response.

  13. Ant colony optimization algorithm for continuous domains based on position distribution model of ant colony foraging.

    Science.gov (United States)

    Liu, Liqiang; Dai, Yuntao; Gao, Jinyu

    2014-01-01

    Ant colony optimization algorithm for continuous domains is a major research direction for ant colony optimization algorithm. In this paper, we propose a distribution model of ant colony foraging, through analysis of the relationship between the position distribution and food source in the process of ant colony foraging. We design a continuous domain optimization algorithm based on the model and give the form of solution for the algorithm, the distribution model of pheromone, the update rules of ant colony position, and the processing method of constraint condition. Algorithm performance against a set of test trials was unconstrained optimization test functions and a set of optimization test functions, and test results of other algorithms are compared and analyzed to verify the correctness and effectiveness of the proposed algorithm.

  14. Ant Colony Optimization Algorithm for Continuous Domains Based on Position Distribution Model of Ant Colony Foraging

    Directory of Open Access Journals (Sweden)

    Liqiang Liu

    2014-01-01

    Full Text Available Ant colony optimization algorithm for continuous domains is a major research direction for ant colony optimization algorithm. In this paper, we propose a distribution model of ant colony foraging, through analysis of the relationship between the position distribution and food source in the process of ant colony foraging. We design a continuous domain optimization algorithm based on the model and give the form of solution for the algorithm, the distribution model of pheromone, the update rules of ant colony position, and the processing method of constraint condition. Algorithm performance against a set of test trials was unconstrained optimization test functions and a set of optimization test functions, and test results of other algorithms are compared and analyzed to verify the correctness and effectiveness of the proposed algorithm.

  15. 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.

  16. 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.

    2015-01-01

    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. PMID:25909856

  17. 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.

  18. Convergence analysis of artificial bee colony algorithm%人工蜂群算法的收敛性分析

    Institute of Scientific and Technical Information of China (English)

    宁爱平; 张雪英

    2013-01-01

    The convergence of artificial bee colony algorithm is analyzed theoretically by using the stochastic process theory. Some mathematical definitions of artificial bee colony algorithm and one step transition probability of nectar source position are given and the Markov chain model of the algorithm is established. Some properties of the Markov chain are analyzed, and the conclusions that the artificial bee colony state sequence is a finite homogeneous of Markov chain and the state space of artificial bee colony is irreducible are obtained. It is proved that the artificial bee colony algorithm ensures the global convergence as the algorithm meets two assumptions of the random search algorithm for the global convergence.%利用随机过程理论,对人工蜂群算法收敛性进行理论分析,给出人工蜂群算法的一些数学定义和蜜源位置的一步转移概率,建立人工蜂群算法的Markov链模型,分析此Markov链的一些性质,论证了人工蜂群状态序列是有限齐次Markov链,且状态空间是不可约的。结合随机搜索算法的全局收敛准则,证明了人工蜂群算法能够满足随机搜索算法全局收敛的两个假设,保证算法的全局收敛。

  19. Autumn invasion rates of Varroa destructor (Mesostigmata: Varroidae) into honey bee (Hymenoptera: Apidae) colonies and the resulting increase in mite populations.

    Science.gov (United States)

    Frey, Eva; Rosenkranz, Peter

    2014-04-01

    The honey bee parasite Varroa destructor Anderson & Trueman can disperse and invade honey bee colonies by attaching to "drifting" and "robbing" honey bees that move into nonnatal colonies. We quantified the weekly invasion rates and the subsequent mite population growth from the end of July to November 2011 in 28 honey bee colonies kept in two apiaries that had high (HBD) and low (LBD) densities of neighboring colonies. At each apiary, half (seven) of the colonies were continuously treated with acaricides to kill all Varroa mites and thereby determine the invasion rates. The other group of colonies was only treated before the beginning of the experiment and then left untreated to record Varroa population growth until a final treatment in November. The numbers of bees and brood cells of all colonies were estimated according to the Liebefeld evaluation method. The invasion rates varied among individual colonies but revealed highly significant differences between the study sites. The average invasion rate per colony over the entire 3.5-mo period ranged from 266 to 1,171 mites at the HBD site compared with only 72 to 248 mites at the LBD apiary. In the untreated colonies, the Varroa population reached an average final infestation in November of 2,082 mites per colony (HBD) and 340 mites per colony (LBD). All colonies survived the winter; however, the higher infested colonies lost about three times more bees compared with the lower infested colonies. Therefore, mite invasion and late-year population growth must be considered more carefully for future treatment concepts in temperate regions.

  20. 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.

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

    Directory of Open Access Journals (Sweden)

    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.

  2. An Improved Artificial Bee Colony Algorithm for Solving Hybrid Flexible Flowshop With Dynamic Operation Skipping.

    Science.gov (United States)

    Li, Jun-Qing; Pan, Quan-Ke; Duan, Pei-Yong

    2016-06-01

    In this paper, we propose an improved discrete artificial bee colony (DABC) algorithm to solve the hybrid flexible flowshop scheduling problem with dynamic operation skipping features in molten iron systems. First, each solution is represented by a two-vector-based solution representation, and a dynamic encoding mechanism is developed. Second, a flexible decoding strategy is designed. Next, a right-shift strategy considering the problem characteristics is developed, which can clearly improve the solution quality. In addition, several skipping and scheduling neighborhood structures are presented to balance the exploration and exploitation ability. Finally, an enhanced local search is embedded in the proposed algorithm to further improve the exploitation ability. The proposed algorithm is tested on sets of the instances that are generated based on the realistic production. Through comprehensive computational comparisons and statistical analysis, the highly effective performance of the proposed DABC algorithm is favorably compared against several presented algorithms, both in solution quality and efficiency.

  3. Condition assessment of transformer insulation using dielectric frequency response analysis by artificial bee colony algorithm

    Directory of Open Access Journals (Sweden)

    Bigdeli Mehdi

    2016-03-01

    Full Text Available Transformers are one of the most important components of the power system. It is important to maintain and assess the condition. Transformer lifetime depends on the life of its insulation and insulation life is also strongly influenced by moisture in the insulation. Due to importance of this issue, in this paper a new method is introduced for determining the moisture content of the transformer insulation system using dielectric response analysis in the frequency domain based on artificial bee colony algorithm. First, the master curve of dielectric response is modeled. Then, using proposed method the master curve and the measured dielectric response curves are compared. By analyzing the results of the comparison, the moisture content of paper insulation, electrical conductivity of the insulating oil and dielectric model dimensions are determined. Finally, the proposed method is applied to several practical samples to demonstrate its capabilities compared with the well-known conventional method.

  4. 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.

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

    Directory of Open Access Journals (Sweden)

    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. New insights into honey bee (Apis mellifera pheromone communication. Is the queen mandibular pheromone alone in colony regulation?

    Directory of Open Access Journals (Sweden)

    Plettner Erika

    2010-06-01

    Full Text Available Abstract Background In social insects, the queen is essential to the functioning and homeostasis of the colony. This influence has been demonstrated to be mediated through pheromone communication. However, the only social insect for which any queen pheromone has been identified is the honey bee (Apis mellifera with its well-known queen mandibular pheromone (QMP. Although pleiotropic effects on colony regulation are accredited to the QMP, this pheromone does not trigger the full behavioral and physiological response observed in the presence of the queen, suggesting the presence of additional compounds. We tested the hypothesis of a pheromone redundancy in honey bee queens by comparing the influence of queens with and without mandibular glands on worker behavior and physiology. Results Demandibulated queens had no detectable (E-9-oxodec-2-enoic acid (9-ODA, the major compound in QMP, yet they controlled worker behavior (cell construction and queen retinue and physiology (ovary inhibition as efficiently as intact queens. Conclusions We demonstrated that the queen uses other pheromones as powerful as QMP to control the colony. It follows that queens appear to have multiple active compounds with similar functions in the colony (pheromone redundancy. Our findings support two hypotheses in the biology of social insects: (1 that multiple semiochemicals with synonymous meaning exist in the honey bee, (2 that this extensive semiochemical vocabulary exists because it confers an evolutionary advantage to the colony.

  7. 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.

  8. 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.

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

    Science.gov (United States)

    Jacques, Antoine; Laurent, Marion; Ribière-Chabert, Magali; Saussac, Mathilde; Bougeard, Stéphanie; Budge, Giles E.; Hendrikx, Pascal; Chauzat, Marie-Pierre

    2017-01-01

    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. PMID:28278255

  10. 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.

  11. 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.

  12. IAPV, a bee-affecting virus associated with Colony Collapse Disorder can be silenced by dsRNA ingestion.

    Science.gov (United States)

    Maori, E; Paldi, N; Shafir, S; Kalev, H; Tsur, E; Glick, E; Sela, I

    2009-02-01

    Colony Collapse Disorder (CCD) has been associated with Israeli acute paralysis virus (IAPV). CCD poses a serious threat to apiculture and agriculture as a whole, due to the consequent inability to provide the necessary amount of bees for pollination of critical crops. Here we report on RNAi-silencing of IAPV infection by feeding bees with double-stranded RNA, as an efficient and feasible way of controlling this viral disease. The association of CCD with IAPV is discussed, as well as the potential of controlling CCD.

  13. 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.

  14. Generating and prioritizing optimal paths using ant colony optimization

    Directory of Open Access Journals (Sweden)

    Mukesh Mann

    2015-03-01

    Full Text Available The assurance of software reliability partially depends on testing. Numbers of approaches for software testing are available with their proclaimed advantages and limitations, but accessibility of any one of them is a subject dependent. Time is a critical factor in deciding cost of any project. A deep insight has shown that executing test cases are time consuming and tedious activity. Thus stress has been given to develop algorithms which can suggest better pathways for testing. One such algorithm called Path Prioritization -Ant Colony Optimization (PP-ACO has been suggested in this paper which is inspired by real Ant's foraging behavior to generate optimal paths sequence of a decision to decision (DD path of a graph. The algorithm does full path coverage and suggests the best optimal sequences of path in path testing and prioritizes them according to path strength.

  15. 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

  16. 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.

  17. 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.

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

    OpenAIRE

    Al Toufailia, Hasan M; Amiri, Esmaeil; Scandian, Luciano; Kryger, Per; Ratnieks, Francis L. W.

    2014-01-01

    Hygienic behaviour in the honey bee, Apis mellifera, is the uncapping and removal of dead, diseased or infected brood from sealed cells by worker bees. We determined the effect of hygienic behaviour on varroa population growth and incidence of deformed wing virus (DWV), which can be transmitted 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 ...

  19. Treatment with synthetic brood pheromone (SuperBoost) enhances honey production and improves overwintering survival of package honey bee (Hymenoptera: Apidae) colonies.

    Science.gov (United States)

    Lait, Cameron G; Borden, John H; Kovacs, Ervin; Moeri, Onour E; Campbell, Michael; Machial, Cristina M

    2012-04-01

    We evaluated a year-long treatment regime testing synthetic, 10-component, honey bee, Apis mellifera L. (Hymenoptera: Apidae), brood pheromone (SuperBoost; Contech Enterprises Inc., Delta, BC, Canada) on the productivity and vigor of package bee colonies in the lower Fraser Valley of British Columbia, Canada. Fifty-eight newlyestablished 1.3-kg (3-lb) colonies treated three times with SuperBoost at 5-wk intervals starting 30 April 2009 were compared with 52 untreated control colonies. Treated colonies produced 84.3% more honey than untreated control colonies. By 8 September 2009, SuperBoost-treated colonies had 35.4% more adults than untreated colonies. By 28 September, net survival of treated and control colonies was 72.4 and 67.3%, respectively. On 5 October, treated and control colonies were divided into two additional groups, making up four cohorts: SuperBoost-treated colonies treated again during fall and spring build-up feeding with pollen substitute diet (BeePro, Mann Lake Ltd., Hackensack, MN; TIT); controls that remained untreated throughout the year (CCC); colonies treated with SuperBoost in spring-summer 2009 but not treated thereafter (TCC); and original control colonies treated with SuperBoost during the fall and spring build-up feeding periods (CTT). There was no difference among cohorts in consumption of BeePro during fall feeding, but TTT colonies (including daughter colonies split off from parent colonies) consumed 50.8% more diet than CCC colonies during spring build-up feeding. By 21 April, the normalized percentages of the original number of colonies remaining (dead colonies partially offset by splits) were as follows: CCC, 31.4%; CTT, 43.8%; TCC, 53.59%; and TTT, 80.0%. The net benefit of placing 100 newly established package bee colonies on a year-long six-treatment regime with SuperBoost would be US$6,202 (US$62.02 per colony). We conclude that treatment with SuperBoost enhanced the productivity and survival of package bee colonies and

  20. 基于人工蜂群算法的多目标最优潮流问题的研究%Research on power flow optimization based on multi-objective artificial bee colony algorithm

    Institute of Scientific and Technical Information of China (English)

    刘前进; 许慧铭; 施超

    2015-01-01

    Taking the minimum pollutant emission and active loss as objective functions, this paper builds a multi-objective optimization model for power flow optimization of power system. In the proposed algorithm, an external archive of non-dominated solutions is kept which is updated at each iteration. Moreover, a method based on fuzzy set theory is employed to extract one of the Pareto-optimal solutions set as the best compromise one to provide the scientific decision basis for decision-makers. Simulation of IEEE-30 bus system and IEEE-57 bus system testify that this algorithm can avoid the local convergence effectively compared with other multi-objective optimization algorithm.%以污染气体排放量、网损最小为目标,建立多目标电力系统最优潮流数学模型,并提出一种基于人工蜂群的多目标算法对其进行求解。该算法利用外部存档技术来保存进化过程中已经找到的Pareto最优解,并在每次迭代后更新。最后根据模糊集理论从Pareto最优解集中选取最优折衷解,为决策者提供科学的决策依据。通过IEEE-30节点系统及IEEE-57节点系统的仿真,验证了该算法在求解大规模电力系统多目标问题上的有效性,相比其他多目标算法能有效避免局部收敛。

  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. Powdered sugar shake to monitor and oxalic acid treatments to control varroa mites (Parasitiformes: Varroidae) in honey bee (Hymenoptera: Apidae) colonies

    Science.gov (United States)

    Effective monitoring and alternative strategies to control the ectoparasitic mite, Varroa destructor Anderson and Truemann (Parasitiformes: Varroidae), (varroa) are crucial for determining when to apply effective treatments to honey bee, Apis mellifera L. (Hymenoptera: Apidae), colonies. Using simpl...

  3. 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...

  4. Artificial Bee Colony Algorithm Merged with Pheromone Communication Mechanism for the 0-1 Multidimensional Knapsack Problem

    Directory of Open Access Journals (Sweden)

    Junzhong Ji

    2013-01-01

    Full Text Available Given a set of n objects, the objective of the 0-1 multidimensional knapsack problem (MKP_01 is to find a subset of the object set that maximizes the total profit of the objects in the subset while satisfying m knapsack constraints. In this paper, we have proposed a new artificial bee colony (ABC algorithm for the MKP_01. The new ABC algorithm introduces a novel communication mechanism among bees, which bases on the updating and diffusion of inductive pheromone produced by bees. In a number of experiments and comparisons, our approach obtains better quality solutions in shorter time than the ABC algorithm without the mechanism. We have also compared the solution performance of our approach against some stochastic approaches recently reported in the literature. Computational results demonstrate the superiority of the new ABC approach over all the other approaches.

  5. 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.

  6. The hyper-cube framework for ant colony optimization.

    Science.gov (United States)

    Blum, Christian; Dorigo, Marco

    2004-04-01

    Ant colony optimization is a metaheuristic approach belonging to the class of model-based search algorithms. In this paper, we propose a new framework for implementing ant colony optimization algorithms called the hyper-cube framework for ant colony optimization. In contrast to the usual way of implementing ant colony optimization algorithms, this framework limits the pheromone values to the interval [0,1]. This is obtained by introducing changes in the pheromone value update rule. These changes can in general be applied to any pheromone value update rule used in ant colony optimization. We discuss the benefits coming with this new framework. The benefits are twofold. On the theoretical side, the new framework allows us to prove that in Ant System, the ancestor of all ant colony optimization algorithms, the average quality of the solutions produced increases in expectation over time when applied to unconstrained problems. On the practical side, the new framework automatically handles the scaling of the objective function values. We experimentally show that this leads on average to a more robust behavior of ant colony optimization algorithms.

  7. 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.

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

    OpenAIRE

    Fei Song; Shiyin Qin

    2014-01-01

    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 flywhe...

  10. Division of foraging labor in the bumble bee, Bombus impatiens : effect of removing pollen specialists and colony adoption

    OpenAIRE

    Hagbery, Jessica

    2011-01-01

    Foraging specialization plays an important role in the ability of social Hymenoptera to efficiently allocate labor and adapt to environmental changes. However, relatively little is known about whether bumble bees, important social pollinators, can flexibly allocate their foraging. I removed pollen specialists at different stages in the life of a Bombus impatiens colony and recorded the pollen and nectar foraging of every forager on each foraging trip over the lifetimes of five established col...

  11. Determinants of between-year burrow re-occupation in a colony of the European bee-eater Merops apiaster.

    Science.gov (United States)

    Brust, Vera; Bastian, Hans-Valentin; Bastian, Anita; Schmoll, Tim

    2015-08-01

    Re-occupation of existing nesting burrows in the European bee-eater Merops apiaster has only rarely - and if so mostly anecdotically - been documented in the literature record, although such behavior would substantially save time and energy. In this study, we quantify burrow re-occupation in a German colony over a period of eleven years and identify ecological variables determining reuse probability. Of 179 recorded broods, 54% took place in a reused burrow and the overall probability that one of 75 individually recognized burrows would be reused in a given subsequent year was estimated as 26.4%. This indicates that between-year burrow reuse is a common behavior in the study colony which contrasts with findings from studies in other colonies. Furthermore, burrow re-occupation probability declined highly significantly with increasing age of the breeding wall. Statistical separation of within- and between-burrow effects of the age of the breeding wall revealed that a decline in re-occupation probability with individual burrow age was responsible for this and not a selective disappearance of burrows with high re-occupation probability over time. Limited duty cycles of individual burrows may be caused by accumulating detritus or decreasing stability with increasing burrow age. Alternatively, burrow fidelity may presuppose pair fidelity which may also explain the observed restricted burrow reuse duty cycles. A consequent next step would be to extend our within-colony approach to other colonies and compare the ecological circumstances under which bee-eaters reuse breeding burrows.

  12. Discover for Yourself: An Optimal Control Model in Insect Colonies

    Science.gov (United States)

    Winkel, Brian

    2013-01-01

    We describe the enlightening path of self-discovery afforded to the teacher of undergraduate mathematics. This is demonstrated as we find and develop background material on an application of optimal control theory to model the evolutionary strategy of an insect colony to produce the maximum number of queen or reproducer insects in the colony at…

  13. Discover for Yourself: An Optimal Control Model in Insect Colonies

    Science.gov (United States)

    Winkel, Brian

    2013-01-01

    We describe the enlightening path of self-discovery afforded to the teacher of undergraduate mathematics. This is demonstrated as we find and develop background material on an application of optimal control theory to model the evolutionary strategy of an insect colony to produce the maximum number of queen or reproducer insects in the colony at…

  14. A novel approach to speckle noise filtering based on Artificial Bee Colony algorithm: an ultrasound image application.

    Science.gov (United States)

    Latifoğlu, Fatma

    2013-09-01

    In this study a novel approach based on 2D FIR filters is presented for denoising digital images. In this approach the filter coefficients of 2D FIR filters were optimized using the Artificial Bee Colony (ABC) algorithm. To obtain the best filter design, the filter coefficients were tested with different numbers (3×3, 5×5, 7×7, 11×11) and connection types (cascade and parallel) during optimization. First, the speckle noise with variances of 1, 0.6, 0.8 and 0.2 respectively was added to the synthetic test image. Later, these noisy images were denoised with both the proposed approach and other well-known filter types such as Gaussian, mean and average filters. For image quality determination metrics such as mean square error (MSE), peak signal-to-noise ratio (PSNR) and signal-to-noise ratio (SNR) were used. Even in the case of noise having maximum variance (the most noisy), the proposed approach performed better than other filtering methods did on the noisy test images. In addition to test images, speckle noise with a variance of 1 was added to a fetal ultrasound image, and this noisy image was denoised with very high PSNR and SNR values. The performance of the proposed approach was also tested on several clinical ultrasound images such as those obtained from ovarian, abdomen and liver tissues. The results of this study showed that the 2D FIR filters designed based on ABC optimization can eliminate speckle noise quite well on noise added test images and intrinsically noisy ultrasound images. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

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

    Science.gov (United States)

    Amiri, Esmaeil; Strand, Micheline K; Rueppell, Olav; Tarpy, David R

    2017-05-08

    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.

  16. Associations of parameters related to the fall of Varroa destructor (Mesostigmata: Varroidae) in Russian and Italian honey bee (Hymenoptera: Apidae) colonies.

    Science.gov (United States)

    Rinderer, Thomas E; De Guzman, Lilia I; Frake, Amanda M

    2013-04-01

    Varroa destructor (Anderson and Truman) trapped on bottom boards were assessed as indirect measurements of colony mite populations and mite fall in colonies of Russian and Italian honey bees using 29 candidate measurements. Measurements included damaged and nondamaged younger mites, damaged and nondamaged older mites, fresh mites and all mites, each as a proportion of total mites in the colonies and as a proportion of all trapped mites or all trapped fresh mites. Regression analyses were used to determine the relationships of these candidate measurements to the number of mites in the colonies. The largest positive regressions were found for trapped younger mites (Y) and trapped fresh mites (F). Measurments of Y and F across time could be used to estimate mite population growth for the purposes of selective breeding. The largest negative regressions with colony mites were observed for: trapped older mites/trapped mites (O/T), trapped older mites/trapped younger mites (O/Y), and trapped injured older mites/injured mites (IO/I). O/T and O/Y are significantly higher for Russian honey bee colonies suggesting that they are related to at least some of the mechanisms used by Russian honey bee to resist Varroa population growth. O/T and O/Y have strong negative relationships with colony mites for both Russian honey bee and Italian colonies suggesting that both strains possibly could be selected for reduced colony mites using O/T or O/Y.

  17. Varroa destructor (Mesostigmata: Varroidae) in Costa Rica: population dynamics and its influence on the colony condition of Africanized honey bees (Hymenoptera: Apidae).

    Science.gov (United States)

    Calderón, Rafael A; van Veen, Johan W

    2008-12-01

    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, P varroa infestation coincided with a decrease in the amount of brood. Furthermore, adult bees with deformed wings or even without wings crawling in front of their hive occurred in highly infested colonies (mite infestation = 10.0% or more).

  18. Hyperspectral image clustering method based on artificial bee colony algorithm and Markov random fields

    Science.gov (United States)

    Sun, Xu; Yang, Lina; Gao, Lianru; Zhang, Bing; Li, Shanshan; Li, Jun

    2015-01-01

    Center-oriented hyperspectral image clustering methods have been widely applied to hyperspectral remote sensing image processing; however, the drawbacks are obvious, including the over-simplicity of computing models and underutilized spatial information. In recent years, some studies have been conducted trying to improve this situation. We introduce the artificial bee colony (ABC) and Markov random field (MRF) algorithms to propose an ABC-MRF-cluster model to solve the problems mentioned above. In this model, a typical ABC algorithm framework is adopted in which cluster centers and iteration conditional model algorithm's results are considered as feasible solutions and objective functions separately, and MRF is modified to be capable of dealing with the clustering problem. Finally, four datasets and two indices are used to show that the application of ABC-cluster and ABC-MRF-cluster methods could help to obtain better image accuracy than conventional methods. Specifically, the ABC-cluster method is superior when used for a higher power of spectral discrimination, whereas the ABC-MRF-cluster method can provide better results when used for an adjusted random index. In experiments on simulated images with different signal-to-noise ratios, ABC-cluster and ABC-MRF-cluster showed good stability.

  19. Sequential Insertion Heuristic with Adaptive Bee Colony Optimisation Algorithm for Vehicle Routing Problem with Time Windows.

    Science.gov (United States)

    Jawarneh, Sana; Abdullah, Salwani

    2015-01-01

    This paper presents a bee colony optimisation (BCO) algorithm to tackle the vehicle routing problem with time window (VRPTW). The VRPTW involves recovering an ideal set of routes for a fleet of vehicles serving a defined number of customers. The BCO algorithm is a population-based algorithm that mimics the social communication patterns of honeybees in solving problems. The performance of the BCO algorithm is dependent on its parameters, so the online (self-adaptive) parameter tuning strategy is used to improve its effectiveness and robustness. Compared with the basic BCO, the adaptive BCO performs better. Diversification is crucial to the performance of the population-based algorithm, but the initial population in the BCO algorithm is generated using a greedy heuristic, which has insufficient diversification. Therefore the ways in which the sequential insertion heuristic (SIH) for the initial population drives the population toward improved solutions are examined. Experimental comparisons indicate that the proposed adaptive BCO-SIH algorithm works well across all instances and is able to obtain 11 best results in comparison with the best-known results in the literature when tested on Solomon's 56 VRPTW 100 customer instances. Also, a statistical test shows that there is a significant difference between the results.

  20. Population growth of Varroa destructor (Acari: Varroidae) in honey bee colonies is affected by the number of foragers with mites.

    Science.gov (United States)

    DeGrandi-Hoffman, Gloria; Ahumada, Fabiana; Zazueta, Victor; Chambers, Mona; Hidalgo, Geoffrey; deJong, Emily Watkins

    2016-05-01

    Varroa mites are a serious pest of honey bees and the leading cause of colony losses. Varroa have relatively low reproductive rates, so populations should not increase rapidly, but often they do. Other factors might contribute to the growth of varroa populations including mite migration into colonies on foragers from other hives. We measured the proportion of foragers carrying mites on their bodies while entering and leaving hives, and determined its relationship to the growth of varroa populations in those hives at two apiary sites. We also compared the estimates of mite population growth with predictions from a varroa population dynamics model that generates estimates of mite population growth based on mite reproduction. Samples of capped brood and adult bees indicated that the proportion of brood cells infested with mites and adult bees with phoretic mites was low through the summer but increased sharply in the fall especially at site 1. The frequency of capturing foragers with mites on their bodies while entering or leaving hives also increased in the fall. The growth of varroa populations at both sites was not significantly related to our colony estimates of successful mite reproduction, but instead to the total number of foragers with mites (entering and leaving the colony). There were more foragers with mites at site 1 than site 2, and mite populations at site 1 were larger especially in the fall. The model accurately estimated phoretic mite populations and infested brood cells until November when predictions were much lower than those measured in colonies. The rapid growth of mite populations particularly in the fall being a product of mite migration rather than mite reproduction only is discussed.

  1. Ant Colony Optimization for Train Scheduling: An Analysis

    OpenAIRE

    Sudip Kumar Sahana; Aruna Jain; Prabhat Kumar Mahanti

    2014-01-01

    This paper deals on cargo train scheduling between source station and destination station in Indian railways scenario. It uses Ant Colony Optimization (ACO) technique which is based on ant’s food finding behavior. Iteration wise convergence process and the convergence time for the algorithm are studied and analyzed. Finally, the run time analysis of Ant Colony Optimization Train Scheduling (ACOTS) and Standard Train Scheduling (STS) algorithm has been performed.

  2. Ant Colony Optimization for Train Scheduling: An Analysis

    Directory of Open Access Journals (Sweden)

    Sudip Kumar Sahana

    2014-01-01

    Full Text Available This paper deals on cargo train scheduling between source station and destination station in Indian railways scenario. It uses Ant Colony Optimization (ACO technique which is based on ant’s food finding behavior. Iteration wise convergence process and the convergence time for the algorithm are studied and analyzed. Finally, the run time analysis of Ant Colony Optimization Train Scheduling (ACOTS and Standard Train Scheduling (STS algorithm has been performed.

  3. Application of honey-bee mating optimization on state estimation of a power distribution system including distributed generators

    Institute of Scientific and Technical Information of China (English)

    Taher NIKNAM

    2008-01-01

    We present a new approach based on honey-bee mating optimization to estimate the state variables in distribution networks including distributed generators. The proposed method considers practical models of electrical equipments such as smilc var compensators,voltage regulators,and under-load tap changer transformers,which have usually nonlinear and discrete characteristics. The feasibility of the proposed approach is demonstrated by comparison with the methods based on neural networks,ant colony optimization,and genetic algorithms for two test systems,a network with 34-bus radial test feeders and a realistic 80-bus 20 kV network.

  4. 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......Hygienic behaviour in the honey bee, Apis mellifera, is the uncapping and removal of dead, diseased or infected brood from sealed cells by worker bees. We determined the effect of hygienic behaviour on varroa population growth and incidence of deformed wing virus (DWV), which can be transmitted...... the summer the hygienic behaviour in each colony was quantified four times using the Freeze Killed Brood (FKB) removal assay, and ranged from 27.5 % to 100 %. Varroa population increased greatly over the season, and there was a significant negative correlation between varroa increase and FKB removal...

  5. 混合蚁群蜂群算法在旅行Agent问题中的应用%Application of hybrid ant colony and bee colony algorithm in traveling Agent problem

    Institute of Scientific and Technical Information of China (English)

    宋佩莉; 祁飞; 张鹏

    2012-01-01

    Aiming at the defects such as long search time and tending to be trapped by local optimization for ant colony algorithm on solving Traveling Agent Problem (TAP), this paper proposes an improves algorithm which integrates ant and bee colony algorithm. The algorithm is more suitable for the characteristics of TAP by modifying the state transition probability and pheromone updating rules. This algorithm makes the ants search for the optimal solution as soon as possible by introducing the thought of follow bees. The algorithm adding the obstruction factor avoids the shortcoming that the solution is trapped easily in the local optimum. The simulation results show that the algorithm for resolving the TAP avoids effectively the above-mentioned disadvantages of the ant colony algorithm, and also show that the algorithm is superior to other related methods on the performance of solution.%针对蚁群算法在解决旅行Agent问题(TAP)时存在搜索时间长和易陷入局部最优的缺点,提出一种将蜂群和蚁群算法相结合的新型算法.通过修改状态转移概率和信息素更新规则使算法更符合TAP问题的特征,引入跟随蜂思想使蚂蚁尽快搜索到问题最优解,加入阻塞度因子以避免算法陷入局部最优.仿真结果表明,该算法在解决旅行Agent问题时有效避免了蚁群算法的上述缺点,且在解的性能上优于相关算法.

  6. 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...

  7. Optimization of PID Controllers Using Ant Colony and Genetic Algorithms

    CERN Document Server

    Ünal, Muhammet; Topuz, Vedat; Erdal, Hasan

    2013-01-01

    Artificial neural networks, genetic algorithms and the ant colony optimization algorithm have become a highly effective tool for solving hard optimization problems. As their popularity has increased, applications of these algorithms have grown in more than equal measure. While many of the books available on these subjects only provide a cursory discussion of theory, the present book gives special emphasis to the theoretical background that is behind these algorithms and their applications. Moreover, this book introduces a novel real time control algorithm, that uses genetic algorithm and ant colony optimization algorithms for optimizing PID controller parameters. In general, the present book represents a solid survey on artificial neural networks, genetic algorithms and the ant colony optimization algorithm and introduces novel practical elements related to the application of these methods to  process system control.

  8. Monitoring Aethina tumida (Coleoptera: Nitidulidae) with baited bottom board traps: occurrence and seasonal abundance in honey bee colonies in Kenya.

    Science.gov (United States)

    Torto, Baldwyn; Fombong, Ayuka T; Arbogast, Richard T; Teal, Peter E A

    2010-12-01

    The population dynamics of the honey bee pest Aethina tumida Murray (small hive beetle) have been studied in the United States with flight and Langstroth hive bottom board traps baited with pollen dough inoculated with a yeast Kodamaea ohmeri associated with the beetle. However, little is known about the population dynamics of the beetle in its native host range. Similarly baited Langstroth hive bottom board traps were used to monitor the occurrence and seasonal abundance of the beetle in honey bee colonies at two beekeeping locations in Kenya. Trap captures indicated that the beetle was present in honey bee colonies in low numbers all year round, but it was most abundant during the rainy season, with over 80% trapped during this period. The survival of larvae was tested in field releases under dry and wet soil conditions, and predators of larvae were identified. The actvity and survival of the beetle were strongly influenced by a combination of abiotic and biotic factors. Larval survival was higher during wet (28%) than dry (1.1%) conditions, with pupation occurring mostly at 0-15 cm and 11-20 cm, respectively, beneath the surface soil during these periods. The ant Pheidole megacephala was identified as a key predator of larvae at this site, and more active during the dry than wet seasons. These observations imply that intensive trapping during the rainy season could reduce the population of beetles infesting hives in subsequent seasons especially in places where the beetle is a serious pest.

  9. 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.

  10. 基于粒子群人工蜂群算法的灌区渠-塘-田优化调配耦合模型%Coupled allocation model for optimizing water in canal-pond-field based on artificial bee colony and particle swarm algorithm

    Institute of Scientific and Technical Information of China (English)

    陈述; 邵东国; 李浩鑫; 徐保利

    2014-01-01

    With the development of Chinese society and economy, contradiction between water supply and demand has become increasingly prominent. Consequently, the problem of agricultural water shortage becomes more serious. What’s more, seasonal droughts have frequently taken place in the south of China in recent years, resulting in a serious impact on agricultural production. In order to alleviate the contradiction between water supply and demand of agricultural, to reduce losses caused by seasonal droughts, we proposed a model to optimize the allocation of water resources. Based on the consideration of the complex relationship of water conversion between the canals, ponds and fields in the southern irrigation area and the regulatory role of ponds which is less taken into consideration in the models built before, canal-pond optimal regulation has been proposed. Water flowed into ponds from canals in lower-intensity periods, which were used to irrigate crops in peak periods in the mode of canal-pond optimal regulation. Then an optimal operation model for coupling canal-pond regulation and water allocation between crops has been set up, with the goal of maximizing the economic benefits in the whole irrigated region. Channel water diversion and crop irrigation water at each period were treated as decision variables in the model. The responses of different crops to water deficit during the same period, the responses of the same crop to water deficit in different periods and the regulatory role of ponds were all taken into consideration in the model. Problems solved by the model were high-dimensional, complex, non-linear optimization problems. And the PSO-ABC hybrid algorithm was used to solve the model according to the characteristics of the model. Artificial bee colony algorithm is one of the current best evolutionary algorithms with advantages of simple principle, easy implementation, less parameters and quick convergence speed. But it still suffered from the problems of

  11. 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...

  12. Improved Modeling of Intelligent Tutoring Systems Using Ant Colony Optimization

    Science.gov (United States)

    Rastegarmoghadam, Mahin; Ziarati, Koorush

    2017-01-01

    Swarm intelligence approaches, such as ant colony optimization (ACO), are used in adaptive e-learning systems and provide an effective method for finding optimal learning paths based on self-organization. The aim of this paper is to develop an improved modeling of adaptive tutoring systems using ACO. In this model, the learning object is…

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

    Directory of Open Access Journals (Sweden)

    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.

  14. Parallelizing Ant Colony Optimization via Area of Expertise Learning

    Science.gov (United States)

    2007-09-13

    lutions for all but the most trivial instances. Ant colony optimization (ACO) is a simple metaheuristic that can effectively solve problems in these...expertise” technique is applied to two problem domains: gridworld and the traveling salesman problem. 1.1 Motivation ACO is a metaheuristic that generates...independent ant agents, an obvious extension of the ant colony framework is to implement the algorithm in a parallel environment. One of the main

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

    Directory of Open Access Journals (Sweden)

    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.

  16. 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.

  17. Could Computing Service Composition Based on Discrete Artificial Bee Colony Algorithm%基于离散人工群算法的云制造服务组合

    Institute of Scientific and Technical Information of China (English)

    常瑞云; 周井泉; 许斌; 亓晋

    2016-01-01

    With the rapid development of network technology such as Internet,cloud computing and so on,single manufacturing service has already not satisfied the increasingly complex tasks for users. So,cloud manufacturing service composition,as a NP hard problem,has been the applied and research hotspot in recent years. As to service composition optimal selection,a Location Search Discrete Artificial Bee Colony ( LSDABC) is proposed in this paper based on improvement of original ABC to provide the service composition execution path with optimal QoS for users. This algorithm introduces selection probability based on population and local search strategy to improve the exploitation ability and convergence speed and to avoid falling into local optimum. Finally,LSDABC is applied to the cloud manufac-turing service composition. The experiment shows that the LSDABC has better quality and robustness compared with the original ABC, DE and PSO.%随着互联网、云计算等网络技术的快速发展,单一制造服务已无法满足用户日益复杂的制造任务,所以云制造服务组合问题一直是近年来应用和研究的热点,为典型NP难题。文中针对云制造服务组合优选问题,改进原始人工蜂群算法( Artificial Bee Colony,ABC),提出了一种基于局部搜索离散蜂群算法( Location Search Discrete Artificial Bee Colony,LSD-ABC),从而为用户选择服务质量( Quality of Service,QoS)最优的服务组合执行路径。该算法引入种群的选择概率和对最优解的局部搜索策略,提升算法的开采能力、收敛速度,同时避免出现搜索停滞陷入局部最优。最后将LSDABC应用于云制造服务组合优选中进行仿真实验,并将结果与原始ABC、DE、PSO算法进行对比。实验结果表明,LSDABC具有较好的求解质量和鲁棒性。

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

    Science.gov (United States)

    Delaplane, Keith S.; Pietravalle, Stéphane; Brown, Mike A.; Budge, Giles E.

    2015-01-01

    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. PMID:26691845

  19. 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.

  20. 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.

  1. Tuning PID Controller Using Multiobjective Ant Colony Optimization

    Directory of Open Access Journals (Sweden)

    Ibtissem Chiha

    2012-01-01

    Full Text Available This paper treats a tuning of PID controllers method using multiobjective ant colony optimization. The design objective was to apply the ant colony algorithm in the aim of tuning the optimum solution of the PID controllers (Kp, Ki, and Kd by minimizing the multiobjective function. The potential of using multiobjective ant algorithms is to identify the Pareto optimal solution. The other methods are applied to make comparisons between a classic approach based on the “Ziegler-Nichols” method and a metaheuristic approach based on the genetic algorithms. Simulation results demonstrate that the new tuning method using multiobjective ant colony optimization has a better control system performance compared with the classic approach and the genetic algorithms.

  2. 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.

  3. 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

  4. 模糊人工蜂群算法的置换流水车间调度问题求解%Fuzzy Artificial Bees Colony Algorithm for Solving Permutation Flow Shop Scheduling Problem

    Institute of Scientific and Technical Information of China (English)

    柳寅; 马良; 黄钰

    2013-01-01

    针对传统人工蜂群算法早熟收敛问题,基于模糊化处理和蜂群寻优的特点,提出一种模糊人工蜂群算法.将模糊输入输出机制引入到算法中来保持蜜源访问概率的动态更新.根据算法计算过程中的不同阶段对蜜源访问概率有效调整,避免算法陷入局部极值.通过对置换流水车间调度问题的仿真实验和与其他算法的比较,表明本算法可行有效,有良好的鲁棒性.%Aiming at the premature convergence problem in traditional artificial bees colony algorithm,fuzzy artificial bees colony algorithm is proposed,which is based on the principles of fuzzy processing and bees colony behavior.Fuzzy inputs and fuzzy outputs are introduced into the algorithm to maintain dynamic updates of the nectar access probability.According to effective adjustment on nectar access probability during the different stages of algorithm calculation,the algorithm avoids local optima.Simulated tests of permutation flow shop scheduling problem and comparisons with other algorithms show the algorithm is feasible and effective and the algorithm has strong global optimization ability.

  5. Implementation of Travelling Salesman Problem Using ant Colony Optimization

    Directory of Open Access Journals (Sweden)

    Gaurav Singh,

    2014-04-01

    Full Text Available Within the Artificial Intelligence community, there is great need for fast and accurate traversal algorithms, specifically those that find a path from a start to goal with minimum cost. Cost can be distance, time, money, energy, etc. Travelling salesman problem (TSP is a combinatorial optimization problem. TSP is the most intensively studied problem in the area of optimization. Ant colony optimization (ACO is a population-based metaheuristic that can be used to find approximate solutions to difficult optimization problems. There have been many efforts in the past to provide time efficient solutions for the problem, both exact and approximate. This paper demonstrates the implementation of TSP using ant colony optimization(ACO.The solution to this problem enjoys wide applicability in a variety of practical fields.TSP in its purest form has several applications such as planning, logistics, and manufacture of microchips, military and traffic.

  6. 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.

  7. Colony Failure Linked to Low Sperm Viability in Honey Bee (Apis mellifera Queens and an Exploration of Potential Causative Factors.

    Directory of Open Access Journals (Sweden)

    Jeffery S Pettis

    Full Text Available Queen health is closely linked to colony performance in honey bees as a single queen is normally responsible for all egg laying and brood production within the colony. In the U. S. in recent years, queens have been failing at a high rate; with 50% or greater of queens replaced in colonies within 6 months when historically a queen might live one to two years. This high rate of queen failure coincides with the high mortality rates of colonies in the US, some years with >50% of colonies dying. In the current study, surveys of sperm viability in US queens were made to determine if sperm viability plays a role in queen or colony failure. Wide variation was observed in sperm viability from four sets of queens removed from colonies that beekeepers rated as in good health (n = 12; average viability = 92%, were replacing as part of normal management (n = 28; 57%, or where rated as failing (n = 18 and 19; 54% and 55%. Two additional paired set of queens showed a statistically significant difference in viability between colonies rated by the beekeeper as failing or in good health from the same apiaries. Queens removed from colonies rated in good health averaged high viability (ca. 85% while those rated as failing or in poor health had significantly lower viability (ca. 50%. Thus low sperm viability was indicative of, or linked to, colony performance. To explore the source of low sperm viability, six commercial queen breeders were surveyed and wide variation in viability (range 60-90% was documented between breeders. This variability could originate from the drones the queens mate with or temperature extremes that queens are exposed to during shipment. The role of shipping temperature as a possible explanation for low sperm viability was explored. We documented that during shipment queens are exposed to temperature spikes ( 40°C and these spikes can kill 50% or more of the sperm stored in queen spermathecae in live queens. Clearly low sperm viability is

  8. Colony Failure Linked to Low Sperm Viability in Honey Bee (Apis mellifera) Queens and an Exploration of Potential Causative Factors.

    Science.gov (United States)

    Pettis, Jeffery S; Rice, Nathan; Joselow, Katie; vanEngelsdorp, Dennis; Chaimanee, Veeranan

    2016-01-01

    Queen health is closely linked to colony performance in honey bees as a single queen is normally responsible for all egg laying and brood production within the colony. In the U. S. in recent years, queens have been failing at a high rate; with 50% or greater of queens replaced in colonies within 6 months when historically a queen might live one to two years. This high rate of queen failure coincides with the high mortality rates of colonies in the US, some years with >50% of colonies dying. In the current study, surveys of sperm viability in US queens were made to determine if sperm viability plays a role in queen or colony failure. Wide variation was observed in sperm viability from four sets of queens removed from colonies that beekeepers rated as in good health (n = 12; average viability = 92%), were replacing as part of normal management (n = 28; 57%), or where rated as failing (n = 18 and 19; 54% and 55%). Two additional paired set of queens showed a statistically significant difference in viability between colonies rated by the beekeeper as failing or in good health from the same apiaries. Queens removed from colonies rated in good health averaged high viability (ca. 85%) while those rated as failing or in poor health had significantly lower viability (ca. 50%). Thus low sperm viability was indicative of, or linked to, colony performance. To explore the source of low sperm viability, six commercial queen breeders were surveyed and wide variation in viability (range 60-90%) was documented between breeders. This variability could originate from the drones the queens mate with or temperature extremes that queens are exposed to during shipment. The role of shipping temperature as a possible explanation for low sperm viability was explored. We documented that during shipment queens are exposed to temperature spikes ( 40°C) and these spikes can kill 50% or more of the sperm stored in queen spermathecae in live queens. Clearly low sperm viability is linked to

  9. Colony Failure Linked to Low Sperm Viability in Honey Bee (Apis mellifera) Queens and an Exploration of Potential Causative Factors

    Science.gov (United States)

    Pettis, Jeffery S.; Rice, Nathan; Joselow, Katie; vanEngelsdorp, Dennis; Chaimanee, Veeranan

    2016-01-01

    Queen health is closely linked to colony performance in honey bees as a single queen is normally responsible for all egg laying and brood production within the colony. In the U. S. in recent years, queens have been failing at a high rate; with 50% or greater of queens replaced in colonies within 6 months when historically a queen might live one to two years. This high rate of queen failure coincides with the high mortality rates of colonies in the US, some years with >50% of colonies dying. In the current study, surveys of sperm viability in US queens were made to determine if sperm viability plays a role in queen or colony failure. Wide variation was observed in sperm viability from four sets of queens removed from colonies that beekeepers rated as in good health (n = 12; average viability = 92%), were replacing as part of normal management (n = 28; 57%), or where rated as failing (n = 18 and 19; 54% and 55%). Two additional paired set of queens showed a statistically significant difference in viability between colonies rated by the beekeeper as failing or in good health from the same apiaries. Queens removed from colonies rated in good health averaged high viability (ca. 85%) while those rated as failing or in poor health had significantly lower viability (ca. 50%). Thus low sperm viability was indicative of, or linked to, colony performance. To explore the source of low sperm viability, six commercial queen breeders were surveyed and wide variation in viability (range 60–90%) was documented between breeders. This variability could originate from the drones the queens mate with or temperature extremes that queens are exposed to during shipment. The role of shipping temperature as a possible explanation for low sperm viability was explored. We documented that during shipment queens are exposed to temperature spikes ( 40°C) and these spikes can kill 50% or more of the sperm stored in queen spermathecae in live queens. Clearly low sperm viability is linked to

  10. 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.

  11. PARAMETER ESTIMATION OF VALVE STICTION USING ANT COLONY OPTIMIZATION

    Directory of Open Access Journals (Sweden)

    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.

  12. Evaluation of spring organic treatments against Varroa destructor (Acari: Varroidae) in honey bee Apis mellifera (Hymenoptera: Apidae) colonies in eastern Canada.

    Science.gov (United States)

    Giovenazzo, Pierre; Dubreuil, Pascal

    2011-09-01

    The objective of this study was to measure the efficacy of two organic acid treatments, formic acid (FA) and oxalic acid (OA) for the spring control of Varroa destructor (Anderson and Trueman) in honey bee (Apis mellifera L.) colonies. Forty-eight varroa-infested colonies were randomly distributed amongst six experimental groups (n = 8 colonies per group): one control group (G1); two groups tested applications of different dosages of a 40 g OA/l sugar solution 1:1 trickled on bees (G2 and G3); three groups tested different applications of FA: 35 ml of 65% FA in an absorbent Dri-Loc(®) pad (G4); 35 ml of 65% FA poured directly on the hive bottom board (G5) and MiteAwayII™ (G6). The efficacy of treatments (varroa drop), colony development, honey yield and hive survival were monitored from May until September. Five honey bee queens died during this research, all of which were in the FA treated colonies (G4, G5 and G6). G6 colonies had significantly lower brood build-up during the beekeeping season. Brood populations at the end of summer were significantly higher in G2 colonies. Spring honey yield per colony was significantly lower in G6 and higher in G1. Summer honey flow was significantly lower in G6 and higher in G3 and G5. During the treatment period, there was an increase of mite drop in all the treated colonies. Varroa daily drop at the end of the beekeeping season (September) was significantly higher in G1 and significantly lower in G6. The average number of dead bees found in front of hives during treatment was significantly lower in G1, G2 and G3 versus G4, G5 and G6. Results suggest that varroa control is obtained from all spring treatment options. However, all groups treated with FA showed slower summer hive population build-up resulting in reduced honey flow and weaker hives at the end of summer. FA had an immediate toxic effect on bees that resulted in queen death in five colonies. The OA treatments that were tested have minimal toxic impacts on the

  13. 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 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.

  14. 模糊人工蜂群算法的旅行商问题求解%Fuzzy artificial bees colony algorithm for solving traveling salesman problem

    Institute of Scientific and Technical Information of China (English)

    柳寅; 马良

    2013-01-01

    Aiming at the premature convergence problem in traditional intelligent optimization algorithm,this paper proposed a fuzzy artificial bees colony algorithm,it based on the principles of fuzzy processing and bees colony behavior.It introduced fuzzy inputs and fuzzy outputs into the algorithm to maintain dynamic updates of the nectar access probability.According to effective adjustment on nectar access probability during the different stages of algorithm calculation,the algorithm avoided local optima.Simulation tests of traveling salesman problem and comparisons with other algorithms show the performance of proposed algorithm.The computational results prove the algorithm is feasible and effective.%针对传统人工智能算法早熟收敛问题,基于模糊化处理和蜂群寻优的特点,提出一种模糊人工蜂群算法,将模糊输入/输出机制引入到算法中来保持蜜源访问概率的动态更新.根据算法计算过程中的不同阶段对蜜源访问概率有效调整,避免算法陷入局部极值.通过对旅行商问题的仿真实验和与其他算法的比较来验证算法的性能.计算结果表明,该算法有良好的鲁棒性和有效性.

  15. A Multiple Pheromone Table Based Ant Colony Optimization for Clustering

    OpenAIRE

    Kai-Cheng Hu; Chun-Wei Tsai; Ming-Chao Chiang; Chu-Sing Yang

    2015-01-01

    Ant colony optimization (ACO) is an efficient heuristic algorithm for combinatorial optimization problems, such as clustering. Because the search strategy of ACO is similar to those of other well-known heuristics, the probability of searching particular regions will be increased if better results are found and kept. Although this kind of search strategy may find a better approximate solution, it also has a high probability of losing the potential search directions. To prevent the ACO from los...

  16. Stress indicator gene expression profiles, colony dynamics and tissue development of honey bees exposed to sub-lethal doses of imidacloprid in laboratory and field experiments

    Science.gov (United States)

    Ioannidis, Pavlos; Hamamtzoglou, Anna; Schoonvaere, Karel; Francis, Frédéric; Meeus, Ivan; Smagghe, Guy; de Graaf, Dirk C.

    2017-01-01

    In this study, different context-dependent effects of imidacloprid exposure on the honey bee response were studied. Honey bees were exposed to different concentrations of imidacloprid during a time period of 40 days. Next to these variables, a laboratory-field comparison was conducted. The influence of the chronic exposure on gene expression levels was determined using an in-house developed microarray targeting different immunity-related and detoxification genes to determine stress-related gene expression changes. Increased levels of the detoxification genes encoding, CYP9Q3 and CYT P450, were detected in imidacloprid-exposed honey bees. The different context-dependent effects of imidacloprid exposure on honey bees were confirmed physiologically by decreased hypopharyngeal gland sizes. Honey bees exposed to imidacloprid in laboratory cages showed a general immunosuppression and no detoxification mechanisms were triggered significantly, while honey bees in-field showed a resilient response with an immune stimulation at later time points. However, the treated colonies had a brood and population decline tendency after the first brood cycle in the field. In conclusion, this study highlighted the different context-dependent effects of imidacloprid exposure on the honey bee response. These findings warn for possible pitfalls concerning the generalization of results based on specific experiments with short exposure times. The increased levels of CYT P450 and CYP9Q3 combined with an immune response reaction can be used as markers for bees which are exposed to pesticides in the field. PMID:28182641

  17. Stress indicator gene expression profiles, colony dynamics and tissue development of honey bees exposed to sub-lethal doses of imidacloprid in laboratory and field experiments.

    Science.gov (United States)

    De Smet, Lina; Hatjina, Fani; Ioannidis, Pavlos; Hamamtzoglou, Anna; Schoonvaere, Karel; Francis, Frédéric; Meeus, Ivan; Smagghe, Guy; de Graaf, Dirk C

    2017-01-01

    In this study, different context-dependent effects of imidacloprid exposure on the honey bee response were studied. Honey bees were exposed to different concentrations of imidacloprid during a time period of 40 days. Next to these variables, a laboratory-field comparison was conducted. The influence of the chronic exposure on gene expression levels was determined using an in-house developed microarray targeting different immunity-related and detoxification genes to determine stress-related gene expression changes. Increased levels of the detoxification genes encoding, CYP9Q3 and CYT P450, were detected in imidacloprid-exposed honey bees. The different context-dependent effects of imidacloprid exposure on honey bees were confirmed physiologically by decreased hypopharyngeal gland sizes. Honey bees exposed to imidacloprid in laboratory cages showed a general immunosuppression and no detoxification mechanisms were triggered significantly, while honey bees in-field showed a resilient response with an immune stimulation at later time points. However, the treated colonies had a brood and population decline tendency after the first brood cycle in the field. In conclusion, this study highlighted the different context-dependent effects of imidacloprid exposure on the honey bee response. These findings warn for possible pitfalls concerning the generalization of results based on specific experiments with short exposure times. The increased levels of CYT P450 and CYP9Q3 combined with an immune response reaction can be used as markers for bees which are exposed to pesticides in the field.

  18. Review of field and monitoring studies investigating the role of nitro-substituted neonicotinoid insecticides in the reported losses of honey bee colonies (Apis mellifera).

    Science.gov (United States)

    Schmuck, Richard; Lewis, Gavin

    2016-11-01

    The nitro-substituted neonicotinoid insecticides, which include imidacloprid, thiamethoxam and clothianidin, are widely used to control a range of important agricultural pests both by foliar applications and also as seed dressings and by soil application. Since they exhibit systemic properties, exposure of bees may occur as a result of residues present in the nectar and/or pollen of seed- or soil-treated crop plants and so they have been the subject of much debate about whether they cause adverse effects in pollinating insects under field conditions. Due to these perceived concerns, the use of the three neonicotinoids imidacloprid, clothianidin and thiamethoxam has been temporarily suspended in the European Union for seed treatment, soil application and foliar treatment in crops attractive to bees. Monitoring data from a number of countries are available to assess the presence of neonicotinoid residues in honey bee samples and possible impacts at the colony level and these are reviewed here together with a number of field studies which have looked at the impact of clothiandin on honey bees in relation to specific crop use and in particular with oilseed rape. Currently there is considerable uncertainty with regards to the regulatory testing requirements for field studies. Accordingly, a testing protocol was developed to address any acute and chronic risks from oilseed rape seeds containing a coating with 10 g clothianidin and 2 g beta-cyfluthrin per kg seeds (Elado(®)) for managed honey bee (Apis mellifera) colonies, commercially bred bumble bee (Bombus terrestris) colonies and red mason bees (Osmia bicornis) as a representative solitary bee species. This is described here together with a summary of the results obtained as an introduction to the study details given in the following papers in this issue.

  19. 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.

  20. AN IMPROVED ANT COLONY ALGORITHM IN CONTINUOUS OPTIMIZATION

    Institute of Scientific and Technical Information of China (English)

    Ling CHEN; Jie SHEN; Ling QIN; Hongjian CHEN

    2003-01-01

    A modified ant colony algorithm for solving optimization problem with continuous parameters is presented. In the method, groups of candidate values of the components are constructed, and each value in the group has its trail information. In each iteration of the ant colony algorithm, the method first chooses initial values of the components using the trail information. Then GA operations of crossover and mutation can determine the values of the components in the solution. Our experimental results on the problem of nonlinear programming show that our method has a much higher convergence speed and stability than those of simulated annealing (SA) and GA.

  1. A Multiobjective Optimization Algorithm Based on Discrete Bacterial Colony Chemotaxis

    Directory of Open Access Journals (Sweden)

    Zhigang Lu

    2014-01-01

    Full Text Available Bacterial colony chemotaxis algorithm was originally developed for optimal problem with continuous space. In this paper the discrete bacterial colony chemotaxis (DBCC algorithm is developed to solve multiobjective optimization problems. The basic DBCC algorithm has the disadvantage of being trapped into the local minimum. Therefore, some improvements are adopted in the new algorithm, such as adding chaos transfer mechanism when the bacterium choose their next locations and the crowding distance operation to maintain the population diversity in the Pareto Front. The definition of chaos transfer mechanism is used to retain the elite solution produced during the operation, and the definition of crowding distance is used to guide the bacteria for determinate variation, thus enabling the algorithm obtain well-distributed solution in the Pareto optimal set. The convergence properties of the DBCC strategy are tested on some test functions. At last, some numerical results are given to demonstrate the effectiveness of the results obtained by the new algorithm.

  2. Modeling Honey Bee Populations.

    Directory of Open Access Journals (Sweden)

    David J Torres

    Full Text Available Eusocial honey bee populations (Apis mellifera employ an age stratification organization of egg, larvae, pupae, hive bees and foraging bees. Understanding the recent decline in honey bee colonies hinges on understanding the factors that impact each of these different age castes. We first perform an analysis of steady state bee populations given mortality rates within each bee caste and find that the honey bee colony is highly susceptible to hive and pupae mortality rates. Subsequently, we study transient bee population dynamics by building upon the modeling foundation established by Schmickl and Crailsheim and Khoury et al. Our transient model based on differential equations accounts for the effects of pheromones in slowing the maturation of hive bees to foraging bees, the increased mortality of larvae in the absence of sufficient hive bees, and the effects of food scarcity. We also conduct sensitivity studies and show the effects of parameter variations on the colony population.

  3. Modeling Honey Bee Populations

    Science.gov (United States)

    Torres, David J.; Ricoy, Ulises M.; Roybal, Shanae

    2015-01-01

    Eusocial honey bee populations (Apis mellifera) employ an age stratification organization of egg, larvae, pupae, hive bees and foraging bees. Understanding the recent decline in honey bee colonies hinges on understanding the factors that impact each of these different age castes. We first perform an analysis of steady state bee populations given mortality rates within each bee caste and find that the honey bee colony is highly susceptible to hive and pupae mortality rates. Subsequently, we study transient bee population dynamics by building upon the modeling foundation established by Schmickl and Crailsheim and Khoury et al. Our transient model based on differential equations accounts for the effects of pheromones in slowing the maturation of hive bees to foraging bees, the increased mortality of larvae in the absence of sufficient hive bees, and the effects of food scarcity. We also conduct sensitivity studies and show the effects of parameter variations on the colony population. PMID:26148010

  4. 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.

  5. Global path planning approach based on ant colony optimization algorithm

    Institute of Scientific and Technical Information of China (English)

    WEN Zhi-qiang; CAI Zi-xing

    2006-01-01

    Ant colony optimization (ACO) algorithm was modified to optimize the global path. In order to simulate the real ant colonies, according to the foraging behavior of ant colonies and the characteristic of food, conceptions of neighboring area and smell area were presented. The former can ensure the diversity of paths and the latter ensures that each ant can reach the goal. Then the whole path was divided into three parts and ACO was used to search the second part path. When the three parts pathes were adjusted,the final path was found. The valid path and invalid path were defined to ensure the path valid. Finally, the strategies of the pheromone search were applied to search the optimum path. However, when only the pheromone was used to search the optimum path, ACO converges easily. In order to avoid this premature convergence, combining pheromone search and random search, a hybrid ant colony algorithm(HACO) was used to find the optimum path. The comparison between ACO and HACO shows that HACO can be used to find the shortest path.

  6. 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...

  7. 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...

  8. 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...

  9. A scientific note on detection of honey bee viruses in the darkling beetle (Alphitobius diaperinus), an inhabitant in Apis cerana colonies

    Science.gov (United States)

    The darkling beetles, Alphitobius diaperinus (Panzer), are omnivorous arthropods and pose significant danger to the poultry industry by acting as reservoir and vector of poultry pathogens. Here, the A. diaperinus was first found in the Asian honey bee Apis cerana colonies, and 10 of the 29 hives wer...

  10. 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 ...

  11. 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 ...

  12. Chemical basis for inter-colonial aggression in the stingless bee Scaptotrigona bipunctata (Hymenoptera: Apidae).

    Science.gov (United States)

    Jungnickel, H; da Costa, A J S; Tentschert, J; Patricio, Eda Flávia L R A; Imperatriz-Fonseca, V L; Drijfhout, F; Morgan, E D

    2004-08-01

    Inter-colonial aggression was tested using three colonies of Scaptotrigona bipunctata in a natural setting when their nests were moved and by artificial contact between individuals. Examination of the cuticular lipids of individuals from two colonies kept under identical conditions showed clear differences in their cuticular hydrocarbon profiles. The cuticular lipids were a mixture of hydrocarbons (saturated and unsaturated alkanes and alkenes) within the range of C23-C29. The use of multivariate analysis (PCA and discriminant analysis) showed that seven of the identified surface compounds are enough to separate workers from colonies A and B from each other.

  13. 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.

  14. Ant colony optimized planning for unmanned surface marine vehicles

    OpenAIRE

    Benítez, J.M.; Jiménez, Juan F.; Jose M. Girón-Sierra

    2010-01-01

    This paper presents some results achieved from a preliminary study on the use of the Ant Colony Algorithm to plan feasible optimal or suboptimal trajectories for an autonomous ship manoeuvring. The scenario, for this preliminary work, comprises only open sea manoeuvres. The goal involves obtaining the least time consuming ship trajectory between to points, departing from the start point with arbitrary initial speed and attitude values and arriving to the end point with prede...

  15. 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.

  16. 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

  17. Genetic and artificial bee colony algorithms for scheduling of multi-skilled manpower in combined manpower-vehicle routing problem

    Directory of Open Access Journals (Sweden)

    Hany Seidgar

    2016-01-01

    Full Text Available This paper investigates a scheduling combined manpower-vehicle routing problem with a central depot in and a set of multi-skilled manpower for serving to customers. Teams are in different range of competencies that it will affect the service time duration. Vehicles are in different moving speeds and costs and not all the vehicles are capable to move toward all the customers’ sites. The objective is to minimize the total cost of servicing, routing, and lateness penalties. This paper presents a mixed integer programming model and two meta-heuristic approaches of genetic algorithm (GA and artificial bee colony algorithm (ABC are developed to solve the generated problems. Furthermore, Taguchi experimental design method is applied to set the proper values of parameters. The available results show the higher performance of proposed GA compared with ABC, in quality of solutions.

  18. 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.

  19. A novel kernel extreme learning machine algorithm based on self-adaptive artificial bee colony optimisation strategy

    Science.gov (United States)

    Ma, Chao; Ouyang, Jihong; Chen, Hui-Ling; Ji, Jin-Chao

    2016-04-01

    In this paper, we propose a novel learning algorithm, named SABC-MKELM, based on a kernel extreme learning machine (KELM) method for single-hidden-layer feedforward networks. In SABC-MKELM, the combination of Gaussian kernels is used as the activate function of KELM instead of simple fixed kernel learning, where the related parameters of kernels and the weights of kernels can be optimised by a novel self-adaptive artificial bee colony (SABC) approach simultaneously. SABC-MKELM outperforms six other state-of-the-art approaches in general, as it could effectively determine solution updating strategies and suitable parameters to produce a flexible kernel function involved in SABC. Simulations have demonstrated that the proposed algorithm not only self-adaptively determines suitable parameters and solution updating strategies learning from the previous experiences, but also achieves better generalisation performances than several related methods, and the results show good stability of the proposed algorithm.

  20. New Miticides for Integrated Pest Management of Varroa destructor (Acari: Varroidae) in Honey Bee Colonies on the Canadian Prairies.

    Science.gov (United States)

    Vandervalk, L P; Nasr, M E; Dosdall, L M

    2014-12-01

    Varroa destructor Anderson and Trueman 2000 (Acari: Varroidae) is an ectoparasitic mite of the honey bee, Apis mellifera L. (Hymenoptera: Apidae). Honey bee colonies require extensive management to prevent mortality caused by varroa mites and the viruses they vector. New miticides (Thymovar and HopGuard) to manage varroa mites were evaluated during the spring and fall treatment windows of the Canadian prairies to determine their effectiveness as part of an integrated management strategy. Thymovar and HopGuard were evaluated alongside the currently used industry standards: Apivar and formic acid. Results demonstrated that Apivar and formic acid remain effective V. destructor management options under spring and fall conditions. Applications of Thymovar during spring were associated with a reduction in brood area, and therefore should be limited to the fall season. The miticide HopGuard was not effective in managing V. destructor, and alteration of the current delivery system is necessary. This study demonstrates the potential for new effective treatment options to supplement currently used V. destructor integrated pest management systems.

  1. Short term hydrothermal scheduling via improved honey-bee mating optimization algorithm

    Directory of Open Access Journals (Sweden)

    hamed baradaran tavakoli

    2012-11-01

    Full Text Available In this paper, a new approach for solving short term hydrothermal scheduling problem is suggested, to minimize the total production cost and to produce electrical energy in an optimized way, by using honey-bee mating optimization algorithm. In the proposed method, lots of the hydrothermal system constraints such as power balance, water balance, time delay between reservoirs, volume limits and the operation limits of hydro and thermal plants, are considered. Therefore, the problem of short term hydrothermal scheduling becomes a complicated and nonlinear problem. In this paper, in addition to implementing the honey-bee mating optimization on a sample system, the improved honey-bee mating optimization algorithm has also been tested and analyzed. With regard to the simulation results, it is apparent that the improved honey-bee mating optimization has far higher convergence speed and takes less time, and less total cost in comparison with honey-bee mating optimization algorithm, genetic algorithm, particle swarm optimization algorithm and other optimization methods.

  2. Solving the Travelling Salesman Problem Using the Ant Colony Optimization

    Directory of Open Access Journals (Sweden)

    Zuzana Čičková

    2011-12-01

    Full Text Available In this article, we study a possibility of solving the well-known Travelling Salesman Problem (TSP, which ranges among NP-hard problems, and offer a theoretical overview of some methods used for solving this problem. We discuss the Ant Colony Optimization (ACO, which belongs to the group of evolutionary techniques and presents the approach used in the application of ACO to the TSP. We study the impact of some control parameters by implementing this algorithm. The quality of the solution is compared with the optimal solution.

  3. Operations planning for agricultural harvesters using ant colony optimization

    Directory of Open Access Journals (Sweden)

    A. Bakhtiari

    2013-07-01

    Full Text Available An approach based on ant colony optimization for the generation for optimal field coverage plans for the harvesting operations using the optimal track sequence principle B-patterns was presented. The case where the harvester unloads to a stationary facility located out of the field area, or in the field boundary, was examined. In this operation type there are capacity constraints to the load that a primary unit, or a harvester in this specific case, can carry and consequently, it is not able to complete the task of harvesting a field area and therefore it has to leave the field area, to unload, and return to continue the task one or more times. Results from comparing the optimal plans with conventional plans generated by operators show reductions in the in-field nonworking distance in the range of 19.3-42.1% while the savings in the total non-working distance were in the range of 18-43.8%. These savings provide a high potential for the implementation of the ant colony optimization approach for the case of harvesting operations that are not supported by transport carts for the out-of-the-field removal of the crops, a practice case that is normally followed in developing countries, due to lack of resources.

  4. New approach for determination of an optimum honeybee colony's carrying capacity based on productivity and nectar secretion potential of bee forage species.

    Science.gov (United States)

    Al-Ghamdi, Ahmed; Adgaba, Nuru; Getachew, Awraris; Tadesse, Yilma

    2016-01-01

    The present study was carried out to determine an optimum honeybee colony's carrying capacity of selected valleys dominated by Ziziphus spina-christi and Acacia tortilis in the Al-Baha region, Kingdom of Saudi Arabia. The study was conducted based on the assessment of the number of colonies kept, their productivities and the existing productive bee forage resources in the target valleys with its economic implication. In the existing beekeeping practice, the average number of managed honeybee colonies introduced per square kilometer was 530 and 317 during the flowering period of Z. spina-christi and A. tortilis, respectively. Furthermore, the overall ratios of productive bee forage plants to the number of honeybee colonies introduced were 0.55 and 11.12 to Ziziphus trees and A. tortilis shrubs respectively. In the existing situation the average honey production potential of 5.21 and 0.34 kg was recorded per Ziziphus and A. tortilis plants per flowering season, respectively. The present study, revealed that the number of honeybee colonies introduced in relation to the existing bee forage potential was extremely overcrowding which is beyond the carrying capacity of bee forage resources in selected valleys and it has been observed to affect the productivities and subsequent profitability of beekeeping. The study infers that, by keeping the optimum honeybee colony's carrying capacity of valleys (88 traditional hives/km(2) or 54 Langstroth hives/km(2) in Ziziphus field and 72 traditional hives/km(2) or 44 Langstroth hives/km(2) in A. tortilis field), profitability of beekeeping can be boosted up to 130.39% and 207.98% during Z. spina-christi and A. tortilis, flowering seasons, respectively.

  5. All-Optical Implementation of the Ant Colony Optimization Algorithm

    Science.gov (United States)

    Hu, Wenchao; Wu, Kan; Shum, Perry Ping; Zheludev, Nikolay I.; Soci, Cesare

    2016-05-01

    We report all-optical implementation of the optimization algorithm for the famous “ant colony” problem. Ant colonies progressively optimize pathway to food discovered by one of the ants through identifying the discovered route with volatile chemicals (pheromones) secreted on the way back from the food deposit. Mathematically this is an important example of graph optimization problem with dynamically changing parameters. Using an optical network with nonlinear waveguides to represent the graph and a feedback loop, we experimentally show that photons traveling through the network behave like ants that dynamically modify the environment to find the shortest pathway to any chosen point in the graph. This proof-of-principle demonstration illustrates how transient nonlinearity in the optical system can be exploited to tackle complex optimization problems directly, on the hardware level, which may be used for self-routing of optical signals in transparent communication networks and energy flow in photonic systems.

  6. 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.

  7. 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.

  8. Task scheduling based on ant colony optimization in cloud environment

    Science.gov (United States)

    Guo, Qiang

    2017-04-01

    In order to optimize the task scheduling strategy in cloud environment, we propose a cloud computing task scheduling algorithm based on ant colony algorithm. The main goal of this algorithm is to minimize the makespan and the total cost of the tasks, while making the system load more balanced. In this paper, we establish the objective function of the makespan and costs of the tasks, define the load balance function. Meanwhile, we also improve the initialization of the pheromone, the heuristic function and the pheromone update method in the ant colony algorithm. Then, some experiments were carried out on the Cloudsim platform, and the results were compared with algorithms of ACO and Min-Min. The results shows that the algorithm is more efficient than the other two algorithms in makespan, costs and system load balancing.

  9. Self-tuning PID Parameters by Using Artificial Bee Colony Algorithm%人工蜂群算法整定PID控制器参数

    Institute of Scientific and Technical Information of China (English)

    蔡超; 周武能

    2015-01-01

    针对工业控制中常用的PID控制器参数整定困难的问题,提出一种基于人工蜂群算法的参数整定方法。将PID控制器待整定参数看作蜜源,利用蜂群特有的角色转变机制搜索优质的参数组合;选取绝对误差矩积分性能指标作为参数寻优的目标函数。仿真实验结果表明,所采用的算法能够提高控制系统的动态性能,增强系统的快速性和稳定性,适用于PID 控制器的自整定。%Aiming at the difficult problems in parameter tuning of PID controllers in industrial control, the parameter tuning method based on artificial bee colony algorithm is proposed. In this algorithm, the parameter of PID controller need to be tuned is seen as the nectar source;the high-quality combination of parameters is searched using the unique role change mechanism of the bees;and the ITAE index is selected as the objective function for parameter optimization. The simulation experiments show that this algorithm can enhance dynamic performance of the control system, and strengthen the speediness and stability of the system, it’ s suitable for PID controller self-tuning.

  10. Open Vehicle Routing Problem by Ant Colony Optimization

    Directory of Open Access Journals (Sweden)

    Er. Gurpreet Singh

    2014-01-01

    Full Text Available Vehicle routing problem (VRP is real-world combinatorial optimization problem which determine the optimal route of a vehicle. Generally, toprovide the efficientvehicle serving to the customer through different services by visiting the number of cities or stops. The VRP follows the Travelling Salesman Problem (TSP, in which each of vehicle visiting a set of cities such that every city is visited by exactly one vehicle only once. This work proposes the Ant Colony Optimization (ACO-TSP algorithm to eliminate the tour loop for Open Vehicle routing Problem (OVRP. A key aspect of this algorithm is to plan the routes of buses that must pick up and deliver the school students from various bus stops on time, especially in the case of far distance covered by the vehicle in a rural area and find out the efficient and safe vehicle route.

  11. An evaluation of the associations of parameters related to the fall of Varroa destructor (Acari: Varroidae) from commercial honey bee (Hymenoptera: Apidae) colonies as tools for selective breeding for mite resistance.

    Science.gov (United States)

    Rinderer, Thomas E; De Guzman, Lilia I; Frake, Amanda M; Tarver, Matthew R; Khongphinitbunjong, Kitiphong

    2014-04-01

    Varroa destructor (Anderson and Trueman) trapped on bottom boards were assessed as indirect measurements of colony mite population differences and potential indicators of mite resistance in commercial colonies of Russian and Italian honey bees (Apis mellifera L.) by using 35 candidate measurements. Measurements included numbers of damaged and nondamaged younger mites, nymphs, damaged and nondamaged older mites, fresh mites, and all mites, each as a proportion of total mites in the colonies and as a proportion of all trapped mites or all trapped fresh mites. Several measurements differed strongly between the stocks, suggesting that the detailed characteristics of trapped mites may reflect the operation of resistance mechanisms in the Russian honey bees. Regression analyses were used to determine the relationships of these candidate measurements with the number of mites in the colonies. The largest positive regressions differed for the two stocks (Italian honey bees: trapped mites and trapped younger mites; Russian honey bees: trapped younger mites and trapped fresh mites). Also, the regressions for Italian honey bees were substantially stronger. The largest negative regressions with colony mites for both stocks were for the proportion of older mites out of all trapped mites. Although these regressions were statistically significant and consistent with those previously reported, they were weaker than those previously reported. The numbers of mites in the colonies were low, especially in the Russian honey bee colonies, which may have negatively influenced the precision of the regressions.

  12. 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...... in intensive agricultural practices, the forage availability around a honey bee colony can have a strong impact on its success. In the first part of the thesis, I focus on investigating whether the immediate type of landscape around a colony is a determining factor in the productivity of that colony. Using...... of the year. The successful running of the colony is also affected by the numerous pests mentioned above. Part two of the thesis deals with what effects a microsporidian gut parasite, Nosema ceranae can have on the behaviour of groups of honey bees exposed from early-on in their adult life. The creation...

  13. Multiple-Agent Task Allocation Algorithm Utilizing Ant Colony Optimization

    Directory of Open Access Journals (Sweden)

    Kai Zhao

    2013-11-01

    Full Text Available Task allocation in multiple agent system has been widely applied many application fields, such as unmanned aerial vehicle, multi-robot system and manufacturing system et al. Therefore, it becomes one of the hot topics in distributed artificial intelligence research field for several years. Therefore, in this paper, we propose a novel task allocation algorithm in multiple agent systems utilizing ant colony optimization. Firstly, the basic structure of agent organization is described, which include context-aware module, information processing module, the executing module, decision-making and intelligent control module, knowledge base and task table. Based the above agent structure, these module utilize the knowledge in the external environment to process the information in agent communicating. Secondly, we point out that task allocation process in multiple agent systems can be implement by creating the space to the mapping of the multi-agent organization. Thirdly, a modified multiple agent system oriented ant colony optimization algorithm is given, which contain pre-processing steps and the task allocation results are obtained by executing the trust region sqp algorithm in local solver. Finally, performance evaluation is conducted by experiments comparing with Random strategy and Instant optimal strategy, and very positive results are obtained

  14. 坦克分队 WTA 问题的改进人$蜂群算法%An Improved Artificial Bee Colony Algorithm for Tank Unit WTA Problem

    Institute of Scientific and Technical Information of China (English)

    常天庆; 陈军伟; 张雷; 杨国振

    2015-01-01

    针对目前智能算法初期收敛速度难以满足坦克分队武器目标分配(Weapon-Target Assignment,WTA)要求的问题,提出了一种改进人工蜂群算法。该算法结合 NEH 启发式算法和随机方法对种群进行初始化,利用变邻域搜索和模拟退火方法改进了采蜜蜂算法,并简化了跟随蜂算法,提出了一种全局最优限制算法。最后,结合不同规模的 WTA 问题,给出了该算法参数的确定方法。仿真结果表明:改进人工蜂群算法相比于其他算法在初始种群质量和算法初期收敛速度方面具有明显优势,特别适合求解坦克分队 WTA 问题。%Aiming at the problem that it is difficult for the current intelligent algorithms to meet the re-quirement of tank unit Weapon-Target Assignment (WTA)for faster convergence speed on the early stage,an improved Artificial Bee Colony (ABC)algorithm is proposed.This algorithm adopts a combina-tion method of NEH heuristic algorithm and random method for population initialization,uses variable neighborhood search and simulated annealing method for improving employed bees algorithm and simplify-ing unemployed bees algorithm,and introduces a global optimal limit algorithm.At last,combining dif-ferent scale of WTA problem,the method used to determine algorithm parameters are given.The simula-tion results reveal that the improved ABC has a significant advantage on the quality of the initial popula-tion and the convergence speed on the early stage over other algorithms,and it is particularly suitable for tank unit WTA problem.

  15. Collective Intelligence for Optimal Power Flow Solution Using Ant Colony Optimization

    Directory of Open Access Journals (Sweden)

    Boumediène ALLAOUA

    2008-12-01

    Full Text Available This paper presents the performance ant collective intelligence efficiency for electrical network. Solutions for Optimal Power Flow (OPF problem of a power system deliberate via an ant colony optimization metaheuristic method. The objective is to minimize the total fuel cost of thermal generating units and also conserve an acceptable system performance in terms of limits on generator real and reactive power outputs, bus voltages, shunt capacitors/reactors, transformers tap-setting and power flow of transmission lines. Simulation results on the IEEE 30-bus electrical network show that the ant colony optimization method converges quickly to the global optimum.

  16. Fuzzy Artificial Bees Colony Algorithm for Solving Multi-choice Multidimensional Knapsack Problem%模糊人工蜂群算法的多选择多维背包问题求解

    Institute of Scientific and Technical Information of China (English)

    柳寅; 马良; 黄钰

    2013-01-01

    针对传统人工蜂群算法早熟收敛问题,基于模糊化处理和蜂群寻优的特点,提出一种模糊人工蜂群算法。将模糊输入输出机制引入到算法中来保持蜜源访问概率的动态更新。根据算法计算过程中的不同阶段对蜜源访问概率有效调整,避免算法陷入局部极值。通过对多选择多维背包问题的仿真实验和与其他算法的比较,表明本算法可行有效,有良好的鲁棒性。%Aiming at the premature convergence problem in traditional artificial bees colony algorithm , fuzzy artificial bees colony algorithm is proposed , which is based on the principles of fuzzy processing and bees colony behavior .Fuzzy inputs and fuzzy outputs are introduced into the algorithm to maintain dynamic updates of the nectar access probability .According to effective adjustment on nectar access probability during the different stages of algorithm calculation , the algorithm avoids local optima .Simulated tests of multi-choice multidimen-sional knapsack problem and comparisons with other algorithms show the algorithm is feasible and effective and the algorithm has strong global optimization ability .

  17. An ant colony optimization method for generalized TSP problem

    Institute of Scientific and Technical Information of China (English)

    Jinhui Yang; Xiaohu Shi; Maurizio Marchese; Yanchun Liang

    2008-01-01

    Focused on a variation of the euclidean traveling salesman problem (TSP), namely, the generalized traveling salesman problem (GTSP), this paper extends the ant colony optimization method from TSP to this field. By considering the group influence, an improved method is further improved. To avoid locking into local minima, a mutation process and a local searching technique are also introduced into this method. Numerical results show that the proposed method can deal with the GTSP problems fairly well, and the developed mutation process and local search technique are effective.

  18. Brood removal influences fall of Varroa destructor (Mesostigmata: Varroidae) in honey bee (Hymenoptera: Apidae) colonies

    Science.gov (United States)

    The hygienic removal of brood infested with Varroa destructor by Apis mellifera disrupts the reproduction of the infesting mites and exposes the foundress mites to potential removal from the colony by grooming. Using brood deliberately infested with marked Varroa, we investigated the association bet...

  19. Controlling Varroa destructor (Acari: Varroidae in honeybee Apis mellifera (Hymenoptera: Apidae colonies by using Thymovar® and BeeVital®

    Directory of Open Access Journals (Sweden)

    Halil Yeninar

    2010-01-01

    Full Text Available This study was carried out to determine the effects of Thymovar® and BeeVital® on reducing Varroa mite (Varroa destructor damage in honey bee (Apis mellifera L. colonies in spring season. Average percentage of Varroa infestation level was determined as 24.27 on adult workers before the treatments. The drugs were applied two times on 25 September and 16 October 2006. Average percentage of Varroa infestation levels were determined as 5.18%, 10.78% and 35.45% after the first application, 1.90%, 7.05% and 61.15% after the second application in Thymovar®, BeeVital® and control groups, respectively. Average efficacies of Thymovar® and BeeVital® were found to be 96.91% and 88.66%, respectively. Difference between drug efficacies on Varroa mite was found significant (P<0.01. There was no queen, brood and adult honeybee mortality in all group colonies during the research.

  20. The analysis of the convergence of ant colony optimization algorithm

    Institute of Scientific and Technical Information of China (English)

    ZHU Qingbao; WANG Lingling

    2007-01-01

    The ant colony optimization algorithm has been widely studied and many important results have been obtained.Though this algorithm has been applied to many fields.the analysis about its convergence is much less,which will influence the improvement of this algorithm.Therefore,the convergence of this algorithm applied to the traveling salesman problem(TSP)was analyzed in detail.The conclusion that this algorithm will definitely converge to the optimal solution under the condition of 0<q0<1 was proved true.In addition,the influence on its convergence caused by the properties of the closed path,heuristic functions,the pheromone and q0 was analyzed.Based on the above-mentioned,some conclusions about how to improve the speed of its convergence are obtained.

  1. Annealing Ant Colony Optimization with Mutation Operator for Solving TSP

    Directory of Open Access Journals (Sweden)

    Abdulqader M. Mohsen

    2016-01-01

    Full Text Available Ant Colony Optimization (ACO has been successfully applied to solve a wide range of combinatorial optimization problems such as minimum spanning tree, traveling salesman problem, and quadratic assignment problem. Basic ACO has drawbacks of trapping into local minimum and low convergence rate. Simulated annealing (SA and mutation operator have the jumping ability and global convergence; and local search has the ability to speed up the convergence. Therefore, this paper proposed a hybrid ACO algorithm integrating the advantages of ACO, SA, mutation operator, and local search procedure to solve the traveling salesman problem. The core of algorithm is based on the ACO. SA and mutation operator were used to increase the ants population diversity from time to time and the local search was used to exploit the current search area efficiently. The comparative experiments, using 24 TSP instances from TSPLIB, show that the proposed algorithm outperformed some well-known algorithms in the literature in terms of solution quality.

  2. Annealing Ant Colony Optimization with Mutation Operator for Solving TSP.

    Science.gov (United States)

    Mohsen, Abdulqader M

    2016-01-01

    Ant Colony Optimization (ACO) has been successfully applied to solve a wide range of combinatorial optimization problems such as minimum spanning tree, traveling salesman problem, and quadratic assignment problem. Basic ACO has drawbacks of trapping into local minimum and low convergence rate. Simulated annealing (SA) and mutation operator have the jumping ability and global convergence; and local search has the ability to speed up the convergence. Therefore, this paper proposed a hybrid ACO algorithm integrating the advantages of ACO, SA, mutation operator, and local search procedure to solve the traveling salesman problem. The core of algorithm is based on the ACO. SA and mutation operator were used to increase the ants population diversity from time to time and the local search was used to exploit the current search area efficiently. The comparative experiments, using 24 TSP instances from TSPLIB, show that the proposed algorithm outperformed some well-known algorithms in the literature in terms of solution quality.

  3. Ant colony optimization approach to estimate energy demand of Turkey

    Energy Technology Data Exchange (ETDEWEB)

    Duran Toksari, M. [Erciyes University, Kayseri (Turkey). Engineering Faculty, Industrial Engineering Department

    2007-08-15

    This paper attempts to shed light on the determinants of energy demand in Turkey. Energy demand model is first proposed using the ant colony optimization (ACO) approach. It is multi-agent systems in which the behavior of each ant is inspired by the foraging behavior of real ants to solve optimization problem. ACO energy demand estimation (ACOEDE) model is developed using population, gross domestic product (GDP), import and export. All equations proposed here are linear and quadratic. Quadratic{sub A}COEDE provided better-fit solution due to fluctuations of the economic indicators. The ACOEDE model plans the energy demand of Turkey until 2025 according to three scenarios. The relative estimation errors of the ACOEDE model are the lowest when they are compared with the Ministry of Energy and Natural Resources (MENR) projection. (author)

  4. An Ant Colony Optimization Algorithm for Microwave Corrugated Filters Design

    Directory of Open Access Journals (Sweden)

    Ivan A. Mantilla-Gaviria

    2013-01-01

    Full Text Available A practical and useful application of the Ant Colony Optimization (ACO method for microwave corrugated filter design is shown. The classical, general purpose ACO method is adapted to deal with the microwave filter design problem. The design strategy used in this paper is an iterative procedure based on the use of an optimization method along with an electromagnetic simulator. The designs of high-pass and band-pass microwave rectangular waveguide filters working in the C-band and X-band, respectively, for communication applications, are shown. The average convergence performance of the ACO method is characterized by means of Monte Carlo simulations and compared with that obtained with the well-known Genetic Algorithm (GA. The overall performance, for the simulations presented herein, of the ACO is found to be better than that of the GA.

  5. Hybrid bee colony algorithm for flexible Job Shop scheduling problem%混合蜂群算法求解柔性作业车间调度问题

    Institute of Scientific and Technical Information of China (English)

    李修琳; 鲁建厦; 柴国钟; 汤洪涛

    2011-01-01

    To solve the flexible Job Shop scheduling problem, a hybrid intelligent optimization algorithm based on bee colony model to improve the searching accuracy and efficiency was proposed. Combined bee colony optimization with stochastic methods, a novel method for generating initial population was put forward to improve initial population quality. To improve search accuracy, simulated annealing was used to update onlooker bees, and annealing coefficient was used to refine neighbor domains. Aiming at characteristics of flexible Job Shop scheduling problem, update method for neighborhood with controlled scale was established. By using flexible Job Shop standard algorithm, the validity and superiority of the algorithm was proved by comparing the simulation program with other algorithm.%为解决柔性作业车间调度问题,提出一种基于蜂群模型的混合群智能优化算法.在算法初始化阶段提出了蜂群优化算法结合随机方法的种群初始化方法,提高了初始种群质量;为提高算法搜索精度,在观察蜂阶段采用模拟退火算法更新观察蜂群,并以退温系数调节邻域规模,随算法进程细化搜索范围;针对柔性作业车间调度问题特点,建立了可控规模的邻域更新方法.采用柔性作业车间标准算例,通过仿真编程和与其他算法的比较,验证了算法的有效性和优越性.

  6. The Bio-Politics of Bees: Industrial Farming and Colony Collapse Disorder

    OpenAIRE

    Nimmo, Richie

    2015-01-01

    Everywhere, honeybees and other insect pollinators are dwindling and dying, in a slowly but relentlessly unfolding crisis that has come to be known as Colony Collapse Disorder. This article draws upon theoretical currents from animal studies, environmental sociology and ecofeminism in order to explore the aetiology and significance of this crisis, an animal-techno-ecological assemblage of forbidding complexity and intense controversy. It is argued that the critical animal studies concept of t...

  7. Optimization of PID controller based on The Bees Algorithm for one leg of a quadruped robot

    Directory of Open Access Journals (Sweden)

    Bakırcıoğlu Veli

    2016-01-01

    Full Text Available In this paper, we apply The Bees Algorithm to find optimal PID controller gains to control angular positions of robot leg joints with the minimum position error. In order to present more realistic simulation, system modelled in MATLAB/Simulink environment which is close to experimental set up. Solid model of system, which has two degrees of freedom, drawn by using a CAD software. Required physical specifications of robot leg for MATLAB/Simulink modelling is obtained from this CAD model. Controller of the system is designed in MATLAB/Simulink interface. Simulation results derived to show effectiveness of The Bees Algorithm to find optimal PID controller gains.

  8. 基于人工蜂群算法的贝叶斯网络结构学习%Structure learning of Bayesian networks by use of the artificial bee colony algorithm

    Institute of Scientific and Technical Information of China (English)

    张平; 刘三阳; 朱明敏

    2014-01-01

    从数据集中学习贝叶斯网络结构是一个NP难问题。针对此问题提出基于遗传算子的人工蜂群算法。首先,将贝叶斯网络结构映射为一种二进制编码;其次,根据贝叶斯网络的结构特点,设计了蜜源的更新策略,从而将学习贝叶斯网络结构的过程转化为蜂群寻找最优蜜源的过程。实验结果表明,该算法应用于贝叶斯网络结构学习中的有效性。%The learning structure of Bayesian networks from a data set is an NP-hard problem .To deal with this problem , an artificial bee colony algorithm based on genetic operators is proposed in this paper .The structure of the Bayesian network is mapped to binary encoding , and the updated strategy of nectar is designed according to the characteristics of the Bayesian network structure .Thus the process of structure learning of the Bayesian network is transformed into the process of the bee colony finding the optimal nectar .The experimental results show that the al-gorithm is valid in the structure learning of Bayesian networks .

  9. 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.

  10. Land-use change reduces habitat suitability for supporting managed honey bee colonies in the Northern Great Plains.

    Science.gov (United States)

    Otto, Clint R V; Roth, Cali L; Carlson, Benjamin L; Smart, Matthew D

    2016-09-13

    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.

  11. Key management practices to prevent high infestation levels of Varroa destructor in honey bee colonies at the beginning of the honey yield season.

    Science.gov (United States)

    Giacobino, Agostina; Molineri, Ana; Bulacio Cagnolo, Natalia; Merke, Julieta; Orellano, Emanuel; Bertozzi, Ezequiel; Masciangelo, Germán; Pietronave, Hernán; Pacini, Adriana; Salto, Cesar; Signorini, Marcelo

    2016-09-01

    Varroa destructor is considered one of the main threats to worldwide apiculture causing a variety of physiological effects at individual and colony level. Also, Varroa mites are often associated with several honey bee viruses presence. Relatively low levels of Varroa during the spring, at the beginning of the honey yield season, can have a significant economic impact on honey production and colony health. Winter treatments against Varroa and certain management practices may delay mite population growth during following spring and summer improving colonies performance during the honey yield season. The aim of this study was to identify risk factors associated with the presence of Varroa destructor in late spring in apiaries from temperate climate. A longitudinal study was carried out in 48 apiaries, randomly selected to evaluate V. destructor infestation level throughout the year. The percentage of infestation with V. destructor was assessed four times during one year and the beekeepers answered a survey concerning all management practices applied in the colonies. We used a generalized linear mixed model to determine association between risk of achieving 2% infestation on adult bees at the beginning of the honey yield season and all potential explanatory variables. The complete dataset was scanned to identify colonies clusters with a higher probability of achieving damage thresholds throughout the year. Colonies that achieved ≥2% of infestation with V. destructor during spring were owned by less experienced beekeepers. Moreover, as Varroa populations increase exponentially during spring and summer, if the spring sampling time is later this growth remains unobserved. Monitoring and winter treatment can be critical for controlling mite population during the honey production cycle. Spatial distribution of colonies with a higher risk of achieving high Varroa levels seems to be better explained by management practices than a geographical condition. Copyright © 2016

  12. 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...... are investigated. The influence of the evaporation factor in the pheromone update mechanism and the robustness of this parameter w.r.t. the runtime behavior have been determined for the example function OneMax.This work puts forward the rigorous runtime analysis of the 1-ANT on the example functions Leading......Ones and BinVal. With respect to Evolutionary Algorithms (EAs), such analyses were essential to develop methods for the analysis on more complicated problems. The proof techniques required for the 1-ANT, unfortunately, differ significantly from those for EAs, which means that a new reservoir of methods has...

  13. DETECTION OF MASSES IN MAMMOGRAM IMAGES USING ANT COLONY OPTIMIZATION

    Directory of Open Access Journals (Sweden)

    Varsha Patankar

    2014-04-01

    Full Text Available This paper proposes the advances in edge detection techniques, which is used for the mammogram images for cancer diagnosis. It compares the evaluation of edge detection with the proposed method ant colony optimization. The study shows that the edge detection technique is applied on the mammogram images because it will clearly identify the masses in mammogram images. This will help to identify the type of cancer at the early stage. ACO edge detector is best in detecting the edges when compared to the other edge detectors. The quality of various edge detectors is calculated based on the parameters such as Peak signal to noise ratio (PSNR and Mean square error (MSE.

  14. 人工蜂群算法的无人机航路规划与平滑%Smooth trajectory planning of an unmanned aerial vehicle using an artificial bee colony algorithm

    Institute of Scientific and Technical Information of China (English)

    刘敏; 邹杰; 冯星; 赵振宇

    2011-01-01

    航路规划是无人机(UAV)作战任务规划系统的关键组成部分,目标是在适当的时间内为UAV计算出最优或次最优的飞行航路.人工蜂群(ABC)算法是一种最新发展的模拟昆虫王国中蜜蜂群体寻找优良蜜源的群体智能优化算法.采用人工蜂群算法完成无人机的平滑航路规划,首先阐述了人工蜂群算法的基本原理,然后将无人机航路规划问题通过建模转换成为一个多维函数优化问题,利用人工蜂群算法的优势,找到多维函数的最优解,最后对优化后的航路进行了平滑,使UAV对规划后的航路可飞.仿真实验结果表明,此方法可有效规划出航路,且所规划的航路可飞.%Trajectory is a key issue for an unmanned aerial vehicle ( UAV) , which aims to obtain an optimal or sub-optimal trajectory within proper time. The artificial bee colony ( ABC ) is a new algorithm based on how a bee colony finds food. On the basis of introducing the basic principle of the ABC, and the description of threatening models of a UAV, the UAV trajectory planning was transformed into an optimization problem through modeling. Then the optimal solution of the multi-dimensional function was given by taking advantage of the artificial bee colony algorithm. Finally, the smoothing strategy was adopted to obtain a feasible path. The feasibility and effectiveness of the proposed approach was verified by experimental results.

  15. 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.

  16. 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.

  17. Ant system: optimization by a colony of cooperating agents.

    Science.gov (United States)

    Dorigo, M; Maniezzo, V; Colorni, A

    1996-01-01

    An analogy with the way ant colonies function has suggested the definition of a new computational paradigm, which we call ant system (AS). We propose it as a viable new approach to stochastic combinatorial optimization. The main characteristics of this model are positive feedback, distributed computation, and the use of a constructive greedy heuristic. Positive feedback accounts for rapid discovery of good solutions, distributed computation avoids premature convergence, and the greedy heuristic helps find acceptable solutions in the early stages of the search process. We apply the proposed methodology to the classical traveling salesman problem (TSP), and report simulation results. We also discuss parameter selection and the early setups of the model, and compare it with tabu search and simulated annealing using TSP. To demonstrate the robustness of the approach, we show how the ant system (AS) can be applied to other optimization problems like the asymmetric traveling salesman, the quadratic assignment and the job-shop scheduling. Finally we discuss the salient characteristics-global data structure revision, distributed communication and probabilistic transitions of the AS.

  18. Ant Colony Optimization for Capacitated Vehicle Routing Problem

    Directory of Open Access Journals (Sweden)

    H. V. Seow

    2012-01-01

    Full Text Available Problem statement: The Capacitated Vehicle Routing Problem (CVRP is a well-known combinatorial optimization problem which is concerned with the distribution of goods between the depot and customers. It is of economic importance to businesses as approximately 10-20% of the final cost of the goods is contributed by the transportation process. Approach: This problem was tackled using an Ant Colony Optimization (ACO combined with heuristic approaches that act as the route improvement strategies. The proposed ACO utilized a pheromone evaporation procedure of standard ant algorithm in order to introduce an evaporation rate that depends on the solutions found by the artificial ants. Results: Computational experiments were conducted on benchmark data set and the results obtained from the proposed algorithms shown that the application of combination of two different heuristics in the ACO had the capability to improve the ants’ solutions better than ACO embedded with only one heuristic. Conclusion: ACO with swap and 3-opt heuristic has the capability to tackle the CVRP with satisfactory solution quality and run time. It is a viable alternative for solving the CVRP.

  19. Ant colony optimization for solving university facility layout problem

    Science.gov (United States)

    Mohd Jani, Nurul Hafiza; Mohd Radzi, Nor Haizan; Ngadiman, Mohd Salihin

    2013-04-01

    Quadratic Assignment Problems (QAP) is classified as the NP hard problem. It has been used to model a lot of problem in several areas such as operational research, combinatorial data analysis and also parallel and distributed computing, optimization problem such as graph portioning and Travel Salesman Problem (TSP). In the literature, researcher use exact algorithm, heuristics algorithm and metaheuristic approaches to solve QAP problem. QAP is largely applied in facility layout problem (FLP). In this paper we used QAP to model university facility layout problem. There are 8 facilities that need to be assigned to 8 locations. Hence we have modeled a QAP problem with n ≤ 10 and developed an Ant Colony Optimization (ACO) algorithm to solve the university facility layout problem. The objective is to assign n facilities to n locations such that the minimum product of flows and distances is obtained. Flow is the movement from one to another facility, whereas distance is the distance between one locations of a facility to other facilities locations. The objective of the QAP is to obtain minimum total walking (flow) of lecturers from one destination to another (distance).

  20. Ant colony Optimization: A Solution of Load balancing in Cloud

    Directory of Open Access Journals (Sweden)

    Ratan Mishra

    2012-05-01

    Full Text Available As the cloud computing is a new style of computing over internet. It has many advantages along with some crucial issues to be resolved in order to improve reliability of cloud environment. These issues are related with the load management, fault tolerance and different security issues in cloud environment. In this paper the main concern is load balancing in cloud computing. The load can be CPU load, memory capacity, delay or network load. Load balancing is the process of distributing the load among various nodes of adistributed system to improve both resource utilization and job response time while also avoiding a situation where some of the nodes are heavily loaded while other nodes are idle or doing very little work. Load balancing ensures that all the processor in the system or every node in the network does approximately the equal amount of work at any instant of time. Many methods to resolve this problem has been came into existence like Particle Swarm Optimization, hash method, genetic algorithms and severalscheduling based algorithms are there. In this paper we are proposing a method based on Ant Colony optimization to resolve the problem of load balancing in cloud environment.

  1. 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.

  2. Mobile Anonymous Trust Based Routing Using Ant Colony Optimization

    Directory of Open Access Journals (Sweden)

    R. Kalpana

    2012-01-01

    Full Text Available Problem statement: Ad hoc networks are susceptible to malicious attacks through denial of services, traffic analysis and spoofing. The security of the ad hoc routing protocol depends upon encryption, authentication, anonymity and trust factors. End-to-end security of data is provided by encryption and authentication, topology information of the nodes can be obtained by studying traffic and routing data. This security problem of ad hoc network is addressed by the use of anonymity mechanisms and trust levels. Identification information like traffic flow, network topology, paths from malicious attackers is hidden in anonymous networks. Similarly, trust plays a very important role in the intermediate node selection in ad hoc networks. Trust is essential as selfish and malicious nodes not only pose a security issue but also decreases the Quality of Service. Approach: In this study, a routing to address anonymous routing with a trust which improves the overall security of the ad hoc network was proposed. A new approach for an on demand ad-hoc routing algorithm, which was based on swarm intelligence. Ant colony algorithms were a subset of swarm intelligence and considered the ability of simple ants to solve complex problems by cooperation. The interesting point was, that the ants do not need any direct communication for the solution process, instead they communicate by stigmergy. The notion of stigmergy means the indirect communication of individuals through modifying their environment. Several algorithms which were based on ant colony problems were introduced in recent years to solve different problems, e.g., optimization problems. Results and Conclusion: It is observed that the overall security in the network improves when the trust factor is considered. It is seen that non performing nodes are not considered due to the proposed ACO technique.

  3. Novel Approach to Nonlinear PID Parameter Optimization Using Ant Colony Optimization Algorithm

    Institute of Scientific and Technical Information of China (English)

    Duan Hai-bin; Wang Dao-bo; Yu Xiu-fen

    2006-01-01

    This paper presents an application of an Ant Colony Optimization (ACO) algorithm to optimize the parameters in the design of a type of nonlinear PID controller. The ACO algorithm is a novel heuristic bionic algorithm, which is based on the behaviour of real ants in nature searching for food. In order to optimize the parameters of the nonlinear PID controller using ACO algorithm,an objective function based on position tracing error was constructed, and elitist strategy was adopted in the improved ACO algorithm. Detailed simulation steps are presented. This nonlinear PID controller using the ACO algorithm has high precision of control and quick response.

  4. New Variants of Ant Colony Optimization for Network Routing

    Directory of Open Access Journals (Sweden)

    Debasmita Mukherjee

    2012-12-01

    Full Text Available This paper suggests new variants of Ant Colony Optimization(ACOTechniques for Network Routing. There are three existing variants of ACO based on pheromone deposit calculation. In our earlier work we suggested three different heuristics for selecting the next node at each step of iteration. Incorporation of these heuristics in each of the above three variants result into nine variations. In this paper the performance of these nine variations has been studied. Moreover, we have modified the pheromone deposit calculation considering the transmission time of each successful packet(ant and incorporated this new pheromone update formula in each of the nine variants. As a result, we have obtained nine new variants of ACO. The performance of these new nine variants has been compared with previous ones with respect to the speed of execution, throughput and the number of successful packets. The experiments have been performed over two different network topologies. In one of the variant a tabu list has been incorporated. The length of the tabu list plays a vital role in improving the performance of the routing algorithm. In this paper it has been observed that the new variations of ACO have outperformed the previous ones. These new variants can perform efficient network routing in an environment having variable transmission time along the paths due to congestion or poor link quality.

  5. Improved Ant Colony Optimization Algorithm based Expert System on Nephrology

    Directory of Open Access Journals (Sweden)

    Sri.N.V.Ramana Murty

    2010-07-01

    Full Text Available Expert system Nephrology is a computer program that exhibits, within a specific domain, a degree of expertise in problem solving that is comparable to that of a human expert. The knowledge base consistsof information about a particular problem area. This information is collected from domain experts (doctors. This system mainly contains two modules one is Information System and the other is Expert Advisory system. The Information System contains the static information about different diseases and drugs in the field of Nephrology. This information system helps the patients /users to know about the problems related to kidneys. The Nephrology Advisory system helps the Patients /users to get the required and suitable advice depending on their queries. This medical expert system is developedusing Java Server Pages (JSP as front-end and MYSQL database as Backend in such a way that all the activities are carried out in a user-friendly manner. Improved Ant Colony Optimization Algorithm (ACO along with RETE algorithm is also used for better results.

  6. 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.

  7. Ant colony optimization-based firewall anomaly mitigation engine.

    Science.gov (United States)

    Penmatsa, Ravi Kiran Varma; Vatsavayi, Valli Kumari; Samayamantula, Srinivas Kumar

    2016-01-01

    A firewall is the most essential component of network perimeter security. Due to human error and the involvement of multiple administrators in configuring firewall rules, there exist common anomalies in firewall rulesets such as Shadowing, Generalization, Correlation, and Redundancy. There is a need for research on efficient ways of resolving such anomalies. The challenge is also to see that the reordered or resolved ruleset conforms to the organization's framed security policy. This study proposes an ant colony optimization (ACO)-based anomaly resolution and reordering of firewall rules called ACO-based firewall anomaly mitigation engine. Modified strategies are also introduced to automatically detect these anomalies and to minimize manual intervention of the administrator. Furthermore, an adaptive reordering strategy is proposed to aid faster reordering when a new rule is appended. The proposed approach was tested with different firewall policy sets. The results were found to be promising in terms of the number of conflicts resolved, with minimal availability loss and marginal security risk. This work demonstrated the application of a metaheuristic search technique, ACO, in improving the performance of a packet-filter firewall with respect to mitigating anomalies in the rules, and at the same time demonstrated conformance to the security policy.

  8. Ant Colony Optimization for Solving Solid Waste Collection Scheduling Problems

    Directory of Open Access Journals (Sweden)

    Z. Ismail

    2009-01-01

    Full Text Available Problem statement: Southern Waste Management environment (SWM environment is a company responsible for the collection and disposal of solid waste for the city of Johor Bahru, a city with over one million populations. The company is implementing an integrated solid waste management system where it involved in the optimization of resources to ensure the effectiveness of its services. Formulating this real life problem into vehicle routing problem with stochastic demand model and using some designed algorithms to minimize operation cost of solid waste management. Approach: The implementation of Ant Colony Optimization (ACO for solving solid waste collection problem as a VRPSD model was described. A set of data modified from the well known 50 customers problems were used to find the route such that the expected traveling cost was minimized. The total cost was minimized by adopting a preventive restocking policy which was trading off the extra cost of returning to depot after a stock-out with the cost of returning depot for restocking before a stock-out actually occurs. For comparison purposes, Simulated Annealing (SA was used to generate the solution under the same condition. Results: For the problem size with 12 customers with vehicle capacity 10 units, both algorithms obtained the same best cost which is 69.4358 units. But the percentage deviations of averages from the associated best cost are 0.1322 and 0.7064 for ACS and SA. The results indicated that for all demand ranges, proposed ACO algorithm showed better performance than SA algorithm. Conclusion: SA was able to obtain good solutions for small ranges especially small size of problem. For ACS, it is always provide good results for all tested ranges and problems sizes of the tested problem.

  9. 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

  10. A review of methods used in some European countries for assessing the quality of honey bee queens through their physical characters and the performance of their colonies

    DEFF Research Database (Denmark)

    Hatjina, Fani; Bienkowska, Malgorzata; Charistos, Leonidas

    2014-01-01

    The term “quality” in relation to queens and drones refers to certain quantitative physical and / or behavioural characters. It is generally believed that a high quality queen should have the following physical characteristics: high live weight; high number of ovarioles; large size of spermatheca......; high number of spermatozoa in spermatheca; and be free from diseases and pests. It is, however, also known that the performance of a honey bee colony is the result of its queen’s function as well as of that of the drones that mated with her. These two approaches are often considered together and give...

  11. 改进人工蜂群算法在饲料配比中的应用研究%Improve Artificial Bee Colony Algorithm and Its Application of Feed Ratio

    Institute of Scientific and Technical Information of China (English)

    鄢靖丰; 郭超峰

    2012-01-01

    人工蜂群算法是一种模拟蜜蜂采蜜的群智能优化算法.针对传统的人工蜂群算法收敛速度慢,容易陷于局部最优进行了改进,引入了扰动控制频率来指导引领峰搜寻蜜源,增强算法局部搜索能力.提出了自适应动态变异算子,提高了算法收敛速度.融合了Boltzmann策略选择机制,动态调整了算法的搜索范围,增强了种群的多样性.算法成功地应用到求解动物饲料配比问题.结果显示,在运行效率、最优解质量、稳定性均优于被比较的其它算法.%Artificial colony algorithm is a kind of simulation in the group of honey bees group of intelligent optimization algorithms. The traditional artificial colony algorithm has slow convergence speed, and limits to local optimum . An improved algorithm is proposed, which introduces disturbance control frequency to guide employed bees searching for source, enhance algorithm local searching ability; introducing the adaptive dynamic mutation operator factor to improve the algorithm convergence speed; the strategy of choice for Boltzmann is adopted with dynamic adjustment the search area of the algorithm, and enhancement the diversity of population, the algorithms of are successfully applied to practical animal feed in proportion problems. The experimental results show that above algorithm is better than other compared algorithms in efficiency, the optimal solution quality and stability.

  12. 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.

  13. 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.

  14. S-ABC-Service Domain-Oriented Artificial Bee Colony Algorithm Paradigm%S-ABC--面向服务领域的人工蜂群算法范型

    Institute of Scientific and Technical Information of China (English)

    徐晓飞; 刘志中; 王忠杰; 闵寻优; 刘睿霖; 王海芳

    2015-01-01

    In the service computing field,the typical service optimization problems (such as service selection,service composition and service resource scheduling)become more and more complicated with the rapid development of cloud computing,internet of things and big data.Meanwhile,in many service sectors,the service domain features (such as service priori,correlation and similarity) have been gradually formed with the long-term evolution of service business.These domain features have strong influences on solutions of service optimization problems.If the service domain features are not considered adequately,the service optimization problems can not be solved effectively and efficiently.Therefore,how to design service domain-oriented optimization algorithm paradigm, efficient algorithms and optimization strategies become the critical challenges.This paper analyzes the influences on service optimization problems by service domain features.Then,based on the improved strategies of artificial bee colony algorithm,a service domain-oriented artificial bee colony algorithm paradigm (S-ABC)is presented.The optimization principle of S-ABC paradigm is described in detail.The better optimization results are verified by means of confirmatory experiment. This research work shows a new and better method for solving service optimization problems with the support of service domain features,and to extend the theory of swarm intelligence optimization in service computing field.%服务计算优化问题(如服务选择、服务组合、服务资源调度等)随着云计算、物联网、大数据的快速发展而变得日益复杂。另一方面,各服务行业在其长期演化中逐渐形成了特有的领域特性(如服务先验性、关联性、相似性等)。这些特性对服务优化问题求解有重要影响,如果对其考虑不充分,将导致服务优化问题求解的效率与效果不理想。因此,如何构建面向服务领域的服务优化算法范型及

  15. Routing in Wireless Sensor Networks Using an Ant Colony Optimization (ACO) Router Chip

    OpenAIRE

    Dervis Karaboga; Selcuk Okdem

    2009-01-01

    Wireless Sensor Networks consisting of nodes with limited power are deployed to gather useful information from the field. In WSNs it is critical to collect the information in an energy efficient manner. Ant Colony Optimization, a swarm intelligence based optimization technique, is widely used in network routing. A novel routing approach using an Ant Colony Optimization algorithm is proposed for Wireless Sensor Networks consisting of stable nodes. Illustrative examples, detailed descriptions a...

  16. 基于人工蜂群算法的胶囊内窥镜位姿磁定位研究%Magnetic Localization and Orientation of Capsule Endoscope Base on Artiifcial Bee Colony Algorithm

    Institute of Scientific and Technical Information of China (English)

    郑子昭; 何小其; 胡超

    2014-01-01

    The technology of the capsule endoscope for the detection of the gastrointestinal disease has made a major breakthrough. But the clinical feedback show that some problems are to be solved, including the problem of the location and tracking. Among the possible localization technologies, the magnetic positioning technology with advantages of no need for power and not much space occupation and so on, is very suitable to be applied to the problem of localization of the capsule endoscope. In this paper, for the positioning model of the magnetic dipole based on the magnetic positioning technology, a new algorithm -- hybrid artiifcial bee colony algorithm was proposed and compared with the artiifcial bee colony (ABC) algorithm and the nonlinear optimization algorithms that the random complex algorithm and the levenberg - marquart (LM) algorithm. The experimental results show that in terms of the positioning accuracy, the stability and the anti-noise ability, the hybrid artiifcial bee colony algorithm has better performance than the other three methods.%胶囊内窥镜技术是胃肠道内窥镜检查的一个重大突破,但其临床反馈尚有一些问题待解决,其中一个问题就是定位跟踪。在各种可能的定位技术中,磁体定位技术具有不用供电且体积小等特点,应用于胶囊内窥镜的定位问题是非常合适的。文章针对磁体定位技术中的磁偶极子定位模型,提出一种新的算法--混合人工蜂群算法,并将该算法与标准人工蜂群算法、随机复合形算法、Levenberg-Marquart(LM)算法进行对比。实验结果表明,混合人工蜂群算法在算法定位精度、算法稳定性和抗噪能力等方面,比其他三种算法具有更优越的性能。

  17. 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.

  18. A Hybrid Ant Colony Optimization for the Prediction of Protein Secondary Structure

    Institute of Scientific and Technical Information of China (English)

    Chao CHEN; Yuan Xin TIAN; Xiao Yong ZOU; Pei Xiang CAI; Jin Yuan MO

    2005-01-01

    Based on the concept of ant colony optimization and the idea of population in genetic algorithm, a novel global optimization algorithm, called the hybrid ant colony optimization (HACO), is proposed in this paper to tackle continuous-space optimization problems. It was compared with other well-known stochastic methods in the optimization of the benchmark functions and was also used to solve the problem of selecting appropriate dilation efficiently by optimizing the wavelet power spectrum of the hydrophobic sequence of protein, which is thc key step on using continuous wavelet transform (CWT) to predict a-helices and connecting peptides.

  19. 基于混沌鲶鱼效应的人工蜂群算法及应用%Artificial Bee Colony Algorithm with Chaotic Catfish Effect and Its Application

    Institute of Scientific and Technical Information of China (English)

    王生生; 杨娟娟; 柴胜

    2014-01-01

    There are the disadvantages of easily falling into premature convergence and local optimal solution which the ele-mentary artificial bee colony algorithm had in some degree .Chaotic Catfish effect was hence adopted in this paper to achieve the op-timum performance of artificial bee colony algorithm ,in which ,chaotic mechanism was conducted to instantiate each individual of the swarm firstly owing to its marvelous intrinsic randomness .Then the efficacious competition and coordination mechanism among Catfish bees which were derived from the integration of Chaos theory with Catfish effect and originals were intended to boost the ca-pabilities of them leaping out of local optimal solution and converging expeditiously .The practicability of Support Vector Machines (SVM )is excessively affected due to the difficulty of selecting appropriate penalty factor C and kernel function parameter of SVM . Conversely ,all of the common SVM parameters optimization methods have their respective disadvantages with some degree of com-petence .We utilized the improved artificial bee colony algorithm to optimize the two parameters of SVM ,simultaneously ,the public datasets from the University of California-Irvine (UCI )and the activity recognition reality data were employed for evaluating the pro-posed model .Experimental results demonstrate that the classification accuracy obtained by the developed SVM was higher.%针对目前人工蜂群算法的早熟收敛、陷入局部极值等问题,提出一种基于混沌鲶鱼效应的改进人工蜂群算法。首先,采用随机性更高的混沌序列初始化蜂群以扩大其遍布范围;其次,集成了鲶鱼效应和混沌理论提出了混沌鲶鱼蜂,并引入了它与跌入局部极值的蜂群之间的有效竞争协调机制,从而增进蜜蜂群体跳出局部最优解、加速收敛的能力。支持向量机的学习能力主要取决于其惩罚因子 C和核函数参数的合理选择,对其参数的优化可

  20. Honey Bees: Sweetness and Mites

    Science.gov (United States)

    Honey bee colony losses have been in the news lately and the potential reasons for these losses have taken up much space in the news media. In order to clarify what role mites play in the current loss (2006-2007) of bee colonies, called Colony Collapse Disorder, a better understanding of what a mit...

  1. Occurrence of parasites and pathogens in honey bee colonies used in a European genotype-environment interactions experiment

    DEFF Research Database (Denmark)

    Meixner, Marina Doris; Francis, Roy Mathew; Gajda, Anna;

    2014-01-01

    Diseases are known to be one of the major contributors to colony losses. Within a Europe-wide experiment on genotype - environment interactions, an initial 621 colonies were set up and maintained from 2009 to 2012. The colonies were monitored to investigate the occurrence and levels of key pathog...

  2. The optimal time-frequency atom search based on a modified ant colony algorithm

    Institute of Scientific and Technical Information of China (English)

    GUO Jun-feng; LI Yan-jun; YU Rui-xing; ZHANG Ke

    2008-01-01

    In this paper,a new optimal time-frequency atom search method based on a modified ant colony algorithm is proposed to improve the precision of the traditional methods.First,the discretization formula of finite length time-frequency atom is inferred at length.Second; a modified ant colony algorithm in continuous space is proposed.Finally,the optimal timefrequency atom search algorithm based on the modified ant colony algorithm is described in detail and the simulation experiment is carried on.The result indicates that the developed algorithm is valid and stable,and the precision of the method is higher than that of the traditional method.

  3. The genetic origin of honey bee colonies used in the COLOSS Genotype-Environment Interactions Experiment: a comparison of methods

    DEFF Research Database (Denmark)

    Francis, Roy M; Kryger, Per; Meixner, Marina;

    2014-01-01

    to describe the genetic background and population allocation of the bees used in this experiment. Two wing morphometric and two genetic methods were employed to discriminate bee populations. Classical morphometry of 11 angles on the wings were carried out on 350 bees. Geometric morphometry on 19 wing...... landmarks was carried out on 381 individuals. DNA microsatellite analysis was carried out on 315 individuals using 24 loci. Allozyme analysis was performed on 90 individuals using six enzyme systems. DNA microsatellite markers produced the best discrimination between the subspecies (Apis mellifera carnica......, A. m. ligustica, A. m. macedonica, A. m. mellifera and A. m. siciliana) used in the experiment. Morphometric methods generally showed an intermediate level of discrimination, usually best separating A. m. siciliana and A. m. ligustica from the remaining populations. Allozyme markers lack power...

  4. An improved ant colony optimization approach for optimization of process planning.

    Science.gov (United States)

    Wang, JinFeng; Fan, XiaoLiang; Ding, Haimin

    2014-01-01

    Computer-aided process planning (CAPP) is an important interface between computer-aided design (CAD) and computer-aided manufacturing (CAM) in computer-integrated manufacturing environments (CIMs). In this paper, process planning problem is described based on a weighted graph, and an ant colony optimization (ACO) approach is improved to deal with it effectively. The weighted graph consists of nodes, directed arcs, and undirected arcs, which denote operations, precedence constraints among operation, and the possible visited path among operations, respectively. Ant colony goes through the necessary nodes on the graph to achieve the optimal solution with the objective of minimizing total production costs (TPCs). A pheromone updating strategy proposed in this paper is incorporated in the standard ACO, which includes Global Update Rule and Local Update Rule. A simple method by controlling the repeated number of the same process plans is designed to avoid the local convergence. A case has been carried out to study the influence of various parameters of ACO on the system performance. Extensive comparative experiments have been carried out to validate the feasibility and efficiency of the proposed approach.

  5. Arc Based Ant Colony Optimization Algorithm for optimal design of gravitational sewer networks

    Directory of Open Access Journals (Sweden)

    R. Moeini

    2017-06-01

    Full Text Available In this paper, constrained and unconstrained versions of a new formulation of Ant Colony Optimization Algorithm (ACOA named Arc Based Ant Colony Optimization Algorithm (ABACOA are augmented with the Tree Growing Algorithm (TGA and used for the optimal layout and pipe size design of gravitational sewer networks. The main advantages offered by the proposed ABACOA formulation are proper definition of heuristic information, a useful component of the ant-based algorithms, and proper trade-off between the two conflicting search attributes of exploration and exploitation. In both the formulations, the TGA is used to incrementally construct feasible tree-like layouts out of the base layout. In the first formulation, unconstrained version of ABACOA is used to determine the nodal cover depths of sewer pipes while in the second formulation, a constrained version of ABACOA is used to determine the nodal cover depths of sewer pipes which satisfy the pipe slopes constraint. Three different methods of cut determination are also proposed to complete the construction of a tree-like network containing all base layout pipes, here. The proposed formulations are used to solve three test examples of different scales and the results are presented and compared with other available results in the literature. Comparison of the results shows that best results are obtained using the third cutting method in both the formulations. In addition, the results indicate the ability of the proposed methods and in particular the constrained version of ABACOA equipped with TGA to solve sewer networks design optimization problem. To be specific, the constrained version of ABACOA has been able to produce results 0.1%, 1% and 2.1% cheaper than those obtained by the unconstrained version of ABACOA for the first, second and the third test examples, respectively.

  6. 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

    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......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...

  7. Optimization design of drilling string by screw coal miner based on ant colony algorithm

    Institute of Scientific and Technical Information of China (English)

    ZHANG Qiang; MAO Jun; DING Fei

    2008-01-01

    It took that the weight minimum and drive efficiency maximal were as double optimizing target,the optimization model had built the drilling string,and the optimization solution was used of the ant colony algorithm to find in progress.Adopted a two-layer search of the continuous space ant colony algorithm with overlapping or variation global ant search operation strategy and conjugated gradient partial ant search operation strategy.The experiment indicates that the spiral drill weight reduces 16.77% and transports the efficiency enhance 7.05% through the optimization design,the ant colony algorithm application on the spiral drill optimized design has provided the basis for the system research screw coal mine machine.

  8. Optimization design of drilling string by screw coal miner based on ant colony algorithm

    Institute of Scientific and Technical Information of China (English)

    ZHANG Qiang; MAO Jun; DING Fei

    2008-01-01

    It took that the weight minimum and drive efficiency maximal were as double optimizing target, the optimization model had built the drilling string, and the optimization solution was used of the ant colony algorithm to find in progress. Adopted a two-layer search of the continuous space ant colony algorithm with overlapping or variation global ant search operation strategy and conjugated gradient partial ant search operation strat-egy. The experiment indicates that the spiral drill weight reduces 16.77% and transports the efficiency enhance 7.05% through the optimization design, the ant colony algorithm application on the spiral drill optimized design has provided the basis for the system re-search screw coal mine machine.

  9. Monitoring colony phenology using within-day variability in continuous weight and temperature of honey bee hives

    Science.gov (United States)

    Continuous weight and temperature data were collected for honey bee hives in two locations in Arizona, and those data were evaluated with respect to separate measurements of hive phenology to develop methods for monitoring hives non-invasively. Both the weight and temperature data were divided into ...

  10. 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.

  11. Ant Colony Optimization with Memory and Its Application to Traveling Salesman Problem

    Science.gov (United States)

    Wang, Rong-Long; Zhao, Li-Qing; Zhou, Xiao-Fan

    Ant Colony Optimization (ACO) is one of the most recent techniques for solving combinatorial optimization problems, and has been unexpectedly successful. Therefore, many improvements have been proposed to improve the performance of the ACO algorithm. In this paper an ant colony optimization with memory is proposed, which is applied to the classical traveling salesman problem (TSP). In the proposed algorithm, each ant searches the solution not only according to the pheromone and heuristic information but also based on the memory which is from the solution of the last iteration. A large number of simulation runs are performed, and simulation results illustrate that the proposed algorithm performs better than the compared algorithms.

  12. Application of ant colony optimization approach to severe accident management measures of Maanshan nuclear power plant

    Energy Technology Data Exchange (ETDEWEB)

    Tsai, C.-M.; Wang, S.-J. [Inst. of Nuclear Energy Research, Taiwan (China)

    2011-07-01

    The first three guidelines in the Maanshan SAMG were respectively evaluated for the effects in the SBO incident. The MAAP5 code was used to simulate the sequence of events and physical phenomena in the plant. The results show that the priority optimization should be carried out at two separated scenarios, i.e. the power recovered prior or after hot-leg creep rupture. The performance indices in the ant colony optimization could be the vessel life and the hydrogen generation from core for ant colony optimization. (author)

  13. An adaptive ant colony system algorithm for continuous-space optimization problems

    Institute of Scientific and Technical Information of China (English)

    李艳君; 吴铁军

    2003-01-01

    Ant colony algorithms comprise a novel category of evolutionary computation methods for optimization problems, especially for sequencing-type combinatorial optimization problems. An adaptive ant colony algorithm is proposed in this paper to tackle continuous-space optimization problems, using a new objective-function-based heuristic pheromone assignment approach for pheromone update to filtrate solution candidates.Global optimal solutions can be reached more rapidly by self-adjusting the path searching behaviors of the ants according to objective values. The performance of the proposed algorithm is compared with a basic ant colony algorithm and a Square Quadratic Programming approach in solving two benchmark problems with multiple extremes. The results indicated that the efficiency and reliability of the proposed algorithm were greatly improved.

  14. An adaptive ant colony system algorithm for continuous-space optimization problems

    Institute of Scientific and Technical Information of China (English)

    李艳君; 吴铁军

    2003-01-01

    Ant colony algorithms comprise a novel category of evolutionary computation methods for optimization problems, especially for sequencing-type combinatorial optimization problems. An adaptive ant colony algorithm is proposed in this paper to tackle continuous-space optimization problems, using a new objective-function-based heuristic pheromone assignment approach for pheromone update to filtrate solution candidates. Global optimal solutions can be reached more rapidly by self-adjusting the path searching behaviors of the ants according to objective values. The performance of the proposed algorithm is compared with a basic ant colony algorithm and a Square Quadratic Programming approach in solving two benchmark problems with multiple extremes. The results indicated that the efficiency and reliability of the proposed algorithm were greatly improved.

  15. Vehicle Routing Optimization in Logistics Distribution Using Hybrid Ant Colony Algorithm

    Directory of Open Access Journals (Sweden)

    Chengming Qi

    2013-09-01

    Full Text Available The Vehicle Routing Problem (VRP is an important management problem in the field of physical distribution and logistics. Good vehicle routing can not only increase the profit of logistics but also make logistics management more scientific. The Capacitated Vehicle Routing Problem (CVRP constrained by the capacity of a vehicle is the extension of VRP. Our research applies a two-phase algorithm to address CVRP. It takes the advantages of Simulated Annealing (SA and ant colony optimization for solving the capacitated vehicle routing problem. In the first phase of proposed algorithm, simulated an