Polyethism in a colony of artificial ants
Marriott, Chris
2011-01-01
We explore self-organizing strategies for role assignment in a foraging task carried out by a colony of artificial agents. Our strategies are inspired by various mechanisms of division of labor (polyethism) observed in eusocial insects like ants, termites, or bees. Specifically we instantiate models of caste polyethism and age or temporal polyethism to evaluated the benefits to foraging in a dynamic environment. Our experiment is directly related to the exploration/exploitation trade of in machine learning.
A nuclear reactor core fuel reload optimization using artificial ant colony connective networks
Energy Technology Data Exchange (ETDEWEB)
Lima, Alan M.M. de [Universidade Federal do Rio de Janeiro, PEN/COPPE - UFRJ, Ilha do Fundao s/n, CEP 21945-970 Rio de Janeiro (Brazil)], E-mail: alanmmlima@yahoo.com.br; Schirru, Roberto [Universidade Federal do Rio de Janeiro, PEN/COPPE - UFRJ, Ilha do Fundao s/n, CEP 21945-970 Rio de Janeiro (Brazil)], E-mail: schirru@lmp.ufrj.br; Carvalho da Silva, Fernando [Universidade Federal do Rio de Janeiro, PEN/COPPE - UFRJ, Ilha do Fundao s/n, CEP 21945-970 Rio de Janeiro (Brazil)], E-mail: fernando@con.ufrj.br; Medeiros, Jose Antonio Carlos Canedo [Universidade Federal do Rio de Janeiro, PEN/COPPE - UFRJ, Ilha do Fundao s/n, CEP 21945-970 Rio de Janeiro (Brazil)], E-mail: canedo@lmp.ufrj.br
2008-09-15
The core of a nuclear Pressurized Water Reactor (PWR) may be reloaded every time the fuel burn-up is such that it is not more possible to maintain the reactor operating at nominal power. The nuclear core fuel reload optimization problem consists in finding a pattern of burned-up and fresh-fuel assemblies that maximize the number of full operational days. This is an NP-Hard problem, meaning that complexity grows exponentially with the number of fuel assemblies in the core. Moreover, the problem is non-linear and its search space is highly discontinuous and multi-modal. Ant Colony System (ACS) is an optimization algorithm based on artificial ants that uses the reinforcement learning technique. The ACS was originally developed to solve the Traveling Salesman Problem (TSP), which is conceptually similar to the nuclear core fuel reload problem. In this work a parallel computational system based on the ACS, called Artificial Ant Colony Networks is introduced to solve the core fuel reload optimization problem.
Kavitha, Ganesan; Ramakrishnan, Swaminathan
2010-01-01
Optic disc and retinal vasculature are important anatomical structures in the retina of the eye and any changes observed in these structures provide vital information on severity of various diseases. Digital retinal images are shown to provide a meaningful way of documenting and assessing some of the key elements inside the eye including the optic nerve and the tiny retinal blood vessels. In this work, an attempt has been made to detect and differentiate abnormalities of the retina using Digital image processing together with Optimization based segmentation and Artificial Neural Network methods. The retinal fundus images were recorded using standard protocols. Ant Colony Optimization is employed to extract the most significant objects namely the optic disc and blood vessel. The features related to these objects are obtained and corresponding indices are also derived. Further, these features are subjected to classification using Radial Basis Function Neural Networks and compared with conventional training algorithms. Results show that the Ant Colony Optimization is efficient in extracting useful information from retinal images. The features derived are effective for classification of normal and abnormal images using Radial basis function networks compared to other methods. As Optic disc and blood vessels are significant markers of abnormality in retinal images, the method proposed appears to be useful for mass screening. In this paper, the objectives of the study, methodology and significant observations are presented.
Ant colonies for the travelling salesman problem.
Dorigo, M; Gambardella, L M
1997-01-01
We describe an artificial ant colony capable of solving the travelling salesman problem (TSP). Ants of the artificial colony are able to generate successively shorter feasible tours by using information accumulated in the form of a pheromone trail deposited on the edges of the TSP graph. Computer simulations demonstrate that the artificial ant colony is capable of generating good solutions to both symmetric and asymmetric instances of the TSP. The method is an example, like simulated annealing, neural networks and evolutionary computation, of the successful use of a natural metaphor to design an optimization algorithm.
Evolutional Ant Colony Method Using PSO
Morii, Nobuto; Aiyoshi, Eitarou
The ant colony method is one of heuristic methods capable of solving the traveling salesman problem (TSP), in which a good tour is generated by the artificial ant's probabilistic behavior. However, the generated tour length depends on the parameter describing the ant's behavior, and the best parameters corresponding to the problem to be solved is unknown. In this technical note, the evolutional strategy is presented to find the best parameter of the ant colony by using Particle Swarm Optimization (PSO) in the parameter space. Numerical simulations for benchmarks demonstrate effectiveness of the evolutional ant colony method.
Ant Colony Optimization for Control
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
Directory of Open Access Journals (Sweden)
Nigsch Florian
2008-10-01
Full Text Available Abstract Background We present a novel feature selection algorithm, Winnowing Artificial Ant Colony (WAAC, that performs simultaneous feature selection and model parameter optimisation for the development of predictive quantitative structure-property relationship (QSPR models. The WAAC algorithm is an extension of the modified ant colony algorithm of Shen et al. (J Chem Inf Model 2005, 45: 1024–1029. We test the ability of the algorithm to develop a predictive partial least squares model for the Karthikeyan dataset (J Chem Inf Model 2005, 45: 581–590 of melting point values. We also test its ability to perform feature selection on a support vector machine model for the same dataset. Results Starting from an initial set of 203 descriptors, the WAAC algorithm selected a PLS model with 68 descriptors which has an RMSE on an external test set of 46.6°C and R2 of 0.51. The number of components chosen for the model was 49, which was close to optimal for this feature selection. The selected SVM model has 28 descriptors (cost of 5, ε of 0.21 and an RMSE of 45.1°C and R2 of 0.54. This model outperforms a kNN model (RMSE of 48.3°C, R2 of 0.47 for the same data and has similar performance to a Random Forest model (RMSE of 44.5°C, R2 of 0.55. However it is much less prone to bias at the extremes of the range of melting points as shown by the slope of the line through the residuals: -0.43 for WAAC/SVM, -0.53 for Random Forest. Conclusion With a careful choice of objective function, the WAAC algorithm can be used to optimise machine learning and regression models that suffer from overfitting. Where model parameters also need to be tuned, as is the case with support vector machine and partial least squares models, it can optimise these simultaneously. The moving probabilities used by the algorithm are easily interpreted in terms of the best and current models of the ants, and the winnowing procedure promotes the removal of irrelevant descriptors.
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.
GRID SCHEDULING USING ENHANCED ANT COLONY ALGORITHM
Directory of Open Access Journals (Sweden)
P. Mathiyalagan
2010-10-01
Full Text Available Grid computing is a high performance computing used to solve larger scale computational demands. Task scheduling is a major issue in grid computing systems. Scheduling of tasks is the NP hard problem. The heuristic approach provides optimal solution for NP hard problems .The ant colony algorithm provides optimal solution. The existing ant colony algorithm takes more time to schedule the tasks. In this paper ant colony algorithm improved by enhancing pheromone updating rule such that it schedules the tasks efficiently and better resource utilization. The simulation results prove that proposed method reduces the execution time of tasks compared to existing ant colony algorithm.
Optimization of PID Controllers Using Ant Colony and Genetic Algorithms
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.
Nestmate and kin recognition in interspecific mixed colonies of ants.
Carlin, N F; Hölldobler, B
1983-12-01
Recognition of nestmates and discrimination against aliens is the rule in the social insects. The principal mechanism of nestmate recognition in carpenter ants (Camponotus) appears to be odor labels or "discriminators" that originate from the queen and are distributed among, and learned by, all adult colony members. The acquired odor labels are sufficiently powerful to produce indiscriminate acceptance among workers of different species raised together in artificially mixed colonies and rejection of genetic sisters reared by different heterospecific queens.
Improving Emergency Management by Modeling Ant Colonies
2015-03-01
brood. The brood stages include the egg, the larval, and the pupa.27 The brood is dependent on the colony for nourishment and warmth until fully...night for rest and to relocate the colony. The bivouac is what is created when army ants huddle together in a ball instead of building a physical nest
Ant Colony Optimisation for Backward Production Scheduling
Directory of Open Access Journals (Sweden)
Leandro Pereira dos Santos
2012-01-01
Full Text Available The main objective of a production scheduling system is to assign tasks (orders or jobs to resources and sequence them as efficiently and economically (optimised as possible. Achieving this goal is a difficult task in complex environment where capacity is usually limited. In these scenarios, finding an optimal solution—if possible—demands a large amount of computer time. For this reason, in many cases, a good solution that is quickly found is preferred. In such situations, the use of metaheuristics is an appropriate strategy. In these last two decades, some out-of-the-shelf systems have been developed using such techniques. This paper presents and analyses the development of a shop-floor scheduling system that uses ant colony optimisation (ACO in a backward scheduling problem in a manufacturing scenario with single-stage processing, parallel resources, and flexible routings. This scenario was found in a large food industry where the corresponding author worked as consultant for more than a year. This work demonstrates the applicability of this artificial intelligence technique. In fact, ACO proved to be as efficient as branch-and-bound, however, executing much faster.
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.
Ant colony optimization and constraint programming
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
A Novel Parser Design Algorithm Based on Artificial Ants
Maiti, Deepyaman; Konar, Amit; Ramadoss, Janarthanan
2008-01-01
This article presents a unique design for a parser using the Ant Colony Optimization algorithm. The paper implements the intuitive thought process of human mind through the activities of artificial ants. The scheme presented here uses a bottom-up approach and the parsing program can directly use ambiguous or redundant grammars. We allocate a node corresponding to each production rule present in the given grammar. Each node is connected to all other nodes (representing other production rules), thereby establishing a completely connected graph susceptible to the movement of artificial ants. Each ant tries to modify this sentential form by the production rule present in the node and upgrades its position until the sentential form reduces to the start symbol S. Successful ants deposit pheromone on the links that they have traversed through. Eventually, the optimum path is discovered by the links carrying maximum amount of pheromone concentration. The design is simple, versatile, robust and effective and obviates ...
Ant Colony Optimization and Hypergraph Covering Problems
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 ...
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.
Improvement and Implementation of Best-worst Ant Colony Algorithm
Directory of Open Access Journals (Sweden)
Xianmin Wei
2013-05-01
Full Text Available In this study, we introduced the ant colony algorithm of best-worst ant system based on the pheromone update. By update improvements of local pheromone and global pheromone, as well as the optimal solution enhancement to a greater extent and the weakening of the worst solution, the algorithm further increased the difference of pheromone amount between the edge of the optimal path and the edge of the worst path and allowed the ant colony search behavior more focused near the optimal solution. Finally, through simulation experiments to prove that the algorithm can get the optimal solution and the convergence rate is faster than the average ant colony algorithm.
An ant colony algorithm on continuous searching space
Xie, Jing; Cai, Chao
2015-12-01
Ant colony algorithm is heuristic, bionic and parallel. Because of it is property of positive feedback, parallelism and simplicity to cooperate with other method, it is widely adopted in planning on discrete space. But it is still not good at planning on continuous space. After a basic introduction to the basic ant colony algorithm, we will propose an ant colony algorithm on continuous space. Our method makes use of the following three tricks. We search for the next nodes of the route according to fixed-step to guarantee the continuity of solution. When storing pheromone, it discretizes field of pheromone, clusters states and sums up the values of pheromone of these states. When updating pheromone, it makes good resolutions measured in relative score functions leave more pheromone, so that ant colony algorithm can find a sub-optimal solution in shorter time. The simulated experiment shows that our ant colony algorithm can find sub-optimal solution in relatively shorter time.
Remote Sensing Image Feature Extracting Based Multiple Ant Colonies Cooperation
Directory of Open Access Journals (Sweden)
Zhang Zhi-long
2014-02-01
Full Text Available This paper presents a novel feature extraction method for remote sensing imagery based on the cooperation of multiple ant colonies. First, multiresolution expression of the input remote sensing imagery is created, and two different ant colonies are spread on different resolution images. The ant colony in the low-resolution image uses phase congruency as the inspiration information, whereas that in the high-resolution image uses gradient magnitude. The two ant colonies cooperate to detect features in the image by sharing the same pheromone matrix. Finally, the image features are extracted on the basis of the pheromone matrix threshold. Because a substantial amount of information in the input image is used as inspiration information of the ant colonies, the proposed method shows higher intelligence and acquires more complete and meaningful image features than those of other simple edge detectors.
Parallelizing Ant Colony Optimization via Area of Expertise Learning
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
The hyper-cube framework for ant colony optimization.
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.
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
Incremental Web Usage Mining Based on Active Ant Colony Clustering
Institute of Scientific and Technical Information of China (English)
SHEN Jie; LIN Ying; CHEN Zhimin
2006-01-01
To alleviate the scalability problem caused by the increasing Web using and changing users' interests, this paper presents a novel Web Usage Mining algorithm-Incremental Web Usage Mining algorithm based on Active Ant Colony Clustering. Firstly, an active movement strategy about direction selection and speed, different with the positive strategy employed by other Ant Colony Clustering algorithms, is proposed to construct an Active Ant Colony Clustering algorithm, which avoid the idle and "flying over the plane" moving phenomenon, effectively improve the quality and speed of clustering on large dataset. Then a mechanism of decomposing clusters based on above methods is introduced to form new clusters when users' interests change. Empirical studies on a real Web dataset show the active ant colony clustering algorithm has better performance than the previous algorithms, and the incremental approach based on the proposed mechanism can efficiently implement incremental Web usage mining.
Ant Colony Optimization for Train Scheduling: An Analysis
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.
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.
Finding the Minimum Ratio Traveling Salesman Tour by Artificial Ants
Institute of Scientific and Technical Information of China (English)
马良; 崔雪丽; 姚俭
2003-01-01
Ants of artificial colony are able to generate good solutions to the famous traveling salesman problem (TSP).We propose an artificial ants algorithm for solving the minimum ratio TSP, which is more general than the standard TSP in combinatorial optimization area. In the minimum ratio TSP, another criterion concerning each edge is added, that is,the traveling salesman can have a benefit if he travels from one city to another. The objective is to minimize the ratio between total costs or distances and total benefits. The idea of this type of optimization is in some sense quite similar to that of traditional cost-benefit analysis in management science. Computational results substantiate the solution quality and efficiency of the algorithm.
Ant Colony Algorithm for Solving QoS Routing Problem
Institute of Scientific and Technical Information of China (English)
SUN Li-juan; WANG Liang-jun; WANG Ru-chuan
2004-01-01
Based on the state transition rule, the local updating rule and the global updating rule of ant colony algorithm, we propose an improved ant colony algorithm of the least-cost quality of service (QoS) unicast routing. The algorithm is used for solving the routing problem with delay, delay jitter, bandwidth, and packet loss-constrained. In the simulation, about 52.33% ants find the successful QoS routing , and converge to the best. It is proved that the algorithm is efficient and effective.
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.
Ant Colony Search Algorithm for Solving Unit Commitment Problem
Directory of Open Access Journals (Sweden)
M.Surya Kalavathi
2013-07-01
Full Text Available In this paper Ant Colony Search Algorithm is proposed to solve thermal unit commitment problem. Ant colony search (ACS studies are inspired from the behavior of real ant colonies that are used to solve function or combinatorial optimization problems. In the ACSA a set of cooperating agents called ants cooperates to find good solution of unit commitment problem of thermal units. The UC problem is to determine a minimal cost turn-on and turn-off schedule of a set of electrical power generating units to meet a load demand while satisfying a set of operational constraints. This proposed approach is a tested on 10 unit power system and compared to conventional methods.
Runtime analysis of the 1-ANT ant colony optimizer
DEFF Research Database (Denmark)
Doerr, Benjamin; Neumann, Frank; Sudholt, Dirk;
2011-01-01
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...
Introduction to Ant Colony Algorithm and Its Application in CIMS
Institute of Scientific and Technical Information of China (English)
无
2002-01-01
Ant colony algorithm is a novel simulated ecosystem e volutionary algorithm, which is proposed firstly by Italian scholars M.Dorigo, A . Colormi and V. Maniezzo. Enlightened by the process of ants searching for food , scholars bring forward this new evolutionary algorithm. This algorithm has sev eral characteristics such as positive feedback, distributed computing and stro nger robustness. Positive feedback and distributed computing make it easier to find better solutions. Based on these characteristics...
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.
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.
Core Business Selection Based on Ant Colony Clustering Algorithm
Directory of Open Access Journals (Sweden)
Yu Lan
2014-01-01
Full Text Available Core business is the most important business to the enterprise in diversified business. In this paper, we first introduce the definition and characteristics of the core business and then descript the ant colony clustering algorithm. In order to test the effectiveness of the proposed method, Tianjin Port Logistics Development Co., Ltd. is selected as the research object. Based on the current situation of the development of the company, the core business of the company can be acquired by ant colony clustering algorithm. Thus, the results indicate that the proposed method is an effective way to determine the core business for company.
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.
Colony life history and lifetime reproductive success of red harvester ant colonies.
Ingram, Krista K; Pilko, Anna; Heer, Jeffrey; Gordon, Deborah M
2013-05-01
1. We estimate colony reproductive success, in numbers of offspring colonies arising from a colony's daughter queens, of colonies of the red harvester ant, Pogonomyrmex barbatus. 2. A measure of lifetime reproductive success is essential to understand the relation of ecological factors, phenotype and fitness in a natural population. This was possible for the first time in a natural population of ant colonies using data from long-term study of a population of colonies in south-eastern Arizona, for which ages of all colonies are known from census data collected since 1985. 3. Parentage analyses of microsatellite data from 5 highly polymorphic loci were used to assign offspring colonies to maternal parent colonies in a population of about 265 colonies, ages 1-28 years, sampled in 2010. 4. The estimated population growth rate Ro was 1.69 and generation time was 7.8 years. There was considerable variation among colonies in reproductive success: of 199 possible parent colonies, only 49 (˜ 25%) had offspring colonies on the site. The mean number of offspring colonies per maternal parent colony was 2.94 and ranged from 1 to 8. A parent was identified for the queen of 146 of 247 offspring colonies. There was no evidence for reproductive senescence; fecundity was about the same throughout the 25-30 year lifespan of a colony. 5. There were no trends in the distance or direction of the dispersal of an offspring relative to its maternal parent colony. There was no relationship between the number of gynes produced by a colony in 1 year and the number of offspring colonies subsequently founded by its daughter reproductive females. The results provide the first estimate of a life table for a population of ant colonies and the first estimate of the female component of colony lifetime reproductive success. 6. The results suggest that commonly used measures of reproductive output may not be correlated with realized reproductive success. This is the starting point for future
Hybrid ant colony algorithm for traveling salesman problem
Institute of Scientific and Technical Information of China (English)
无
2003-01-01
A hybrid approach based on ant colony algorithm for the traveling salesman problem is proposed, which is an improved algorithm characterized by adding a local search mechanism, a cross-removing strategy and candidate lists. Experimental results show that it is competitive in terms of solution quality and computation time.
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...
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
Pringle, Elizabeth G; Dirzo, Rodolfo; Gordon, Deborah M
2012-11-01
The effects of herbivory on plant fitness are integrated over a plant's lifetime, mediated by ontogenetic changes in plant defense, tolerance, and herbivore pressure. In symbiotic ant-plant mutualisms, plants provide nesting space and food for ants, and ants defend plants against herbivores. The benefit to the plant of sustaining the growth of symbiotic ant colonies depends on whether defense by the growing ant colony outpaces the plant's growth in defendable area and associated herbivore pressure. These relationships were investigated in the symbiotic mutualism between Cordia alliodora trees and Azteca pittieri ants in a Mexican tropical dry forest. As ant colonies grew, worker production remained constant relative to ant-colony size. As trees grew, leaf production increased relative to tree size. Moreover, larger trees hosted lower densities of ants, suggesting that ant-colony growth did not keep pace with tree growth. On leaves with ants experimentally excluded, herbivory per unit leaf area increased exponentially with tree size, indicating that larger trees experienced higher herbivore pressure per leaf area than smaller trees. Even with ant defense, herbivory increased with tree size. Therefore, although larger trees had larger ant colonies, ant density was lower in larger trees, and the ant colonies did not provide sufficient defense to compensate for the higher herbivore pressure in larger trees. These results suggest that in this system the tree can decrease herbivory by promoting ant-colony growth, i.e., sustaining space and food investment in ants, as long as the tree continues to grow.
All-Optical Implementation of the Ant Colony Optimization Algorithm
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.
Solution to the problem of ant being stuck by ant colony routing algorithm
Institute of Scientific and Technical Information of China (English)
ZHAO Jing; TONG Wei-ming
2009-01-01
Many ant colony routing (ACR) algorithms have been presented in recent years, but few have studied the problem that ants will get stuck with probability in any terminal host when they are searching paths to route packets around a network. The problem has to be faced when designing and implementing the ACR algorithm. This article analyzes in detail the differences between the ACR and the ant colony optimization (ACO). Besides, particular restrictions on the ACR are pointed out and the three causes of ant being-stuck problem are obtained. Furthermore, this article proposes a new ant searching mechanism through dual path-checking and online routing loop removing by every intermediate node an ant visited and the destination host respectively, to solve the problem of ant being stuck and routing loop simultaneously. The result of numerical simulation is abstracted from one real network. Compared with existing two typical ACR algorithms, it shows that the proposed algorithm can settle the problem of ant being stuck and achieve more effective searching outcome for optimization path.
Research on the Perceptual Law of Artificial Ants
Institute of Scientific and Technical Information of China (English)
ZHENG Zhaobao
2005-01-01
Beginning with the analysis of the behavior of natural ants, this paper illuminates the principle and method that, by adopting image texture energy as pheromone and finding their way on the track of the pheromone, artificial ants have the ability to identify and remember through similar measurement of pheromone. Based on the quantity of experiments, this paper analyzes some factors that influence the ability of artificial ants and draws some conclusions about the law of ant perception.
Emigration of a colony of the leaf-cutting ant Acromyrmex heyeri Forel (Hymenoptera, Formicidae
Directory of Open Access Journals (Sweden)
Mariane Aparecida Nickele
2012-09-01
Full Text Available Emigration of a colony of the leaf-cutting ant Acromyrmex heyeri Forel (Hymenoptera, Formicidae. Colony migration is a poorly studied phenomenon in leaf-cutting ants. Here we report on the emigration of a colony of the leaf-cutting ant A. heyeri in Brazil. The colony emigrated to a new location 47.4 m away from the original nest site, possibly because it had undergone considerable stress due to competitive interactions with a colony of Acromyrmex crassispinus.
An ant colony algorithm for solving Max-cut problem
Institute of Scientific and Technical Information of China (English)
Lin Gao; Yan Zeng; Anguo Dong
2008-01-01
Max-cut problem is an NP-complete and classical combinatorial optimization problem that has a wide range of appfications in dif-ferent domains,such as bioinformatics,network optimization,statistical physics,and very large scale integration design.In this paper we investigate the capabilities of the ant colony optimization(ACO)heuristic for solving the Max-cut problem and present an AntCut algo-rithm.A large number of simulation experiments show that the algorithm can solve the Max-cut problem more efficiently and effectively.
Comparative Analysis and Survey of Ant Colony Optimization based Rule Miners
Directory of Open Access Journals (Sweden)
Zulfiqar Ali
2017-01-01
Full Text Available In this research study, we analyze the performance of bio inspired classification approaches by selecting Ant-Miners (Ant-Miner, cAnt_Miner, cAnt_Miner2 and cAnt_MinerPB for the discovery of classification rules in terms of accuracy, terms per rule, number of rules, running time and model size discovered by the corresponding rule mining algorithm. Classification rule discovery is still a challenging and emerging research problem in the field of data mining and knowledge discovery. Rule based classification has become cutting edge research area due to its importance and popular application areas in the banking, market basket analysis, credit card fraud detection, costumer behaviour, stock market prediction and protein sequence analysis. There are various approaches proposed for the discovery of classification rules like Artificial Neural Networks, Genetic Algorithm, Evolutionary Programming, SVM and Swarm Intelligence. This research study is focused on classification rule discovery by Ant Colony Optimization. For the performance analysis, Myra Tool is used for experiments on the 18 public datasets (available on the UCI repository. Data sets are selected with varying number of instances, number of attributes and number of classes. This research paper also provides focused survey of Ant-Miners for the discovery of classification rules.
The use of artificial nests by weaver ants: a preliminary field observation
DEFF Research Database (Denmark)
Offenberg, Joachim
2014-01-01
populations or destroy colonies. The ants, however, show adaptive nesting behavior, which may mitigate storm impact. This study tested whether Oecophylla smaragdina was willing to use plastic bottles as safe artificial nesting sites, and whether adoption of artificial nests was seasonally related to harsh...... weather. Bottles were used for nesting throughout the stormy rainy season in a pomelo plantation with an open canopy, whereas in a mango plantation with a denser canopy the ants, after initial colonisation, left the bottles again at the end of the rainy season, especially in the calmer part...... of the plantation. This suggests that exposure to harsh weather triggered the use of artificial nests. It was also found that ants preferred to nest in bottles covered with aluminum foil compared to transparent bottles. These findings document an opportunistic nesting behavior of weaver ants and suggest...
Ant colonies prefer infected over uninfected nest sites
DEFF Research Database (Denmark)
Pontieri, Luigi; Vojvodic, Svjetlana; Graham, Riley;
2014-01-01
During colony relocation, the selection of a new nest involves exploration and assessment of potential sites followed by colony movement on the basis of a collective decision making process. Hygiene and pathogen load of the potential nest sites are factors worker scouts might evaluate, given...... and low genetic diversity, likely making these species particularly vulnerable to parasites and diseases. We investigated the nest site preference of the invasive pharaoh ant, Monomorium pharaonis, through binary choice tests between three nest types: nests containing dead nestmates overgrown...
AN ANT COLONY ALGORITHM FOR MINIMUM UNSATISFIABLE CORE EXTRACTION
Institute of Scientific and Technical Information of China (English)
Zhang Jianmin; Shen Shengyu; Li Sikun
2008-01-01
Explaining the causes of infeasibility of Boolean formulas has many practical applications in electronic design automation and formal verification of hardware. Furthermore,a minimum explanation of infeasibility that excludes all irrelevant information is generally of interest. A smallest-cardinality unsatisfiable subset called a minimum unsatisfiable core can provide a succinct explanation of infea-sibility and is valuable for applications. However,little attention has been concentrated on extraction of minimum unsatisfiable core. In this paper,the relationship between maximal satisfiability and mini-mum unsatisfiability is presented and proved,then an efficient ant colony algorithm is proposed to derive an exact or ncarly exact minimum unsatisfiable core based on the relationship. Finally,ex-perimental results on practical benchmarks compared with the best known approach are reported,and the results show that the ant colony algorithm strongly outperforms the best previous algorithm.
Ant colony optimized planning for unmanned surface marine vehicles
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...
A Multiple Pheromone Table Based Ant Colony Optimization for Clustering
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...
Rationality in collective decision-making by ant colonies.
Edwards, Susan C; Pratt, Stephen C
2009-10-22
Economic models of animal behaviour assume that decision-makers are rational, meaning that they assess options according to intrinsic fitness value and not by comparison with available alternatives. This expectation is frequently violated, but the significance of irrational behaviour remains controversial. One possibility is that irrationality arises from cognitive constraints that necessitate short cuts like comparative evaluation. If so, the study of whether and when irrationality occurs can illuminate cognitive mechanisms. We applied this logic in a novel setting: the collective decisions of insect societies. We tested for irrationality in colonies of Temnothorax ants choosing between two nest sites that varied in multiple attributes, such that neither site was clearly superior. In similar situations, individual animals show irrational changes in preference when a third relatively unattractive option is introduced. In contrast, we found no such effect in colonies. We suggest that immunity to irrationality in this case may result from the ants' decentralized decision mechanism. A colony's choice does not depend on site comparison by individuals, but instead self-organizes from the interactions of multiple ants, most of which are aware of only a single site. This strategy may filter out comparative effects, preventing systematic errors that would otherwise arise from the cognitive limitations of individuals.
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.
Cooperation-based Ant Colony Algorithm in WSN
Directory of Open Access Journals (Sweden)
Jianbin Xue
2013-04-01
Full Text Available This paper proposed a routing algorithm based on ant colony algorithm. The traditional ant colony algorithm updates pheromone according to the path length, to get the shortest path from the initial node to destination node. But MIMO system is different from the SISO system. The distance is farther but the energy is not bigger. Similarly, the closer the distance, the smaller the energy is not necessarily. So need to select the path according to the energy consumption of the path. This paper is based on the energy consumption to update the pheromone which from the cluster head node to the next hop node. Then, can find a path which the communication energy consumption is least. This algorithm can save more energy consumption of the network. The simulation results of MATLAB show that the path chosen by the algorithm is better than the simple ant colony algorithm, and the algorithm can save the network energy consumption better and can prolong the life cycle of the network.
Random walk models of worker sorting in ant colonies.
Sendova-Franks, Ana B; Van Lent, Jan
2002-07-21
Sorting can be an important mechanism for the transfer of information from one level of biological organization to another. Here we study the algorithm underlying worker sorting in Leptothorax ant colonies. Worker sorting is related to task allocation and therefore to the adaptive advantages associated with an efficient system for the division of labour in ant colonies. We considered four spatially explicit individual-based models founded on two-dimensional correlated random walk. Our aim was to establish whether sorting at the level of the worker population could occur with minimal assumptions about the behavioural algorithm of individual workers. The behaviour of an individual worker in the models could be summarized by the rule "move if you can, turn always". We assume that the turning angle of a worker is individually specific and negatively dependent on the magnitude of an internal parameter micro which could be regarded as a measure of individual experience or task specialization. All four models attained a level of worker sortedness that was compatible with results from experiments onLeptothorax ant colonies. We found that the presence of a sorting pivot, such as the nest wall or an attraction force towards the centre of the worker population, was crucial for sorting. We make a distinction between such pivots and templates and discuss the biological implications of their difference.
A Novel Algorithm for Manets using Ant Colony
Directory of Open Access Journals (Sweden)
Javad Pashaei Barbin
2012-01-01
Full Text Available Mobile Ad-hoc Networks have recently attracted a lot of attention in the research community as well as the industry. Quality of Service support for MANETs is an exigent task due to dynamic topology and limited resource. Routing, the act of moving information across network from a source to a destination. Conventional routing algorithms are difficult to be applied to a dynamic network topology, therefore modeling and design an efficient routing protocol in such dynamic networks is an important issue. It is important that MANETs should provide QoS support routing, such as acceptable delay, jitter and energy in the case of multimedia and real time applications. One of the meta-heuristic algorithms which are inspired by the behavior of real ants is called Ant Colony Optimization algorithm. In this paper we propose a new on demand QoS routing algorithm "Ant Routing for Mobile Ad Hoc Networks" based on ant colony. The proposed algorithm will be highly adaptive, efficient and scalable and mainly reduces end-to-end delay in high mobility cases.
DEFF Research Database (Denmark)
Offenberg, Hans Joachim; Nielsen, Mogens Gissel; Peng, Renkang
2011-01-01
Weaver ants (Oecophylla spp.) are increasingly being used for biocontrol and are targeted for future production of insect protein in ant farms. An efficient production of live ant colonies may facilitate the utilization of these ants but the production of mature colonies is hampered by the long...... time it takes for newly established colonies to grow to a suitable size. In this study we followed the growth of newly founded O. smaragdina colonies with 2, 3 or 4 founding queens during 12 days of development, following the transplantation of 0, 30 or 60 pupae from a mature donor colony. Colony...... growth (number of individuals) increased with increasing numbers of queens (pleometrosis) as well as with the transplantation of pupae. Transplanted pupae were accepted and developed into mature workers and not only added extra individuals to the receiver colonies but also increased the egg production...
Directory of Open Access Journals (Sweden)
Antonio Carlos Da-Silva
2012-07-01
Leafcutter ants (Atta sexdens rubropilosa (Forel 1908 have an elaborate social organization, complete with caste divisions. Activities carried out by specialist groups contribute to the overall success and survival of the colony when it is confronted with environmental challenges such as dehydration. Ants detect variations in humidity inside the nest and react by activating several types of behavior that enhance water uptake and decrease water loss, but it is not clear whether or not a single caste collects water regardless of the cost of bringing this resource back to the colony. Accordingly, we investigated water collection activities in three colonies of Atta sexdens rubropilosa experimentally exposed to water stress. Specifically, we analyzed whether or not the same ant caste foraged for water, regardless of the absolute energetic cost (distance of transporting this resource back to the colony. Our experimental design offered water sources at 0 m, 1 m and 10 m from the nest. We studied the body size of ants near the water sources from the initial offer of water (time = 0 to 120 min, and tested for specialization. We observed a reduction in the average size and variance of ants that corroborated the specialization hypothesis. Although the temporal course of specialization changed with distance, the final outcome was similar among distances. Thus, we conclude that, for this species, a specialist (our use of the word “specialist” does not mean exclusive task force is responsible for collecting water, regardless of the cost of transporting water back to the colony.
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.
Annealing Ant Colony Optimization with Mutation Operator for Solving TSP.
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.
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.
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.
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.
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.
Microsatellites reveal high genetic diversity within colonies of Camponotus ants.
Gertsch, P; Pamilo, P; Varvio, S L
1995-04-01
In order to characterize the sociogenetic structure of colonies in the carpenter ants Camponotus herculeanus and C. ligniperda, we have developed microsatellite markers. The three loci studied were either fixed for different alleles in the two species or showed different patterns of polymorphisms. Genotyping of workers and males showed that the broods of C. ligniperda include several matrilines, a rare phenomenon in the genus. Five alleles from a locus polymorphic in both species were sequenced from the respective PCR-products. A part of the length variation appeared to be due to changes outside the repeat sequence, and some PCR products of an equal length had a different number of dinucleotide repeats.
JOB SHOP METHODOLOGY BASED ON AN ANT COLONY
Directory of Open Access Journals (Sweden)
OMAR CASTRILLON
2009-01-01
Full Text Available The purpose of this study is to reduce the total process time (Makespan and to increase the machines working time, in a job shop environment, using a heuristic based on ant colony optimization. This work is developed in two phases: The first stage describes the identification and definition of heuristics for the sequential processes in the job shop. The second stage shows the effectiveness of the system in the traditional programming of production. A good solution, with 99% efficiency is found using this technique.
Modeling of Vector Quantization Image Coding in an Ant Colony System
Institute of Scientific and Technical Information of China (English)
LIXia; LUOXuehui; ZHANGJihong
2004-01-01
Ant colony algorithm is a newly emerged stochastic searching optimization algorithm in recent years. In this paper, vector quantization image coding is modeled as a stochastic optimization problem in an Ant colony system (ACS). An appropriately adapted ant colony algorithm is proposed for vector quantization codebook design. Experimental results show that the ACS-based algorithm can produce a better codebook and the improvement of Pixel signal-to-noise ratio (PSNR) exceeds 1dB compared with the conventional LBG algorithm.
Binary-Coding-Based Ant Colony Optimization and Its Convergence
Institute of Scientific and Technical Information of China (English)
Tian-Ming Bu; Song-Nian Yu; Hui-Wei Guan
2004-01-01
Ant colony optimization(ACO for short)is a meta-heuristics for hard combinatorial optimization problems.It is a population-based approach that uses exploitation of positive feedback as well as greedy search.In this paper,genetic algorithm's(GA for short)ideas are introduced into ACO to present a new binary-coding based ant colony optimization.Compared with the typical ACO,the algorithm is intended to replace the problem's parameter-space with coding-space,which links ACO with GA so that the fruits of GA can be applied to ACO directly.Furthermore,it can not only solve general combinatorial optimization problems,but also other problems such as function optimization.Based on the algorithm,it is proved that if the pheromone remainder factor ρ is under the condition of ρ≥ 1,the algorithm can promise to converge at the optimal,whereas if 0 ＜ρ＜ 1,it does not.
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.
Ant system: optimization by a colony of cooperating agents.
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.
A Dynamic Job Shop Scheduling Method Based on Ant Colony Coordination System
Institute of Scientific and Technical Information of China (English)
ZHU Qiong; WU Li-hui; ZHANG Jie
2009-01-01
Due to the stubborn nature of dynamic job shop scheduling problem, a novel ant colony coordination mechanism is proposed in this paper to search for an optimal schedule in dynamic environment. In ant colony coordination mechanism, the dynamic .job shop is composed of several autonomous ants. These ants coordinate with each other by simulating the ant foraging behavior of spreading pheromone on the trails, by which they can make information available globally, and further more guide ants make optimal decisions. The proposed mechanism is tested by several instances and the results confirm the validity of it.
Population and colony structure of the carpenter ant Camponotus floridanus.
Gadau, J; Heinze, J; Hölldobler, B; Schmid, M
1996-12-01
The colony and population structure of the carpenter ant, Camponotus floridanus, were investigated by multilocus DNA fingerprinting using simple repeat motifs as probes [e.g. (GATA)4]. The mating frequency of 15 queens was determined by comparing the fingerprint patterns of the queen and 17-33 of her progeny workers. C. floridanus queens are most probably singly mated, i.e. this species is monandrous and monogynous (one queen per colony). C. floridanus occurs in all counties of mainland Florida and also inhabits most of the Key islands in the southern part of Florida. We tested whether the two mainland populations and the island populations are genetically isolated. Wright's FST and Nei's D-value of genetic distance were calculated from intercolonial bandsharing-coefficients. The population of C. floridanus is substructured (FST = 0.19 +/- 0.09) and the highest degree of genetic distance was found between one of the mainland populations and the island populations (D = 0.35). Our fingerprinting technique could successfully be transferred to 12 other Camponotus species and here also revealed sufficient variability to analyse the genetic structure. In three of these species (C. ligniperdus, C. herculeanus and C. gigas) we could determine the mating frequency of the queen in one or two colonies, respectively.
Departure Traj ectory Design Based on Pareto Ant Colony Algorithm
Institute of Scientific and Technical Information of China (English)
SunFanrong; HanSongchen; QianGe
2016-01-01
Due to the ever-increasing air traffic flow,the influence of aircraft noise around the airport has become significant.As most airlines are trying to decrease operation cost,stringent requirements for more simple and effi-cient departure traj ectory are on a rise.Therefore,a departure traj ectory design was established for performance-based navigation technology,and a multi-obj ective optimization model was developed,with constraints of safety and noise influence,as well as optimization targets of efficiency and simplicity.An improved ant colony algorithm was then proposed to solve the optimization problem.Finally,an experiment was conducted using the Lanzhou ter-minal airspace operation data,and the results showed that the designed departure traj ectory was feasible and effi-cient in decreasing the aircraft noise influence.
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.
Ant Colony Based Path Planning Algorithm for Autonomous Robotic Vehicles
Directory of Open Access Journals (Sweden)
Yogita Gigras
2012-11-01
Full Text Available The requirement of an autonomous robotic vehicles demand highly efficient algorithm as well as software. Today’s advanced computer hardware technology does not provide these types of extensive processing capabilities, so there is still a major space and time limitation for the technologies that are available for autonomous robotic applications. Now days, small to miniature mobile robots are required for investigation, surveillance and hazardous material detection for military and industrial applications. But these small sized robots have limited power capacity as well as memory and processing resources. A number of algorithms exist for producing optimal path for dynamically cost. This paper presents a new ant colony based approach which is helpful in solving path planning problem for autonomous robotic application. The experiment of simulation verified its validity of algorithm in terms of time.
Time-Based Dynamic Trust Model Using Ant Colony Algorithm
Institute of Scientific and Technical Information of China (English)
TANG Zhuo; LU Zhengding; LI Kai
2006-01-01
The trust in distributed environment is uncertain, which is variation for various factors. This paper introduces TDTM, a model for time-based dynamic trust. Every entity in the distribute environment is endowed with a trust-vector, which figures the trust intensity between this entity and the others. The trust intensity is dynamic due to the time and the inter-operation between two entities, a method is proposed to quantify this change based on the mind of ant colony algorithm and then an algorithm for the transfer of trust relation is also proposed. Furthermore, this paper analyses the influence to the trust intensity among all entities that is aroused by the change of trust intensity between the two entities, and presents an algorithm to resolve the problem. Finally, we show the process of the trusts'change that is aroused by the time' lapse and the inter-operation through an instance.
Power Efficient Resource Allocation for Clouds Using Ant Colony Framework
Chimakurthi, Lskrao
2011-01-01
Cloud computing is one of the rapidly improving technologies. It provides scalable resources needed for the ap- plications hosted on it. As cloud-based services become more dynamic, resource provisioning becomes more challenging. The QoS constrained resource allocation problem is considered in this paper, in which customers are willing to host their applications on the provider's cloud with a given SLA requirements for performance such as throughput and response time. Since, the data centers hosting the applications consume huge amounts of energy and cause huge operational costs, solutions that reduce energy consumption as well as operational costs are gaining importance. In this work, we propose an energy efficient mechanism that allocates the cloud resources to the applications without violating the given service level agreements(SLA) using Ant colony framework.
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.
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.
Strong Combination of Ant Colony Optimization with Constraint Programming Optimization
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.
Aircraft technology portfolio optimization using ant colony optimization
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.
Road Network Vulnerability Analysis Based on Improved Ant Colony Algorithm
Directory of Open Access Journals (Sweden)
Yunpeng Wang
2014-01-01
Full Text Available We present an improved ant colony algorithm-based approach to assess the vulnerability of a road network and identify the critical infrastructures. This approach improves computational efficiency and allows for its applications in large-scale road networks. This research involves defining the vulnerability conception, modeling the traffic utility index and the vulnerability of the road network, and identifying the critical infrastructures of the road network. We apply the approach to a simple test road network and a real road network to verify the methodology. The results show that vulnerability is directly related to traffic demand and increases significantly when the demand approaches capacity. The proposed approach reduces the computational burden and may be applied in large-scale road network analysis. It can be used as a decision-supporting tool for identifying critical infrastructures in transportation planning and management.
Multiple ant colony algorithm method for selecting tag SNPs.
Liao, Bo; Li, Xiong; Zhu, Wen; Li, Renfa; Wang, Shulin
2012-10-01
The search for the association between complex disease and single nucleotide polymorphisms (SNPs) or haplotypes has recently received great attention. Finding a set of tag SNPs for haplotyping in a great number of samples is an important step to reduce cost for association study. Therefore, it is essential to select tag SNPs with more efficient algorithms. In this paper, we model problem of selection tag SNPs by MINIMUM TEST SET and use multiple ant colony algorithm (MACA) to search a smaller set of tag SNPs for haplotyping. The various experimental results on various datasets show that the running time of our method is less than GTagger and MLR. And MACA can find the most representative SNPs for haplotyping, so that MACA is more stable and the number of tag SNPs is also smaller than other evolutionary methods (like GTagger and NSGA-II). Our software is available upon request to the corresponding author.
Nest site and weather affect the personality of harvester ant colonies.
Pinter-Wollman, Noa; Gordon, Deborah M; Holmes, Susan
2012-09-01
Environmental conditions and physical constraints both influence an animal's behavior. We investigate whether behavioral variation among colonies of the black harvester ant, Messor andrei, remains consistent across foraging and disturbance situations and ask whether consistent colony behavior is affected by nest site and weather. We examined variation among colonies in responsiveness to food baits and to disturbance, measured as a change in numbers of active ants, and in the speed with which colonies retrieved food and removed debris. Colonies differed consistently, across foraging and disturbance situations, in both responsiveness and speed. Increased activity in response to food was associated with a smaller decrease in response to alarm. Speed of retrieving food was correlated with speed of removing debris. In all colonies, speed was greater in dry conditions, reducing the amount of time ants spent outside the nest. While a colony occupied a certain nest site, its responsiveness was consistent in both foraging and disturbance situations, suggesting that nest structure influences colony personality.
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.
Directory of Open Access Journals (Sweden)
Milinkovitch Michel C
2007-11-01
Full Text Available Abstract Background Distance matrix methods constitute a major family of phylogenetic estimation methods, and the minimum evolution (ME principle (aiming at recovering the phylogeny with shortest length is one of the most commonly used optimality criteria for estimating phylogenetic trees. The major difficulty for its application is that the number of possible phylogenies grows exponentially with the number of taxa analyzed and the minimum evolution principle is known to belong to the NP MathType@MTEF@5@5@+=feaafiart1ev1aaatCvAUfKttLearuWrP9MDH5MBPbIqV92AaeXatLxBI9gBaebbnrfifHhDYfgasaacPC6xNi=xH8viVGI8Gi=hEeeu0xXdbba9frFj0xb9qqpG0dXdb9aspeI8k8fiI+fsY=rqGqVepae9pg0db9vqaiVgFr0xfr=xfr=xc9adbaqaaeGacaGaaiaabeqaaeqabiWaaaGcbaWenfgDOvwBHrxAJfwnHbqeg0uy0HwzTfgDPnwy1aaceaGae8xdX7Kaeeiuaafaaa@3888@-hard class of problems. Results In this paper, we introduce an Ant Colony Optimization (ACO algorithm to estimate phylogenies under the minimum evolution principle. ACO is an optimization technique inspired from the foraging behavior of real ant colonies. This behavior is exploited in artificial ant colonies for the search of approximate solutions to discrete optimization problems. Conclusion We show that the ACO algorithm is potentially competitive in comparison with state-of-the-art algorithms for the minimum evolution principle. This is the first application of an ACO algorithm to the phylogenetic estimation problem.
Gao, Wei
2016-05-01
The objective function of displacement back analysis for rock parameters in underground engineering is a very complicated nonlinear multiple hump function. The global optimization method can solve this problem very well. However, many numerical simulations must be performed during the optimization process, which is very time consuming. Therefore, it is important to improve the computational efficiency of optimization back analysis. To improve optimization back analysis, a new global optimization, immunized continuous ant colony optimization, is proposed. This is an improved continuous ant colony optimization using the basic principles of an artificial immune system and evolutionary algorithm. Based on this new global optimization, a new displacement optimization back analysis for rock parameters is proposed. The computational performance of the new back analysis is verified through a numerical example and a real engineering example. The results show that this new method can be used to obtain suitable parameters of rock mass with higher accuracy and less effort than previous methods. Moreover, the new back analysis is very robust.
Yeasts associated with the infrabuccal pocket and colonies of the carpenter ant Camponotus vicinus.
Mankowski, M E; Morrell, J J
2004-01-01
After scanning electron microscopy indicated that the infrabuccal pockets of carpenter ants (Camponotus vicinus) contained numerous yeast-like cells, yeast associations were examined in six colonies of carpenter ants from two locations in Benton County in western Oregon. Samples from the infrabuccal-pocket contents and worker ant exoskeletons, interior galleries of each colony, and detritus and soil around the colonies were plated on yeast-extract/ malt-extract agar augmented with 1 M hydrochloric acid and incubated at 25 C. Yeasts were identified on the basis of morphological characteristics and physiological attributes with the BIOLOG(®) microbial identification system. Yeast populations from carpenter ant nest material and material surrounding the nest differed from those obtained from the infrabuccal pocket. Debaryomyces polymorphus was isolated more often from the infrabuccal pocket than from other material. This species has also been isolated from other ant species, but its role in colony nutrition is unknown.
Ji, Junzhong; Song, Xiangjing; Liu, Chunnian; Zhang, Xiuzhen
2013-08-01
Community structure detection in complex networks has been intensively investigated in recent years. In this paper, we propose an adaptive approach based on ant colony clustering to discover communities in a complex network. The focus of the method is the clustering process of an ant colony in a virtual grid, where each ant represents a node in the complex network. During the ant colony search, the method uses a new fitness function to percept local environment and employs a pheromone diffusion model as a global information feedback mechanism to realize information exchange among ants. A significant advantage of our method is that the locations in the grid environment and the connections of the complex network structure are simultaneously taken into account in ants moving. Experimental results on computer-generated and real-world networks show the capability of our method to successfully detect community structures.
Colony variation in the collective regulation of foraging by harvester ants.
Gordon, Deborah M; Guetz, Adam; Greene, Michael J; Holmes, Susan
2011-03-01
This study investigates variation in collective behavior in a natural population of colonies of the harvester ant, Pogonomyrmex barbatus. Harvester ant colonies regulate foraging activity to adjust to current food availability; the rate at which inactive foragers leave the nest on the next trip depends on the rate at which successful foragers return with food. This study investigates differences among colonies in foraging activity and how these differences are associated with variation among colonies in the regulation of foraging. Colonies differ in the baseline rate at which patrollers leave the nest, without stimulation from returning ants. This baseline rate predicts a colony's foraging activity, suggesting there is a colony-specific activity level that influences how quickly any ant leaves the nest. When a colony's foraging activity is high, the colony is more likely to regulate foraging. Moreover, colonies differ in the propensity to adjust the rate of outgoing foragers to the rate of forager return. Naturally occurring variation in the regulation of foraging may lead to variation in colony survival and reproductive success.
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.
Directory of Open Access Journals (Sweden)
Yogeesha C.B
2014-09-01
Full Text Available The classical methods have limited scope in practical applications as some of them involve objective functions which are not continuous and/or differentiable. Evolutionary Computation is a subfield of artificial intelligence that involves combinatorial optimization problems. Travelling Salesperson Problem (TSP, which considered being a classic example for Combinatorial Optimization problem. It is said to be NP-Complete problem that cannot be solved conventionally particularly when number of cities increase. So Evolutionary techniques is the feasible solution to such problem. This paper explores an evolutionary technique: Geometric Hopfield Neural Network model to solve Travelling Salesperson Problem. Paper also achieves the results of Geometric TSP and compares the result with one of the existing widely used nature inspired heuristic approach Ant Colony Optimization Algorithms (ACA/ACO to solve Travelling Salesperson Problem.
Construction of Learning Path Using Ant Colony Optimization from a Frequent Pattern Graph
Directory of Open Access Journals (Sweden)
Souvik Sengupta
2011-11-01
Full Text Available In an e-Learning system a learner may come across multiple unknown terms, which are generally hyperlinked, while reading a text definition or theory on any topic. It becomes even harder when one tries to understand those unknown terms through further such links and they again find some new terms that have new links. As a consequence they get confused where to initiate from and what are the prerequisites. So it is very obvious for the learner to make a choice of what should be learnt before what. In this paper we have taken the data mining based frequent pattern graph model to define the association and sequencing between the words and then adopted the Ant Colony Optimization, an artificial intelligence approach, to derive a searching technique to obtain an efficient and optimized learning path to reach to a unknown term.
Ant colony optimization and neural networks applied to nuclear power plant monitoring
Energy Technology Data Exchange (ETDEWEB)
Santos, Gean Ribeiro dos; Andrade, Delvonei Alves de; Pereira, Iraci Martinez, E-mail: gean@usp.br, E-mail: delvonei@ipen.br, E-mail: martinez@ipen.br [Instituto de Pesquisas Energeticas e Nucleares (IPEN/CNEN-SP), Sao Paulo, SP (Brazil)
2015-07-01
A recurring challenge in production processes is the development of monitoring and diagnosis systems. Those systems help on detecting unexpected changes and interruptions, preventing losses and mitigating risks. Artificial Neural Networks (ANNs) have been extensively used in creating monitoring systems. Usually the ANNs created to solve this kind of problem are created by taking into account only parameters as the number of inputs, outputs, and hidden layers. The result networks are generally fully connected and have no improvements in its topology. This work intends to use an Ant Colony Optimization (ACO) algorithm to create a tuned neural network. The ACO search algorithm will use Back Error Propagation (BP) to optimize the network topology by suggesting the best neuron connections. The result ANN will be applied to monitoring the IEA-R1 research reactor at IPEN. (author)
Information cascade, Kirman's ant colony model, and kinetic Ising model
Hisakado, Masato
2014-01-01
In this paper, we discuss a voting model in which voters can obtain information from a finite number of previous voters. There exist three groups of voters: (i) digital herders and independent voters, (ii) analog herders and independent voters, and (iii) tanh-type herders. In our previous paper, we used the mean field approximation for case (i). In that study, if the reference number r is above three, phase transition occurs and the solution converges to one of the equilibria. In contrast, in the current study, the solution oscillates between the two equilibria, that is, good and bad equilibria. In this paper, we show that there is no phase transition when r is finite. If the annealing schedule is adequately slow from finite r to infinite r, the voting rate converges only to the good equilibrium. In case (ii), the state of reference votes is equivalent to that of Kirman's ant colony model, and it follows beta binomial distribution. In case (iii), we show that the model is equivalent to the finite-size kinetic...
Parallelization Strategies for Ant Colony Optimisation on GPUs
Cecilia, Jose M; Ujaldon, Manuel; Nisbet, Andy; Amos, Martyn
2011-01-01
Ant Colony Optimisation (ACO) is an effective population-based meta-heuristic for the solution of a wide variety of problems. As a population-based algorithm, its computation is intrinsically massively parallel, and it is there- fore theoretically well-suited for implementation on Graphics Processing Units (GPUs). The ACO algorithm comprises two main stages: Tour construction and Pheromone update. The former has been previously implemented on the GPU, using a task-based parallelism approach. However, up until now, the latter has always been implemented on the CPU. In this paper, we discuss several parallelisation strategies for both stages of the ACO algorithm on the GPU. We propose an alternative data-based parallelism scheme for Tour construction, which fits better on the GPU architecture. We also describe novel GPU programming strategies for the Pheromone update stage. Our results show a total speed-up exceeding 28x for the Tour construction stage, and 20x for Pheromone update, and suggest that ACO is a po...
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.
Enhanced ant colony optimization for inventory routing problem
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.
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.
Ant colony optimization for solving university facility layout problem
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).
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.
A Novel Polymorphic Ant Colony -Based Clustering Mechanism for Wireless Sensor Networks
Directory of Open Access Journals (Sweden)
Min Xiang
2012-10-01
Full Text Available In wireless sensor networks, sensor nodes are extremely power constrained, so energy efficient clustering mechanism is mainly considered in the network topology management. A new clustering mechanism based on the polymorphic ant colony (PAC is designed for dynamically controlling the networks clustering structure. According to different functions, the nodes of the networks are respectively defined as the queen ant, the scout ant and worker ant. Based on the calculated cost function and real-time pheromone, the queen ant restructures an optimum clustering structure. Furthermore, the worker ants and the scout ants can send or receive sensing data with optional communication path based on their pheromones. With the mechanism, the energy consumption in inter-cluster and intra-cluster communication for the worker ants and scout ants can be reduced. The simulation results demonstrate that the proposed mechanism can effectively remodel the clustering structure and improve the energy efficiency of the networks.
Amador-Vargas, Sabrina; Gronenberg, Wulfila; Wcislo, William T; Mueller, Ulrich
2015-02-22
Group size in both multicellular organisms and animal societies can correlate with the degree of division of labour. For ants, the task specialization hypothesis (TSH) proposes that increased behavioural specialization enabled by larger group size corresponds to anatomical specialization of worker brains. Alternatively, the social brain hypothesis proposes that increased levels of social stimuli in larger colonies lead to enlarged brain regions in all workers, regardless of their task specialization. We tested these hypotheses in acacia ants (Pseudomyrmex spinicola), which exhibit behavioural but not morphological task specialization. In wild colonies, we marked, followed and tested ant workers involved in foraging tasks on the leaves (leaf-ants) and in defensive tasks on the host tree trunk (trunk-ants). Task specialization increased with colony size, especially in defensive tasks. The relationship between colony size and brain region volume was task-dependent, supporting the TSH. Specifically, as colony size increased, the relative size of regions within the mushroom bodies of the brain decreased in trunk-ants but increased in leaf-ants; those regions play important roles in learning and memory. Our findings suggest that workers specialized in defence may have reduced learning abilities relative to leaf-ants; these inferences remain to be tested. In societies with monomorphic workers, brain polymorphism enhanced by group size could be a mechanism by which division of labour is achieved.
Pezon, Antoine; Denis, Damien; Cerdan, Philippe; Valenzuela, Jorge; Fresneau, Dominique
2005-01-01
In ants, nest relocations are frequent but nevertheless perilous, especially for the reproductive caste. During emigrations, queens are exposed to predation and face the risk of becoming lost. Therefore the optimal strategy should be to move the queen(s) swiftly to a better location, while maintaining maximum worker protection at all times in the new and old nests. The timing of that event is a crucial strategic issue for the colony and may depend on queen number. In monogynous colonies, the queen is vital for colony survival, whereas in polygynous colonies a queen is less essential, if not dispensable. We tested the null hypothesis that queen movement occurs at random within the sequence of emigration events in both monogynous and polygynous colonies of the ponerine ant Pachycondyla obscuricornis. Our study, based on 16 monogynous and 16 polygynous colony emigrations, demonstrates for the first time that regardless of the number of queens per colony, the emigration serial number of a queen occurs in the middle of all emigration events and adult ant emigration events, but not during brood transport events. It therefore appears that the number of workers in both nests plays an essential role in the timing of queen movement. Our results correspond to a robust colony-level strategy since queen emigration is related neither to colony size nor to queen number. Such an optimal strategy is characteristic of ant societies working as highly integrated units and represents a new instance of group-level adaptive behaviors in social insect colonies.
Polygynous supercolonies of the acacia-ant Pseudomyrmex peperi, an inferior colony founder.
Kautz, S; Pauls, S U; Ballhorn, D J; Lumbsch, H T; Heil, M
2009-12-01
In ant-plant protection mutualisms, plants provide nesting space and nutrition to defending ants. Several plant-ants are polygynous. Possessing more than one queen per colony can reduce nestmate relatedness and consequently the inclusive fitness of workers. Here, we investigated the colony structure of the obligate acacia-ant Pseudomyrmex peperi, which competes for nesting space with several congeneric and sympatric species. Pseudomyrmex peperi had a lower colony founding success than its congeners and thus, appears to be competitively inferior during the early stages of colony development. Aggression assays showed that P. peperi establishes distinct, but highly polygynous supercolonies, which can inhabit large clusters of host trees. Analysing queens, workers, males and virgin queens from two supercolonies with eight polymorphic microsatellite markers revealed a maximum of three alleles per locus within a colony and, thus, high relatedness among nestmates. Colonies had probably been founded by one singly mated queen and supercolonies resulted from intranidal mating among colony-derived males and daughter queens. This strategy allows colonies to grow by budding and to occupy individual plant clusters for time spans that are longer than an individual queen's life. Ancestral states reconstruction indicated that polygyny represents the derived state within obligate acacia-ants. We suggest that the extreme polygyny of Pseudomyrmex peperi, which is achieved by intranidal mating and thereby maintains high nestmate relatedness, might play an important role for species coexistence in a dynamic and competitive habitat.
Text clustering based on fusion of ant colony and genetic algorithms
Institute of Scientific and Technical Information of China (English)
Yun ZHANG; Boqin FENG; Shouqiang MA; Lianmeng LIU
2009-01-01
Focusing on the problem that the ant colony algorithm gets into stagnation easily and cannot fully search in solution space,a text clustering approach based on the fusion of the ant colony and genetic algorithms is proposed.The four parameters that influence the performance of the ant colony algorithm are encoded as chromosomes,thereby the fitness function,selection,crossover and mutation operator are designed to find the combination of optimal parameters through a number of iteration,and then it is applied to text clustering.The simulation.results show that compared with the classical k-means clustering and the basic ant colony clustering algorithm,the proposed algorithm has better performance and the value of F-Measure is enhanced by 5.69%,48.60% and 69.60%,respectively,in 3 test datasets.Therefore,it is more suitable for processing a larger dataset.
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.
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.
A Survey Paper on Solving TSP using Ant Colony Optimization on GPU
Directory of Open Access Journals (Sweden)
Khushbu Khatri
2014-12-01
Full Text Available Ant Colony Optimization (ACO is meta-heuristic algorithm inspired from nature to solve many combinatorial optimization problem such as Travelling Salesman Problem (TSP. There are many versions of ACO used to solve TSP like, Ant System, Elitist Ant System, Max-Min Ant System, Rank based Ant System algorithm. For improved performance, these methods can be implemented in parallel architecture like GPU, CUDA architecture. Graphics Processing Unit (GPU provides highly parallel and fully programmable platform. GPUs which have many processing units with an off-chip global memory can be used for general purpose parallel computation. This paper presents a survey on different solving TSP using ACO on GPU.
A SURVEY PAPER ON SOLVING TSP USING ANT COLONY OPTIMIZATION ON GPU
Directory of Open Access Journals (Sweden)
Khushbu khatri
2015-10-01
Full Text Available Ant Colony Optimization (ACO is meta-heuristic algorithm inspired from nature to solve many combinatorial optimization problem such as Travelling Salesman Problem (TSP. There are many versions of ACO used to solve TSP like, Ant System, Elitist Ant System, Max-Min Ant System, Rank based Ant System algorithm. For improved performance, these methods can be implemented in parallel architecture like GPU, CUDA architecture. Graphics Processing Unit (GPU provides highly parallel and fully programmable platform. GPUs which have many processing units with an off-chip global memory can be used for general purpose parallel computation. This paper presents a survey on different solving TSP using ACO on GPU.
A review on the ant colony optimization metaheuristic: basis, models and new trends
2002-01-01
Ant Colony Optimization (ACO) is a recent metaheuristic method that is inspired by the behavior of real ant colonies. In this paper, we review the underlying ideas of this approach that lead from the biological inspiration to the ACO metaheuristic, which gives a set of rules of how to apply ACO algorithms to challenging combinatorial problems. We present some of the algorithms that were developed under this framework, give an overview of current applications, and analyze ...
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.
Artificial ants deposit pheromone to search for regulatory DNA elements
Directory of Open Access Journals (Sweden)
Liu Yunlong
2006-08-01
Full Text Available Abstract Background Identification of transcription-factor binding motifs (DNA sequences can be formulated as a combinatorial problem, where an efficient algorithm is indispensable to predict the role of multiple binding motifs. An ant algorithm is a biology-inspired computational technique, through which a combinatorial problem is solved by mimicking the behavior of social insects such as ants. We developed a unique version of ant algorithms to select a set of binding motifs by considering a potential contribution of each of all random DNA sequences of 4- to 7-bp in length. Results Human chondrogenesis was used as a model system. The results revealed that the ant algorithm was able to identify biologically known binding motifs in chondrogenesis such as AP-1, NFκB, and sox9. Some of the predicted motifs were identical to those previously derived with the genetic algorithm. Unlike the genetic algorithm, however, the ant algorithm was able to evaluate a contribution of individual binding motifs as a spectrum of distributed information and predict core consensus motifs from a wider DNA pool. Conclusion The ant algorithm offers an efficient, reproducible procedure to predict a role of individual transcription-factor binding motifs using a unique definition of artificial ants.
DEFF Research Database (Denmark)
Ouagoussounon, Issa; Sinzogan, Antonio; Offenberg, Joachim;
2013-01-01
Oecophylla ants are currently used for biological control in fruit plantations in Australia, Asia and Africa and for protein production in Asia. To further improve the technology and implement it on a large scale, effective and fast production of live colonies is desirable. Early colony developme...
Extreme queen-mating frequency and colony fission in African army ants
DEFF Research Database (Denmark)
Kronauer, Daniel J C; Schoning, Caspar; Pedersen, Jes S
2004-01-01
, which have so far been regarded as odd exceptions within the social Hymenoptera. Army ants and honeybees are fundamentally different in morphology and life history, but are the only social insects known that combine obligate multiple mating with reproduction by colony fission and extremely male......-mating frequencies reported so far for ants. This confirms that obligate multiple queen-mating in social insects is associated with large colony size and advanced social organization, but also raises several novel questions. First, these high estimates place army ants in the range of mating frequencies of honeybees...
Ant Colony Optimization with Memory and Its Application to Traveling Salesman Problem
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.
Identification of Dynamic Parameters Based on Pseudo-Parallel Ant Colony Optimization Algorithm
Institute of Scientific and Technical Information of China (English)
ZHAO Feng-yao; MA Zhen-yue; ZHANG Yun-liang
2007-01-01
For the parameter identification of dynamic problems, a pseudo-parallel ant colony optimization (PPACO) algorithm based on graph-based ant system (AS) was introduced. On the platform of ANSYS dynamic analysis, the PPACO algorithm was applied to the identification of dynamic parameters successfully. Using simulated data of forces and displacements, elastic modulus E and damping ratio ξ was identified for a designed 3D finite element model, and the detailed identification step was given. Mathematical example and simulation example show that the proposed method has higher precision, faster convergence speed and stronger antinoise ability compared with the standard genetic algorithm and the ant colony optimization (ACO) algorithms.
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.
Directory of Open Access Journals (Sweden)
Lamiaa F. Ibrahim
2011-01-01
Full Text Available Problem statement: The process of network planning is divided into two sub steps. The first step is determining the location of the Multi Service Access Node (MSAN. The second step is the construction of subscriber network lines from MSAN to subscribers to satisfy optimization criteria and design constraints. Due to the complexity of this process artificial intelligence and clustering techniques have been successfully deployed to solve many problems. The problems of the locations of MSAN, the cabling layout and the computation of optimum cable network layouts have been addressed in this study. The proposed algorithm, Clustering density-Based Spatial of Applications with Noise original, minimal Spanning tree and modified Ant-Colony-Based algorithm (CBSCAN-SPANT, used two clustering algorithms which are density-based and agglomerative clustering algorithm using distances which are shortest paths distance and satisfying the network constraints. This algorithm used wire and wireless technology to serve the subscribers demand and place the switches in a real optimal place. Approach: The density-based Spatial Clustering of Applications with Noise original (DBSCAN algorithm has been modified and a new algorithm (NetPlan algorithm has been proposed by the author in a recent work to solve the first step in the problem of network planning. In the present study, the NetPlan algorithm is modified by introduce the modified Ant-Colony-Based algorithm to find the optimal path between any node and the corresponding MSAN node in the first step of network planning process to determine nodes belonging to each cluster. The second step, in the process of network planning, is also introduced in the present study. For each cluster, the optimal cabling layout from each MSAN to the subscriber premises is determining by introduce the Prime algorithm which construct minimal spanning tree. Results: Experimental results and analysis indicate that the
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.
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.
The rewards of restraint in the collective regulation of foraging by harvester ant colonies.
Gordon, Deborah M
2013-06-01
Collective behaviour, arising from local interactions, allows groups to respond to changing conditions. Long-term studies have shown that the traits of individual mammals and birds are associated with their reproductive success, but little is known about the evolutionary ecology of collective behaviour in natural populations. An ant colony operates without central control, regulating its activity through a network of local interactions. This work shows that variation among harvester ant (Pogonomyrmex barbatus) colonies in collective response to changing conditions is related to variation in colony lifetime reproductive success in the production of offspring colonies. Desiccation costs are high for harvester ants foraging in the desert. More successful colonies tend to forage less when conditions are dry, and show relatively stable foraging activity when conditions are more humid. Restraint from foraging does not compromise a colony's long-term survival; colonies that fail to forage at all on many days survive as long, over the colony's 20-30-year lifespan, as those that forage more regularly. Sensitivity to conditions in which to reduce foraging activity may be transmissible from parent to offspring colony. These results indicate that natural selection is shaping the collective behaviour that regulates foraging activity, and that the selection pressure, related to climate, may grow stronger if the current drought in their habitat persists.
A cuckoo-like parasitic moth leads African weaver ant colonies to their ruin.
Dejean, Alain; Orivel, Jérôme; Azémar, Frédéric; Hérault, Bruno; Corbara, Bruno
2016-03-29
In myrmecophilous Lepidoptera, mostly lycaenids and riodinids, caterpillars trick ants into transporting them to the ant nest where they feed on the brood or, in the more derived "cuckoo strategy", trigger regurgitations (trophallaxis) from the ants and obtain trophic eggs. We show for the first time that the caterpillars of a moth (Eublemma albifascia; Noctuidae; Acontiinae) also use this strategy to obtain regurgitations and trophic eggs from ants (Oecophylla longinoda). Females short-circuit the adoption process by laying eggs directly on the ant nests, and workers carry just-hatched caterpillars inside. Parasitized colonies sheltered 44 to 359 caterpillars, each receiving more trophallaxis and trophic eggs than control queens. The thus-starved queens lose weight, stop laying eggs (which transport the pheromones that induce infertility in the workers) and die. Consequently, the workers lay male-destined eggs before and after the queen's death, allowing the colony to invest its remaining resources in male production before it vanishes.
A Hybrid Routing Algorithm Based on Ant Colony and ZHLS Routing Protocol for MANET
Rafsanjani, Marjan Kuchaki; Asadinia, Sanaz; Pakzad, Farzaneh
Mobile Ad hoc networks (MANETs) require dynamic routing schemes for adequate performance. This paper, presents a new routing algorithm for MANETs, which combines the idea of ant colony optimization with Zone-based Hierarchical Link State (ZHLS) protocol. Ant colony optimization (ACO) is a class of Swarm Intelligence (SI) algorithms. SI is the local interaction of many simple agents to achieve a global goal. SI is based on social insect for solving different types of problems. ACO algorithm uses mobile agents called ants to explore network. Ants help to find paths between two nodes in the network. Our algorithm is based on ants jump from one zone to the next zones which contains of the proactive routing within a zone and reactive routing between the zones. Our proposed algorithm improves the performance of the network such as delay, packet delivery ratio and overhead than traditional routing algorithms.
The Role of Non-Foraging Nests in Polydomous Wood Ant Colonies.
Ellis, Samuel; Robinson, Elva J H
2015-01-01
A colony of red wood ants can inhabit more than one spatially separated nest, in a strategy called polydomy. Some nests within these polydomous colonies have no foraging trails to aphid colonies in the canopy. In this study we identify and investigate the possible roles of non-foraging nests in polydomous colonies of the wood ant Formica lugubris. To investigate the role of non-foraging nests we: (i) monitored colonies for three years; (ii) observed the resources being transported between non-foraging nests and the rest of the colony; (iii) measured the amount of extra-nest activity around non-foraging and foraging nests. We used these datasets to investigate the extent to which non-foraging nests within polydomous colonies are acting as: part of the colony expansion process; hunting and scavenging specialists; brood-development specialists; seasonal foragers; or a selfish strategy exploiting the foraging effort of the rest of the colony. We found that, rather than having a specialised role, non-foraging nests are part of the process of colony expansion. Polydomous colonies expand by founding new nests in the area surrounding the existing nests. Nests founded near food begin foraging and become part of the colony; other nests are not founded near food sources and do not initially forage. Some of these non-foraging nests eventually begin foraging; others do not and are abandoned. This is a method of colony growth not available to colonies inhabiting a single nest, and may be an important advantage of the polydomous nesting strategy, allowing the colony to expand into profitable areas.
Breeding system, colony and population structure in the weaver ant Oecophylla smaragdina.
Schlüns, E A; Wegener, B J; Schlüns, H; Azuma, N; Robson, S K A; Crozier, R H
2009-01-01
Weaver ants (Oecophylla smaragdina) are dominant ants in open forests from India, Australia, China and Southeast Asia, whose leaf nests are held together with larval silk. The species, together with its sole congener O. longinoda, has been important in research on biological control, communication, territoriality and colony integration. Over most of the range, only one queen has been found per colony, but the occurrence of several queens per nest has been reported for the Australian Northern Territory. The number of males mating with each queen is little known. Here we report on the colony structure of O. smaragdina using published and new microsatellite markers. Worker genotype arrays reflect the occurrence of habitual polygyny (more than one queen per colony) in 18 colonies from Darwin, Northern Australia, with up to five queens inferred per colony. Monogyny (one queen per colony) with occasional polygyny was inferred for 14 colonies from Queensland, Australia, and 20 colonies from Java, Indonesia. Direct genotyping of the sperm carried by 77 Queensland queens and worker genotypic arrays of established colonies yielded similar results, indicating that less than half of the queens mate only once and some mate up to five times. Worker genotype arrays indicated that queens from Java and the Northern Territory also often mate with more than one male, but less often than those from Queensland. A strong isolation-by-distance effect was found for Queensland samples. The variation uncovered means that O. smaragdina is a more versatile study system than previously supposed.
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...
Ant colonies prefer infected over uninfected nest sites
DEFF Research Database (Denmark)
Pontieri, Luigi; Vojvodic, Svjetlana; Graham, Riley
2014-01-01
and low genetic diversity, likely making these species particularly vulnerable to parasites and diseases. We investigated the nest site preference of the invasive pharaoh ant, Monomorium pharaonis, through binary choice tests between three nest types: nests containing dead nestmates overgrown...
An Ant Colony Optimization Based Dimension Reduction Method for High-Dimensional Datasets
Institute of Scientific and Technical Information of China (English)
Ying Li; Gang Wang; Huiling Chen; Lian Shi; Lei Qin
2013-01-01
In this paper,a bionic optimization algorithm based dimension reduction method named Ant Colony Optimization -Selection (ACO-S) is proposed for high-dimensional datasets.Because microarray datasets comprise tens of thousands of features (genes),they are usually used to test the dimension reduction techniques.ACO-S consists of two stages in which two well-known ACO algorithms,namely ant system and ant colony system,are utilized to seek for genes,respectively.In the first stage,a modified ant system is used to filter the nonsignificant genes from high-dimensional space,and a number of promising genes are reserved in the next step.In the second stage,an improved ant colony system is applied to gene selection.In order to enhance the search ability of ACOs,we propose a method for calculating priori available heuristic information and design a fuzzy logic controller to dynamically adjust the number of ants in ant colony system.Furthermore,we devise another fuzzy logic controller to tune the parameter (q0) in ant colony system.We evaluate the performance of ACO-S on five microarray datasets,which have dimensions varying from 7129 to 12000.We also compare the performance of ACO-S with the results obtained from four existing well-known bionic optimization algorithms.The comparison results show that ACO-S has a notable ability to generate a gene subset with the smallest size and salient features while yielding high classification accuracy.The comparative results generated by ACO-S adopting different classifiers are also given.The proposed method is shown to be a promising and effective tool for mining high-dimension data and mobile robot navigation.
Blochmannia endosymbionts improve colony growth and immune defence in the ant Camponotus fellah
Directory of Open Access Journals (Sweden)
Depoix Delphine
2009-02-01
Full Text Available Abstract Background Microorganisms are a large and diverse form of life. Many of them live in association with large multicellular organisms, developing symbiotic relations with the host and some have even evolved to form obligate endosymbiosis 1. All Carpenter ants (genus Camponotus studied hitherto harbour primary endosymbiotic bacteria of the Blochmannia genus. The role of these bacteria in ant nutrition has been demonstrated 2 but the omnivorous diet of these ants lead us to hypothesize that the bacteria might provide additional advantages to their host. In this study, we establish links between Blochmannia, growth of starting new colonies and the host immune response. Results We manipulated the number of bacterial endosymbionts in incipient laboratory-reared colonies of Camponotus fellah by administrating doses of an antibiotic (Rifampin mixed in honey-solution. Efficiency of the treatment was estimated by quantitative polymerase chain reaction and Fluorescent in situ hybridization (FISH, using Blochmannia specific primers (qPCR and two fluorescent probes (one for all Eubacterial and other specific for Blochmannia. Very few or no bacteria could be detected in treated ants. Incipient Rifampin treated colonies had significantly lower numbers of brood and adult workers than control colonies. The immune response of ants from control and treated colonies was estimated by inserting nylon filaments in the gaster and removing it after 24 h. In the control colonies, the encapsulation response was positively correlated to the bacterial amount, while no correlation was observed in treated colonies. Indeed, antibiotic treatment increased the encapsulation response of the workers, probably due to stress conditions. Conclusion The increased growth rate observed in non-treated colonies confirms the importance of Blochmannia in this phase of colony development. This would provide an important selective advantage during colony founding, where the colonies
Akpinar, Sener; Mirac Bayhan, G.
2014-06-01
The aim of this article is to compare the performances of iterative ant colony optimization (ACO)-based solution strategies on a mixed-model assembly line balancing problem of type II (MMALBP-II) by addressing some particular features of real-world assembly line balancing problems such as parallel workstations and zoning constraints. To solve the problem, where the objective is to minimize the cycle time (i.e. maximize the production rate) for a predefined number of workstations in an existing assembly line, two ACO-based approaches which differ in the mission assigned to artificial ants are used. Furthermore, each ACO-based approach is conducted with two different pheromone release strategies: global and local pheromone updating rules. The four ACO-based approaches are used for solving 20 representative MMALBP-II to compare their performance in terms of computational time and solution quality. Detailed comparison results are presented.
Directory of Open Access Journals (Sweden)
Puneet Rai
2014-02-01
Full Text Available Ant Colony Optimization (ACO is nature inspired algorithm based on foraging behavior of ants. The algorithm is based on the fact how ants deposit pheromone while searching for food. ACO generates a pheromone matrix which gives the edge information present at each pixel position of image, formed by ants dispatched on image. The movement of ants depends on local variance of image's intensity value. This paper proposes an improved method based on heuristic which assigns weight to the neighborhood. Thus by assigning the weights or priority to the neighboring pixels, the ant decides in which direction it can move. The method is applied on Medical images and experimental results are provided to support the superior performance of the proposed approach and the existing method.
Hayashi, Masayuki; Nomura, Masashi
2014-08-01
In ant-aphid mutualisms, ants usually attack and exclude enemies of aphids. However, larvae of the green lacewing Mallada desjardinsi (Navas) prey on ant-tended aphids without being excluded by ants; these larvae protect themselves from ants by carrying aphid carcasses on their backs. Eggs of M. desjardinsi laid at the tips of stalks have also been observed in ant-tended aphid colonies in the field. Here, we examined whether the egg stalks of M. desjardinsi protect the eggs from ants and predators. When exposed to ants, almost all eggs with intact stalks were untouched, whereas 50-80% of eggs in which stalks had been severed at their bases were destroyed by ants. In contrast, most eggs were preyed upon by larvae of the lacewing Chrysoperla nipponensis (Okamoto), an intraguild predator of M. desjardinsi, regardless of whether their stalks had been severed. These findings suggest that egg stalks provide protection from ants but not from C. nipponensis larvae. To test whether M. desjardinsi eggs are protected from predators by aphid-tending ants, we introduced C. nipponensis larvae onto plants colonized by ant-tended aphids. A significantly greater number of eggs survived in the presence of ants because aphid-tending ants excluded larvae of C. nipponensis. This finding indicates that M. desjardinsi eggs are indirectly protected from predators by ants in ant-tended aphid colonies.
A Graph-based Ant Colony Optimization Approach for Integrated Process Planning and Scheduling
Institute of Scientific and Technical Information of China (English)
Jinfeng Wang; Xiaoliang Fan; Chaowei Zhang; Shuting Wan
2014-01-01
This paper considers an ant colony optimization algorithm based on AND/OR graph for integrated process planning and scheduling (IPPS). General y, the process planning and scheduling are studied separately. Due to the complexity of manufacturing system, IPPS combining both process planning and scheduling can depict the real situation of a manufacturing system. The IPPS is represented on AND/OR graph consisting of nodes, and undirected and directed arcs. The nodes denote operations of jobs, and undirected/directed arcs denote possible visiting path among the nodes. Ant colony goes through the necessary nodes on the graph from the starting node to the end node to obtain the optimal solution with the objective of minimizing makespan. In order to avoid local convergence and low convergence, some improved strategy is incorporated in the standard ant colony optimiza-tion algorithm. Extensive computational experiments are carried out to study the influence of various parameters on the system performance.
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 annealing provides a good initial solution for ant colony optimization. In the second phase, Iterative Local Search (ILS method is employed to seeking the close-to-optimal solution in local scope based on the capacity of the vehicle. Experimental results show that the proposed algorithm is superior to original ant colony optimization and simulated annealing separately reported on partial benchmark problems.
Application of ant colony algorithm in plant leaves classification based on infrared spectroscopy
Guo, Tiantai; Hong, Bo; Kong, Ming; Zhao, Jun
2014-04-01
This paper proposes to use ant colony algorithm in the analysis of spectral data of plant leaves to achieve the best classification of different plants within a short time. Intelligent classification is realized according to different components of featured information included in near infrared spectrum data of plants. The near infrared diffusive emission spectrum curves of the leaves of Cinnamomum camphora and Acer saccharum Marsh are acquired, which have 75 leaves respectively, and are divided into two groups. Then, the acquired data are processed using ant colony algorithm and the same kind of leaves can be classified as a class by ant colony clustering algorithm. Finally, the two groups of data are classified into two classes. Experiment results show that the algorithm can distinguish different species up to the percentage of 100%. The classification of plant leaves has important application value in agricultural development, research of species invasion, floriculture etc.
Institute of Scientific and Technical Information of China (English)
Wang Yanxia; Qian Longjun; Guo Zhi; Ma Lifeng
2008-01-01
A weapon target assignment (WTA) model satisfying expected damage probabilities with an ant colony algorithm is proposed.In order to save armament resource and attack the targets effectively,the strategy of the weapon assignment is that the target with greater threat degree has higher priority to be intercepted.The effect of this WTA model is not maximizing the damage probability but satisfying the whole assignment result.Ant colony algorithm has been successfully used in many fields,especially in combination optimization.The ant colony algorithm for this WTA problem is described by analyzing path selection,pheromone update,and tabu table update.The effectiveness of the model and the algorithm is demonstrated with an example.
Application of Modified Ant Colony Optimization (MACO for Multicast Routing Problem
Directory of Open Access Journals (Sweden)
Sudip Kumar Sahana
2016-04-01
Full Text Available It is well known that multicast routing is combinatorial problem finds the optimal path between source destination pairs. Traditional approaches solve this problem by establishment of the spanning tree for the network which is mapped as an undirected weighted graph. This paper proposes a Modified Ant Colony Optimization (MACO algorithm which is based on Ant Colony System (ACS with some modification in the configuration of starting movement and in local updation technique to overcome the basic limitations of ACS such as poor initialization and slow convergence rate. It is shown that the proposed Modified Ant Colony Optimization (MACO shows better convergence speed and consumes less time than the conventional ACS to achieve the desired solution.
Directory of Open Access Journals (Sweden)
L.Yang
2015-12-01
Full Text Available Three-dimensional path planning for underwater vehicles is an important problem that focuses on optimizing the route with consideration of various constraints in a complex underwater environment. In this paper, an improved ant colony optimization (IACO algorithm based on pheromone exclusion is proposed to solve the underwater vehicle 3D path planning problem. The IACO algorithm can balance the tasks of exploration and development in the ant search path, and enable the ants in the search process to explore initially and develop subsequently. Then, the underwater vehicle can find the safe path by connecting the chosen nodes of the 3D mesh while avoiding the threat area. This new approach can overcome common disadvantages of the basic ant colony algorithm, such as falling into local extremum, poor quality, and low accuracy. Experimental comparative results demonstrate that this proposed IACO method is more effective and feasible in underwater vehicle 3D path planning than the basic ACO model.
Blackboard Mechanism Based Ant Colony Theory for Dynamic Deployment of Mobile Sensor Networks
Institute of Scientific and Technical Information of China (English)
Guang-ping Qi; Ping Song; Ke-jie Li
2008-01-01
A novel bionic swarm intelligence algorithm, called ant colony algorithm based on a blackboard mechanism, is proposed to solve the autonomy and dynamic deployment of mobiles sensor networks effectively. A blackboard mechanism is introduced into the system for making pheromone and completing the algorithm. Every node, which can be looked as an ant, makes one information zone in its memory for communicating with other nodes and leaves pheromone, which is created by ant itself in nature. Then ant colony theory is used to find the optimization scheme for path planning and deployment of mobile Wireless Sensor Network (WSN). We test the algorithm in a dynamic and unconfigurable environment. The results indicate that the algorithm can reduce the power consumption by 13% averagely, enhance the efficiency of path planning and deployment of mobile WSN by 15% averagely.
Mechanisms of social regulation change across colony development in an ant
Directory of Open Access Journals (Sweden)
Liebig Jürgen
2010-10-01
Full Text Available Abstract Background Mutual policing is an important mechanism for reducing conflict in cooperative groups. In societies of ants, bees, and wasps, mutual policing of worker reproduction can evolve when workers are more closely related to the queen's sons than to the sons of workers or when the costs of worker reproduction lower the inclusive fitness of workers. During colony growth, relatedness within the colony remains the same, but the costs of worker reproduction may change. The costs of worker reproduction are predicted to be greatest in incipient colonies. If the costs associated with worker reproduction outweigh the individual direct benefits to workers, policing mechanisms as found in larger colonies may be absent in incipient colonies. Results We investigated policing behaviour across colony growth in the ant Camponotus floridanus. In large colonies of this species, worker reproduction is policed by the destruction of worker-laid eggs. We found workers from incipient colonies do not exhibit policing behaviour, and instead tolerate all conspecific eggs. The change in policing behaviour is consistent with changes in egg surface hydrocarbons, which provide the informational basis for policing; eggs laid by queens from incipient colonies lack the characteristic hydrocarbons on the surface of eggs laid by queens from large colonies, making them chemically indistinguishable from worker-laid eggs. We also tested the response to fertility information in the context of queen tolerance. Workers from incipient colonies attacked foreign queens from large colonies; whereas workers from large colonies tolerated such queens. Workers from both incipient and large colonies attacked foreign queens from incipient colonies. Conclusions Our results provide novel insights into the regulation of worker reproduction in social insects at both the proximate and ultimate levels. At the proximate level, our results show that mechanisms of social regulation, such as
An ant colony based resilience approach to cascading failures in cluster supply network
Wang, Yingcong; Xiao, Renbin
2016-11-01
Cluster supply chain network is a typical complex network and easily suffers cascading failures under disruption events, which is caused by the under-load of enterprises. Improving network resilience can increase the ability of recovery from cascading failures. Social resilience is found in ant colony and comes from ant's spatial fidelity zones (SFZ). Starting from the under-load failures, this paper proposes a resilience method to cascading failures in cluster supply chain network by leveraging on social resilience of ant colony. First, the mapping between ant colony SFZ and cluster supply chain network SFZ is presented. Second, a new cascading model for cluster supply chain network is constructed based on under-load failures. Then, the SFZ-based resilience method and index to cascading failures are developed according to ant colony's social resilience. Finally, a numerical simulation and a case study are used to verify the validity of the cascading model and the resilience method. Experimental results show that, the cluster supply chain network becomes resilient to cascading failures under the SFZ-based resilience method, and the cluster supply chain network resilience can be enhanced by improving the ability of enterprises to recover and adjust.
Directory of Open Access Journals (Sweden)
Thenmozhi Srinivasan
2015-01-01
Full Text Available Clusters of high-dimensional data techniques are emerging, according to data noisy and poor quality challenges. This paper has been developed to cluster data using high-dimensional similarity based PCM (SPCM, with ant colony optimization intelligence which is effective in clustering nonspatial data without getting knowledge about cluster number from the user. The PCM becomes similarity based by using mountain method with it. Though this is efficient clustering, it is checked for optimization using ant colony algorithm with swarm intelligence. Thus the scalable clustering technique is obtained and the evaluation results are checked with synthetic datasets.
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.
Adaptive Ant Colony Algorithm for the VRP Solution of Logistics Distribution
Directory of Open Access Journals (Sweden)
Yu-Ping Wang
2013-06-01
Full Text Available In order to conquer the premature convergence problem and lower the cost of computing of the basic Ant Colony Algorithm (ACA, we present an adaptive ant colony algorithm, named AACA, coupled with a Pareto Local Search (PLS algorithm and apply to the Vehicle Routing Problem (VRP and Capacitated VRP (CVRP. By using the information entropy, the algorithm adjusts the pheromone updating strategy adaptively. Experiments on various aspects of the algorithm and computational results for some benchmark problems are reported. We compare our approach with some classic, powerful meta-heuristics and show that the proposed approach can obtain the better quality of the solutions.
DEFF Research Database (Denmark)
van Zweden, Jelle Stijn; Brask, Josefine B.; Christensen, Jan H.
2010-01-01
members to create a Gestalt odour. Although earlier studies have established that hydrocarbon profiles are influenced by heritable factors, transfer among nestmates and additional environmental factors, no studies have quantified these relative contributions for separate compounds. Here, we use the ant...... discrimination or as nestmate recognition cues. These results indicate that heritable compounds are suitable for establishing a genetic Gestalt for efficient nestmate recognition, but that recognition cues within colonies are insufficiently distinct to allow nepotistic kin discrimination....
A Preliminary Study of Automatic Delineation of Eyes on CT Images Using Ant Colony Optimization
Institute of Scientific and Technical Information of China (English)
LI Yong-jie; XIE Wei-fu; YAO De-zhong
2007-01-01
Eyes are important organs-at-risk (OARs) that should be protected during the radiation treatment of those head tumors. Correct delineation of the eyes on CT images is one of important issues for treatment planning to protect the eyes as much as possible. In this paper, we propose a new method, named ant colony optimization (ACO), to delineate the eyes automatically.In the proposed algorithm, each ant tries to find a closed path, and some pheromone is deposited on the visited path when the ant finds a path. After all ants finish a circle, the best ant will lay some pheromone to enforce the best path. The proposed algorithm is verified on several CT images, and the preliminary results demonstrate the feasibility of ACO for the delineation problem.
Orthogonal Methods Based Ant Colony Search for Solving Continuous Optimization Problems
Institute of Scientific and Technical Information of China (English)
Xiao-Min Hu; Jun Zhang; Yun Li
2008-01-01
Research into ant colony algorithms for solving continuous optimization problems forms one of the most significant and promising areas in swarm computation. Although traditional ant algorithms are designed for combinatorial optimization, they have shown great potential in solving a wide range of optimization problems, including continuous optimization. Aimed at solving continuous problems effectively, this paper develops a novel ant algorithm termed "continuous orthogonal ant colony" (COAC), whose pheromone deposit mechanisms would enable ants to search for solutions collaboratively and effectively. By using the orthogonal design method, ants in the feasible domain can explore their chosen regions rapidly and efficiently. By implementing an "adaptive regional radius" method, the proposed algorithm can reduce the probability of being trapped in local optima and therefore enhance the global search capability and accuracy. An elitist strategy is also employed to reserve the most valuable points. The performance of the COAC is compared with two other ant algorithms for continuous optimization -- API and CACO by testing seventeen functions in the continuous domain. The results demonstrate that the proposed COAC algorithm outperforms the others.
Social prophylaxis: group interaction promotes collective immunity in ant colonies
DEFF Research Database (Denmark)
Ugelvig, Line V; Cremer, Sylvia
2007-01-01
Life in a social group increases the risk of disease transmission. To counteract this threat, social insects have evolved manifold antiparasite defenses, ranging from social exclusion of infected group members to intensive care. It is generally assumed that individuals performing hygienic behaviors...... risk infecting themselves, suggesting a high direct cost of helping. Our work instead indicates the opposite for garden ants. Social contact with individual workers, which were experimentally exposed to a fungal parasite, provided a clear survival benefit to nontreated, naive group members upon later...... challenge with the same parasite. This first demonstration of contact immunity in Social Hymenoptera and complementary results from other animal groups and plants suggest its general importance in both antiparasite and antiherbivore defense. In addition to this physiological prophylaxis of adult ants...
DEFF Research Database (Denmark)
Peng, Renkang; Nielsen, Mogens Gissel; Offenberg, Joachim
2013-01-01
Weaver ants (Oecophylla smaragdina Fabricius) have been increasingly used as biocontrol agents of insect pests and as insect protein for human food and animals. For either of these purposes, mature ant colonies are essential. However, for a newly established colony to develop to a suitable mature...
Disease in the Society: Infectious Cadavers Result in Collapse of Ant Sub-Colonies
Loreto, Raquel G.; Hughes, David P.
2016-01-01
Despite the growing number of experimental studies on mechanisms of social immunity in ant societies, little is known about how social behavior relates to disease progression within the nests of ants. In fact, when empirically studying disease in ant societies, it is common to remove dead ants from experiments to confirm infection by the studied parasite. This unfortunately does not allow disease to progress within the nest as it may be assumed would happen under natural conditions. Therefore, the approach taken so far has resulted in a limited knowledge of diseases dynamics within the nest environment. Here we introduced a single infectious cadaver killed by the fungus Beauveria bassiana into small nests of the ant Camponotus castaneus. We then observed the natural progression of the disease by not removing the corpses of the ants that died following the first entry of the disease. Because some behaviors such as social isolation of sick individuals or the removal of cadavers by nestmates are considered social immune functions and thus adaptations at the colony level that reduce disease spread, we also experimentally confined some sub-colonies to one or two chamber nests to prevent the expression of such behaviors. Based on 51 small nests and survival studies in 1,003 ants we found that a single introduced infectious cadaver was able to transmit within the nest, and social immunity did not prevent the collapse of the small sub-colonies here tested. This was true whether ants did or did not have the option to remove the infectious cadaver. Therefore, we found no evidence that the typically studied social immunity behaviors can reduce disease spread in the conditions here tested. PMID:27529548
Colony fusion and worker reproduction after queen loss in army ants
DEFF Research Database (Denmark)
Kronauer, Daniel J C; Schöning, Caspar; d'Ettorre, Patrizia
2010-01-01
Theory predicts that altruism is only evolutionarily stable if it is preferentially directed towards relatives, so that any such behaviour towards seemingly unrelated individuals requires scrutiny. Queenless army ant colonies, which have anecdotally been reported to fuse with queenright foreign c...... loss might be more widespread, especially in spatially structured populations of social insects where worker reproduction is not profitable....... their reproductive success. We show that worker chemical recognition profiles remain similar after queen loss, but rapidly change into a mixed colony Gestalt odour after fusion, consistent with indiscriminate acceptance of alien workers that are no longer aggressive. We hypothesize that colony fusion after queen...
Ant Colony Optimization Analysis on Overall Stability of High Arch Dam Basis of Field Monitoring
Directory of Open Access Journals (Sweden)
Peng Lin
2014-01-01
Full Text Available A dam ant colony optimization (D-ACO analysis of the overall stability of high arch dams on complicated foundations is presented in this paper. A modified ant colony optimization (ACO model is proposed for obtaining dam concrete and rock mechanical parameters. A typical dam parameter feedback problem is proposed for nonlinear back-analysis numerical model based on field monitoring deformation and ACO. The basic principle of the proposed model is the establishment of the objective function of optimizing real concrete and rock mechanical parameter. The feedback analysis is then implemented with a modified ant colony algorithm. The algorithm performance is satisfactory, and the accuracy is verified. The m groups of feedback parameters, used to run a nonlinear FEM code, and the displacement and stress distribution are discussed. A feedback analysis of the deformation of the Lijiaxia arch dam and based on the modified ant colony optimization method is also conducted. By considering various material parameters obtained using different analysis methods, comparative analyses were conducted on dam displacements, stress distribution characteristics, and overall dam stability. The comparison results show that the proposal model can effectively solve for feedback multiple parameters of dam concrete and rock material and basically satisfy assessment requirements for geotechnical structural engineering discipline.
Ant colony optimization analysis on overall stability of high arch dam basis of field monitoring.
Lin, Peng; Liu, Xiaoli; Chen, Hong-Xin; Kim, Jinxie
2014-01-01
A dam ant colony optimization (D-ACO) analysis of the overall stability of high arch dams on complicated foundations is presented in this paper. A modified ant colony optimization (ACO) model is proposed for obtaining dam concrete and rock mechanical parameters. A typical dam parameter feedback problem is proposed for nonlinear back-analysis numerical model based on field monitoring deformation and ACO. The basic principle of the proposed model is the establishment of the objective function of optimizing real concrete and rock mechanical parameter. The feedback analysis is then implemented with a modified ant colony algorithm. The algorithm performance is satisfactory, and the accuracy is verified. The m groups of feedback parameters, used to run a nonlinear FEM code, and the displacement and stress distribution are discussed. A feedback analysis of the deformation of the Lijiaxia arch dam and based on the modified ant colony optimization method is also conducted. By considering various material parameters obtained using different analysis methods, comparative analyses were conducted on dam displacements, stress distribution characteristics, and overall dam stability. The comparison results show that the proposal model can effectively solve for feedback multiple parameters of dam concrete and rock material and basically satisfy assessment requirements for geotechnical structural engineering discipline.
Biomantling and bioturbation by colonies of the Florida harvester ant, Pogonomyrmex badius.
Tschinkel, Walter R
2015-01-01
In much of the world, soil-nesting ants are among the leading agents of biomantling and bioturbation, depositing excavated soil on the surface or in underground chambers. Colonies of the Florida harvester ant, Pogonomyrmex badius excavate a new nest once a year on average, depositing 0.1 to 12 L (3 L average) of soil on the surface. Repeated surveys of a population of about 400 colonies yielded the frequency of moves (approximately once per year), the distance moved (mean 4 m), and the direction moved (random). The area of the soil disc correlated well with the volume and maximum depth of the nest, as determined by excavation and mapping of chambers. The population-wide frequency distribution of disc areas thus yielded the frequency distribution of nest volumes and maximum depths. For each surveyed colony, the volume of soil excavated from six specified depth ranges and deposited on the surface was estimated. These parameters were used in a simulation to estimate the amount of soil mantled over time by the observed population of P. badius colonies. Spread evenly, P. badius mantling would create a soil layer averaging 0.43 cm thick in a millennium, with 10-15% of the soil deriving from depths greater than 1 m. Biomantling by P. badius is discussed in the context of the ant community of which it is a part, and in relation to literature reports of ant biomantling.
Biomantling and bioturbation by colonies of the Florida harvester ant, Pogonomyrmex badius.
Directory of Open Access Journals (Sweden)
Walter R Tschinkel
Full Text Available In much of the world, soil-nesting ants are among the leading agents of biomantling and bioturbation, depositing excavated soil on the surface or in underground chambers. Colonies of the Florida harvester ant, Pogonomyrmex badius excavate a new nest once a year on average, depositing 0.1 to 12 L (3 L average of soil on the surface. Repeated surveys of a population of about 400 colonies yielded the frequency of moves (approximately once per year, the distance moved (mean 4 m, and the direction moved (random. The area of the soil disc correlated well with the volume and maximum depth of the nest, as determined by excavation and mapping of chambers. The population-wide frequency distribution of disc areas thus yielded the frequency distribution of nest volumes and maximum depths. For each surveyed colony, the volume of soil excavated from six specified depth ranges and deposited on the surface was estimated. These parameters were used in a simulation to estimate the amount of soil mantled over time by the observed population of P. badius colonies. Spread evenly, P. badius mantling would create a soil layer averaging 0.43 cm thick in a millennium, with 10-15% of the soil deriving from depths greater than 1 m. Biomantling by P. badius is discussed in the context of the ant community of which it is a part, and in relation to literature reports of ant biomantling.
Assessment Guidelines for Ant Colony Algorithms when Solving Quadratic Assignment Problems
See, Phen Chiak; Yew Wong, Kuan; Komarudin, Komarudin
2009-08-01
To date, no consensus exists on how to evaluate the performance of a new Ant Colony Optimization (ACO) algorithm when solving Quadratic Assignment Problems (QAPs). Different performance measures and problems sets are used by researchers to evaluate their algorithms. This paper is aimed to provide a recapitulation of the relevant issues and suggest some guidelines for assessing the performance of new ACO algorithms.
Leite, Walter L.; Huang, I-Chan; Marcoulides, George A.
2008-01-01
This article presents the use of an ant colony optimization (ACO) algorithm for the development of short forms of scales. An example 22-item short form is developed for the Diabetes-39 scale, a quality-of-life scale for diabetes patients, using a sample of 265 diabetes patients. A simulation study comparing the performance of the ACO algorithm and…
Most diets for rearing fire ants and other ants contain insects such as crickets or mealworms. Unfortunately, insect diets are expensive, especially for large rearing operations, and are not always easily available. This study was designed to examine colony growth of Solenopsis fire ants on beef liv...
Study on Increasing the Accuracy of Classification Based on Ant Colony algorithm
Yu, M.; Chen, D.-W.; Dai, C.-Y.; Li, Z.-L.
2013-05-01
The application for GIS advances the ability of data analysis on remote sensing image. The classification and distill of remote sensing image is the primary information source for GIS in LUCC application. How to increase the accuracy of classification is an important content of remote sensing research. Adding features and researching new classification methods are the ways to improve accuracy of classification. Ant colony algorithm based on mode framework defined, agents of the algorithms in nature-inspired computation field can show a kind of uniform intelligent computation mode. It is applied in remote sensing image classification is a new method of preliminary swarm intelligence. Studying the applicability of ant colony algorithm based on more features and exploring the advantages and performance of ant colony algorithm are provided with very important significance. The study takes the outskirts of Fuzhou with complicated land use in Fujian Province as study area. The multi-source database which contains the integration of spectral information (TM1-5, TM7, NDVI, NDBI) and topography characters (DEM, Slope, Aspect) and textural information (Mean, Variance, Homogeneity, Contrast, Dissimilarity, Entropy, Second Moment, Correlation) were built. Classification rules based different characters are discovered from the samples through ant colony algorithm and the classification test is performed based on these rules. At the same time, we compare with traditional maximum likelihood method, C4.5 algorithm and rough sets classifications for checking over the accuracies. The study showed that the accuracy of classification based on the ant colony algorithm is higher than other methods. In addition, the land use and cover changes in Fuzhou for the near term is studied and display the figures by using remote sensing technology based on ant colony algorithm. In addition, the land use and cover changes in Fuzhou for the near term is studied and display the figures by using
Harvester ant colony variation in foraging activity and response to humidity.
Gordon, Deborah M; Dektar, Katherine N; Pinter-Wollman, Noa
2013-01-01
Collective behavior is produced by interactions among individuals. Differences among groups in individual response to interactions can lead to ecologically important variation among groups in collective behavior. Here we examine variation among colonies in the foraging behavior of the harvester ant, Pogonomyrmex barbatus. Previous work shows how colonies regulate foraging in response to food availability and desiccation costs: the rate at which outgoing foragers leave the nest depends on the rate at which foragers return with food. To examine how colonies vary in response to humidity and in foraging rate, we performed field experiments that manipulated forager return rate in 94 trials with 17 colonies over 3 years. We found that the effect of returning foragers on the rate of outgoing foragers increases with humidity. There are consistent differences among colonies in foraging activity that persist from year to year.
ACOustic: A Nature-Inspired Exploration Indicator for Ant Colony Optimization.
Sagban, Rafid; Ku-Mahamud, Ku Ruhana; Abu Bakar, Muhamad Shahbani
2015-01-01
A statistical machine learning indicator, ACOustic, is proposed to evaluate the exploration behavior in the iterations of ant colony optimization algorithms. This idea is inspired by the behavior of some parasites in their mimicry to the queens' acoustics of their ant hosts. The parasites' reaction results from their ability to indicate the state of penetration. The proposed indicator solves the problem of robustness that results from the difference of magnitudes in the distance's matrix, especially when combinatorial optimization problems with rugged fitness landscape are applied. The performance of the proposed indicator is evaluated against the existing indicators in six variants of ant colony optimization algorithms. Instances for travelling salesman problem and quadratic assignment problem are used in the experimental evaluation. The analytical results showed that the proposed indicator is more informative and more robust.
ACOustic: A Nature-Inspired Exploration Indicator for Ant Colony Optimization
Directory of Open Access Journals (Sweden)
Rafid Sagban
2015-01-01
Full Text Available A statistical machine learning indicator, ACOustic, is proposed to evaluate the exploration behavior in the iterations of ant colony optimization algorithms. This idea is inspired by the behavior of some parasites in their mimicry to the queens’ acoustics of their ant hosts. The parasites’ reaction results from their ability to indicate the state of penetration. The proposed indicator solves the problem of robustness that results from the difference of magnitudes in the distance’s matrix, especially when combinatorial optimization problems with rugged fitness landscape are applied. The performance of the proposed indicator is evaluated against the existing indicators in six variants of ant colony optimization algorithms. Instances for travelling salesman problem and quadratic assignment problem are used in the experimental evaluation. The analytical results showed that the proposed indicator is more informative and more robust.
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.
Artificial bee colony algorithm for constrained possibilistic portfolio optimization problem
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.
Ant Colony Algorithm and Simulation for Robust Airport Gate Assignment
Directory of Open Access Journals (Sweden)
Hui Zhao
2014-01-01
Full Text Available Airport gate assignment is core task for airport ground operations. Due to the fact that the departure and arrival time of flights may be influenced by many random factors, the airport gate assignment scheme may encounter gate conflict and many other problems. This paper aims at finding a robust solution for airport gate assignment problem. A mixed integer model is proposed to formulate the problem, and colony algorithm is designed to solve this model. Simulation result shows that, in consideration of robustness, the ability of antidisturbance for airport gate assignment scheme has much improved.
Ant-cuckoo colony optimization for feature selection in digital mammogram.
Jona, J B; Nagaveni, N
2014-01-15
Digital mammogram is the only effective screening method to detect the breast cancer. Gray Level Co-occurrence Matrix (GLCM) textural features are extracted from the mammogram. All the features are not essential to detect the mammogram. Therefore identifying the relevant feature is the aim of this work. Feature selection improves the classification rate and accuracy of any classifier. In this study, a new hybrid metaheuristic named Ant-Cuckoo Colony Optimization a hybrid of Ant Colony Optimization (ACO) and Cuckoo Search (CS) is proposed for feature selection in Digital Mammogram. ACO is a good metaheuristic optimization technique but the drawback of this algorithm is that the ant will walk through the path where the pheromone density is high which makes the whole process slow hence CS is employed to carry out the local search of ACO. Support Vector Machine (SVM) classifier with Radial Basis Kernal Function (RBF) is done along with the ACO to classify the normal mammogram from the abnormal mammogram. Experiments are conducted in miniMIAS database. The performance of the new hybrid algorithm is compared with the ACO and PSO algorithm. The results show that the hybrid Ant-Cuckoo Colony Optimization algorithm is more accurate than the other techniques.
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.
Akhoondzadeh, M.
2015-04-01
This study attempts to acknowledge AOD (Aerosol Optical Depth) seismo-atmospheric anomalies around the time of the Chile earthquake of 27 February 2010. Since AOD precursor alone might not be useful as an accurate and stand alone criteria for the earthquake anomalies detection, therefore it would be more appropriate to use and integrate a variety of other precursors to reduce the uncertainty of potential detected seismic anomalies. To achieve this aim, eight other precursors including GPS-TEC (Total Electron Content), H+, He+, O+ densities (cm-3) and total ion density (cm-3) from IAP experiment, electron density (cm-3) and electron temperature (K) from ISL experiment and VLF electric field from ICE experiment have been surveyed to detect unusual variations around the time and location of the Chile earthquake. Moreover, three methods including Interquartile, ANN (Artificial Neural Network) and ACO (Ant Colony Optimization) have been implemented to observe the discord patterns in time series of the AOD precursor. All of the methods indicate a clear abnormal increase in time series of AOD data, 2 days prior to event. Also a striking anomaly is observed in time series of TEC data, 6 days preceding the earthquake. Using the analysis of ICE data, a prominent anomaly is detected in the VLF electric field measurement, 1 day before the earthquake. The time series of H+, He+, O+ densities (cm-3) and total ion density (cm-3) from IAP and also electron density (cm-3) and electron temperature (K) from ISL, illustrate the abnormal behaviors, 3 days before the event. It should be noted that the acknowledgment of the different lead times in outcomes of the implemented precursors strictly depend on the proper understanding of Lithosphere-Atmosphere-Ionosphere (LAI) coupling mechanism during seismic activities. It means that these different anomalies dates between LAI precursors can be a hint of truthfulness of multi-precursors analysis.
Institute of Scientific and Technical Information of China (English)
DUAN Hai-bin; WANG Dao-bo; YU Xiu-fen
2006-01-01
Although ant colony algorithm for the heuristic solution of hard combinational optimization problems enjoy a rapidly growing popularity, but little is known about its convergence properties. Based on the introduction of the basic principle and mathematical model, a novel approach to the convergence proof that applies directly to the ant colony algorithm is proposed in this paper. Then, a MATLAB GUI- based ant colony algorithm simulation platform is developed, and the interface of this simulation platform is very friendly, easy to use and to modify.
Sirviö, A; Gadau, J; Rueppell, O; Lamatsch, D; Boomsma, J J; Pamilo, P; Page, R E
2006-09-01
Honeybees are known to have genetically diverse colonies because queens mate with many males and the recombination rate is extremely high. Genetic diversity among social insect workers has been hypothesized to improve general performance of large and complex colonies, but this idea has not been tested in other social insects. Here, we present a linkage map and an estimate of the recombination rate for Acromyrmex echinatior, a leaf-cutting ant that resembles the honeybee in having multiple mating of queens and colonies of approximately the same size. A map of 145 AFLP markers in 22 linkage groups yielded a total recombinational size of 2076 cM and an inferred recombination rate of 161 kb cM(-1) (or 6.2 cM Mb(-1)). This estimate is lower than in the honeybee but, as far as the mapping criteria can be compared, higher than in any other insect mapped so far. Earlier studies on A. echinatior have demonstrated that variation in division of labour and pathogen resistance has a genetic component and that genotypic diversity among workers may thus give colonies of this leaf-cutting ant a functional advantage. The present result is therefore consistent with the hypothesis that complex social life can select for an increased recombination rate through effects on genotypic diversity and colony performance.
Automated Software Testing Using Metahurestic Technique Based on An Ant Colony Optimization
Srivastava, Praveen Ranjan
2011-01-01
Software testing is an important and valuable part of the software development life cycle. Due to time, cost and other circumstances, exhaustive testing is not feasible that's why there is a need to automate the software testing process. Testing effectiveness can be achieved by the State Transition Testing (STT) which is commonly used in real time, embedded and web-based type of software systems. Aim of the current paper is to present an algorithm by applying an ant colony optimization technique, for generation of optimal and minimal test sequences for behavior specification of software. Present paper approach generates test sequence in order to obtain the complete software coverage. This paper also discusses the comparison between two metaheuristic techniques (Genetic Algorithm and Ant Colony optimization) for transition based testing
Routing in Wireless Sensor Networks Using an Ant Colony Optimization (ACO) Router Chip.
Okdem, Selcuk; Karaboga, Dervis
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 and comparative performance test results of the proposed approach are included. The approach is also implemented to a small sized hardware component as a router chip. Simulation results show that proposed algorithm provides promising solutions allowing node designers to efficiently operate routing tasks.
Application of the dynamic ant colony algorithm on the optimal operation of cascade reservoirs
Tong, X. X.; Xu, W. S.; Wang, Y. F.; Zhang, Y. W.; Zhang, P. C.
2016-08-01
Due to the lack of dynamic adjustments between global searches and local optimization, it is difficult to maintain high diversity and overcome local optimum problems for Ant Colony Algorithms (ACA). Therefore, this paper proposes an improved ACA, Dynamic Ant Colony Algorithm (DACA). DACA applies dynamic adjustments on heuristic factor changes to balance global searches and local optimization in ACA, which decreases cosines. At the same time, by utilizing the randomness and ergodicity of the chaotic search, DACA implements the chaos disturbance on the path found in each ACA iteration to improve the algorithm's ability to jump out of the local optimum and avoid premature convergence. We conducted a case study with DACA for optimal joint operation of the Dadu River cascade reservoirs. The simulation results were compared with the results of the gradual optimization method and the standard ACA, which demonstrated the advantages of DACA in speed and precision.
Directory of Open Access Journals (Sweden)
Khaled Loukhaoukha
2013-01-01
Full Text Available We present a new optimal watermarking scheme based on discrete wavelet transform (DWT and singular value decomposition (SVD using multiobjective ant colony optimization (MOACO. A binary watermark is decomposed using a singular value decomposition. Then, the singular values are embedded in a detailed subband of host image. The trade-off between watermark transparency and robustness is controlled by multiple scaling factors (MSFs instead of a single scaling factor (SSF. Determining the optimal values of the multiple scaling factors (MSFs is a difficult problem. However, a multiobjective ant colony optimization is used to determine these values. Experimental results show much improved performances of the proposed scheme in terms of transparency and robustness compared to other watermarking schemes. Furthermore, it does not suffer from the problem of high probability of false positive detection of the watermarks.
Detection Of Ventricular Late Potentials Using Wavelet Transform And ANT Colony Optimization
Subramanian, A. Sankara; Gurusamy, G.; Selvakumar, G.
2010-10-01
Ventricular late Potentials (VLPs) are low-level high frequency signals that are usually found with in the terminal part of the QRS complex from patients after Myocardial Infraction. Patients with VLPs are at risk of developing Ventricular Tachycardia, which is the major cause of death if patients suffering from heart disease. In this paper the Discrete Wavelet Transform was used to detect VLPs and then ANT colony optimization (ACO) was applied to classify subjects with and without VLPs. A set of Discrete Wavelet Transform (DWT) coefficients is selected from the wavelet decomposition. Three standard parameters of VLPs such as QRST, D40 and V40 are also established. After that a novel clustering algorithm based on Ant Colony Optimization is developed for classifying arrhythmia types. The wavelet decomposition enabled us to perform the task efficiently and produced reliable results.
Multi-Robot Dynamic Task Allocation Using Modified Ant Colony System
Xu, Zhenzhen; Xia, Feng; Zhang, Xianchao
This paper presents a dynamic task allocation algorithm for multiple robots to visit multiple targets. This algorithm is specifically designed for the environment where robots have dissimilar starting and ending locations. And the constraint of balancing the number of targets visited by each robot is considered. More importantly, this paper takes into account the dynamicity of multi-robot system and the obstacles in the environment. This problem is modeled as a constrained MTSP which can not be transformed to TSP and can not be solved by classical Ant Colony System (ACS). The Modified Ant Colony System (MACS) is presented to solve this problem and the unvisited targets are allocated to appropriate robots dynamically. The simulation results show that the output of the proposed algorithm can satisfy the constraints and dynamicity for the problem of multi-robot task allocation.
Ant colony optimization approach for test scheduling of system on chip
Institute of Scientific and Technical Information of China (English)
CHEN Ling; PAN Zhong-liang
2009-01-01
It is necessary to perform the test of system on chip, the test scheduling determines the test start and finishing time of every core in the system on chip such that the overall test time is minimized. A new test scheduling approach based on chaotic ant colony algorithm is presented in this paper. The optimization model of test scheduling was studied, the model uses the information such as the scale of test sets of both cores and user defined logic. An approach based on chaotic ant colony algorithm was proposed to solve the optimization model of test scheduling. The test of signal integrity faults such as crosstalk were also investigated when performing the test scheduling. Experimental results on many circuits show that the proposed approach can be used to solve test scheduling problems.
Clarifying Cutting and Sewing Processes with Due Windows Using an Effective Ant Colony Optimization
Directory of Open Access Journals (Sweden)
Rong-Hwa Huang
2013-01-01
Full Text Available The cutting and sewing process is a traditional flow shop scheduling problem in the real world. This two-stage flexible flow shop is often commonly associated with manufacturing in the fashion and textiles industry. Many investigations have demonstrated that the ant colony optimization (ACO algorithm is effective and efficient for solving scheduling problems. This work applies a novel effective ant colony optimization (EACO algorithm to solve two-stage flexible flow shop scheduling problems and thereby minimize earliness, tardiness, and makespan. Computational results reveal that for both small and large problems, EACO is more effective and robust than both the particle swarm optimization (PSO algorithm and the ACO algorithm. Importantly, this work demonstrates that EACO can solve complex scheduling problems in an acceptable period of time.
Colony insularity through queen control on worker social motivation in ants.
Boulay, Raphaël; Katzav-Gozansky, Tamar; Vander Meer, Robert K; Hefetz, Abraham
2003-05-01
We investigated the relative contribution of the queen and workers to colony nestmate recognition cues and on colony insularity in the Carpenter ant Camponotus fellah. Workers were either individually isolated, preventing contact with both queen and workers (colonial deprived, CD), kept in queenless groups, allowing only worker-worker interactions (queen deprived, QD) or in queenright (QR) groups. Two weeks post-separation QD and QR workers were amicable towards each other but both rejected their CD nestmates, which suggests that the queen does not measurably influence the colony recognition cues. By contrast, aggression between QD and QR workers from the same original colony was apparent only after six months of separation. This clearly demonstrates the power of the Gestalt and indicates that the queen is not a dominant contributor to the nestmate recognition cues in this species. Aggression between nestmates was correlated with a greater hydrocarbon (HC) profile divergence for CD than for QD and QR workers, supporting the importance of worker-worker interactions in maintaining the colony Gestalt odour. While the queen does not significantly influence nestmate recognition cues, she does influence colony insularity since within 3 days QD (queenless for six months) workers from different colony origins merged to form a single queenless colony. By contrast, the corresponding QR colonies maintained their territoriality and did not merge. The originally divergent cuticular and postpharyngeal gland HC profiles became congruent following the merger. Therefore, while workers supply and blend the recognition signal, the queen affects worker-worker interaction by reducing social motivation and tolerance of alien conspecifics.
Ant Colony Search Algorithm for Optimal Generators Startup during Power System Restoration
A. Ketabi; Feuillet, R
2010-01-01
Generators startup sequence plays a significant role in achieving a suitable and effective restoration strategy. This paper outlines an ant colony search algorithm in order to determine the generator starting times during the bulk power system restoration. The algorithm attempts to maximize the system generation capability over a restoration period, where the dynamic characteristics of different types of units and system constraints are considered. Applying this method for the 39-bus New Engl...
Ant Colony System for a Fuzzy Adjacent Multiple-Level Warehouse Layout Problem
Institute of Scientific and Technical Information of China (English)
ZHANG Qiang; YU Ying-zi; LAI K K
2006-01-01
A warehouse layout problem where the warehouse has more than one level and both the distance from the cell to the receive/exit bay and demand of item types are fuzzy variables is proposed. The problem is to find a layout with the minimum transportation cost subject to adjacency and other constraints. A fuzzy expected value model is given and an ant colony system is designed to solve the problem. Computational results indicate the efficiency and effectiveness of the method.
Inductive Synthesis of Cover-Grammars with the Help of Ant Colony Optimization
Directory of Open Access Journals (Sweden)
Wieczorek Wojciech
2016-11-01
Full Text Available A cover-grammar of a finite language is a context-free grammar that accepts all words in the language and possibly other words that are longer than any word in the language. In this paper, we describe an efficient algorithm aided by Ant Colony System that, for a given finite language, synthesizes (constructs a small cover-grammar of the language. We also check its ability to solve a grammatical inference task through the series of experiments.
Amador-Vargas, Sabrina; Gronenberg, Wulfila; Wcislo, William T.; Mueller, Ulrich
2015-01-01
Group size in both multicellular organisms and animal societies can correlate with the degree of division of labour. For ants, the task specialization hypothesis (TSH) proposes that increased behavioural specialization enabled by larger group size corresponds to anatomical specialization of worker brains. Alternatively, the social brain hypothesis proposes that increased levels of social stimuli in larger colonies lead to enlarged brain regions in all workers, regardless of their task special...
An artificial bee colony algorithm for uncertain portfolio selection.
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.
Ant colony system (ACS with hybrid local search to solve vehicle routing problems
Directory of Open Access Journals (Sweden)
Suphan Sodsoon
2016-02-01
Full Text Available This research applied an Ant Colony System algorithm with a Hybrid Local Search to solve Vehicle Routing Problems (VRP from a single depot when the customers’ requirements are known. VRP is an NP-hard optimization problem and has usually been successfully solved optimum by heuristics. A fleet of vehicles of a specific capacity are used to serve a number of customers at minimum cost, without violating the constraints of vehicle capacity. There are meta-heuristic approaches to solve these problems, such as Simulated Annealing, Genetic Algorithm, Tabu Search and the Ant Colony System algorithm. In this case a hybrid local search was used (Cross-Exchange, Or-Opt and 2-Opt algorithm with an Ant Colony System algorithm. The Experimental Design was tested on 7 various problems from the data set online in the OR-Library. There are five different problems in which customers are randomly distributed with the depot in an approximately central location. The customers were grouped into clusters. The results are evaluated in terms of optimal routes using optimal distances. The experimental results are compared with those obtained from meta-heuristics and they show that the proposed method outperforms six meta-heuristics in the literature.
Selective Marketing for Retailers to promote Stock using improved Ant Colony Algorithm
Directory of Open Access Journals (Sweden)
S.SURIYA
2013-10-01
Full Text Available Data mining is a knowledge discovery process which deals with analysing large storage of data in order to identify the relevant data. It is a powerful tool to uncover relationships within the data.Association rule mining is an important data mining model to mine frequent items in huge repository of data. It frames out association rules with the help of minimum support and confidence value which inturns paves way to identify the occurrence of frequent item sets. Frequent pattern mining starts from analysis of customers buying habits. From which various associations between the different items that the customers purchase are identified. With the help of such associations retailers perform selective marketing to promote their business. Biologically inspired algorithms have their process observed in nature as their origin. The best feature of Ant colony algorithm, which is a bio inspired algorithm based on the behaviour of natural ant colonies, is its parallel search over the problem data and previously obtained results from it. Dynamic memory management is done by pheromone updating operation. During each cycle, solutions are constructed by evaluation of the transition probability throughpheromone level modification. An improved pheromone updating rule is used to find out all the frequent items. The proposed approach was tested using MATLAB along with WEKA toolkit. The experimental results prove that the stigmeric communication of improved ant colony algorithm helps in mining the frequent items faster and effectively than the existing algorithms.
Advancement on techniques for the separation and maintenance of the red imported fire ant colonies
Institute of Scientific and Technical Information of China (English)
JIAN CHEN
2007-01-01
Advancement has recently been made on the techniques for separating andmaintaining colonies of red imported fire ants, Solenopsis invicta Buren. A new brood rescuemethod significantly improved the efficiency in separating colony from mound soil.Furthermore, a new method was developed to separate brood from the colony using fire antrepellants. Finally, a cost-effective method was developed to coat containers with dilutedFluon(R) (AGC Chemicals America, Inc, Moorestown, NJ, USA), an aqueouspolytetrafluoroethylene, to prevent housed ants from escaping a container. Usually theoriginal Fluon(R) solution is directly applied to the wall of the containers. Reduced concentrations of Fluon(R) were found to be equally effective in preventing ant escape. The use ofdiluted Fluon(R) solutions to coat the containers was recommended because of environmentaland cost-saving benefits. Application of these new techniques can significantly reduce labor,cost and environmental contamination. This review paper collates all the new techniques inone reference which readers can use as a manual.
Co-evolutionary design of discrete structures based on the ant colony optimization
Institute of Scientific and Technical Information of China (English)
无
2007-01-01
In order to optimize the sizing and topology of discrete structures together and resist the combinatorial explosion of the solution space, a co-evolutionary design (CED) method based on ant colony optimization (ACO) for discrete structures is proposed. The tailored ant colony optimization for the sizing of structures (TACO-SS) and the tailored ant colony optimization for the topology of structures (TACO-TS) are implemented respectively. Theoretical analysis shows that the computation complexity of each sub-process in CED based on ACO above is polynomial and it guarantees the efficiency of this method. After the parameter settings in TACO-SS and TACO-TS are discussed, the convergence history of a sub-process is studied through an instance in detail. Finally, according to the design examples of the 10-bar and 15-bar trusses under different cases, the effectiveness of the CED based on ACO is validated and the effects of the loads and the deflection constraints on the co-evolutionary design are discussed.
Nourelfath, M.; Nahas, N.; Montreuil, B.
2007-12-01
This article uses a hybrid optimization approach to solve the discrete facility layout problem (FLP), modelled as a quadratic assignment problem (QAP). The idea of this approach design is inspired by the ant colony meta-heuristic optimization method, combined with the extended great deluge (EGD) local search technique. Comparative computational experiments are carried out on benchmarks taken from the QAP-library and from real life problems. The performance of the proposed algorithm is compared to construction and improvement heuristics such as H63, HC63-66, CRAFT and Bubble Search, as well as other existing meta-heuristics developed in the literature based on simulated annealing (SA), tabu search and genetic algorithms (GAs). This algorithm is compared also to other ant colony implementations for QAP. The experimental results show that the proposed ant colony optimization/extended great deluge (ACO/EGD) performs significantly better than the existing construction and improvement algorithms. The experimental results indicate also that the ACO/EGD heuristic methodology offers advantages over other algorithms based on meta-heuristics in terms of solution quality.
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...
Goodisman, Michael A D; Hahn, Daniel A
2005-10-01
All social insects live in highly organized societies. However, different social insect species display striking variation in social structure. This variation can significantly affect the genetic structure within populations and, consequently, the divergence between species. The purpose of this study was to determine if variation in social structure was associated with species diversification in the Camponotus festinatus desert carpenter ant species complex. We used polymorphic DNA microsatellite markers to dissect the breeding system of these ants and to determine if distinct C. festinatus forms hybridized in their natural range. Our analysis of single-queen colonies established in the laboratory revealed that queens typically mated with only a single male. The genotypes of workers sampled from a field population suggested that multiple, related queens occasionally reproduced within colonies and that colonies inhabited multiple nests. Camponotus festinatus workers derived from colonies of the same form originating at different locales were strongly differentiated, suggesting that gene flow was geographically restricted. Overall, our data indicate that C. festinatus populations are highly structured. Distinct C. festinatus forms possess similar social systems but are genetically isolated. Consequently, our data suggest that diversification in the C. festinatus species complex is not necessarily associated with a shift in social structure.
Institute of Scientific and Technical Information of China (English)
KE Xi-zheng; HE Hua; WU Chang-li
2011-01-01
Aiming at the unidirectional links coming from nodes with different transmitting power and the obstacle blocking in UV mesh wireless communication network and the traditional ant colony algorithm only supporting bidirectional links, a new ant colony based routing algorithm with unidirectional link in UV mesh communication wireless network is proposed. The simulation results show that the proposed algorithm can improve the overall network connectivity and the survivability by supporting the combination of unidirectional link and bidirectional link.
Sociogenomics of cooperation and conflict during colony founding in the fire ant Solenopsis invicta.
Directory of Open Access Journals (Sweden)
Fabio Manfredini
Full Text Available One of the fundamental questions in biology is how cooperative and altruistic behaviors evolved. The majority of studies seeking to identify the genes regulating these behaviors have been performed in systems where behavioral and physiological differences are relatively fixed, such as in the honey bee. During colony founding in the monogyne (one queen per colony social form of the fire ant Solenopsis invicta, newly-mated queens may start new colonies either individually (haplometrosis or in groups (pleometrosis. However, only one queen (the "winner" in pleometrotic associations survives and takes the lead of the young colony while the others (the "losers" are executed. Thus, colony founding in fire ants provides an excellent system in which to examine the genes underpinning cooperative behavior and how the social environment shapes the expression of these genes. We developed a new whole genome microarray platform for S. invicta to characterize the gene expression patterns associated with colony founding behavior. First, we compared haplometrotic queens, pleometrotic winners and pleometrotic losers. Second, we manipulated pleometrotic couples in order to switch or maintain the social ranks of the two cofoundresses. Haplometrotic and pleometrotic queens differed in the expression of genes involved in stress response, aging, immunity, reproduction and lipid biosynthesis. Smaller sets of genes were differentially expressed between winners and losers. In the second experiment, switching social rank had a much greater impact on gene expression patterns than the initial/final rank. Expression differences for several candidate genes involved in key biological processes were confirmed using qRT-PCR. Our findings indicate that, in S. invicta, social environment plays a major role in the determination of the patterns of gene expression, while the queen's physiological state is secondary. These results highlight the powerful influence of social environment
A Stochastic Inversion Method for Potential Field Data: Ant Colony Optimization
Liu, Shuang; Hu, Xiangyun; Liu, Tianyou
2014-07-01
Simulating natural ants' foraging behavior, the ant colony optimization (ACO) algorithm performs excellently in combinational optimization problems, for example the traveling salesman problem and the quadratic assignment problem. However, the ACO is seldom used to inverted for gravitational and magnetic data. On the basis of the continuous and multi-dimensional objective function for potential field data optimization inversion, we present the node partition strategy ACO (NP-ACO) algorithm for inversion of model variables of fixed shape and recovery of physical property distributions of complicated shape models. We divide the continuous variables into discrete nodes and ants directionally tour the nodes by use of transition probabilities. We update the pheromone trails by use of Gaussian mapping between the objective function value and the quantity of pheromone. It can analyze the search results in real time and promote the rate of convergence and precision of inversion. Traditional mapping, including the ant-cycle system, weaken the differences between ant individuals and lead to premature convergence. We tested our method by use of synthetic data and real data from scenarios involving gravity and magnetic anomalies. The inverted model variables and recovered physical property distributions were in good agreement with the true values. The ACO algorithm for binary representation imaging and full imaging can recover sharper physical property distributions than traditional linear inversion methods. The ACO has good optimization capability and some excellent characteristics, for example robustness, parallel implementation, and portability, compared with other stochastic metaheuristics.
Directory of Open Access Journals (Sweden)
Meenakshi R Patel
2012-03-01
Full Text Available ACO algorithms for datagram networks was given by Di Caro Dorigo, in year 1996. Basic mechanisms in typical ACO routing algorithms is Ant-like agents are proactively generated at the nodes to find/check paths toward assigned destinations Ants move hop-by-hop according to a exploratory routing policy based on the local routing .After reaching their destination, ants retrace their path and update nodes routing information according to the quality of the path. Routing information is statistical estimates of the time-to-go to the destination maintained in pheromone arrays. Data are probabilistically spread over the paths according to their estimated quality as stored in the pheromone variables. AntNet algorithms may cause the network congestion and stagnation as the routing table converges. In this paper we perform a survey on modified AntNet routing algorithm using Multiple Ant-Colony Optimization. Multiple ant colonies with different pheromone updating mechanism have different searching traits. By leveraging this feature, much of work is done by designing a set of adaptive rules to facilitate the collaboration between these colonies. This approach can balance the diversity and convergence of solutions generated by different ant colonies and also overcome the problem of Stagnation.
Directory of Open Access Journals (Sweden)
Gabriela Pérez-Lachaud
Full Text Available INTRODUCTION: Systematic surveys of macrofaunal diversity within ant colonies are lacking, particularly for ants nesting in microhabitats that are difficult to sample. Species associated with ants are generally small and rarely collected organisms, which makes them more likely to be unnoticed. We assumed that this tendency is greater for arthropod communities in microhabitats with low accessibility, such as those found in the nests of arboreal ants that may constitute a source of cryptic biodiversity. MATERIALS AND METHODS: We investigated the invertebrate diversity associated with an undescribed, but already threatened, Neotropical Camponotus weaver ant. As most of the common sampling methods used in studies of ant diversity are not suited for evaluating myrmecophile diversity within ant nests, we evaluated the macrofauna within ant nests through exhaustive colony sampling of three nests and examination of more than 80,000 individuals. RESULTS: We identified invertebrates from three classes belonging to 18 taxa, some of which were new to science, and recorded the first instance of the co-occurrence of two brood parasitoid wasp families attacking the same ant host colony. This diversity of ant associates corresponded to a highly complex interaction network. Agonistic interactions prevailed, but the prevalence of myrmecophiles was remarkably low. CONCLUSIONS: Our data support the hypothesis of the evolution of low virulence in a variety of symbionts associated with large insect societies. Because most myrmecophiles found in this work are rare, strictly specific, and exhibit highly specialized biology, the risk of extinction for these hitherto unknown invertebrates and their natural enemies is high. The cryptic, far unappreciated diversity within arboreal ant nests in areas at high risk of habitat loss qualifies these nests as 'hot-points' of biodiversity that urgently require special attention as a component of conservation and management
An Improved Artificial Bee Colony-Based Approach for Zoning Protected Ecological Areas.
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.
Parametric Optimization of Nd:YAG Laser Beam Machining Process Using Artificial Bee Colony Algorithm
Directory of Open Access Journals (Sweden)
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.
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.
Optimization of Straight Cylindrical Turning Using Artificial Bee Colony (ABC) Algorithm
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.
Flood risk zoning using a rule mining based on ant colony algorithm
Lai, Chengguang; Shao, Quanxi; Chen, Xiaohong; Wang, Zhaoli; Zhou, Xiaowen; Yang, Bing; Zhang, Lilan
2016-11-01
Risk assessment is a preliminary step in flood management and mitigation, and risk zoning provides a quantitative measure of flood risk. The difficulty in flood risk zoning is to deal with the complicated non-linear relationship among indices and risk levels. To solve this problem, the ant colony algorithm based on rule mining (Ant-Miner) is promoted in this paper to map the regional flood risk at grid scale. For the case study in the Dongjiang River Basin in Southern China, 11 and 14 indices (without and with the socio-economic indices considered) are respectively chosen to construct the zoning model based on Ant-Miner. The results show that Ant-Miner exhibits higher accuracy and more simple rules that can be used to generate flood risk zoning map quickly and easily than decision tree method (DT); compared to random forest (RF) and fuzzy comprehensive evaluation (FCE), Ant-Miner has significant advantages both in implementation step-reducing and computing time-saving. Although the comprehensive measure and natural hazard measure of flood risk distributed similarly over the entire region, the former one which considered the socio-economic indices is more reasonable in term of real impact to natural and socio-economy. The areas with high-risk level obtained in this paper matched well with the integrated risk zoning map and the inundation areas of historical floods, suggesting that the proposed Ant-Miner method is capable of zoning the flood risk at grid scale. This study shows the potential to provide a novel and successful approach to flood risk zoning. Evaluation results provide a reference for flood risk management, prevention, and reduction of natural disasters in the study basin.
Institute of Scientific and Technical Information of China (English)
无
2005-01-01
A novel method based on ant colony optimization (ACO), algorithm for solving the ill-conditioned linear systems of equations is proposed. ACO is a parallelized bionic optimization algorithm which is inspired from the behavior of real ants. ACO algorithm is first introduced, a kind of positive feedback mechanism is adopted in ACO. Then, the solution problem of linear systems of equations was reformulated as an unconstrained optimization problem for solution by an ACO algorithm. Finally, the ACO with other traditional methods is applied to solve a kind of multi-dimensional Hilbert ill-conditioned linear equations. The numerical results demonstrate that ACO is effective, robust and recommendable in solving ill-conditioned linear systems of equations.
Directory of Open Access Journals (Sweden)
Kanchan Singla
2014-06-01
Full Text Available MC CDMA is a rising candidate for future generation broadband wireless communication and gained great attention from researchers. It provides benefits of both OFDM and CDMA. Main challenging problem of MC CDMA is high PAPR. It occurs in HPA and reduces system efficiency. There are many PAPR reduction techniques for MC CDMA. In this paper we proposed Ant colony optimization algorithm to reduce PAPR with different number of user using BPSK and QPSK modulation. ACO is a metaheuristic technique and based on the foraging behavior of real ants. It provides solution to many complex problems. Simulation result proves that ACO using BPSK modulation is effective for reducing PAPR in MC CDMA.
Topology optimum design of compliant mechanisms using modified ant colony optimization
Energy Technology Data Exchange (ETDEWEB)
Yoo, Kwang Seon; Han, Seog Young [Hanyang University, Seoul (Korea, Republic of)
2015-08-15
A Modified ant colony optimization (MACO) algorithm was suggested for topology optimal design of compliant mechanisms since standard ACO cannot provide an appropriate optimal topology. In order to improve computational efficiency and suitability of standard ACO algorithm in topology optimization for compliant mechanisms, a continuous variable, called the 'Element contribution significance (ECS),'is employed, which serves to replace the positions of ants in the standard ACO algorithm, and assess the importance of each element in the optimization process. MACO algorithm was applied to topology optimizations of both linear and geometrically nonlinear compliant mechanisms using three kinds of objective functions, and optimized topologies were compared each other. From the comparisons, it was concluded that MACO algorithm can effectively be applied to topology optimizations of linear and geometrically nonlinear compliant mechanisms, and the ratio of Mutual potential energy (MPE) to Strain energy (SE) type of objective function is the best for topology optimal design of compliant mechanisms.
A Multi-pipe Path Planning by Modified Ant Colony Optimization
Institute of Scientific and Technical Information of China (English)
QU Yan-feng; JIANG Dan; LIU Bin
2011-01-01
Path planning in 3D geometry space is used to find an optimal path in the restricted environment, according to a certain evaluation criteria. To solve the problem of long searching time and slow solving speed in 3D path planning, a modified ant colony optimization is proposed in this paper. Firstly, the grid method for environment modeling is adopted. Heuristic information is connected with the planning space. A semi-iterative global pheromone update mechanism is proposed. Secondly, the optimal ants mutate the paths to improve the diversity of the algorithm after a defined iterative number. Thirdly, co-evolutionary algorithm is used. Finally, the simulation result shows the effectiveness of the proposed algorithm in solving the problem of 3D pipe path planning.
Directory of Open Access Journals (Sweden)
Osmar Viera Carcache
2017-03-01
Full Text Available This paper presents a computational proposal for the solution of the Cell Planning Problem. The importance of this problem in the area of Telecommunications imposes it as a reference in the search for new methods of optimization. Due to the complexity of the problem, this work uses a discrete relaxation and proposes a mathematical model for the application of the Meta-heuristic Ant Colony Optimization (ACO. For the analysis of the results, 5 instances of the problem of different sizes were selected and the Ants System (AS algorithm was applied. The results show that the proposal efficiently explores the search space, finding the optimal solution for each instance with a relatively low computational cost. These results are compared with 3 evolutionary alternatives of international reference that have been applied to the same study instances, showing a significant improvement by our proposal.
Hierarchical Artificial Bee Colony Algorithm for RFID Network Planning Optimization
Directory of Open Access Journals (Sweden)
Lianbo Ma
2014-01-01
Full Text Available This paper presents a novel optimization algorithm, namely, hierarchical artificial bee colony optimization, called HABC, to tackle the radio frequency identification network planning (RNP problem. In the proposed multilevel model, the higher-level species can be aggregated by the subpopulations from lower level. In the bottom level, each subpopulation employing the canonical ABC method searches the part-dimensional optimum in parallel, which can be constructed into a complete solution for the upper level. At the same time, the comprehensive learning method with crossover and mutation operators is applied to enhance the global search ability between species. Experiments are conducted on a set of 10 benchmark optimization problems. The results demonstrate that the proposed HABC obtains remarkable performance on most chosen benchmark functions when compared to several successful swarm intelligence and evolutionary algorithms. Then HABC is used for solving the real-world RNP problem on two instances with different scales. Simulation results show that the proposed algorithm is superior for solving RNP, in terms of optimization accuracy and computation robustness.
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.
Lévy flight artificial bee colony algorithm
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.
Social transfer of pathogenic fungus promotes active immunisation in ant colonies.
Directory of Open Access Journals (Sweden)
Matthias Konrad
Full Text Available Due to the omnipresent risk of epidemics, insect societies have evolved sophisticated disease defences at the individual and colony level. An intriguing yet little understood phenomenon is that social contact to pathogen-exposed individuals reduces susceptibility of previously naive nestmates to this pathogen. We tested whether such social immunisation in Lasius ants against the entomopathogenic fungus Metarhizium anisopliae is based on active upregulation of the immune system of nestmates following contact to an infectious individual or passive protection via transfer of immune effectors among group members--that is, active versus passive immunisation. We found no evidence for involvement of passive immunisation via transfer of antimicrobials among colony members. Instead, intensive allogrooming behaviour between naive and pathogen-exposed ants before fungal conidia firmly attached to their cuticle suggested passage of the pathogen from the exposed individuals to their nestmates. By tracing fluorescence-labelled conidia we indeed detected frequent pathogen transfer to the nestmates, where they caused low-level infections as revealed by growth of small numbers of fungal colony forming units from their dissected body content. These infections rarely led to death, but instead promoted an enhanced ability to inhibit fungal growth and an active upregulation of immune genes involved in antifungal defences (defensin and prophenoloxidase, PPO. Contrarily, there was no upregulation of the gene cathepsin L, which is associated with antibacterial and antiviral defences, and we found no increased antibacterial activity of nestmates of fungus-exposed ants. This indicates that social immunisation after fungal exposure is specific, similar to recent findings for individual-level immune priming in invertebrates. Epidemiological modeling further suggests that active social immunisation is adaptive, as it leads to faster elimination of the disease and lower
Senthiil, P. V.; Selladurai, V.; Rajesh, R.
This study introduces a new approach for decentralized scheduling in a parallel machine environment based on the ant colonies optimization algorithm. The algorithm extends the use of the traveling salesman problem for scheduling in one single machine, to a multiple machine problem. The results are presented using simple and illustrative examples and show that the algorithm is able to optimize the different scheduling problems. Using the same parameters, the completion time of the tasks is minimized and the processing time of the parallel machines is balanced.
Ant Colony Search Algorithm for Optimal Generators Startup during Power System Restoration
Directory of Open Access Journals (Sweden)
A. Ketabi
2010-01-01
Full Text Available Generators startup sequence plays a significant role in achieving a suitable and effective restoration strategy. This paper outlines an ant colony search algorithm in order to determine the generator starting times during the bulk power system restoration. The algorithm attempts to maximize the system generation capability over a restoration period, where the dynamic characteristics of different types of units and system constraints are considered. Applying this method for the 39-bus New England test system, and comparing the results with backtracking-search and P/t methods, it is found that proposed algorithm improved generation capability.
Ant colony optimization techniques for the hamiltonian p-median problem
Directory of Open Access Journals (Sweden)
M. Zohrehbandian
2010-12-01
Full Text Available Location-Routing problems involve locating a number of facilitiesamong candidate sites and establishing delivery routes to a set of users in such a way that the total system cost is minimized. A special case of these problems is Hamiltonian p-Median problem (HpMP. This research applies the metaheuristic method of ant colony optimization (ACO to solve the HpMP. Modifications are made to the ACO algorithm used to solve the traditional vehicle routing problem (VRP in order to allow the search of the optimal solution of the HpMP. Regarding this metaheuristic algorithm a computational experiment is reported as well.
CACER:A Novel E-commerce Recommendation Model Based on Crazy Ant Colony Algorithms
Institute of Scientific and Technical Information of China (English)
王征; 刘庆强
2013-01-01
In order to deal with the problems of E-commerce online marketing, a novel E-commerce recommendation system model was given to lead consumers to efficient retrieval and consumption. And the system model was built with a crazy ant colony algorithm. Then its model, message structures and working flows were presented as following. At last, an application example and compared results were given to be analyzed. Simulation results show the model can perform better in real-time and customer satisfaction than the olds do.
Ant Colony Algorithm and Optimization of Test Conditions in Analytical Chemistry
Institute of Scientific and Technical Information of China (English)
丁亚平; 吴庆生; 苏庆德
2003-01-01
The research for the new algorithm is in the forward position and an issue of general interest in chemometrics all along.A novel chemometrics method,Chemical Ant Colony Algorithm,has first been developed.In this paper,the basic principle,theevaluation function,and the parameter choice were discussed.This method has been successfully applied to the fitting of nonlinear multivariate function and the optimization of test conditions in chrome-azure-S-Al spctrophotometric system.The sum of residual square of the results is 0.0009,which has reached a good convergence result.
Optimum Distribution Generator Placement in Power Distribution System Using Ant Colony Algorithm
Directory of Open Access Journals (Sweden)
Mehdi Mahdavi
2009-03-01
Full Text Available The recent development in renewable energy systems and the high demand for having clean and low cost energy sources encourage people to use distributed generator (DG systems. Proper addition and placement of DG units can increase reliability and reduce the loss and production cost. In this paper using Ant Colony method, we developed an optimum placing scheme for DGs. The proposed method is tested on an IEEE 34-shinhe system. Results show that if DGs are able to generate active power, their effectiveness will increase.
Optimal Path Identification Using ANT Colony Optimisation in Wireless Sensor Network
Directory of Open Access Journals (Sweden)
Aniket. A. Gurav
2013-05-01
Full Text Available Wireless Sensor Network WSN is tightly constrained for energy, computational power and memory. All applications of WSN require to forward data from remote sensor node SN to base station BS. The path length and numbers of nodes in path by which data is forwarded affect the basic performance of WSN. In this paper we present bio-Inspired Ant Colony Optimisation ACO algorithm for Optimal path Identification OPI for p acket transmission to communicate between SN to BS. Our modified algorithm OPI using ACO cons iders the path length and the number of hops in path for data packet transmission, with an aim to reduce communication overheads .
Study on Cloud Computing Resource Scheduling Strategy Based on the Ant Colony Optimization Algorithm
Directory of Open Access Journals (Sweden)
Lingna He
2012-09-01
Full Text Available In order to replace the traditional Internet software usage patterns and enterprise management mode, this paper proposes a new business calculation mode- cloud computing, resources scheduling strategy is the key technology in cloud computing, Based on the study of cloud computing system structure and the mode of operation, The key research for cloud computing the process of the work scheduling and resource allocation problems based on ant colony algorithm , Detailed analysis and design of the specific implementation for cloud resources scheduling . And in CloudSim simulation environment and simulation experiments, the results show that the algorithm has better scheduling performance and load balance than general algorithm.
Ant colony system algorithm for the optimization of beer fermentation control
Institute of Scientific and Technical Information of China (English)
肖杰; 周泽魁; 张光新
2004-01-01
Beer fermentation is a dynamic process that must be guided along a temperature profile to obtain the desired results. Ant colony system algorithm was applied to optimize the kinetic model of this process. During a fixed period of fermentation time, a series of different temperature profiles of the mixture were constructed. An optimal one was chosen at last. Optimal temperature profile maximized the final ethanol production and minimized the byproducts concentration and spoilage risk. The satisfactory results obtained did not require much computation effort.
Ant Colony Optimization In Multi-Agent Systems With NetLogo
Directory of Open Access Journals (Sweden)
Mustafa Tüker
2013-02-01
Full Text Available Multi-agent systems (MAS offer an effective way to model and solve complex optimization problems. In this study, MAS and ant colonies have been used together to solve the Travelling Salesmen Problem (TSP. System simulation has been realized with NetLogo which is an agent-based programming environment. It has been explained in detail with code examples that how to use NetLogo for modeling and simulation of the problem. Algorithm has been tested for different numbers of nodes and obtained results have been discussed.
Tawny crazy ants, Nylanderia fulva, is an invasive ant that are known to readily forage on the liquid, carbohydrate rich honeydew produced by hemipterans such as aphids and scales. There is interest in developing liquid ant baits that can eliminate tawny crazy ant colonies. Preliminary and anecdot...
Role of relative humidity in colony founding and queen survivorship in two carpenter ant species.
Mankowski, Mark E; Morrell, J J
2011-06-01
Conditions necessary for optimal colony foundation in two carpenter ant species, Camponotus modoc Wheeler and Camponotus vicinus Mayr, were studied. Camponotus modoc and C. vicinus queens were placed in Douglas-fir, Pseudotsuga menziesii (Mirb. Franco) and Styrofoam blocks conditioned in sealed chambers at 70, 80, or 100% RH. Nanitic workers produced after 12 wk were used to assess the effects of substrate and moisture content on colony initiation. Queens of C. vicinus in Douglas-fir and Styrofoam produced worker numbers that did not differ significantly with moisture content; however, the number of colonies initiated by C. modoc differed significantly with moisture content. The results indicate that colony founding in C. vicinus is less sensitive to moisture content than C. modoc for Douglas-fir and Styrofoam. In another test, groups of queens of each species were exposed to 20, 50, 70, and 100% RH and the time until 50% mortality occurred was recorded for each species. C. vicinus lived significantly longer at each of the test humidities than C. modoc, suggesting that the former species is adapted to better survive under xeric conditions.
Quantifying the effect of colony size and food distribution on harvester ant foraging.
Directory of Open Access Journals (Sweden)
Tatiana P Flanagan
Full Text Available Desert seed-harvester ants, genus Pogonomyrmex, are central place foragers that search for resources collectively. We quantify how seed harvesters exploit the spatial distribution of seeds to improve their rate of seed collection. We find that foraging rates are significantly influenced by the clumpiness of experimental seed baits. Colonies collected seeds from larger piles faster than randomly distributed seeds. We developed a method to compare foraging rates on clumped versus random seeds across three Pogonomyrmex species that differ substantially in forager population size. The increase in foraging rate when food was clumped in larger piles was indistinguishable across the three species, suggesting that species with larger colonies are no better than species with smaller colonies at collecting clumped seeds. These findings contradict the theoretical expectation that larger groups are more efficient at exploiting clumped resources, thus contributing to our understanding of the importance of the spatial distribution of food sources and colony size for communication and organization in social insects.
T-QoS-aware based parallel ant colony algorithm for services composition
Institute of Scientific and Technical Information of China (English)
Lin Zhang; Kaili Rao; Ruchuan Wang
2015-01-01
In order to make cloud users get credible, high-quality composition of services, the trust quality of service aware (T-QoS-aware) based paral el ant colony algorithm is proposed. Our approach takes the service credibility as the weight of the quality of service, then calculates the trust service quality T-QoS for each service, making the service composition situated in a credible environment. Through the establishment on a per-service T-QoS initialization pheromone matrix, we can reduce the colony’s initial search time. By modifying the pheromone updating rules and intro-ducing two ant colonies to search from different angles in paral el, we can avoid fal ing into the local optimal solution, and quickly find the optimal combination of global solutions. Experiments show that our approach can combine high-quality services and the improve-ment of the operational success rate. Also, the convergence rate and the accuracy of optimal combination are improved.
Scalable unit commitment by memory-bounded ant colony optimization with A{sup *} local search
Energy Technology Data Exchange (ETDEWEB)
Saber, Ahmed Yousuf; Alshareef, Abdulaziz Mohammed [Department of Electrical and Computer Engineering, King Abdulaziz University, P.O. Box 80204, Jeddah 21589 (Saudi Arabia)
2008-07-15
Ant colony optimization (ACO) is successfully applied in optimization problems. Performance of the basic ACO for small problems with moderate dimension and searching space is satisfactory. As the searching space grows exponentially in the large-scale unit commitment problem, the basic ACO is not applicable for the vast size of pheromone matrix of ACO in practical time and physical computer-memory limit. However, memory-bounded methods prune the least-promising nodes to fit the system in computer memory. Therefore, the authors propose memory-bounded ant colony optimization (MACO) in this paper for the scalable (no restriction for system size) unit commitment problem. This MACO intelligently solves the limitation of computer memory, and does not permit the system to grow beyond a bound on memory. In the memory-bounded ACO implementation, A{sup *} heuristic is introduced to increase local searching ability and probabilistic nearest neighbor method is applied to estimate pheromone intensity for the forgotten value. Finally, the benchmark data sets and existing methods are used to show the effectiveness of the proposed method. (author)
Stochastic time-dependent vehicle routing problem: Mathematical models and ant colony algorithm
Directory of Open Access Journals (Sweden)
Zhengyu Duan
2015-11-01
Full Text Available This article addresses the stochastic time-dependent vehicle routing problem. Two mathematical models named robust optimal schedule time model and minimum expected schedule time model are proposed for stochastic time-dependent vehicle routing problem, which can guarantee delivery within the time windows of customers. The robust optimal schedule time model only requires the variation range of link travel time, which can be conveniently derived from historical traffic data. In addition, the robust optimal schedule time model based on robust optimization method can be converted into a time-dependent vehicle routing problem. Moreover, an ant colony optimization algorithm is designed to solve stochastic time-dependent vehicle routing problem. As the improvements in initial solution and transition probability, ant colony optimization algorithm has a good performance in convergence. Through computational instances and Monte Carlo simulation tests, robust optimal schedule time model is proved to be better than minimum expected schedule time model in computational efficiency and coping with the travel time fluctuations. Therefore, robust optimal schedule time model is applicable in real road network.
The Research on Low Carbon Logistics Routing Optimization Based on DNA-Ant Colony Algorithm
Directory of Open Access Journals (Sweden)
Liyi Zhang
2014-01-01
Full Text Available As the energy conservation and emission reduction and sustainable development have become the hot topics in the world, low carbon issues catch more and more attention. Logistics, which is one of the important economic activities, plays a crucial role in the low carbon development. Logistics leads to some significant issues about consuming energy and carbon emissions. Therefore, reducing energy consumption and carbon emissions has become the inevitable trend for logistics industry. Low carbon logistics is introduced in these situations. In this paper, from the microcosmic aspects, we will bring the low carbon idea in the path optimization issues and change the amount of carbon emissions into carbon emissions cost to establish the path optimization model based on the optimization objectives of the lowest cost of carbon emissions. According to different levels of air pollution, we will establish the double objectives path optimization model with the consideration of carbon emissions cost and economy cost. Use DNA-ant colony algorithm to optimize and simulate the model. The simulation indicates that DNA-ant colony algorithm could find a more reasonable solution for low carbon logistics path optimization problems.
Ants Colony Optimisation of a Measuring Path of Prismatic Parts on a CMM
Directory of Open Access Journals (Sweden)
Stojadinovic Slavenko M.
2016-03-01
Full Text Available This paper presents optimisation of a measuring probe path in inspecting the prismatic parts on a CMM. The optimisation model is based on: (i the mathematical model that establishes an initial collision-free path presented by a set of points, and (ii the solution of Travelling Salesman Problem (TSP obtained with Ant Colony Optimisation (ACO. In order to solve TSP, an ACO algorithm that aims to find the shortest path of ant colony movement (i.e. the optimised path is applied. Then, the optimised path is compared with the measuring path obtained with online programming on CMM ZEISS UMM500 and with the measuring path obtained in the CMM inspection module of Pro/ENGINEER® software. The results of comparing the optimised path with the other two generated paths show that the optimised path is at least 20% shorter than the path obtained by on-line programming on CMM ZEISS UMM500, and at least 10% shorter than the path obtained by using the CMM module in Pro/ENGINEER®.
An Improved Ant Colony Algorithm for a Single-machine Scheduling Problem with Setup Times
Institute of Scientific and Technical Information of China (English)
YE Qiang; LIU Xinbao; LIU Lin; YANG Shanglin
2006-01-01
Motivated by industrial applications we study a single-machine scheduling problem in which all the jobs are mutually independent and available at time zero. The machine processes the jobs sequentially and it is not idle if there is any job to be processed. The operation of each job cannot be interrupted. The machine cannot process more than one job at a time. A setup time is needed if the machine switches from one type of job to another. The objective is to find an optimal schedule with the minimal total jobs' completion time. While the sum of jobs' processing time is always a constant, the objective is to minimize the sum of setup times. Ant colony optimization (ACO) is a meta-heuristic that has recently been applied to scheduling problem. In this paper we propose an improved ACO-Branching Ant Colony with Dynamic Perturbation (DPBAC) algorithm for the single-machine scheduling problem. DPBAC improves traditional ACO in following aspects: introducing Branching Method to choose starting points; improving state transition rules; introducing Mutation Method to shorten tours; improving pheromone updating rules and introducing Conditional Dynamic Perturbation Strategy. Computational results show that DPBAC algorithm is superior to the traditional ACO algorithm.
Directory of Open Access Journals (Sweden)
G.Keerthi Lakshmi
2012-03-01
Full Text Available Performing regression testing on a pre production environment is often viewed by software practitioners as a daunting task since often the test execution shall by-pass the stipulated downtime or the test coverage would be non linear. Choosing the exact test cases to match this type of complexity not only needs prior knowledge of the system, but also a right use of calculations to set the goals right. On systems that are just entering the production environment after getting promoted from the staging phase, trade-offs are often needed to between time and the test coverage to ensure the maximum test cases are covered within the stipulated time. There arises a need to refine the test cases to accommodate the maximum test coverage it makes within the stipulated period of time since at most of the times, the most important test cases are often not deemed to qualify under the sanity test suite and any bugs that creped in them would go undetected until it is found out by the actual user at firsthand. Hence An attempt has been made in the paper to layout a testing framework to address the process of improving the regression suite by adopting a modified version of the Ant Colony Algorithm over and thus dynamically injecting dependency over the best route encompassed by the ant colony.
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.
Chaudhuri, Sutapa; Goswami, Sayantika; Das, Debanjana; Middey, Anirban
2014-05-01
Forecasting summer monsoon rainfall with precision becomes crucial for the farmers to plan for harvesting in a country like India where the national economy is mostly based on regional agriculture. The forecast of monsoon rainfall based on artificial neural network is a well-researched problem. In the present study, the meta-heuristic ant colony optimization (ACO) technique is implemented to forecast the amount of summer monsoon rainfall for the next day over Kolkata (22.6°N, 88.4°E), India. The ACO technique belongs to swarm intelligence and simulates the decision-making processes of ant colony similar to other adaptive learning techniques. ACO technique takes inspiration from the foraging behaviour of some ant species. The ants deposit pheromone on the ground in order to mark a favourable path that should be followed by other members of the colony. A range of rainfall amount replicating the pheromone concentration is evaluated during the summer monsoon season. The maximum amount of rainfall during summer monsoon season (June—September) is observed to be within the range of 7.5-35 mm during the period from 1998 to 2007, which is in the range 4 category set by the India Meteorological Department (IMD). The result reveals that the accuracy in forecasting the amount of rainfall for the next day during the summer monsoon season using ACO technique is 95 % where as the forecast accuracy is 83 % with Markov chain model (MCM). The forecast through ACO and MCM are compared with other existing models and validated with IMD observations from 2008 to 2012.
Directory of Open Access Journals (Sweden)
Jhon Jairo Santa Chávez
2016-01-01
Full Text Available This paper presents a multiobjective ant colony algorithm for the Multi-Depot Vehicle Routing Problem with Backhauls (MDVRPB where three objectives of traveled distance, traveling times and total consumption of energy are minimized. An ant colony algorithm is proposed to solve the MDVRPB. The solution scheme allows one to find a set of ordered solutions in Pareto fronts by considering the concept of dominance. The effectiveness of the proposed approach is examined by considering a set of instances adapted from the literature. The computational results show high quality results within short computing times.
Directory of Open Access Journals (Sweden)
Syahid Anuar
2016-10-01
Full Text Available The artificial bee colony (ABC is one of the swarm intelligence algorithms used to solve optimization problems which is inspired by the foraging behaviour of the honey bees. In this paper, artificial bee colony with the rate of change technique which models the behaviour of scout bee to improve the performance of the standard ABC in terms of exploration is introduced. The technique is called artificial bee colony rate of change (ABC-ROC because the scout bee process depends on the rate of change on the performance graph, replace the parameter limit. The performance of ABC-ROC is analysed on a set of benchmark problems and also on the effect of the parameter colony size. Furthermore, the performance of ABC-ROC is compared with the state of the art algorithms.
Pang, Chaoyang; Jiang, Gang; Wang, Shipeng; Hu, Benqiong; Liu, Qingzhong; Deng, Youping; Huang, Xudong
2012-01-01
As Alzheimer's Disease (AD) is the most common form of dementia, the study of AD-related genes via biocomputation is an important research topic. One method of studying AD-related gene is to cluster similar genes together into a gene order. Gene order is a good clustering method as the results can be optimal globally while other clustering methods are only optimal locally. Herein we use the Ant Colony Optimisation (ACO)-based algorithm to calculate the gene order from an Alzheimer's DNA microarray dataset. We test it with four distance measurements: Pearson distance, Spearmen distance, Euclidean distance, and squared Euclidean distance. Our computing results indicate: a different distance formula generated a different quality of gene order, the squared Euclidean distance approach produced the optimal AD-related gene order.
Multiple Optimal Path Identification using Ant Colony Optimisation in Wireless Sensor Network
Directory of Open Access Journals (Sweden)
Aniket. A. Gurav
2013-10-01
Full Text Available Wireless Sensor Network WSN is tightly constrained for resources like energy, computational power andmemory. Many applications of WSN require to communicate sensitive information at sensor nodes SN toBase station BS. The basic performance of WSN depends upon the path length and numbers of nodes in thepath by which data is forwarded to BS. In this paper we present bio-inspired Ant Colony Optimisation ACOalgorithm for Optimal Path Identification OPI for packet transmission to communicate between SN to BS.Our modified algorithm OPI using ACO is base-station driven which considers the path length and thenumber of hops in path for data packet transmission. This modified algorithm finds optimal path OP aswell as several suboptimal paths between SN & BS which are useful for effective communication.
Directory of Open Access Journals (Sweden)
Joko Siswanto
2013-12-01
Full Text Available In the election of the road network usually use road network system optimization considerations with aggregative behavior by determining the shortest route or the lowest cost. Determinants of consumer behavior in route selection and decision-making of the most dominant (disaggregated. Route selection optimization model based on user behavior can be implemented from the results of model development preference with Conjoint analysis. Preferences user behavior seems, is directly proportional to the convenience, the crowd, the facilities, the ease, safety, and inversely proportional to the density. Route selection optimization model with the development of ant-colony optimization formula can be applied with the substitution probability of interaction and preferences as well as the network models incorporate the concept of route selection based on consumer behavior and the physical condition of the network.
Institute of Scientific and Technical Information of China (English)
YAN Shiliang; WANG Yinling
2007-01-01
Travelling Salesman Problem (TSP) is a classical optimization problem and it is one of a class of NP-Problem. The purposes of this work is to apply data mining methodologies to explore the patterns in data generated by an Ant Colony Algorithm (ACA) performing a searching operation and to develop a rule set searcher which approximates the ACA'S searcher. An attribute-oriented induction methodology was used to explore the relationship between an operations' sequence and its attributes and a set of rules has been developed. At the end of this paper, the experimental results have shown that the proposed approach has good performance with respect to the quality of solution and the speed of computation.
DESIGNING DAILY PATROL ROUTES FOR POLICING BASED ON ANT COLONY ALGORITHM
Directory of Open Access Journals (Sweden)
H. Chen
2015-07-01
Full Text Available In this paper, we address the problem of planning police patrol routes to regularly cover street segments of high crime density (hotspots with limited police forces. A good patrolling strategy is required to minimise the average time lag between two consecutive visits to hotspots, as well as coordinating multiple patrollers and imparting unpredictability in patrol routes. Previous studies have designed different police patrol strategies for routing police patrol, but these strategies have difficulty in generalising to real patrolling and meeting various requirements. In this research we develop a new police patrolling strategy based on Bayesian method and ant colony algorithm. In this strategy, virtual marker (pheromone is laid to mark the visiting history of each crime hotspot, and patrollers continuously decide which hotspot to patrol next based on pheromone level and other variables. Simulation results using real data testifies the effective, scalable, unpredictable and extensible nature of this strategy.
Chang, Yung-Chia; Li, Vincent C.; Chiang, Chia-Ju
2014-04-01
Make-to-order or direct-order business models that require close interaction between production and distribution activities have been adopted by many enterprises in order to be competitive in demanding markets. This article considers an integrated production and distribution scheduling problem in which jobs are first processed by one of the unrelated parallel machines and then distributed to corresponding customers by capacitated vehicles without intermediate inventory. The objective is to find a joint production and distribution schedule so that the weighted sum of total weighted job delivery time and the total distribution cost is minimized. This article presents a mathematical model for describing the problem and designs an algorithm using ant colony optimization. Computational experiments illustrate that the algorithm developed is capable of generating near-optimal solutions. The computational results also demonstrate the value of integrating production and distribution in the model for the studied problem.
The Lobe Fissure Tracking by the Modified Ant Colony Optimization Framework in CT Images
Directory of Open Access Journals (Sweden)
Chii-Jen Chen
2014-11-01
Full Text Available Chest computed tomography (CT is the most commonly used technique for the inspection of lung lesions. However, the lobe fissures in lung CT is still difficult to observe owing to its imaging structure. Therefore, in this paper, we aimed to develop an efficient tracking framework to extract the lobe fissures by the proposed modified ant colony optimization (ACO algorithm. We used the method of increasing the consistency of pheromone on lobe fissure to improve the accuracy of path tracking. In order to validate the proposed system, we had tested our method in a database from 15 lung patients. In the experiment, the quantitative assessment shows that the proposed ACO method achieved the average F-measures of 80.9% and 82.84% in left and right lungs, respectively. The experiments indicate our method results more satisfied performance, and can help investigators detect lung lesion for further examination.
Efficiency improvement of ant colony optimization in solving the moderate LTSP
Institute of Scientific and Technical Information of China (English)
Munan Li
2015-01-01
In solving smal- to medium-scale travel ing salesman problems (TSPs) of both symmetric and asymmetric types, the traditional ant colony optimization (ACO) algorithm could work wel , providing high accuracy and satisfactory efficiency. However, when the scale of the TSP increases, ACO, a heuristic algorithm, is greatly chal enged with respect to accuracy and efficiency. A novel pheromone-trail updating strategy that moderately reduces the iteration time required in real optimization problem-solving is proposed. In comparison with the traditional strategy of the ACO in several experiments, the proposed strategy shows advan-tages in performance. Therefore, this strategy of pheromone-trail updating is proposed as a valuable approach that reduces the time-complexity and increases its efficiency with less iteration time in real optimization applications. Moreover, this strategy is espe-cial y applicable in solving the moderate large-scale TSPs based on ACO.
3D sensor placement strategy using the full-range pheromone ant colony system
Shuo, Feng; Jingqing, Jia
2016-07-01
An optimized sensor placement strategy will be extremely beneficial to ensure the safety and cost reduction considerations of structural health monitoring (SHM) systems. The sensors must be placed such that important dynamic information is obtained and the number of sensors is minimized. The practice is to select individual sensor directions by several 1D sensor methods and the triaxial sensors are placed in these directions for monitoring. However, this may lead to non-optimal placement of many triaxial sensors. In this paper, a new method, called FRPACS, is proposed based on the ant colony system (ACS) to solve the optimal placement of triaxial sensors. The triaxial sensors are placed as single units in an optimal fashion. And then the new method is compared with other algorithms using Dalian North Bridge. The computational precision and iteration efficiency of the FRPACS has been greatly improved compared with the original ACS and EFI method.
Energy Efficient Ant Colony Algorithms for Data Aggregation in Wireless Sensor Networks
Lin, Chi; Xia, Feng; Li, Mingchu; Yao, Lin; Pei, Zhongyi
2012-01-01
In this paper, a family of ant colony algorithms called DAACA for data aggregation has been presented which contains three phases: the initialization, packet transmission and operations on pheromones. After initialization, each node estimates the remaining energy and the amount of pheromones to compute the probabilities used for dynamically selecting the next hop. After certain rounds of transmissions, the pheromones adjustment is performed periodically, which combines the advantages of both global and local pheromones adjustment for evaporating or depositing pheromones. Four different pheromones adjustment strategies are designed to achieve the global optimal network lifetime, namely Basic-DAACA, ES-DAACA, MM-DAACA and ACS-DAACA. Compared with some other data aggregation algorithms, DAACA shows higher superiority on average degree of nodes, energy efficiency, prolonging the network lifetime, computation complexity and success ratio of one hop transmission. At last we analyze the characteristic of DAACA in th...
Optimizing Route for Hazardous Materials Logistics Based on Hybrid Ant Colony Algorithm
Directory of Open Access Journals (Sweden)
Haixing Wang
2013-01-01
Full Text Available Optimizing Route for Hazardous Materials Logistics (ORHML belongs to a class of problems referred to as NP-Hard, and a strict constraint of it makes it harder to solve. In order to dealing with ORHML, an improved hybrid ant colony algorithm (HACA was devised. To achieve the purpose of balancing risk and cost for route based on the principle of ACA that used to solve TSP, the improved HACA was designed. Considering the capacity of road network and the maximum expected risk limits, a route optimization model to minimize the total cost is established based on network flow theory. Improvement on route construction rule and pheromone updating rule was adopted on the basis of the former algorithm. An example was analyzed to demonstrate the correctness of the application. It is proved that improved HACA is efficient and feasible in solving ORHML.
Design of FIR Filters with Discrete Coefficients using Ant Colony Optimization
Tsutsumi, Shuntaro; Suyama, Kenji
In this paper, we propose a new design method for linear phase FIR (Finite Impulse Response) filters with discrete coefficients. In a hardware implementation, filter coefficients must be represented as discrete values. The design problem of digital filters with discrete coefficients is formulated as the integer programming problem. Then, an enormous amount of computational time is required to solve the problem in a strict solver. Recently, ACO (Ant Colony Optimization) which is one heuristic approach, is used widely for solving combinational problem like the traveling salesman problem. In our method, we formulate the design problem as the 0-1 integer programming problem and solve it by using the ACO. Several design examples are shown to present effectiveness of the proposed method.
Rescheduling of observing spacecraft using fuzzy neural network and ant colony algorithm
Directory of Open Access Journals (Sweden)
Li Yuqing
2014-06-01
Full Text Available This paper aims at rescheduling of observing spacecraft imaging plans under uncertainties. Firstly, uncertainties in spacecraft observation scheduling are analyzed. Then, considering the uncertainties with fuzzy features, this paper proposes a fuzzy neural network and a hybrid rescheduling policy to deal with them. It then establishes a mathematical model and manages to solve the rescheduling problem by proposing an ant colony algorithm, which introduces an adaptive control mechanism and takes advantage of the information in an existing schedule. Finally, the above method is applied to solve the rescheduling problem of a certain type of earth-observing satellite. The computation of the example shows that the approach is feasible and effective in dealing with uncertainties in spacecraft observation scheduling. The approach designed here can be useful in solving the problem that the original schedule is contaminated by disturbances.
Reliability worth applied to transmission expansion planning based on ant colony system
Energy Technology Data Exchange (ETDEWEB)
Leite da Silva, Armando M.; Rezende, Leandro S. [Institute of Electric Systems and Energy, Federal University of Itajuba, UNIFEI (Brazil); da Fonseca Manso, Luiz A.; de Resende, Leonidas C. [Department of Electrical Engineering, Federal University of Sao Joao del Rei, UFSJ (Brazil)
2010-12-15
This paper proposes a new methodology to solve transmission expansion planning (TEP) problems in power system, based on the metaheuristic ant colony optimisation (ACO). The TEP problem includes the search for the least cost solution, bearing in mind investment cost and reliability worth. Reliability worth is considered through the assessment of the interruption costs represented by the index LOLC - loss of load cost. The focus of this work is the development of a tool for the multi-stage planning of transmission systems and how reliability aspects can influence on the decision-making process. The applications of the proposed methodology are illustrated through case studies carried out using a test system and a real sub-transmission network. (author)
Ant colony optimization algorithm and its application to Neuro-Fuzzy controller design
Institute of Scientific and Technical Information of China (English)
无
2007-01-01
An adaptive ant colony algorithm is proposed based on dynamically adjusting the strategy of updating trail information.The algorithm can keep good balance between accelerating convergence and averting precocity and stagnation.The results of function optimization show that the algorithm has good searching ability and high convergence speed.The algorithm is employed to design a neuro-fuzzy controller for real-time control of an inverted pendulum.In order to avoid the combinatorial explosion of fuzzy.rules due to multivariable inputs,a state variable synthesis scheme is emploved to reduce the number of fuzzy rules greatly.The simulation results show that the designed controller can control the inverted pendulum successfully.
Rescheduling of observing spacecraft using fuzzy neural network and ant colony algorithm
Institute of Scientific and Technical Information of China (English)
Li Yuqing; Wang Rixin; Xu Minqiang
2014-01-01
This paper aims at rescheduling of observing spacecraft imaging plans under uncertain-ties. Firstly, uncertainties in spacecraft observation scheduling are analyzed. Then, considering the uncertainties with fuzzy features, this paper proposes a fuzzy neural network and a hybrid resched-uling policy to deal with them. It then establishes a mathematical model and manages to solve the rescheduling problem by proposing an ant colony algorithm, which introduces an adaptive control mechanism and takes advantage of the information in an existing schedule. Finally, the above method is applied to solve the rescheduling problem of a certain type of earth-observing satellite. The computation of the example shows that the approach is feasible and effective in dealing with uncertainties in spacecraft observation scheduling. The approach designed here can be useful in solving the problem that the original schedule is contaminated by disturbances.
Directory of Open Access Journals (Sweden)
Jiang Ting
2010-01-01
Full Text Available We optimize the cluster structure to solve problems such as the uneven energy consumption of the radar sensor nodes and random cluster head selection in the traditional clustering routing algorithm. According to the defined cost function for clusters, we present the clustering algorithm which is based on radio-free space path loss. In addition, we propose the energy and distance pheromones based on the residual energy and aggregation of the radar sensor nodes. According to bionic heuristic algorithm, a new ant colony-based clustering algorithm for radar sensor networks is also proposed. Simulation results show that this algorithm can get a better balance of the energy consumption and then remarkably prolong the lifetime of the radar sensor network.
Automatic boiling water reactor loading pattern design using ant colony optimization algorithm
Energy Technology Data Exchange (ETDEWEB)
Wang, C.-D. [Department of Engineering and System Science, National Tsing Hua University, 101, Section 2 Kuang Fu Road, Hsinchu 30013, Taiwan (China); Nuclear Engineering Division, Institute of Nuclear Energy Research, No. 1000, Wenhua Rd., Jiaan Village, Longtan Township, Taoyuan County 32546, Taiwan (China)], E-mail: jdwang@iner.gov.tw; Lin Chaung [Department of Engineering and System Science, National Tsing Hua University, 101, Section 2 Kuang Fu Road, Hsinchu 30013, Taiwan (China)
2009-08-15
An automatic boiling water reactor (BWR) loading pattern (LP) design methodology was developed using the rank-based ant system (RAS), which is a variant of the ant colony optimization (ACO) algorithm. To reduce design complexity, only the fuel assemblies (FAs) of one eight-core positions were determined using the RAS algorithm, and then the corresponding FAs were loaded into the other parts of the core. Heuristic information was adopted to exclude the selection of the inappropriate FAs which will reduce search space, and thus, the computation time. When the LP was determined, Haling cycle length, beginning of cycle (BOC) shutdown margin (SDM), and Haling end of cycle (EOC) maximum fraction of limit for critical power ratio (MFLCPR) were calculated using SIMULATE-3 code, which were used to evaluate the LP for updating pheromone of RAS. The developed design methodology was demonstrated using FAs of a reference cycle of the BWR6 nuclear power plant. The results show that, the designed LP can be obtained within reasonable computation time, and has a longer cycle length than that of the original design.
A New Tool Wear Monitoring Method Based on Ant Colony Algorithm
Directory of Open Access Journals (Sweden)
Qianjian Guo
2013-06-01
Full Text Available Tool wear prediction is a major contributor to the dimensional errors of a work piece in precision machining, which plays an important role in industry for higher productivity and product quality. Tool wear monitoring is an effective way to predict the tool wear loss in milling process. In this paper, a new bionic prediction model is presented based on the generation mechanism of tool wear loss. Different milling conditions are estimated as the input variables, tool wear loss is estimated as the output variable, neural network method is proposed to establish the mapping relation and ant algorithm is used to train the weights of BP neural networks during tool wear modeling. Finally, a real-time tool wear loss estimator is developed based on ant colony alogrithm and experiments have been conducted for measuring tool wear based on the estimator in a milling machine. The experimental and estimated results are found to be in satisfactory agreement with average error lower than 6%.
Energy Technology Data Exchange (ETDEWEB)
Esquivel-Estrada, Jaime, E-mail: jaime.esquivel@fi.uaemex.m [Facultad de Ingenieria, Universidad Autonoma del Estado de Mexico, Cerro de Coatepec S/N, Toluca de Lerdo, Estado de Mexico 50000 (Mexico); Instituto Nacional de Investigaciones Nucleares, Carr. Mexico Toluca S/N, Ocoyoacac, Estado de Mexico 52750 (Mexico); Ortiz-Servin, Juan Jose, E-mail: juanjose.ortiz@inin.gob.m [Instituto Nacional de Investigaciones Nucleares, Carr. Mexico Toluca S/N, Ocoyoacac, Estado de Mexico 52750 (Mexico); Castillo, Jose Alejandro; Perusquia, Raul [Instituto Nacional de Investigaciones Nucleares, Carr. Mexico Toluca S/N, Ocoyoacac, Estado de Mexico 52750 (Mexico)
2011-01-15
This paper presents some results of the implementation of several optimization algorithms based on ant colonies, applied to the fuel reload design in a Boiling Water Reactor. The system called Azcaxalli is constructed with the following algorithms: Ant Colony System, Ant System, Best-Worst Ant System and MAX-MIN Ant System. Azcaxalli starts with a random fuel reload. Ants move into reactor core channels according to the State Transition Rule in order to select two fuel assemblies into a 1/8 part of the reactor core and change positions between them. This rule takes into account pheromone trails and acquired knowledge. Acquired knowledge is obtained from load cycle values of fuel assemblies. Azcaxalli claim is to work in order to maximize the cycle length taking into account several safety parameters. Azcaxalli's objective function involves thermal limits at the end of the cycle, cold shutdown margin at the beginning of the cycle and the neutron effective multiplication factor for a given cycle exposure. Those parameters are calculated by CM-PRESTO code. Through the Haling Principle is possible to calculate the end of the cycle. This system was applied to an equilibrium cycle of 18 months of Laguna Verde Nuclear Power Plant in Mexico. The results show that the system obtains fuel reloads with higher cycle lengths than the original fuel reload. Azcaxalli results are compared with genetic algorithms, tabu search and neural networks results.
Wang, Fu; Liu, Bo; Zhang, Lijia; Xin, Xiangjun; Tian, Qinghua; Zhang, Qi; Rao, Lan; Tian, Feng; Luo, Biao; Liu, Yingjun; Tang, Bao
2016-10-01
Elastic Optical Networks are considered to be a promising technology for future high-speed network. In this paper, we propose a RSA algorithm based on the ant colony optimization of minimum consecutiveness loss (ACO-MCL). Based on the effect of the spectrum consecutiveness loss on the pheromone in the ant colony optimization, the path and spectrum of the minimal impact on the network are selected for the service request. When an ant arrives at the destination node from the source node along a path, we assume that this path is selected for the request. We calculate the consecutiveness loss of candidate-neighbor link pairs along this path after the routing and spectrum assignment. Then, the networks update the pheromone according to the value of the consecutiveness loss. We save the path with the smallest value. After multiple iterations of the ant colony optimization, the final selection of the path is assigned for the request. The algorithms are simulated in different networks. The results show that ACO-MCL algorithm performs better in blocking probability and spectrum efficiency than other algorithms. Moreover, the ACO-MCL algorithm can effectively decrease spectrum fragmentation and enhance available spectrum consecutiveness. Compared with other algorithms, the ACO-MCL algorithm can reduce the blocking rate by at least 5.9% in heavy load.
Udiani, Oyita; Pinter-Wollman, Noa; Kang, Yun
2015-02-21
Collective behaviors in social insect societies often emerge from simple local rules. However, little is known about how these behaviors are dynamically regulated in response to environmental changes. Here, we use a compartmental modeling approach to identify factors that allow harvester ant colonies to regulate collective foraging activity in response to their environment. We propose a set of differential equations describing the dynamics of: (1) available foragers inside the nest, (2) active foragers outside the nest, and (3) successful returning foragers, to understand how colony-specific parameters, such as baseline number of foragers, interactions among foragers, food discovery rates, successful forager return rates, and foraging duration might influence collective foraging dynamics, while maintaining functional robustness to perturbations. Our analysis indicates that the model can undergo a forward (transcritical) bifurcation or a backward bifurcation depending on colony-specific parameters. In the former case, foraging activity persists when the average number of recruits per successful returning forager is larger than one. In the latter case, the backward bifurcation creates a region of bistability in which the size and fate of foraging activity depends on the distribution of the foraging workforce among the model's compartments. We validate the model with experimental data from harvester ants (Pogonomyrmex barbatus) and perform sensitivity analysis. Our model provides insights on how simple, local interactions can achieve an emergent and robust regulatory system of collective foraging activity in ant colonies.
Fuel lattice design in a boiling water reactor using an ant-colony-based system
Energy Technology Data Exchange (ETDEWEB)
Montes, Jose Luis, E-mail: joseluis.montes@inin.gob.mx [Instituto Nacional de Investigaciones Nucleares, Carr. Mexico-Toluca S/N, La Marquesa, Ocoyoacac, Estado de Mexico, CP 52750 (Mexico); Facultad de Ciencias, Universidad Autonoma del Estado de Mexico (Mexico); Francois, Juan-Luis, E-mail: juan.luis.francois@gmail.com [Departamento de Sistemas Energeticos, Facultad de Ingenieria, Universidad Nacional Autonoma de Mexico, Paseo Cuauhnahuac 8532, Jiutepec, Mor., CP 62550 (Mexico); Ortiz, Juan Jose, E-mail: juanjose.ortiz@inin.gob.mx [Instituto Nacional de Investigaciones Nucleares, Carr. Mexico-Toluca S/N, La Marquesa, Ocoyoacac, Estado de Mexico, CP 52750 (Mexico); Martin-del-Campo, Cecilia, E-mail: cecilia.martin.del.campo@gmail.com [Departamento de Sistemas Energeticos, Facultad de Ingenieria, Universidad Nacional Autonoma de Mexico, Paseo Cuauhnahuac 8532, Jiutepec, Mor., CP 62550 (Mexico); Perusquia, Raul, E-mail: raul.perusquia@inin.gob.mx [Instituto Nacional de Investigaciones Nucleares, Carr. Mexico-Toluca S/N, La Marquesa, Ocoyoacac, Estado de Mexico, CP 52750 (Mexico)
2011-06-15
Research highlights: > We present an ant-colony-based system for BWR fuel lattice design and optimization. > Assessment of candidate solutions at 0.0 MWd/kg {sup 235}U seems to have a limited scope. > Suitable heuristic rules enable more realistic fuel lattice designs. > The election of the objective has a large impact in CPU time. > ACS enables an important decrease of the initial average U-235 enrichment. - Abstract: This paper presents a new approach to deal with the boiling water reactor radial fuel lattice design. The goal is to optimize the distribution of both, the fissionable material, and the reactivity control poison material inside the fuel lattice at the beginning of its life. An ant-colony-based system was used to search for either: the optimum location of the poisoned pin inside the lattice, or the U{sup 235} enrichment and Gd{sub 2}O{sub 3} concentrations. In the optimization process, in order to know the parameters of the candidate solutions, the neutronic simulator CASMO-4 transport code was used. A typical 10 x 10 BWR fuel lattice with an initial average U{sup 235} enrichment of 4.1%, used in the current operation of Laguna Verde Nuclear Power Plant was taken as a reference. With respect to that reference lattice, it was possible to decrease the average U{sup 235} enrichment up to 3.949%, this obtained value represents a decrease of 3.84% with respect to the reference U{sup 235} enrichment; whereas, the k-infinity was inside the {+-}100 pcm's range, and there was a difference of 0.94% between the local power peaking factor and the lattice reference value. Particular emphasis was made on defining the objective function which is used for making the assessment of candidate solutions. In a typical desktop personal computer, about four hours of CPU time were necessary for the algorithm to fulfill the goals of the optimization process. The results obtained with the application of the implemented system showed that the proposed approach represents a
Spirov, Alexander V; Zamdborg, Leonid; Merelo, Juan J; Levchenko, Vladimir F
2009-01-01
Modern evolutionary computation utilizes heuristic optimizations based upon concepts borrowed from the Darwinian theory of natural selection. We believe that a vital direction in this field must be algorithms that model the activity of genomic parasites, such as transposons, in biological evolution. This publication is our first step in the direction of developing a minimal assortment of algorithms that simulate the role of genomic parasites. Specifically, we started in the domain of genetic algorithms (GA) and selected the Artificial Ant Problem as a test case. We define these artificial transposons as a fragment of an ant's code that possesses properties that cause it to stand apart from the rest. We concluded that artificial transposons, analogous to real transposons, are truly capable of acting as intelligent mutators that adapt in response to an evolutionary problem in the course of co-evolution with their hosts.
Queen-worker caste ratio depends on colony size in the pharaoh ant (Monomorium pharaonis)
DEFF Research Database (Denmark)
Schmidt, Anna Mosegaard; Linksvayer, Timothy Arnold; Boomsma, Jacobus Jan
2011-01-01
The success of an ant colony depends on the simultaneous presence of reproducing queens and nonreproducing workers in a ratio that will maximize colony growth and reproduction. Despite its presumably crucial role, queen–worker caste ratios (the ratio of adult queens to workers) and the factors......, for the first time in a social insect, we confirmed the general life history prediction by Smith and Fretwell (Am Nat 108:499–506, 1974) that offspring number varies more than offspring size. Our findings document a high level of plasticity in energy allocation toward female castes and suggest that polygynous...
Energy Technology Data Exchange (ETDEWEB)
Carvajal, M A; Palma, A J [Departamento de Electronica y Tecnologia de Computadores, Universidad de Granada, E-18071 Granada (Spain); Garcia-Pareja, S [Servicio de Radiofisica Hospitalaria, Hospital Regional Universitario ' Carlos Haya' , Avda Carlos Haya, s/n, E-29010 Malaga (Spain); Guirado, D [Servicio de RadiofIsica, Hospital Universitario ' San Cecilio' , Avda Dr Oloriz, 16, E-18012 Granada (Spain); Vilches, M [Servicio de Fisica y Proteccion Radiologica, Hospital Regional Universitario ' Virgen de las Nieves' , Avda Fuerzas Armadas, 2, E-18014 Granada (Spain); Anguiano, M; Lallena, A M [Departamento de Fisica Atomica, Molecular y Nuclear, Universidad de Granada, E-18071 Granada (Spain)], E-mail: carvajal@ugr.es, E-mail: garciapareja@gmail.com, E-mail: dguirado@ugr.es, E-mail: mvilches@ugr.es, E-mail: mangui@ugr.es, E-mail: ajpalma@ugr.es, E-mail: lallena@ugr.es
2009-10-21
In this work we have developed a simulation tool, based on the PENELOPE code, to study the response of MOSFET devices to irradiation with high-energy photons. The energy deposited in the extremely thin silicon dioxide layer has been calculated. To reduce the statistical uncertainties, an ant colony algorithm has been implemented to drive the application of splitting and Russian roulette as variance reduction techniques. In this way, the uncertainty has been reduced by a factor of {approx}5, while the efficiency is increased by a factor of above 20. As an application, we have studied the dependence of the response of the pMOS transistor 3N163, used as a dosimeter, with the incidence angle of the radiation for three common photons sources used in radiotherapy: a {sup 60}Co Theratron-780 and the 6 and 18 MV beams produced by a Mevatron KDS LINAC. Experimental and simulated results have been obtained for gantry angles of 0 deg., 15 deg., 30 deg., 45 deg., 60 deg. and 75 deg. The agreement obtained has permitted validation of the simulation tool. We have studied how to reduce the angular dependence of the MOSFET response by using an additional encapsulation made of brass in the case of the two LINAC qualities considered.
Graph Theory and ANT Colony Optimization Approach for Forest Patch Connectivity Analysis
Shantala Devi, B. S.; Murthy, M. S. R.; Pujar, G. S.; Debnath, B.
2011-08-01
Forest connectivity is necessary for prioritizing biodiversity conservation. Connectivity indices facilitate to predict the movement pattern of species across complex landscapes. Change in area and inter-patch distance in forest affects the biodiversity, wildlife movement, seed dispersal and other ecological factors. In graph theory components play an important role to analyze the group of patches and its impact with reference to the threshold distance between the patches. The study on link, threshold distance and components showed that with the increase in threshold distance, number of components decreased and number of links increased. Also Integral index of connectivity importance value (dIIC > 0.05) is high for big forest patches and considered to be intact forest. For those less than 0.05 importance value requires protection and conservation. Hence dIIC is categorised into Very low, low, Medium, high and Very high to analyze the degree of connectivity. Choosing correct threshold distance based on the requirement of species movement is preferred. Based on the selection of potential habitat patches shortest path between them is determined using Ant Colony Optimization (ACO) Technique. Vegetation type Map, Slope, Elevation, Disturbance Index, Biological Richness Map and DIIC layers facilitated to analyze the optimal path of the species through ACO for connectivity. Graph Theory and ACO works as a robust tool for Biodiversity Conservation.
An Ant Colony Optimization Based Feature Selection for Web Page Classification
Directory of Open Access Journals (Sweden)
Esra Saraç
2014-01-01
Full Text Available The increased popularity of the web has caused the inclusion of huge amount of information to the web, and as a result of this explosive information growth, automated web page classification systems are needed to improve search engines’ performance. Web pages have a large number of features such as HTML/XML tags, URLs, hyperlinks, and text contents that should be considered during an automated classification process. The aim of this study is to reduce the number of features to be used to improve runtime and accuracy of the classification of web pages. In this study, we used an ant colony optimization (ACO algorithm to select the best features, and then we applied the well-known C4.5, naive Bayes, and k nearest neighbor classifiers to assign class labels to web pages. We used the WebKB and Conference datasets in our experiments, and we showed that using the ACO for feature selection improves both accuracy and runtime performance of classification. We also showed that the proposed ACO based algorithm can select better features with respect to the well-known information gain and chi square feature selection methods.
Directory of Open Access Journals (Sweden)
Jing Yang
2010-05-01
Full Text Available For monitoring burst events in a kind of reactive wireless sensor networks (WSNs, a multipath routing protocol (MRP based on dynamic clustering and ant colony optimization (ACO is proposed.. Such an approach can maximize the network lifetime and reduce the energy consumption. An important attribute of WSNs is their limited power supply, and therefore some metrics (such as energy consumption of communication among nodes, residual energy, path length were considered as very important criteria while designing routing in the MRP. Firstly, a cluster head (CH is selected among nodes located in the event area according to some parameters, such as residual energy. Secondly, an improved ACO algorithm is applied in the search for multiple paths between the CH and sink node. Finally, the CH dynamically chooses a route to transmit data with a probability that depends on many path metrics, such as energy consumption. The simulation results show that MRP can prolong the network lifetime, as well as balance of energy consumption among nodes and reduce the average energy consumption effectively.
Directory of Open Access Journals (Sweden)
N. Kumarasabapathy
2015-01-01
Full Text Available This paper proposes a fuzzy logic based new control scheme for the Unified Power Quality Conditioner (UPQC for minimizing the voltage sag and total harmonic distortion in the distribution system consequently to improve the power quality. UPQC is a recent power electronic module which guarantees better power quality mitigation as it has both series-active and shunt-active power filters (APFs. The fuzzy logic controller has recently attracted a great deal of attention and possesses conceptually the quality of the simplicity by tackling complex systems with vagueness and ambiguity. In this research, the fuzzy logic controller is utilized for the generation of reference signal controlling the UPQC. To enable this, a systematic approach for creating the fuzzy membership functions is carried out by using an ant colony optimization technique for optimal fuzzy logic control. An exhaustive simulation study using the MATLAB/Simulink is carried out to investigate and demonstrate the performance of the proposed fuzzy logic controller and the simulation results are compared with the PI controller in terms of its performance in improving the power quality by minimizing the voltage sag and total harmonic distortion.
A modified ant colony optimization to solve multi products inventory routing problem
Wong, Lily; Moin, Noor Hasnah
2014-07-01
This study considers a one-to-many inventory routing problem (IRP) network consisting of a manufacturer that produces multi products to be transported to many geographically dispersed customers. We consider a finite horizon where a fleet of capacitated homogeneous vehicles, housed at a depot/warehouse, transport products from the warehouse to meet the demand specified by the customers in each period. The demand for each product is deterministic and time varying and each customer requests a distinct product. The inventory holding cost is product specific and is incurred at the customer sites. The objective is to determine the amount on inventory and to construct a delivery schedule that minimizes both the total transportation and inventory holding costs 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 solution) for each problem considered. We propose a modified ant colony optimization (ACO) to solve the problem and the built route is improved by using local search. ACO performs better on large instances compared to the upper bound.
A multiple classifier system based on Ant-Colony Optimization for Hyperspectral image classification
Tang, Ke; Xie, Li; Li, Guangyao
2017-01-01
Hyperspectral images which hold a large quantity of land information enables image classification. Traditional classification methods usually works on multispectral images. However, the high dimensionality in feature space influences the accuracy while using these classification algorithms, such as statistical classifiers or decision trees. This paper proposes a multiple classifier system (MCS) based on ant colony optimization (ACO) algorithm to improve the classification ability. ACO method has been implemented on multispectral images in researches, but seldom to hyperspectral images. In order to overcome the limitation of ACO method on dealing with high dimensionality, MCS is introduced to combine the outputs of each single ACO classifier based on the credibility of rules. Mutual information is applied to discretizing features from the data set and provides the criterion of band selection and band grouping algorithms. The performance of the proposed method is validated with ROSIS Pavia data set, and compared to k-nearest neighbour (KNN) algorithm. Experimental results prove that the proposed method is feasible to classify hyperspectral images.
Directory of Open Access Journals (Sweden)
Guohua Zou
2016-12-01
Full Text Available New medical imaging technology, such as Computed Tomography and Magnetic Resonance Imaging (MRI, has been widely used in all aspects of medical diagnosis. The purpose of these imaging techniques is to obtain various qualitative and quantitative data of the patient comprehensively and accurately, and provide correct digital information for diagnosis, treatment planning and evaluation after surgery. MR has a good imaging diagnostic advantage for brain diseases. However, as the requirements of the brain image definition and quantitative analysis are always increasing, it is necessary to have better segmentation of MR brain images. The FCM (Fuzzy C-means algorithm is widely applied in image segmentation, but it has some shortcomings, such as long computation time and poor anti-noise capability. In this paper, firstly, the Ant Colony algorithm is used to determine the cluster centers and the number of FCM algorithm so as to improve its running speed. Then an improved Markov random field model is used to improve the algorithm, so that its antinoise ability can be improved. Experimental results show that the algorithm put forward in this paper has obvious advantages in image segmentation speed and segmentation effect.
Efficient Stagnation Avoidance for Manets with Local Repair Strategy Using ant Colony Optimization
Directory of Open Access Journals (Sweden)
Sharvani G S
2012-10-01
Full Text Available Wireless networks such as Mobile AdHoc Networks (MANETs have many advantages compared to wired networks. In MANETs the communication is not limited to a certain geometrical region. Swarm Intelligence based ACO algorithms provide interesting solutions to network routing problems. ACO based routing in MANETs will enhance the reliability and efficient packet delivery. They help in reducing control overhead due to their inherent scalable feature. The similarity between ant and nodes, colony and Wireless network helps to use ACO based routing in MANETs. The Termite Algorithms contains several tunable parameters and methods to automate the selection of optimal routes for different network conditions. However, Termite doesn’t contain methods for determination of QoS, Route Maintenance; Load balancing etc. The present work focuses on development of an efficient routing algorithm “Modified Termite algorithms” (MTA for MANETs. The MTA developed by adopting efficient pheromone evaporation technique will address to load balancing problems. By including QoS, efficient route maintenance, local repair strategy by prediction of node failures, the MTA is expected to enhance the performance of the network in terms of throughput, and reduction of End-to-end delay and routing overheads. The results of the analysis are presented in the paper.
Kumarasabapathy, N; Manoharan, P S
2015-01-01
This paper proposes a fuzzy logic based new control scheme for the Unified Power Quality Conditioner (UPQC) for minimizing the voltage sag and total harmonic distortion in the distribution system consequently to improve the power quality. UPQC is a recent power electronic module which guarantees better power quality mitigation as it has both series-active and shunt-active power filters (APFs). The fuzzy logic controller has recently attracted a great deal of attention and possesses conceptually the quality of the simplicity by tackling complex systems with vagueness and ambiguity. In this research, the fuzzy logic controller is utilized for the generation of reference signal controlling the UPQC. To enable this, a systematic approach for creating the fuzzy membership functions is carried out by using an ant colony optimization technique for optimal fuzzy logic control. An exhaustive simulation study using the MATLAB/Simulink is carried out to investigate and demonstrate the performance of the proposed fuzzy logic controller and the simulation results are compared with the PI controller in terms of its performance in improving the power quality by minimizing the voltage sag and total harmonic distortion.
Adaptive Ant Colony Clustering Method Applied to Finding Closely Communicating Community
Directory of Open Access Journals (Sweden)
Yan Liu
2012-02-01
Full Text Available The investigation of community structures in networks is an important issue in many domains and disciplines. Closely communicating community is different from the traditional community which emphasize particularly on structure or context. Our previous method played more emphasis on the feasibility that ant colony algorithm applied to community detection. However the essence of closely communicating community did not be described clearly. In this paper, the deﬁnition of closely communicating community is put forward ﬁrstly, the four features are described and corresponding methods are introduced to achieve the value of features between each pair. Meanwhile, pair propinquity and local propinquity are put forward and used to guide ants’ decision. Based on the previous work, the closely communicating community detection method is improved in four aspects of adaptive adjusting, which are entropy based weight modulation, combining historical paths and random wandering to select next coordination, the strategy of forcing unloading and the adaptive change of ant’s eyesight. The value selection of parameters is discussed in the portion of experiments, and the results also reveal the improvement of our algorithm in adaptive djusting.
Evaluation of Anaerobic Biofilm Reactor Kinetic Parameters Using Ant Colony Optimization.
Satya, Eswari Jujjavarapu; Venkateswarlu, Chimmiri
2013-09-01
Fixed bed reactors with naturally attached biofilms are increasingly used for anaerobic treatment of industry wastewaters due their effective treatment performance. The complex nature of biological reactions in biofilm processes often poses difficulty in analyzing them experimentally, and mathematical models could be very useful for their design and analysis. However, effective application of biofilm reactor models to practical problems suffers due to the lack of knowledge of accurate kinetic models and uncertainty in model parameters. In this work, an inverse modeling approach based on ant colony optimization is proposed and applied to estimate the kinetic and film thickness model parameters of wastewater treatment process in an anaerobic fixed bed biofilm reactor. Experimental data of pharmaceutical industry wastewater treatment process are used to determine the model parameters as a consequence of the solution of the rigorous mathematical models of the process. Results were evaluated for different modeling configurations derived from the combination of mathematical models, kinetic expressions, and optimization algorithms. Analysis of results showed that the two-dimensional mathematical model with Haldane kinetics better represents the pharmaceutical wastewater treatment in the biofilm reactor. The mathematical and kinetic modeling of this work forms a useful basis for the design and optimization of industry wastewater treating biofilm reactors.
An ant colony optimization based feature selection for web page classification.
Saraç, Esra; Özel, Selma Ayşe
2014-01-01
The increased popularity of the web has caused the inclusion of huge amount of information to the web, and as a result of this explosive information growth, automated web page classification systems are needed to improve search engines' performance. Web pages have a large number of features such as HTML/XML tags, URLs, hyperlinks, and text contents that should be considered during an automated classification process. The aim of this study is to reduce the number of features to be used to improve runtime and accuracy of the classification of web pages. In this study, we used an ant colony optimization (ACO) algorithm to select the best features, and then we applied the well-known C4.5, naive Bayes, and k nearest neighbor classifiers to assign class labels to web pages. We used the WebKB and Conference datasets in our experiments, and we showed that using the ACO for feature selection improves both accuracy and runtime performance of classification. We also showed that the proposed ACO based algorithm can select better features with respect to the well-known information gain and chi square feature selection methods.
A Novel Approach for Medical Image Stitching Using Ant Colony Optimization
Directory of Open Access Journals (Sweden)
Amrita
2014-05-01
Full Text Available Image stitching is one of important technologies in medical image processing field. In digital radiography oversized images have to be assembled from multiple exposures as the flat panel of an X-ray system cannot cover all part of a body. The stitching of X-ray images is carried out by employing two basic steps: Registration and Blending. The classical registration methods such as SIFT and SURF search for all the pixels to get the best registration. These methods are slow and cannot perform well for high resolution X-ray images. Therefore a fast and accurate feature based technique using ant colony optimization is implemented in the present work. This technique not only saves time but also gives the accuracy to stitch the image. This technique is also used for finding the edges for land marking and features of different X-ray images. Correlation is found between landmarks to check the alignment between the images and RANSAC algorithm is used to eliminate the spurious feature points. Finally alpha- blending technique is used to stitch the images.
Simulated Annealing-Based Ant Colony Algorithm for Tugboat Scheduling Optimization
Directory of Open Access Journals (Sweden)
Qi Xu
2012-01-01
Full Text Available As the “first service station” for ships in the whole port logistics system, the tugboat operation system is one of the most important systems in port logistics. This paper formulated the tugboat scheduling problem as a multiprocessor task scheduling problem (MTSP after analyzing the characteristics of tugboat operation. The model considers factors of multianchorage bases, different operation modes, and three stages of operations (berthing/shifting-berth/unberthing. The objective is to minimize the total operation times for all tugboats in a port. A hybrid simulated annealing-based ant colony algorithm is proposed to solve the addressed problem. By the numerical experiments without the shifting-berth operation, the effectiveness was verified, and the fact that more effective sailing may be possible if tugboats return to the anchorage base timely was pointed out; by the experiments with the shifting-berth operation, one can see that the objective is most sensitive to the proportion of the shifting-berth operation, influenced slightly by the tugboat deployment scheme, and not sensitive to the handling operation times.
Gilani, Seyed-Omid; Sattarvand, Javad
2016-02-01
Meeting production targets in terms of ore quantity and quality is critical for a successful mining operation. In-situ grade uncertainty causes both deviations from production targets and general financial deficits. A new stochastic optimization algorithm based on ant colony optimization (ACO) approach is developed herein to integrate geological uncertainty described through a series of the simulated ore bodies. Two different strategies were developed based on a single predefined probability value (Prob) and multiple probability values (Pro bnt), respectively in order to improve the initial solutions that created by deterministic ACO procedure. Application at the Sungun copper mine in the northwest of Iran demonstrate the abilities of the stochastic approach to create a single schedule and control the risk of deviating from production targets over time and also increase the project value. A comparison between two strategies and traditional approach illustrates that the multiple probability strategy is able to produce better schedules, however, the single predefined probability is more practical in projects requiring of high flexibility degree.
Research of Ant Colony Optimized Adaptive Control Strategy for Hybrid Electric Vehicle
Directory of Open Access Journals (Sweden)
Linhui Li
2014-01-01
Full Text Available Energy management control strategy of hybrid electric vehicle has a great influence on the vehicle fuel consumption with electric motors adding to the traditional vehicle power system. As vehicle real driving cycles seem to be uncertain, the dynamic driving cycles will have an impact on control strategy’s energy-saving effect. In order to better adapt the dynamic driving cycles, control strategy should have the ability to recognize the real-time driving cycle and adaptively adjust to the corresponding off-line optimal control parameters. In this paper, four types of representative driving cycles are constructed based on the actual vehicle operating data, and a fuzzy driving cycle recognition algorithm is proposed for online recognizing the type of actual driving cycle. Then, based on the equivalent fuel consumption minimization strategy, an ant colony optimization algorithm is utilized to search the optimal control parameters “charge and discharge equivalent factors” for each type of representative driving cycle. At last, the simulation experiments are conducted to verify the accuracy of the proposed fuzzy recognition algorithm and the validity of the designed control strategy optimization method.
Kumarasabapathy, N.; Manoharan, P. S.
2015-01-01
This paper proposes a fuzzy logic based new control scheme for the Unified Power Quality Conditioner (UPQC) for minimizing the voltage sag and total harmonic distortion in the distribution system consequently to improve the power quality. UPQC is a recent power electronic module which guarantees better power quality mitigation as it has both series-active and shunt-active power filters (APFs). The fuzzy logic controller has recently attracted a great deal of attention and possesses conceptually the quality of the simplicity by tackling complex systems with vagueness and ambiguity. In this research, the fuzzy logic controller is utilized for the generation of reference signal controlling the UPQC. To enable this, a systematic approach for creating the fuzzy membership functions is carried out by using an ant colony optimization technique for optimal fuzzy logic control. An exhaustive simulation study using the MATLAB/Simulink is carried out to investigate and demonstrate the performance of the proposed fuzzy logic controller and the simulation results are compared with the PI controller in terms of its performance in improving the power quality by minimizing the voltage sag and total harmonic distortion. PMID:26504895
CA Based Path Planning Method for Mobile Robots Enhanced by ant Colony Inspired Mechanis
Directory of Open Access Journals (Sweden)
Adel Akbarimajd
2011-05-01
Full Text Available In path planning of mobile robots dealing with concave obstacles is a major challenge. More specifically in real-time planning where there is no complete representation of the environment, this challenge would be much more problematic. In such cases local minimums and high computations cost are the most important problems. In this paper, in order to reduce computational cost, cellular automata as a distributed computational method with parallel processing properties is employed as tool for path planning purposes. The environment of the robot is modeled as a two dimensional cellular automata with four states. Evolutionary rules of the automata are proposed to perform the planning task. The proposed method is appropriate for single robot systems as well as multi robot systems. The proposed method is afterwards extended to be employed for concave obstacles using a ant colony inspired technique. The most superior advantage of the proposed method is its capability of real-time path planning of mobile robots with no need to prior representation of the environment.
The Model Identification for Small Unmanned Aerial Rotorcraft Based on Adaptive Ant Colony Algorithm
Institute of Scientific and Technical Information of China (English)
Xusheng Lei; Kexin Guo
2012-01-01
This paper proposes a model identification method to get high performance dynamic model of a small unmanned aerial rotorcraft.With the analysis of flight characteristics,a linear dynamic model is constructed by the small perturbation theory.Using the micro guidance navigation and control module,the system can record the control signals of servos,the state information of attitude and velocity information in sequence.After the data preprocessing,an adaptive ant colony algorithm is proposed to get optimal parameters of the dynamic model.With the adaptive adjustment of the pheromone in the selection process,the proposed model identification method can escape from local minima traps and get the optimal solution quickly.Performance analysis and experiments are conducted to validate the effectiveness of the identified dynamic model.Compared with real flight data,the identified model generated by the proposed method has a better performance than the model generated by the adaptive genetic algorithm.Based on the identified dynamic model,the small unmanned aerial rotorcraft can generate suitable control parameters to realize stable hovering,turning,and straight flight.
Directory of Open Access Journals (Sweden)
Jing Yang
2009-10-01
Full Text Available For monitoring burst events in a kind of reactive wireless sensor networks (WSNs, a multipath routing protocol (MRP based on dynamic clustering and ant colony optimization (ACO is proposed.. Such an approach can maximize the network lifetime and reduce the energy consumption. An important attribute of WSNs is its limited power supply, and therefore in MRP, some metrics (such as energy consumption of communication among nodes, residual energy, path length are considered as very important criteria while designing routing. Firstly, a cluster head (CH is selected among nodes located in the event area according to some parameters, such as residual energy. Secondly, an improved ACO algorithm is applied in search for multiple paths between the CH and sink node. Finally, the CH dynamically chooses a route to transmit data with a probability that depends on many path metrics, such as energy consumption. The simulation results show that MRP can prolong the network lifetime, as well as balance energy consumption among nodes and reduce the average energy consumption effectively.
Optimal management of substrates in anaerobic co-digestion: An ant colony algorithm approach.
Verdaguer, Marta; Molinos-Senante, María; Poch, Manel
2016-04-01
Sewage sludge (SWS) is inevitably produced in urban wastewater treatment plants (WWTPs). The treatment of SWS on site at small WWTPs is not economical; therefore, the SWS is typically transported to an alternative SWS treatment center. There is increased interest in the use of anaerobic digestion (AnD) with co-digestion as an SWS treatment alternative. Although the availability of different co-substrates has been ignored in most of the previous studies, it is an essential issue for the optimization of AnD co-digestion. In a pioneering approach, this paper applies an Ant-Colony-Optimization (ACO) algorithm that maximizes the generation of biogas through AnD co-digestion in order to optimize the discharge of organic waste from different waste sources in real-time. An empirical application is developed based on a virtual case study that involves organic waste from urban WWTPs and agrifood activities. The results illustrate the dominate role of toxicity levels in selecting contributions to the AnD input. The methodology and case study proposed in this paper demonstrate the usefulness of the ACO approach in supporting a decision process that contributes to improving the sustainability of organic waste and SWS management.
Directory of Open Access Journals (Sweden)
P. Mathiyalagan
2013-10-01
Full Text Available As grid is a heterogeneous environment, finding an optimal schedule for the job is always a complex task. In this paper, a hybridization technique using intelligent water drops and Ant colony optimization which are nature-inspired swarm intelligence approaches are used to find the best resource for the job. Intelligent water drops involves in finding out all matching resources for the job requirements and the routing information (optimal path to reach those resources. Ant Colony optimization chooses the best resource among all matching resources for the job. The objective of this approach is to converge to the optimal schedule faster, minimize the make span of the job, improve load balancing of resources and efficient utilization of available resources.
Institute of Scientific and Technical Information of China (English)
李树刚; 吴智铭; 庞小红
2004-01-01
In order to study the capacitated lot sizing problem for a supply chain of corporate multi-location fac-tories to minimize the total costs of production, inventory and transportation under the system capacity restriction and product due date, while at the same time considering the menu distributed balance, the mathematical pro-gramming models are decomposed and reduced from the 3 levels into 2 levels according to the idea of just-in-time production. In order to overcome the premature convergence of ACA (ant colony algorithms) , the idea of mute operation is adopted in genetic algorithms and a PACA (parallel ant colony algorithms) is proposed forsupply chain optimization. Finally, an illustrative example is given, and a comparison is made with standard BAR ( Branch and Bound) and PACA approach. The result shows that the latter is more effective and promis-ing.
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.
Energy Technology Data Exchange (ETDEWEB)
Garcia-Pareja, S. [Servicio de Radiofisica Hospitalaria, Hospital Regional Universitario ' Carlos Haya' , Avda. Carlos Haya, s/n, E-29010 Malaga (Spain)], E-mail: garciapareja@gmail.com; Vilches, M. [Servicio de Fisica y Proteccion Radiologica, Hospital Regional Universitario ' Virgen de las Nieves' , Avda. de las Fuerzas Armadas, 2, E-18014 Granada (Spain); Lallena, A.M. [Departamento de Fisica Atomica, Molecular y Nuclear, Universidad de Granada, E-18071 Granada (Spain)
2007-09-21
The ant colony method is used to control the application of variance reduction techniques to the simulation of clinical electron linear accelerators of use in cancer therapy. In particular, splitting and Russian roulette, two standard variance reduction methods, are considered. The approach can be applied to any accelerator in a straightforward way and permits, in addition, to investigate the 'hot' regions of the accelerator, an information which is basic to develop a source model for this therapy tool.
Hui Zhou; Dongliang Qing; Xiaomei Zhang; Honglin Yuan; Chen Xu
2012-01-01
Routing protocol is an important topic in the wireless sensor networks. For MultiSink wireless sensor networks, the routing protocol designs and implementations are more difficult due to the structure complexity. The paper deals with the problem of a multiple-dimensional tree routing protocol for multisink wireless sensor networks based on ant colony optimization. The proposed protocol is as follows: (1) listening mechanism is used to establish and maintain multidimensional tree routing topol...
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.
A multiuser detector based on artificial bee colony algorithm for DS-UWB systems.
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.
Zamdborg, Leonid; Holloway, David M.; Merelo, Juan J.; Levchenko, Vladimir F.; Spirov, Alexander V.
2015-01-01
Modern evolutionary computation utilizes heuristic optimizations based upon concepts borrowed from the Darwinian theory of natural selection. Their demonstrated efficacy has reawakened an interest in other aspects of contemporary biology as an inspiration for new algorithms. However, amongst the many excellent candidates for study, contemporary models of biological macroevolution attract special attention. We believe that a vital direction in this field must be algorithms that model the activity of “genomic parasites”, such as transposons, in biological evolution. Many evolutionary biologists posit that it is the co-evolution of populations with their genomic parasites that permits the high efficiency of evolutionary searches found in the living world. This publication is our first step in the direction of developing a minimal assortment of algorithms that simulate the role of genomic parasites. Specifically, we started in the domain of genetic algorithms (GA) and selected the Artificial Ant Problem as a test case. This navigation problem is widely known as a classical benchmark test and possesses a large body of literature. We add new objects to the standard toolkit of GA - artificial transposons and a collection of operators that operate on them. We define these artificial transposons as a fragment of an ant's code with properties that cause it to stand apart from the rest. The minimal set of operators for transposons is a transposon mutation operator, and a transposon reproduction operator that causes a transposon to multiply within the population of hosts. An analysis of the population dynamics of transposons within the course of ant evolution showed that transposons are involved in the processes of propagation and selection of blocks of ant navigation programs. During this time, the speed of evolutionary search increases significantly. We concluded that artificial transposons, analogous to real transposons, are truly capable of acting as intelligent
Energy Technology Data Exchange (ETDEWEB)
Hemmateenejad, Bahram, E-mail: hemmatb@sums.ac.ir [Department of Chemistry, Shiraz University, Shiraz (Iran, Islamic Republic of); Medicinal and Natural Products Chemistry Research Center, Shiraz University of Medical Sciences, Shiraz (Iran, Islamic Republic of); Shamsipur, Mojtaba [Department of Chemistry, Razi University, Kermanshah (Iran, Islamic Republic of); Zare-Shahabadi, Vali [Young Researchers Club, Mahshahr Branch, Islamic Azad University, Mahshahr (Iran, Islamic Republic of); Akhond, Morteza [Department of Chemistry, Shiraz University, Shiraz (Iran, Islamic Republic of)
2011-10-17
Highlights: {yields} Ant colony systems help to build optimum classification and regression trees. {yields} Using of genetic algorithm operators in ant colony systems resulted in more appropriate models. {yields} Variable selection in each terminal node of the tree gives promising results. {yields} CART-ACS-GA could model the melting point of organic materials with prediction errors lower than previous models. - Abstract: The classification and regression trees (CART) possess the advantage of being able to handle large data sets and yield readily interpretable models. A conventional method of building a regression tree is recursive partitioning, which results in a good but not optimal tree. Ant colony system (ACS), which is a meta-heuristic algorithm and derived from the observation of real ants, can be used to overcome this problem. The purpose of this study was to explore the use of CART and its combination with ACS for modeling of melting points of a large variety of chemical compounds. Genetic algorithm (GA) operators (e.g., cross averring and mutation operators) were combined with ACS algorithm to select the best solution model. In addition, at each terminal node of the resulted tree, variable selection was done by ACS-GA algorithm to build an appropriate partial least squares (PLS) model. To test the ability of the resulted tree, a set of approximately 4173 structures and their melting points were used (3000 compounds as training set and 1173 as validation set). Further, an external test set containing of 277 drugs was used to validate the prediction ability of the tree. Comparison of the results obtained from both trees showed that the tree constructed by ACS-GA algorithm performs better than that produced by recursive partitioning procedure.
Directory of Open Access Journals (Sweden)
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.
Directory of Open Access Journals (Sweden)
LingaRaj.K
2013-11-01
Full Text Available This paper attempts to undertake the study of maximizing the lifetime of Heterogeneous wireless sensor networks (WSNs. In wireless sensor networks, sensor nodes are typically power-constrained with limited lifetime, and thus it is necessary to know how long the network sustains its networking operations. Heterogeneous WSNs consists of different sensor devices with different capabilities. We can enhance the quality of monitoring in wireless sensor networks by increasing the coverage area. One of major issue in WSNs is finding maximum number of connected coverage. This paper proposed a Swarm Intelligence, Ant Colony Optimization (ACO based approach. Ant colony optimization algorithm provides a natural and intrinsic way of exploration of search space of coverage area. Ants communicate with their nest- mates using chemical scents known as pheromones, Based on Pheromone trail between sensor devices the shortest path is found. The methodology is based on finding the maximum number of connected covers that satisfy both sensing coverage and network connectivity. By finding the coverage area and sensing range, the network lifetime maximized and reduces the energy consumption
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.
Directory of Open Access Journals (Sweden)
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.
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.
Directory of Open Access Journals (Sweden)
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.
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.
Probabilistic dynamic deployment of wireless sensor networks by artificial bee colony algorithm.
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.
On the Effects of Artificial Feeding on Bee Colony Dynamics: A Mathematical Model
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
The effect of symbiotic ant colonies on plant growth: a test using an Azteca-Cecropia system.
Oliveira, Karla N; Coley, Phyllis D; Kursar, Thomas A; Kaminski, Lucas A; Moreira, Marcelo Z; Campos, Ricardo I
2015-01-01
In studies of ant-plant mutualisms, the role that ants play in increasing the growth rates of their plant partners is potentially a key beneficial service. In the field, we measured the growth of Cecropia glaziovii saplings and compared individuals that were naturally colonized by Azteca muelleri ants with uncolonized plants in different seasons (wet and dry). We also measured light availability as well as attributes that could be influenced by the presence of Azteca colonies, such as herbivory, leaf nutrients (total nitrogen and δ(15)N), and investments in defense (total phenolics and leaf mass per area). We found that colonized plants grew faster than uncolonized plants and experienced a lower level of herbivory in both the wet and dry seasons. Colonized plants had higher nitrogen content than uncolonized plants, although the δ(15)N, light environment, total phenolics and leaf mass per area, did not differ between colonized and uncolonized plants. Since colonized and uncolonized plants did not differ in the direct defenses that we evaluated, yet herbivory was lower in colonized plants, we conclude that biotic defenses were the most effective protection against herbivores in our system. This result supports the hypothesis that protection provided by ants is an important factor promoting plant growth. Since C. glaziovii is widely distributed among a variety of forests and ecotones, and since we demonstrated a strong relationship with their ant partners, this system can be useful for comparative studies of ant-plant interactions in different habitats. Also, given this study was carried out near the transition to the subtropics, these results help generalize the geographic distribution of this mutualism and may shed light on the persistence of the interactions in the face of climate change.
The effect of symbiotic ant colonies on plant growth: a test using an Azteca-Cecropia system.
Directory of Open Access Journals (Sweden)
Karla N Oliveira
Full Text Available In studies of ant-plant mutualisms, the role that ants play in increasing the growth rates of their plant partners is potentially a key beneficial service. In the field, we measured the growth of Cecropia glaziovii saplings and compared individuals that were naturally colonized by Azteca muelleri ants with uncolonized plants in different seasons (wet and dry. We also measured light availability as well as attributes that could be influenced by the presence of Azteca colonies, such as herbivory, leaf nutrients (total nitrogen and δ(15N, and investments in defense (total phenolics and leaf mass per area. We found that colonized plants grew faster than uncolonized plants and experienced a lower level of herbivory in both the wet and dry seasons. Colonized plants had higher nitrogen content than uncolonized plants, although the δ(15N, light environment, total phenolics and leaf mass per area, did not differ between colonized and uncolonized plants. Since colonized and uncolonized plants did not differ in the direct defenses that we evaluated, yet herbivory was lower in colonized plants, we conclude that biotic defenses were the most effective protection against herbivores in our system. This result supports the hypothesis that protection provided by ants is an important factor promoting plant growth. Since C. glaziovii is widely distributed among a variety of forests and ecotones, and since we demonstrated a strong relationship with their ant partners, this system can be useful for comparative studies of ant-plant interactions in different habitats. Also, given this study was carried out near the transition to the subtropics, these results help generalize the geographic distribution of this mutualism and may shed light on the persistence of the interactions in the face of climate change.
The Effect of Symbiotic Ant Colonies on Plant Growth: A Test Using an Azteca-Cecropia System
Oliveira, Karla N.; Coley, Phyllis D.; Kursar, Thomas A.; Kaminski, Lucas A.; Moreira, Marcelo Z.; Campos, Ricardo I.
2015-01-01
In studies of ant-plant mutualisms, the role that ants play in increasing the growth rates of their plant partners is potentially a key beneficial service. In the field, we measured the growth of Cecropia glaziovii saplings and compared individuals that were naturally colonized by Azteca muelleri ants with uncolonized plants in different seasons (wet and dry). We also measured light availability as well as attributes that could be influenced by the presence of Azteca colonies, such as herbivory, leaf nutrients (total nitrogen and δ15N), and investments in defense (total phenolics and leaf mass per area). We found that colonized plants grew faster than uncolonized plants and experienced a lower level of herbivory in both the wet and dry seasons. Colonized plants had higher nitrogen content than uncolonized plants, although the δ15N, light environment, total phenolics and leaf mass per area, did not differ between colonized and uncolonized plants. Since colonized and uncolonized plants did not differ in the direct defenses that we evaluated, yet herbivory was lower in colonized plants, we conclude that biotic defenses were the most effective protection against herbivores in our system. This result supports the hypothesis that protection provided by ants is an important factor promoting plant growth. Since C. glaziovii is widely distributed among a variety of forests and ecotones, and since we demonstrated a strong relationship with their ant partners, this system can be useful for comparative studies of ant-plant interactions in different habitats. Also, given this study was carried out near the transition to the subtropics, these results help generalize the geographic distribution of this mutualism and may shed light on the persistence of the interactions in the face of climate change. PMID:25811369
Directory of Open Access Journals (Sweden)
C.Madhumathi
2016-04-01
Full Text Available Quality of Service (QoS and user satisfaction are two of the major requirements considered by the current cloud service providers. In-order to incorporate these qualities in the cloud resource selection framework, user’s requirements must be clearly known. This paper presents an effective cloud package allocation technique that utilizes the user’s logs and fuzzy user inputs to identify the user requirements to perform optimal allocations. Since cloud packages are predefined and do not correspond to the direct user requirements, optimal package allocation is the only option. This process is carried out by Ant Colony Optimization (ACO. Due to the metaheuristic nature of ACO, the results obtained from this selection technique was found to be optimal and the results were obtained faster even with the usage of a large number of agents (ants. Experiments show that ACO provides optimal and fast allocations.
Directory of Open Access Journals (Sweden)
Chander Prabha
2016-09-01
Full Text Available The data loss and disconnection of nodes are frequent in the opportunistic networks. The social information plays an important role in reducing the data loss because it depends on the connectivity of nodes. The appropriate selection of next hop based on social information is critical for improving the performance of routing in opportunistic networks. The frequent disconnection problem is overcome by optimising the social information with Ant Colony Optimization method which depends on the topology of opportunistic network. The proposed protocol is examined thoroughly via analysis and simulation in order to assess their performance in comparison with other social based routing protocols in opportunistic network under various parameters settings.
Institute of Scientific and Technical Information of China (English)
Morteza Atabati; Farzaneh Khandani
2012-01-01
A quantitative structure-property relationship (QSPR) study was suggested for the prediction of λmax of azo dyes.After optimization of 3D geometry of structures,different descriptors were calculated by the HyperChem and Dragon softwares.A major problem of QSPR is the high dimensionality of the descriptor space; therefore,descriptor selection is the most important step for these studies.In this paper,an ant colony optimization (ACO) algorithm was proposed to select the best descriptors.
Directory of Open Access Journals (Sweden)
Deng Lei Lei
2016-01-01
Full Text Available To realize the management and control of the water-saving irrigation of the path pipeline distribution in field plots, get the terrain information through remote sensing technology and analyze the path and the amount of the water in the field plots by the ant colony algorithm according to the matter of the low generality in most parts in China. The result shows that the rules were put forward with shorter path, smaller cost and the most utilization of water eventually. It can be widely used in most areas which is lack of water and scientific technology.
Directory of Open Access Journals (Sweden)
Mohsen Shakibafakhr
2013-11-01
Full Text Available Wireless mesh networks (WMNs are new emerging networks which are anticipated to resolve many limitations of ad-hoc networks, sensor networks and wireless local area networks and improve their performance. But still there are many unresolved research challenge in this area. In this paper we have proposed source-specific multicast protocol for wireless mesh network, which has many application in, multimedia, radio and TV multicasting and distance learning. We have used core-based approach to construct minimum cost tree (MCT among member nodes and optimized this tree for multiple metrics by applying ant colony optimization metaphor.
A Model-Based Approach to Predicting Predator-Prey & Friend-Foe Relationships in Ant Colonies
Narayanaswami, Karthik
2009-01-01
Understanding predator-prey relationships among insects is a challenging task in the domain of insect-colony research. This is due to several factors involved, such as determining whether a particular behavior is the result of a predator-prey interaction, a friend-foe interaction or another kind of interaction. In this paper, we analyze a series of predator-prey and friend-foe interactions in two colonies of carpenter ants to better understand and predict such behavior. Using the data gathered, we have also come up with a preliminary model for predicting such behavior under the specific conditions the experiment was conducted in. In this paper, we present the results of our data analysis as well as an overview of the processes involved.
DEFF Research Database (Denmark)
Sirviö, A.; Gadau, J.; Rueppell, O.;
2006-01-01
Honeybees are known to have genetically diverse colonies because queens mate with many males and the recombination rate is extremely high. Genetic diversity among social insect workers has been hypothesized to improve general performance of large and complex colonies, but this idea has not been t...
Hybrid ant colony and particle swarm algorithm for solving TSP%蚁群与粒子群混合算法求解TSP问题
Institute of Scientific and Technical Information of China (English)
孙凯; 吴红星; 王浩; 丁家栋
2012-01-01
The Traveling Salesman Problem(TSP) is the oldest and most extensively studied combinatorial optimization problem. For the traveling salesman problem, Hybrid Ant colony and Particle swarm Algorithm (HAPA) is proposed. The HAPA divides the ant colony into several ant sub colonies, then optimizes parameters of the ant sub colonies as particles by the particle swarm optimization algorithm, and introduces the operation of swapping the pheromone in each ant sub colony. Results show that the HAPA has more advantages than the traditional algorithm and the similar algorithm in solving the traveling salesman problem.%旅行商问题(TSP)是最古老而且研究最广泛的组合优化问题.针对TSP问题,提出一种蚁群与粒子群混合算法(HAPA).HAPA首先将蚁群划分成多个蚂蚁子群,然后把蚂蚁子群的参数作为粒子,通过粒子群算法来优化蚂蚁子群的参数,并在蚂蚁子群中引入了信息素交换操作.实验结果表明,HAPA在求解TSP问题中比传统算法和同类算法更具优越性.
Mei, Xin; Wang, Qian; Wang, Quanfang; Lin, Wenfang
2009-10-01
Remote sensing image based on the complexity of the background features, has a wealth of spatial information, how to extract huge amounts of data in the region of interest is a serious problem. Image segmentation refers to certain provisions in accordance with the characteristics of the image into different regions, and it is the key of remote sensing image recognition and information extraction. Reasonably fast image segmentation algorithm is the base of image processing; traditional segmentation methods have a lot of the limitations. Traditional threshold segmentation method in essence is an ergodic process, the low efficiency impacts on its application. The ant colony algorithm is a populationbased evolutionary algorithm heuristic biomimetic, since proposed, it has been successfully applied to the TSP, job-shop scheduling problem, network routing problem, vehicle routing problem, as well as other cluster analysis. Ant colony optimization algorithm is a fast heuristic optimization algorithm, easily integrates with other methods, and it is robust. Improved ant colony algorithm can greatly enhance the speed of image segmentation, while reducing the noise on the image. The research background of this paper is land cover classification experiments according to the SPOT images of Qinling area. The image segmentation based on ant colony algorithm is carried out and compared with traditional methods. Experimental results show that improved the ant colony algorithm can quickly and accurately segment target, and it is an effective method of image segmentation, it also has laid a good foundation of image classification for the follow-up work.
Energy Technology Data Exchange (ETDEWEB)
Sandou, G.; Font, S.; Tebbani, S. [Supelec, Dept. d' Automatique, 91 - Gif sur Yvette (France); Hiret, A.; Mondon, Ch. [Electricite de France (EDF), Recherche et Developpement, Dept. Optimisation des Performances des Process, 78 - Chatou (France)
2004-07-01
The control of energy production sites has emerged as a crucial point. However, complexity of such systems sites can be a drawback for their optimal management: corresponding optimization problems are non linear huge mixed integer programming ones. In this article, a meta-heuristic, based on ant colonies, is used to compute the production scheduling. The method is also an hybridizing with an exact solution algorithm which aims to compute real decision variables. Results show that the method, which can be viewed as a stochastic dynamic programming method, allows taking all the constraints into account and can efficiently deal with the feasibility of solutions. A very good solution can be found with low computation times. (authors)
DEFF Research Database (Denmark)
Kronauer, Daniel J C; O'Donnell, Sean; Boomsma, Jacobus J
2011-01-01
Altruism in social insects has evolved between closely related full-siblings. It is therefore of considerable interest why some groups have secondarily evolved low within-colony relatedness, which in turn affects the relatedness incentives of within-colony cooperation and conflict. The highest...... queen mating frequencies, and therefore among the lowest degrees of colony relatedness, occur in Apis honeybees and army ants of the subfamilies Aenictinae, Ecitoninae, and Dorylinae, suggesting that common life history features such as reproduction by colony fission and male biased numerical sex...
蚁群生成树算法研究%Research on Ant Colony Spanning Tree Algorithm
Institute of Scientific and Technical Information of China (English)
周荣敏; 雷延峰; 申海兵
2015-01-01
应用蚁群生成树算法搜索了有34个节点的连接图的生成树，并采用正交设计法和均匀设计法进行了参数优化配置方法研究。结果表明：对于参数较多的蚁群算法，应用正交设计法和均匀设计法进行参数优化配置是一种可行且有效的途径，可有效提高蚁群算法的收敛速度，在求解精度上也有一定优势；充分发挥人类智能与仿生物智能的各自优势是克服单纯靠智能优化方法随机搜索缺点的关键；当蚂蚁数目为100、信息素相对重要性因素为0．3、信息素衰减系数为3．6、信息素挥发系数为0．4、信息素增加强度系数为14时，蚁群生成树算法效果最佳。%The ant colony spanning tree algorithm were used to find the Spanning Tree of the graph which had 34 nodes and the orthogonal design method and the uniform design method were used to optimize its parameters. The results show that for the Ant Colony Algorithm with many parameters,it is a useful and effective way to determine the parameters combination by applying the orthogonal design method and the uniform design method,and it can effectively improve the algorithm convergence and has some advantages in computational accuracy. The key to overcome the shortcomings of only random search relying on intelligent optimization algorithms is to take advantage of the human intelli-gence and bionic intelligence. When the ant numbers is 100,the relative importance of factors of pheromone Beta is 0. 3,the decay coeffi-cient of pheromone Alpha is 3. 6,the evaporation coefficient of pheromone Rho is 0. 4 and the strength coefficient of pheromone Qt is 14,the efficiency of the ant colony spanning tree algorithms is the best.
Directory of Open Access Journals (Sweden)
Dhriti Sundar Maity
2015-02-01
Full Text Available This paper represents The Ant Colony Optimization for MTSP and Swarm Inspired Multipath Data Transmission with Congestion Control in MANET using Total Queue Length based on the behavioral nature in the biological ants. We consider the problem of congestion control for multicast traffic in wireless networks. MANET is multi hop wireless network in which the network components such as PC, mobile phones are mobile in nature. The components can communicate with each other without going through its server. One kind of agent (salesman is engaged in routing. One is Routing agent (salesman, who collects the information about network congestion as well as link failure and same is message agent (salesman that uses this information to get his destination nodes. Though a number of routing protocols exists, which aim to provide effecting routing but few provide a plausible solution to overall network congestion. We attempt to explore the property of the pheromone deposition by the real ant for MTSP. The proposed algorithm using path pheromone scents constantly updates the goodness of choosing a particular path and measuring the congestion in the network using total queue length and Hop-distance.
基于多蚁群的并行ACO算法%Parallel ACO Algorithm Based on Multiple Ant Colony
Institute of Scientific and Technical Information of China (English)
夏鸿斌; 须文波; 刘渊
2009-01-01
This paper proposes and implements a new approach to parallel Ant Colony Optimization(ACO) algorithms by changing the behavior of ACO. In view of the shortcomings for ant algorithms' stagnant, by improving selection strategies, a new selection and search strategies with parallel adaptive mechanisms are implemented, so as to strengthen its global search capability, and the method of data parallel is used to reduce communication time between processors and get a better solution. The performance of the proposed parallel algorithm, applied to the Traveling Salesman Problem(TSP), is investigated and evaluated with respect to solution quality and computational effort. Experimental results demonstrate that the proposed algorithm outperforms the sequential ant colony system as well as the existing parallel ACO algorithms.%通过改变蚁群优化(ACO)算法行为,提出一种新的ACO并行化策略--并行多蚁群ACO算法.针对蚁群算法存在停滞现象的缺点,改进选择策略,实现具有自适应并行机制的选择和搜索策略,以加强其全局搜索能力.并行处理采用数据并行的手段,能减少处理器间的通信时间并获得更好的解.以对称TSP测试集为对象进行比较实验,结果表明,该算法相对于串行算法及现有的并行算法具有一定的优势.
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.
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.
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.
Directory of Open Access Journals (Sweden)
Weixing Su
2017-03-01
Full Text Available There are many dynamic optimization problems in the real world, whose convergence and searching ability is cautiously desired, obviously different from static optimization cases. This requires an optimization algorithm adaptively seek the changing optima over dynamic environments, instead of only finding the global optimal solution in the static environment. This paper proposes a novel comprehensive learning artificial bee colony optimizer (CLABC for optimization in dynamic environments problems, which employs a pool of optimal foraging strategies to balance the exploration and exploitation tradeoff. The main motive of CLABC is to enrich artificial bee foraging behaviors in the ABC model by combining Powell’s pattern search method, life-cycle, and crossover-based social learning strategy. The proposed CLABC is a more bee-colony-realistic model that the bee can reproduce and die dynamically throughout the foraging process and population size varies as the algorithm runs. The experiments for evaluating CLABC are conducted on the dynamic moving peak benchmarks. Furthermore, the proposed algorithm is applied to a real-world application of dynamic RFID network optimization. Statistical analysis of all these cases highlights the significant performance improvement due to the beneficial combination and demonstrates the performance superiority of the proposed algorithm.
Directory of Open Access Journals (Sweden)
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.
Hybrid Artificial Bee Colony Algorithm and Particle Swarm Search for Global Optimization
Directory of Open Access Journals (Sweden)
Wang Chun-Feng
2014-01-01
Full Text Available Artificial bee colony (ABC algorithm is one of the most recent swarm intelligence based algorithms, which has been shown to be competitive to other population-based algorithms. However, there is still an insufficiency in ABC regarding its solution search equation, which is good at exploration but poor at exploitation. To overcome this problem, we propose a novel artificial bee colony algorithm based on particle swarm search mechanism. In this algorithm, for improving the convergence speed, the initial population is generated by using good point set theory rather than random selection firstly. Secondly, in order to enhance the exploitation ability, the employed bee, onlookers, and scouts utilize the mechanism of PSO to search new candidate solutions. Finally, for further improving the searching ability, the chaotic search operator is adopted in the best solution of the current iteration. Our algorithm is tested on some well-known benchmark functions and compared with other algorithms. Results show that our algorithm has good performance.
Directory of Open Access Journals (Sweden)
Hong-Dar Lin
2005-06-01
Full Text Available Thin Film Transistor Liquid Crystal Display (TFT-LCD has excellent properties such as lower voltage to start and less occupied space if comparing with traditional Cathode-Ray Tube (CRT. But screen flaw points and display color deviation defects on image display exist in TFT-LCD products. This research proposes a new automated visual inspection method to solve the problems. We first use multivariate Hotelling T2 statistic for integrating coordinates of color models to construct a T2 energy diagram for inspecting defects and controlling patterns in TFT-LCD display images. An Ant Colony based approach that integrates computer vision techniques is developed to detect the flaw point defects. Then, Back Propagation Network (BPN model is proposed to inspect small deviation defects of the LCD display colors. Experimental results show the proposed system can provide good effects and practicality.
Indian Academy of Sciences (India)
J Satya Eswari; M Anand; C Venkateswarlu
2016-01-01
Central composite rotatable design (CCRD) of experiments was used to obtain data for Lipopeptide and Biomass concentrations from fermentation medium containing the following five components: glucose,monosodium glutamate, yeast extract,MgSO4·7H2O, and K2HPO4. Data was used to develop a second order regression response surface model (RSM) which was coupled with ant colony optimization (ACO) to optimize the media compositions so as to enhance the productivity of lipopeptide. The optimized media by ACO was found to yield 1.501 g/L of lipopeptide concentration which was much higher compared to 1.387 g/L predicted by Nelder–Mead optimization (NMO). The optimum from ACO was validated experimentally. RSM-based ACO is thus shown to be an effective tool for medium optimization of biosurfactant production.
Azcatl-CRP: An ant colony-based system for searching full power control rod patterns in BWRs
Energy Technology Data Exchange (ETDEWEB)
Ortiz, Juan Jose [Dpto. Sistemas Nucleares, ININ, Carr. Mexico-Toluca Km. 36.5, Salazar, Edo. de Mexico (Mexico)]. E-mail: jjortiz@nuclear.inin.mx; Requena, Ignacio [Dpto. Ciencias Computacion e I.A. ETSII Informatica, University of Granada, C. Daniel Saucedo Aranda s/n, 18071 Granada (Spain)]. E-mail: requena@decsai.ugr.es
2006-01-15
We show a new system named AZCATL-CRP to design full power control rod patterns in BWRs. Azcatl-CRP uses an ant colony system and a reactor core simulator for this purpose. Transition and equilibrium cycles of Laguna Verde Nuclear Power Plant (LVNPP) reactor core in Mexico were used to test Azcatl-CRP. LVNPP has 109 control rods grouped in four sequences and currently uses control cell core (CCC) strategy in its fuel reload design. With CCC method only one sequence is employed for reactivity control at full power operation. Several operation scenarios are considered, including core water flow variation throughout the cycle, target different axial power distributions and Haling conditions. Azcatl-CRP designs control rod patterns (CRP) taking into account safety aspects such as k {sub eff} core value and thermal limits. Axial power distributions are also adjusted to a predetermined power shape.
A Global Approach to the Optimal Trajectory Based on an Improved Ant Colony Algorithm for Cold Spray
Cai, Zhenhua; Chen, Tingyang; Zeng, Chunnian; Guo, Xueping; Lian, Huijuan; Zheng, You; Wei, Xiaoxu
2016-12-01
This paper is concerned with finding a global approach to obtain the shortest complete coverage trajectory on complex surfaces for cold spray applications. A slicing algorithm is employed to decompose the free-form complex surface into several small pieces of simple topological type. The problem of finding the optimal arrangement of the pieces is translated into a generalized traveling salesman problem (GTSP). Owing to its high searching capability and convergence performance, an improved ant colony algorithm is then used to solve the GTSP. Through off-line simulation, a robot trajectory is generated based on the optimized result. The approach is applied to coat real components with a complex surface by using the cold spray system with copper as the spraying material.
Directory of Open Access Journals (Sweden)
Hassan Mohammed Mustafa
2010-11-01
Full Text Available Investigational analysis and evaluation of cooperative learning phenomenon is an interdisciplinary and challenging educational research issue. Educationalists have been interesting in modeling of human's cooperative learning to investigate its analogy with some learning aspects of observed social insect behavior. Specifically, this paper presents realistic modeling inspired from interdisciplinary integrated fields of ecology, education ,and animal behavior learning sciences. Presented modeling considers cooperative behavioral learning at ant colony system (ACS. That's motivated by qualitative simulation results obtained after running of an ACS algorithm searching for optimal solution of Travelling Salesman Problem (TSP. In the context of computational intelligence ; cooperative ACS algorithm reaches optimal TSP solution analogously to convergence process of Hebbian coincidence learning paradigm. Moreover, suggested mathematical modeling presents diversity of positive interdependence aspect observed during human's interactive cooperative learning. Interestingly, presented analysis and evaluation of mathematically modeled practical insights of adopted phenomenon, may shed light on promising future enhancement of cooperative learning performance.
Zhang, Zili; Gao, Chao; Liu, Yuxin; Qian, Tao
2014-09-01
Ant colony optimization (ACO) algorithms often fall into the local optimal solution and have lower search efficiency for solving the travelling salesman problem (TSP). According to these shortcomings, this paper proposes a universal optimization strategy for updating the pheromone matrix in the ACO algorithms. The new optimization strategy takes advantages of the unique feature of critical paths reserved in the process of evolving adaptive networks of the Physarum-inspired mathematical model (PMM). The optimized algorithms, denoted as PMACO algorithms, can enhance the amount of pheromone in the critical paths and promote the exploitation of the optimal solution. Experimental results in synthetic and real networks show that the PMACO algorithms are more efficient and robust than the traditional ACO algorithms, which are adaptable to solve the TSP with single or multiple objectives. Meanwhile, we further analyse the influence of parameters on the performance of the PMACO algorithms. Based on these analyses, the best values of these parameters are worked out for the TSP.
Directory of Open Access Journals (Sweden)
Chao Zhang
2016-06-01
Full Text Available An improved ant colony optimization (ACO combined with immunosuppression and parameters switching strategy is proposed in this paper. In this algorithm, a novel judgment criterion for immunosuppression is introduced, that is, if the optimum solution has not changed for default iteration number, the immunosuppressive strategy is carried out. Moreover, two groups of parameters in ACO are switched back and forth according to the change of optimum solution as well. Therefore, the search space is expanded greatly and the problem of the traditional ACO such as falling into local minima easily is avoided effectively. The comparative simulation studies for path planning of landfill inspection robots in Asahikawa, Japan are executed, and the results show that the proposed algorithm has better performance characterized by higher search quality and faster search speed.
Gao, Ming-ke; Chen, Yi-min; Liu, Quan; Huang, Chen; Li, Ze-yu; Zhang, Dian-hua
2015-11-01
Preoperative path planning plays a critical role in vascular access surgery. Vascular access surgery has superior difficulties and requires long training periods as well as precise operation. Yet doctors are on different leves, thus bulky size of blood vessels is usually chosen to undergo surgery and other possible optimal path is not considered. Moreover, patients and surgeons will suffer from X-ray radiation during the surgical procedure. The study proposed an improved ant colony algorithm to plan a vascular optimal three-dimensional path with overall consideration of factors such as catheter diameter, vascular length, diameter as well as the curvature and torsion. To protect the doctor and patient from exposing to X-ray long-term, the paper adopted augmented reality technology to register the reconstructed vascular model and physical model meanwhile, locate catheter by the electromagnetic tracking system and used Head Mounted Display to show the planning path in real time and monitor catheter push procedure. The experiment manifests reasonableness of preoperative path planning and proves the reliability of the algorithm. The augmented reality experiment real time and accurately displays the vascular phantom model, planning path and the catheter trajectory and proves the feasibility of this method. The paper presented a useful and feasible surgical scheme which was based on the improved ant colony algorithm to plan vascular three-dimensional path in augmented reality. The study possessed practical guiding significance in preoperative path planning, intraoperative catheter guiding and surgical training, which provided a theoretical method of path planning for vascular access surgery. It was a safe and reliable path planning approach and possessed practical reference value.
Institute of Scientific and Technical Information of China (English)
Wei-Neng Chen; Jun Zhang
2012-01-01
Project scheduling under uncertainty is a challenging field of research that has attracted increasing attention.While most existing studies only consider the single-mode project scheduling problem under uncertainty,this paper aims to deal with a more realistic model called the stochastic multi-mode resource constrained project scheduling problem with discounted cash flows (S-MRCPSPDCF).In the model,activity durations and costs are given by random variables.The objective is to find an optimal baseline schedule so that the expected net present value (NPV) of cash flows is maximized.To solve the problem,an ant colony system (ACS) based approach is designed.The algorithm dispatches a group of ants to build baseline schedules iteratively using pheromones and an expected discounted cost (EDC) heuristic.Since it is impossible to evaluate the expected NPV directly due to the presence of random variables,the algorithm adopts the Monte Carlo (MC)simulation technique.As the ACS algorithm only uses the best-so-far solution to update pheromone values,it is found that a rough simulation with a small number of random scenarios is enough for evaluation.Thus the computational cost is reduced.Experimental results on 33 instances demonstrate the effectiveness of the proposed model and the ACS approach.
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.
DEFF Research Database (Denmark)
de Fine Licht, Henrik Hjarvard; Schiøtt, Morten; Boomsma, Jacobus Jan
Obligate mutualistic relationships are often inferred to be the result of higher levels of selection. However, because such mutualists consist of separate gene pools, innovative group-selected traits can only become established when they first provide a decisive fitness advantage to one...... visible adaptations in the host ants, such as increased worker dimorphism allowing large workers to cut fresh leaves, but comparative studies of the specific fungal adaptations that accompanied the transition have not been done. Here we report the first large comparative data set on enzymatic fungus...... the ants increased the share of fresh leaves in their forage. However, once in place, this novel enzyme function gave the entire mutualism a significant colony-level advantage, which allowed the leaf-cutting ants to evolve very large long-lived colonies and to become one of the most important...
基于蚁群算法输电线路检修计划的制定%Maintenance scheduling of transmission lines based on ant colony algorithm
Institute of Scientific and Technical Information of China (English)
于宏涛; 高立群; 李丽霞
2011-01-01
为了提高制定输电线路检修计划的工作效率,提出了一种输电线路检修计划模型.该模型为任务量均分的多旅行商问题模型,综合考虑了线路缺陷的严重程度和重要性,在保证线路检修时间始终控制在允许范围内,以可靠性理论中故障率为基础的经济损失风险最小为目标.应用了改进蚁群算法和基本蚁群算法对模型进行仿真比较,结果显示前者求解质量较好,这表明了改进蚁群算法能够改善基本蚁群算法易于陷入局部最优解的缺点.%In order to improve efficiency of making transmission lines maintenance scheduling, presented a model for transmission lines maintenance scheduling. The model based on a multiple traveling salesman problem of equal task, took account of defect severity and importance of lines. Treated the minimal economic loss based on failure rate as the target in searching for the best maintenance scheduling. Limited meanwhile all line' s maintenance time to the range of its maintenance time-choice during the search. Applied both an improved ant colony algorithm and conventional ant colony algorithm to the problem. By contrast, the improved ant colony algorithm was superior to conventional ant colony algorithm in quality. The simulation results show the improved ant colony algorithm can improve the ability of escaping from local optimal solution.
An Improved Multi-Objective Artificial Bee Colony Optimization Algorithm with Regulation Operators
Directory of Open Access Journals (Sweden)
Jiuyuan Huo
2017-02-01
Full Text Available To achieve effective and accurate optimization for multi-objective optimization problems, a multi-objective artificial bee colony algorithm with regulation operators (RMOABC inspired by the intelligent foraging behavior of honey bees was proposed in this paper. The proposed algorithm utilizes the Pareto dominance theory and takes advantage of adaptive grid and regulation operator mechanisms. The adaptive grid technique is used to adaptively assess the Pareto front maintained in an external archive and the regulation operator is used to balance the weights of the local search and the global search in the evolution of the algorithm. The performance of RMOABC was evaluated in comparison with other nature inspired algorithms includes NSGA-II and MOEA/D. The experiments results demonstrated that the RMOABC approach has better accuracy and minimal execution time.
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.
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.
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.
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.
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.
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.
Using Artificial Bee Colony Algorithm for MLP Training on Earthquake Time Series Data Prediction
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...
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.
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.
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.
The Optimization of Running Queries in Relational Databases Using ANT-Colony Algorithm
Directory of Open Access Journals (Sweden)
Adel Alinezhad Kolaei
2013-10-01
Full Text Available The issue of optimizing queries is a cost-sensitive process and with respect to the number of associatedtables in a query, its number of permutations grows exponentially. On one hand, in comparison with otheroperators in relational database, join operator is the most difficult and complicated one in terms ofoptimization for reducing its runtime. Accordingly, various algorithms have so far been proposed to solvethis problem. On the other hand, the success of any database management system (DBMS meansexploiting the query model. In the current paper, the heuristic ant algorithm has been proposed to solve thisproblem and improve the runtime of join operation. Experiments and observed results reveal the efficiencyof this algorithm compared to its similar algorithms.
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.
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.
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...
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.
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。
Path Planning for UAV Based on Mixed Ant Colony Algorithm%基于混合蚁群算法的无人机航路规划
Institute of Scientific and Technical Information of China (English)
税薇; 葛艳; 韩玉; 魏振钢; 孟友新
2011-01-01
The key and difficult problem of UAV path planning is how to satisfy safety and real-time environment,meanwhile, a global path-planning and a local path-planning are considered to improve operational efficiency and survival probability. For this question, according to the existing research of UAV path planning, the method of synthesizing Ant Colony Algorithm (ACA) and Artificial Potential Field (APF) was drscussed. ACA was used as a global route-planning algorithm,and APF was used as a local route-planning algorithm. Simulation results verify that the efficiency of the algorithm can provide some reference value to related researchers.%无人机(UAV)航路规划的热点和难点在于如何满足安全性和实时性的同时,兼顾全局路径规划和局部路径重规划,以提高无人机的作战效率和生存概率.针对这一问题,在现有无人机航路规划研究基础之上,提出采用蚁群算法与人工势场法相结合的方法.蚁群算法用于全局航路规划,人工势场法用于局部路径重规划.仿真结果表明,两种算法结合所得优化航路较好反映了算法的有效性,可以为航路规划辅助决策研究提供借鉴和参考.
Bus Stops Location and Bus Route Planning Using Mean Shift Clustering and Ant Colony in West Jakarta
Supangat, Kenny; Eko Soelistio, Yustinus
2017-03-01
Traffic Jam has been a daily problem for people in Jakarta which is one of the busiest city in Indonesia up until now. Even though the official government has tried to reduce the impact of traffic issues by developing a new public transportation which takes up a lot of resources and time, it failed to diminish the problem. The actual concern to this problem actually lies in how people move between places in Jakarta where they always using their own vehicle like cars, and motorcycles that fill most of the street in Jakarta. Among much other public transportations that roams the street of Jakarta, Buses is believed to be an efficient transportation that can move many people at once. However, the location of the bus stop is now have moved to the middle of the main road, and its too far for the nearby residence to access to it. This paper proposes an optimal location of optimal bus stops in West Jakarta that is experimentally proven to have a maximal distance of 350 m. The optimal location is estimated by means of mean shift clustering method while the optimal routes are calculated using Ant Colony algorithm. The bus stops locations rate of error is 0.07% with overall route area of 32 km. Based on our experiments, we believe our proposed bus stop plan can be an interesting alternative to reduce traffic congestion in West Jakarta.
Energy Technology Data Exchange (ETDEWEB)
Garcia-Pareja, S.; Galan, P.; Manzano, F.; Brualla, L.; Lallena, A. M. [Servicio de Radiofisica Hospitalaria, Hospital Regional Universitario ' ' Carlos Haya' ' , Avda. Carlos Haya s/n, E-29010 Malaga (Spain); Unidad de Radiofisica Hospitalaria, Hospital Xanit Internacional, Avda. de los Argonautas s/n, E-29630 Benalmadena (Malaga) (Spain); NCTeam, Strahlenklinik, Universitaetsklinikum Essen, Hufelandstr. 55, D-45122 Essen (Germany); Departamento de Fisica Atomica, Molecular y Nuclear, Universidad de Granada, E-18071 Granada (Spain)
2010-07-15
Purpose: In this work, the authors describe an approach which has been developed to drive the application of different variance-reduction techniques to the Monte Carlo simulation of photon and electron transport in clinical accelerators. Methods: The new approach considers the following techniques: Russian roulette, splitting, a modified version of the directional bremsstrahlung splitting, and the azimuthal particle redistribution. Their application is controlled by an ant colony algorithm based on an importance map. Results: The procedure has been applied to radiosurgery beams. Specifically, the authors have calculated depth-dose profiles, off-axis ratios, and output factors, quantities usually considered in the commissioning of these beams. The agreement between Monte Carlo results and the corresponding measurements is within {approx}3%/0.3 mm for the central axis percentage depth dose and the dose profiles. The importance map generated in the calculation can be used to discuss simulation details in the different parts of the geometry in a simple way. The simulation CPU times are comparable to those needed within other approaches common in this field. Conclusions: The new approach is competitive with those previously used in this kind of problems (PSF generation or source models) and has some practical advantages that make it to be a good tool to simulate the radiation transport in problems where the quantities of interest are difficult to obtain because of low statistics.
Directory of Open Access Journals (Sweden)
Majid Yousefikhoshbakht
2016-07-01
Full Text Available The TSP is considered one of the most well-known combinatorial optimization tasks and researchers have paid so much attention to the TSP for many years. In this problem, a salesman starts to move from an arbitrary place called depot and after visits all of the nodes, finally comes back to the depot. The objective is to minimize the total distance traveled by the salesman. Because this problem is a non-deterministic polynomial (NP-hard problem in nature, a hybrid meta-heuristic algorithm called REACSGA is used for solving the TSP. In REACSGA, a reactive bone route algorithm that uses the ant colony system (ACS for generating initial diversified solutions and the genetic algorithm (GA as an improved procedure are applied. Since the performance of the Metaheuristic algorithms is significantly influenced by their parameters, Taguchi Method is used to set the parameters of the proposed algorithm. The proposed algorithm is tested on several standard instances involving 24 to 318 nodes from the literature. The computational result shows that the results of the proposed algorithm are competitive with other metaheuristic algorithms for solving the TSP in terms of better quality of solution and computational time respectively. In addition, the proposed REACSGA is significantly efficient and finds closely the best known solutions for most of the instances in which thirteen best known solutions are also found.
Janich, Karl W.
2005-01-01
The At-Least version of the Generalized Minimum Spanning Tree Problem (L-GMST) is a problem in which the optimal solution connects all defined clusters of nodes in a given network at a minimum cost. The L-GMST is NPHard; therefore, metaheuristic algorithms have been used to find reasonable solutions to the problem as opposed to computationally feasible exact algorithms, which many believe do not exist for such a problem. One such metaheuristic uses a swarm-intelligent Ant Colony System (ACS) algorithm, in which agents converge on a solution through the weighing of local heuristics, such as the shortest available path and the number of agents that recently used a given path. However, in a network using a solution derived from the ACS algorithm, some nodes may move around to different clusters and cause small changes in the network makeup. Rerunning the algorithm from the start would be somewhat inefficient due to the significance of the changes, so a genetic algorithm based on the top few solutions found in the ACS algorithm is proposed to quickly and efficiently adapt the network to these small changes.
Indian Academy of Sciences (India)
R Gopalakrishnan; A Krishnan
2013-08-01
Economic load dispatch is one of the vital purposes in electrical power system operation, management and planning. Economic dispatch problem is one of the most important problems in electric power system operation. In large scale system, the problem is more complex and difﬁcult to ﬁnd out optimal solution because it is nonlinear function and it contains number of local optimal. Combined economic emission dispatch (CEED) problem is to schedule the committed generating units outputs to meet the required load demand at minimum operating cost with minimum emission simultaneously. The main aim of economic load dispatch is to reduce the total production cost of the generating system and at the same time the necessary equality and inequality constraints should also be fulﬁlled. This leads to the development of CEED techniques. There are various techniques proposed by several researchers to solve CEED problem based on optimization techniques. But still some problems such as slower convergence and higher computational complexity exist in using the optimization techniques such as GA for solving CEED problem. This paper proposes an efﬁcient and reliable technique for combined fuel cost economic optimization and emission dispatch using the Modiﬁed Ant Colony Optimization algorithm (MACO) to produce better optimal solution. The simulation results reveal the signiﬁcant performance of the proposed MACO approach.
Tashakkori, H.; Rajabifard, A.; Kalantari, M.
2016-10-01
Search and rescue procedures for indoor environments are quite complicated due to the fact that much of the indoor information is unavailable to rescuers before physical entrance to the incident scene. Thus, decision making regarding the number of crew required and the way they should be dispatched in the building considering the various access points and complexities in the buildings in order to cover the search area in minimum time is dependent on prior knowledge and experience of the emergency commanders. Hence, this paper introduces the Search and Rescue Problem (SRP) which aims at finding best search and rescue routes that minimize the overall search time in the buildings. 3D BIM-oriented indoor GIS is integrated in the indoor route graph to find accurate routes based on the building geometric and semantic information. An Ant Colony Based Algorithm is presented that finds the number of first responders required and their individual routes to search all rooms and points of interest inside the building to minimize the overall time spent by all rescuers inside the disaster area. The evaluation of the proposed model for a case study building shows a significant improve in search and rescue time which will lead to a higher chance of saving lives and less exposure of emergency crew to danger.
Directory of Open Access Journals (Sweden)
H. Tashakkori
2016-10-01
Full Text Available Search and rescue procedures for indoor environments are quite complicated due to the fact that much of the indoor information is unavailable to rescuers before physical entrance to the incident scene. Thus, decision making regarding the number of crew required and the way they should be dispatched in the building considering the various access points and complexities in the buildings in order to cover the search area in minimum time is dependent on prior knowledge and experience of the emergency commanders. Hence, this paper introduces the Search and Rescue Problem (SRP which aims at finding best search and rescue routes that minimize the overall search time in the buildings. 3D BIM-oriented indoor GIS is integrated in the indoor route graph to find accurate routes based on the building geometric and semantic information. An Ant Colony Based Algorithm is presented that finds the number of first responders required and their individual routes to search all rooms and points of interest inside the building to minimize the overall time spent by all rescuers inside the disaster area. The evaluation of the proposed model for a case study building shows a significant improve in search and rescue time which will lead to a higher chance of saving lives and less exposure of emergency crew to danger.
Saouane, I.; Chaker, A.; Zaidi, B.; Shekhar, C.
2017-03-01
This paper describes the mathematical model used to determine the amount of solar radiation received on an inclined solar photovoltaic panel. The optimum slope angles for each month, season, and year have also been calculated for a solar photovoltaic panel. The optimization of the procedure to maximize the solar energy collected by the solar panel by varying the tilt angle is also presented. As a first step, the global solar radiation on the horizontal surface of a thermal photovoltaic panel during clear sky is estimated. Thereafter, the Muneer model, which provides the most accurate estimation of the total solar radiation at a given geographical point has been used to determine the optimum collector slope. Also, the Ant Colony Optimization (ACO) algorithm was applied to obtain the optimum tilt angle settings for PV collector to improve the PV collector efficiency. The results show good agreement between calculated and predicted results. Additionally, this paper presents studies carried out on the polycrystalline silicon solar panels for electrical energy generation in the city of Ghardaia. The electrical energy generation has been studied as a function of amount of irradiation received and the angle of optimum orientation of the solar panels.
Directory of Open Access Journals (Sweden)
Hadi Fattahi
2016-12-01
Full Text Available Shear wave velocity (Vs data are key information for petrophysical, geophysical and geomechanical studies. Although compressional wave velocity (Vp measurements exist in almost all wells, shear wave velocity is not recorded for most of elderly wells due to lack of technologic tools. Furthermore, measurement of shear wave velocity is to some extent costly. This study proposes a novel methodology to remove aforementioned problems by use of hybrid adaptive neuro fuzzy inference system (ANFIS with ant colony optimization algorithm (ACO based on fuzzy c–means clustering (FCM and subtractive clustering (SCM. The ACO is combined with two ANFIS models for determining the optimal value of its user–defined parameters. The optimization implementation by the ACO significantly improves the generalization ability of the ANFIS models. These models are used in this study to formulate conventional well log data into Vs in a quick, cheap, and accurate manner. A total of 3030 data points was used for model construction and 833 data points were employed for assessment of ANFIS models. Finally, a comparison among ANFIS models, and six well–known empirical correlations demonstrated ANFIS models outperformed other methods. This strategy was successfully applied in the Marun reservoir, Iran.
Directory of Open Access Journals (Sweden)
Zhiwen Liu
2015-08-01
Full Text Available Sensors play an important role in the modern manufacturing and industrial processes. Their reliability is vital to ensure reliable and accurate information for condition based maintenance. For the gearbox, the critical machine component in the rotating machinery, the vibration signals collected by sensors are usually noisy. At the same time, the fault detection results based on the vibration signals from a single sensor may be unreliable and unstable. To solve this problem, this paper proposes an intelligent multi-sensor data fusion method using the relevance vector machine (RVM based on an ant colony optimization algorithm (ACO-RVM for gearboxes’ fault detection. RVM is a sparse probability model based on support vector machine (SVM. RVM not only has higher detection accuracy, but also better real-time accuracy compared with SVM. The ACO algorithm is used to determine kernel parameters of RVM. Moreover, the ensemble empirical mode decomposition (EEMD is applied to preprocess the raw vibration signals to eliminate the influence caused by noise and other unrelated signals. The distance evaluation technique (DET is employed to select dominant features as input of the ACO-RVM, so that the redundancy and inference in a large amount of features can be removed. Two gearboxes are used to demonstrate the performance of the proposed method. The experimental results show that the ACO-RVM has higher fault detection accuracy than the RVM with normal the cross-validation (CV.
基于蚁群算法的旅行商问题的研究%Research for Solving Traveling Salesman Problem Based on Ant Colony Optimization
Institute of Scientific and Technical Information of China (English)
李辉
2015-01-01
It provides a powerful method of the design of distributed control and optimization for computer scientists that the study of social insect behavior. Research on swarm intelligence to ant colony algorithm as the representative has gradually become a research hotspot. Ant colony algorithm is very useful in real life, such as solving the traveling salesman problem, this paper introduces a solution of complex of the ant colony algorithm TSP, expounds the basic principle and realization process of the algorithm, and trys to use the coding form to be the basic ant colony algorithm applied to solve the traveling salesman problem.%群居性昆虫行为的研究为计算机科学家提供了设计分布式控制和优化算法的有力方法。对以蚁群算法为代表的群集智能的研究已经逐渐成为一个研究热点。蚁群算法在实际的生活中有很大的用处，比如求解旅行商问题，文章介绍了一种求解复杂TSP的蚁群算法，阐述了该算法的基本原理及实现过程，并且在本文中尝试用编码的形式将基本蚁群算法应用到求解旅行商问题中去。
Institute of Scientific and Technical Information of China (English)
ZHENG Xiangquan; GUO Wei; GE Lijia; LIU Renting
2007-01-01
In order to periodically reassess the status of the alternate path route (APR) set and to improve the efficiency of alternate path construction existing in most current alternate path routing protocols,we present a cross-layer design and ant-colony optimization based load-balancing routing protocol for ad-hoc networks (CALRA) in this paper.In CALRA,the APR set maintained in nodes is aged and reassessed by the inherent mechanism of pheromone evaporation of ant-colony optimization algorithm,and load balance of network is achieved by ant-colony optimization combining with cross-layer synthetic optimization.The efficiency of APR set construction is improved by bidirectional and hop-by-hop routing update during routing discovery and routing maintenance process.Moreover,ants in CALRA deposit simulated pheromones as a function of multiple parameters corresponding to the information collected by each layer of each node visited,such as the distance from their source node,the congestion degree of the visited nodes,the current pheromones the nodes possess,the velocity of the nodes,and so on,and provide the information to the visiting nodes to update their pheromone tables by endowing the different parameters corresponding to different information and different weight values,which provides a new method to improve the congestion problem,the shortcut problem,the convergence rate and the heavy overheads commonly existed in existing ant-based routing protocols for ad-hoc networks.The performance of the algorithm is measured by the packet delivery rate,good-put ratio (routing overhead),and end-to-end delay.Simulation results show that CALRA performs well in decreasing the route overheads,balancing traffic load,as well as increasing the packet delivery rate,etc.
一种快速收敛的自适应蚁群算法%Investigation on a Fast Convergent Adaptive Ant Colony Optimization Algorithm
Institute of Scientific and Technical Information of China (English)
潘伟强; 李长云; 胡盛龙
2012-01-01
The ant colony optimization has deficiencies of slow convergence speed and difficult parameters selection.By analyzing the parameters'effect on the algorithm and comparing multiple parameter optimization methods,adopts the hybrid algorithm of particle swarm optimization and ant colony optimization to optimize parameters,and proposes a fast convergent adaptive ant colony optimization.The simulation of the traveling salesman problem shows that the algorithm is feasible and effective.%针对蚁群算法收敛速度慢、参数选择难的不足,通过分析各参数对算法的影响和比较多种参数寻优方法,采用粒子群算法对蚁群算法进行参数寻优,并提出了一种快速收敛的自适应蚁群算法。针对旅行商问题的仿真试验表明,该算法是可行且有效的。
蚁群和遗传混合算法求解旅行商问题%Hybrid Algorithm of Generation and Ant Colony on Traveling Salesman Problem
Institute of Scientific and Technical Information of China (English)
金晓龙
2014-01-01
Traveling salesman is a widely used optimized combined question. The ant colony and geneic hybrid algorithm is adopted to solve the traveling salesman problem. The problem that ant colony algorithm is prone to local optima is solved by using crossover and mutation mechanism of genetic algorithm.The hybrid algorithm is debugged in VBA.Finally, the hybrid algorithm is proved to be better than ant colony algorithm and geneic algorithm by comparing running data.%旅行商是应用广泛的优化组合问题，采用蚁群和遗传混合算法解决旅行商问题，利用遗传算法的交叉、变异机制解决蚁群算法易出现局部最优解的问题，将混合算法在VBA环境调试运行。混合算法与蚁群算法、遗传算法仿真数据比较，混合算法具有较好改进效果。
最大团问题的改进蚁群算法求解%Improved Ant Colony Algorithm for Maximum Clique Problem
Institute of Scientific and Technical Information of China (English)
陈荣
2011-01-01
为了更好的解决最大团问题,提出一种改进的蚁群算法.通过提取图的顶点信息,将图用信息素模型来表示;根据最大团问题的约束条件利用蚁群构造极大团,并进行实时的全局信息素更新和局部信息素更新,直到找到最大团.实验结果表明,算法能较好的实现最大团问题,算法性能高于通用的蚁群算法.%In order to solve maximum clique problem better, an improved ant colony algorithm is proposed. By extracting vertex information of the graph, the graph is represented by pheromone trail.According to the constraints of maximum clique problem, larger clique is constructed by ant colony, and updating global pheromone information and local pheromone information real- time, until finding the maximum clique. Experimental results show that this method can achieve maximum clique problem and the performance is higher than the common ant colony algorithms.
Daugherty, Belinda
2001-01-01
Uses the GEMS guide, "Ants at Home Underground", to explore the life of ants and teach about them in a classroom setting. The activity applies students' knowledge of ants and students learn about ant colonies, what ants eat, and how they live. (SAH)
Pringle, EG; Novo, A; Ableson, I; Barbehenn, RV; Vannette, RL
2014-01-01
© 2014 The Authors. In plant-ant-hemipteran interactions, ants visit plants to consume the honeydew produced by phloem-feeding hemipterans. If genetically based differences in plant phloem chemistry change the chemical composition of hemipteran honeydew, then the plant's genetic constitution could have indirect effects on ants via the hemipterans. If such effects change ant behavior, they could feed back to affect the plant itself. We compared the chemical composition of honeydews produced by...
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.
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.
Modified artificial bee colony for the vehicle routing problems with time windows.
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.
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.
Directory of Open Access Journals (Sweden)
Jingmin Wang
2016-01-01
Full Text Available Electricity consumption forecast is perceived to be a growing hot topic in such a situation that China’s economy has entered a period of new normal and the demand of electric power has slowed down. Therefore, exploring Chinese electricity consumption influence mechanism and forecasting electricity consumption are crucial to formulate electrical energy plan scientifically and guarantee the sustainable economic and social development. Research has identified medium and long term electricity consumption forecast as a difficult study influenced by various factors. This paper proposed an improved Artificial Bee Colony (ABC algorithm which combined with multivariate linear regression (MLR for exploring the influencing mechanism of various factors on Chinese electricity consumption and forecasting electricity consumption in the future. The results indicated that the improved ABC algorithm in view of the various factors is superior to traditional models just considering unilateralism in accuracy and persuasion. The overall findings cast light on this model which provides a new scientific and effective way to forecast the medium and long term electricity consumption.
An efficient artificial bee colony algorithm with application to nonlinear predictive control
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.
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.
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.
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.
Osei, Richard
There are many problems associated with operating a data center. Some of these problems include data security, system performance, increasing infrastructure complexity, increasing storage utilization, keeping up with data growth, and increasing energy costs. Energy cost differs by location, and at most locations fluctuates over time. The rising cost of energy makes it harder for data centers to function properly and provide a good quality of service. With reduced energy cost, data centers will have longer lasting servers/equipment, higher availability of resources, better quality of service, a greener environment, and reduced service and software costs for consumers. Some of the ways that data centers have tried to using to reduce energy costs include dynamically switching on and off servers based on the number of users and some predefined conditions, the use of environmental monitoring sensors, and the use of dynamic voltage and frequency scaling (DVFS), which enables processors to run at different combinations of frequencies with voltages to reduce energy cost. This thesis presents another method by which energy cost at data centers could be reduced. This method involves the use of Ant Colony Optimization (ACO) on a Quadratic Assignment Problem (QAP) in assigning user request to servers in geo-distributed data centers. In this paper, an effort to reduce data center energy cost involves the use of front portals, which handle users' requests, were used as ants to find cost effective ways to assign users requests to a server in heterogeneous geo-distributed data centers. The simulation results indicate that the ACO for Optimal Server Activation and Task Placement algorithm reduces energy cost on a small and large number of users' requests in a geo-distributed data center and its performance increases as the input data grows. In a simulation with 3 geo-distributed data centers, and user's resource request ranging from 25,000 to 25,000,000, the ACO algorithm was able
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.
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.
Parhusip, H. A.; Trihandaru, S.; Susanto, B.; Prasetyo, S. Y. J.; Agus, Y. H.; Simanjuntak, B. H.
2017-03-01
Several algorithms and objective functions on paddy crops have been studied to get optimal paddy crops in Central Java based on the data given from Surakarta and Boyolali. The algorithms are linear solver, least square and Ant Colony Algorithms (ACO) to develop optimization procedures on paddy crops modelled with Modified GSTAR (Generalized Space-Time Autoregressive) and nonlinear models where the nonlinear models are quadratic and power functions. The studied data contain paddy crops from Surakarta and Boyolali determining the best period of planting in the year 1992-2012 for Surakarta where 3 periods for planting are known and the optimal amount of paddy crops in Boyolali in the year 2008-2013. Having these analyses may guide the local agriculture government to give a decision on rice sustainability in its region. The best period for planting in Surakarta is observed, i.e. the best period is in September-December based on the data 1992-2012 by considering the planting area, the cropping area, and the paddy crops are the most important factors to be taken into account. As a result, we can refer the paddy crops in this best period (about 60.4 thousand tons per year) as the optimal results in 1992-2012 where the used objective function is quadratic. According to the research, the optimal paddy crops in Boyolali about 280 thousand tons per year where the studied factors are the amount of rainfalls, the harvested area and the paddy crops in 2008-2013. In this case, linear and power functions are studied to be the objective functions. Compared to all studied algorithms, the linear solver is still recommended to be an optimization tool for a local agriculture government to predict paddy crops in future.
Institute of Scientific and Technical Information of China (English)
张鹏; 魏云霞; 薛宏全; 王永忠
2012-01-01
提出一种基于优胜劣汰规则的异类多种群蚁群算法,该算法由多类不同特性的蚁群构成,彼此间具有潜在的合作性和对抗性.根据蚁群间定期信息交换的结果,引入自然界优胜劣汰准则,设定蚁群间的合作规则、竞争规则、裂变规则.以旅行商问题为例进行相关实验和比较.通过多个种群间的相互合作与竞争,保留优势种群,淘汰劣势种群,提高求解效率,改善解的多样性,使算法更容易收敛到全局最优解.%A Heterogeneous Multiple Ant Colony algorithm based on Survival of Fittest rules(HMACSF) is presented. This algorithm introduces more than one type of ant colony. All types of ant colonies with different pheromone updating mechanism and searching traits have mutual compensation of advantages, as well as mutual competitive exclusion. According to the results of the exchanging, HMACSF retains the dominant colonies, weeds out the inferiors, and improves the solving efficiency and diversity of solutions, to easily converge the global optimal solution. A series of Traveling Salesman Problem(TSP) experiments show that this algorithm can generate solutions with better quality and faster speed.
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...
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...
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...
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.
改进蚁群算法在二次分配问题中的应用%Application of Improved Ant Colony Algorithm for Quadratic Assignment Problems
Institute of Scientific and Technical Information of China (English)
袁东锋; 吕聪颖
2013-01-01
为了解决基本蚁群算法在求解大规模二次分配问题时暴露出的缺陷,本文提出一种改进的蚁群算法.在基本蚂蚁算法中,采用全局信息素更新策略,使用距离及流量作为启发式信息并引入局部优化策略,对每代的最优解进行改进,进一步加快算法的收敛速度.通过对于二次分配问题的3种不同类型的问题进行实验,将改进的蚁群算法与基本蚂蚁算法及混合遗传算法进行比较,结果表明该改进算法具有更优的性能.%In order to solve the problems that the basic ant colony algorithm for solving large scale quadratic assignment has revealed defects, this paper proposes an improved ant colony algorithm. This algorithm adopts the global pheromone update strategy, the use of distance and traffic as heuristic information and the introduction of local optimization strategy. The optimal solution for each generation is to improve and further accelerate the convergence speed. For the quadratic assignment problem through three different types of problems, and improved ant colony algorithm with the basic ant algorithm and the hybrid genetic algorithm are compared, the experiments show that the improved method has better performance.
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.
Pringle, Elizabeth G.; Novo, Alexandria; Ableson, Ian; Barbehenn, Raymond V; Vannette, Rachel L.
2014-01-01
In plant–ant–hemipteran interactions, ants visit plants to consume the honeydew produced by phloem-feeding hemipterans. If genetically based differences in plant phloem chemistry change the chemical composition of hemipteran honeydew, then the plant's genetic constitution could have indirect effects on ants via the hemipterans. If such effects change ant behavior, they could feed back to affect the plant itself. We compared the chemical composition of honeydews produced by Aphis nerii aphid c...
Visual Feedback and Behavior Memory Based Ant Colony Optimization Algorithm%一种使用视觉反馈与行为记忆的蚁群优化算法
Institute of Scientific and Technical Information of China (English)
郭禾; 程童; 陈鑫; 王宇新
2011-01-01
Based on the analysis of exist ant colony optimization (ACO) algorithms and the studies in visual perception and cognitive psychology, this paper proposes a new optimization strategy, the visual feedback and behavioral memory based Max-Min ant colony optimization algorithm (VM-MMACO). The main idea is to enhance the ant's search ability by establishing the learning mechanism of visual feedback and behavioral memory. With artificial visual memory and learning abilities, the ant can not only see the targets around, using visual perception to optimize the heuristic information produced by pheromone in order to improve the search quality, but can also exploit the historical solutions, finding local best segments (called experience) to narrow the searching space smoothly, so that it can accelerate the convergence process. Comparisons of VM-MMACO and existing optimization strategies within a given iteration number are performed on the publicly available TSP instances from TSPLIB. The results demonstrates that VM-MMACO significantly outperforms other optimization strategies. Finally, according to the accumulative learning theory, the learning mechanism could be studied further to make a much more intelligent algorithm.%在分析现有改进算法的基础上,结合视知觉及认知心理学的相关理论,提出一种具备视觉反馈与行为记忆学习能力的新型蚁群算法:首先,建立视觉模型使得蚂蚁能够通过人工视觉感知周围目标城市的分布,用视知觉修正信息素噪声,提高蚂蚁探索质量；其次,建立行为记忆学习模型,使蚂蚁能够从已经走过的局部最优路径中提取经验来指导周游活动,加快算法收敛速度并强化寻优能力.经过与传统改进策略比较发现,新算法在求解质量与求解时间上均有明显改进.
融合遗传蚁群算法的Web服务组合研究%Based Web Service Composition with Genetic Algorithm and Ant Colony Optimization
Institute of Scientific and Technical Information of China (English)
曹腾飞; 符云清; 钟明洋
2012-01-01
为了提高Web服务组合流程中服务选择技术的收敛性能,提出了一种基于遗传算法与蚁群算法相融合的多目标优化策略,用于解决基于QoS的Web服务组合问题.本文首先将Web服务组合的全局最优化问题转化为寻求一条QoS最优解的路径问题,并通过改进遗传算法得到蚁群算法中初始路径的信息素分布,再通过改进蚁群算法来求得最优解.仿真实验结果表明,该改进算法能在较少的进化代数下得到最优路径,提高了Web服务组合的快速全局搜索能力.%To improve the convergence ability of service selection technology in process of Web service composition, the paper presents a multi-objective optimization strategy based on genetic algorithm and ant colony algorithm to solve global optimization problem in QoS-based Web service composition. In the paper, global optimization problem in Web service composition is presented as a QoS optimal routing problem. And then, an improved genetic algorithm is proposed to get pheromone distribution in initial route of ant colony algorithm. At last, an improved ant colony algorithm is presented to get the optimal solution. Simulation result suggests that the improved algorithms can get the optimal routing in less evolutional generation than typical algorithms, and improve global research ability in Web Service compositioa
ON ANT COLONY OPTIMISATION ALGORITHM FOR PROTEIN FOLDING PROBLEM%蛋白质折叠问题的蚁群优化算法研究
Institute of Scientific and Technical Information of China (English)
侯金彪
2013-01-01
Proteins are an important class of biological macromolecules,they occupy a special place in the living creature and are the main bearer of the life.The study on protein folding is one of the topics at the forefront of the field of life sciences.Based on an overview of ant colony algorithm and the 2D HP protein model,an ant colony optimisation algorithm is proposed for the protein folding problem.Then the simulation experiment on it is conducted with a few of typical models,results show that the ant colony optimisation algorithm demonstrates good performance in solving the protein folding problem.The practice indicates that the algorithm has a high application value.%蛋白质是一类重要的生物大分子,在生物体内占有特殊的地位,是生命的主要承担者.而研究蛋白质的折叠,是生命科学领域的前沿课题之一.在概述蚁群算法及2D HP蛋白质模型的基础上,针对蛋白质折叠问题提出一种蚁群优化算法,并用几个比较典型的模型对其进行仿真实验,结果表明该蚁群优化算法在求解蛋白质折叠问题时表现出了良好的性能.实践表明该算法具有很高的应用价值.
Directory of Open Access Journals (Sweden)
Shuai Deng
2016-01-01
Full Text Available This paper presents a closed-loop location-inventory-routing problem model considering both quality defect returns and nondefect returns in e-commerce supply chain system. The objective is to minimize the total cost produced in both forward and reverse logistics networks. We propose a combined optimization algorithm named hybrid ant colony optimization algorithm (HACO to address this model that is an NP-hard problem. Our experimental results show that the proposed HACO is considerably efficient and effective in solving this model.
Institute of Scientific and Technical Information of China (English)
谭冠政; 曾庆冬; 李文斌
2004-01-01
A designing method of intelligent proportional-integral-derivative(PID) controllers was proposed based on the ant system algorithm and fuzzy inference. This kind of controller is called Fuzzy-ant system PID controller. It consists of an off-line part and an on-line part. In the off-line part, for a given control system with a PID controller,by taking the overshoot, setting time and steady-state error of the system unit step response as the performance indexes and by using the ant system algorithm, a group of optimal PID parameters K*p , Ti* and T*d can be obtained, which are used as the initial values for the on-line tuning of PID parameters. In the on-line part, based on Kp* , Ti*and Td* and according to the current system error e and its time derivative, a specific program is written, which is used to optimize and adjust the PID parameters on-line through a fuzzy inference mechanism to ensure that the system response has optimal transient and steady-state performance. This kind of intelligent PID controller can be used to control the motor of the intelligent bionic artificial leg designed by the authors. The result of computer simulation experiment shows that the controller has less overshoot and shorter setting time.
Study on vehicle routing problem based on heuristic ant colony optimization%基于启发式蚁群算法的VRP问题研究
Institute of Scientific and Technical Information of China (English)
刘晓勇; 付辉
2011-01-01
When Ant Colony Optimization algorithm (ACO) is applied to vehicle routing problem, it always spends much time and has worse solutions.This paper uses ACO based on a heuristic method for vehicle routing problem.This heuristic method combines distance matrix with saving route matrix to assign initial pheromone matrix.Three benchmark datasets are chosen to verify performance of the new algorithm. Experiments show that ant colony optimization based on heuristic information has better solution and spends less time.%针对蚁群算法求解VRP问题时收敛速度慢,求解质量不高的缺点,把城市和仓库间的距离矩阵和路径节约矩阵信息融入到初始信息素矩阵中作为启发式信息引入到蚁群算法中用于求解有容量限制的车辆路径规划问题(CVRP),在三个基准数据集上的实验研究表明,基于启发式信息的蚁群算法与基本蚁群算法相比能够以较快的速度收敛到较好的解.
Dynamic Routing Algorithm for Satellite Network Based on Ant Colony Algorithm%基于蚁群算法的卫星网动态路由算法
Institute of Scientific and Technical Information of China (English)
马海滨; 王汝传; 饶元
2011-01-01
卫星网络路由应当具有使用较小的通信开销和处理能力计算出最优路径,并能够适应卫星网络拓扑结构动态变化等特点,这与蚁群算法的特征相匹配,能很好地解决这一问题.以此为背景,提出了一种新型的基于蚁群算法的卫星网动态路由算法(DRAS-ACA),并在NS2网络仿真平台上实现了该路由算法,使用gnuplot分析了仿真结果.%Satellite network routing should have the use of smaller capacity and communication overhead to calculate the optimal path, and be able to adapt to the satellite network topology changes, and other characteristics. The Ant Colony Algorithm should be a good appraach to solve this problem. In this paper, a dynamic routing algorithm for satellite network based on ant colony algorithm (DRAS-ACA) was presented and simulated on NS2 platform,and we also analyzed the simulation results with gnuplot.
Ant Colony Optimization for Solving Traveling Salesman Problem Based on MATLAB%基于MATLAB的蚁群算法求解旅行商问题
Institute of Scientific and Technical Information of China (English)
黄丽韶; 朱喜基
2012-01-01
The traditional method for solving the traveling salesman problem is a genetic algorithm, which is slow convergence and can not obtain the optimal solution. In order to strike the optimal solution of the traveling salesman problem, this paper described the basic principles of ant colony optimization, the model as well as the basis of the process of solving the traveling salesman problem. The other, an ant colony optimization is built for solving the traveling salesman problem based on MATLAB, and finaly through the simulation to obtain the best solution which is the best one currently.%旅行商问题的传统求解方法是遗传算法,此算法收敛速度慢,并不能获得问题的最优解。为了求取旅行商问题的最优解,本文在阐述蚁群算法的基本原理、模型以及在旅行商问题中的实现过程的基础上,提出了一种以蚁群算法构建的基于MATLAB的求解旅行商问题的方法,并最后通过仿真实验获得了目前已知的最好解。
Chaos ant colony algorithm for inverse heat conduction problem%适用于寻源导热逆问题的混沌-蚁群算法
Institute of Scientific and Technical Information of China (English)
陶亮; 卢玫
2015-01-01
When Ant Colony Algorithm(ACA)is applied to find the source in solving Inverse Heat Conduction Problem (IHCP), it is easy to fall into local optimal solution and the convergence speed is very slow. In order to solve this prob-lem, this paper makes use of the ergodicity and initial value sensitivity of Chaotic Algorithm(CA)to establish Chaos Ant Colony Algorithm(CACA)based on chaos path selection mechanism and local search mechanism. Calculation results show that the CACA established can solve the IHCP well and improves the calculation precision and computing speed.%为克服蚁群算法应用于寻源导热逆问题求解时容易陷入局部最优解和收敛速度慢的不足,利用混沌算法的遍历性和对初值的敏感性,将其融入到蚁群算法中,建立了基于混沌路径选择机制和局部混沌搜索机制的混沌-蚁群算法.计算结果表明,建立的混沌-蚁群算法可以很好地解决寻源导热逆问题,较蚁群算法而言,提高了计算精度和计算速度.
Obstacles distance analysis based on evolution ant colony optimization%进化蚁群优化理论实现障碍距离分析
Institute of Scientific and Technical Information of China (English)
谭新莲; 武凤翔; 陆彦辉
2009-01-01
借鉴了机器人路径规划问题的解决思路,将遗传算法中交叉算子引入到蚁群优化算法的路径寻优过程,提出了一种基于进化蚁群优化算法的障碍距离分析算法.实验结果表明,该方法不仅能处理复杂形状的障碍,与基于遗传算法的障碍距离计算方法相比,具有较好的路径寻优能力,并且能够很好地降低搜索陷入局部最优的可能性.%On the basis of the paper used in robet path planning problem solving ideas,and the crossover operation of genetic algorithm is used in the ant colony system for path optimization.This paper proposes a novel analyse algorithm of obstacle distance using ant colony optimization.Experimental results show that the proposed algorithm is capable of handling any complex shape obstacles and has better path planning optimization ability than genetic algorithm,and it can reduce the probability of local optimum.
Improved Ant Colony Algorithm for Solving the Optimal Parking Space Problems%解决最优停车位问题的改进蚁群算法
Institute of Scientific and Technical Information of China (English)
袁静
2013-01-01
In the parking guidance system, generally provide parking lot external the induction, no internal parking guidance. This paper solved the optimal parking space problems using ant colony algorithm, provide internal parking guidance of parking lot. In the solving process, not only consider the shortest path problem, but also according to the driver, vehicles, and parking space characteristics improve ant colony algorithm. And the main steps are given. Make it more accord with the actual parking lot of the optimal selection of parking space.%在停车诱导系统中,一般只提供停车场外的诱导,而没有停车场内部的停车诱导,论文利用蚁群算法求解停车场内部最优停车位,提供停车场内部的停车诱导,在求解过程中不仅考虑最短路径问题,并且根据驾驶员、车辆和停车位的特点,对蚁群算法进行改进,并给出了具体的求解步骤,使之更加符合实际停车场的最优停车位的选择.
Community Detection on Bipartite Networks Based on Ant Colony Optimization%基于蚁群优化的二分网络社区挖掘
Institute of Scientific and Technical Information of China (English)
徐永成; 陈崚
2014-01-01
Detecting communities from networks receives much attention in recent decades, especially from bipartite networks. Detecting communities from bipartite network is very important in the research on the theory and applications of complex network analysis. This paper proposes an algorithm based on ant colony optimization for detecting community structures from bipartite networks. The algorithm firstly transforms the problem of community detection into the problem of ant colony optimization, then constructs a graph model for ants foraging. Meanwhile, this paper redefines heuristic information according the degree of vertexes. Each ant chooses its path according to the pheromone and heuristic information on each path to construct a solution. The quality of solution obtained by each ant is measured by its bipartite modularity. The experimental results show that the proposed algorithm can not only accurately identify the number of communities of a network, but also obtain higher quality of community detection.%近年来，网络社区挖掘得到了极大的关注，尤其是针对二分网络的社区挖掘。二分网络社区挖掘对于研究复杂网络有非常重要的理论意义和实用价值。提出了一个基于蚁群优化的二分网络社区挖掘算法。该算法首先将二分网络社区挖掘问题转化成一个优化问题，建立一个可供蚂蚁搜索的图模型。同时，根据顶点的拓扑结构定义启发式信息。每只蚂蚁根据每条路径上的信息素和启发式信息选择路径，构造出一个社区的划分，再用二分模块度去衡量社区划分的优劣。实验结果表明，该算法不但可以较准确地识别二分网络的社区数，而且可以获得高质量的社区划分。
Falibene, Agustina; Josens, Roxana
2008-05-01
Dynamics of fluid feeding has been deeply studied in insects. However, the ability to vary the nectar-intake rate depending only on the carbohydrate deprivation has been clearly demonstrated only in Camponotus mus ants. When insect morphometry and fluid properties remain constant, changes in intake rate could only be attributed to variations in sucking pump activity. Previous records of the electrical activity generated during feeding in C. mus have revealed two different signal patterns: the regular (RP, frequencies: 2-5 Hz) and the irregular (IP, frequencies: 7-12 Hz). This work studies the mechanism underlying food intake-rate modulation in ants by analysing whether these patterns are involved. Behaviour and electrical activity generated by ants at different starvation levels were analysed during feeding on sucrose solutions. Ants were able to modulate the intake rate for a variety of sucrose concentrations (10, 40 and 60%w/w). The IP only occurred for 60% of solutions and its presence did not affect the intake rate. However, during the RP generated under the starved state, we found frequencies up to 7.5 Hz. RP frequencies positively correlated with the intake-rate for all sucrose concentrations. Hence, intake-rate modulation according to sugar deprivation is mainly achieved by the ant's ability to vary the pumping frequency.
模拟退火蚁群算法求解二次分配问题%Simulated annealing ant colony algorithm for QAP.
Institute of Scientific and Technical Information of China (English)
朱经纬; 芮挺; 蒋新胜; 张金林
2011-01-01
A simulated annealing ant colony algorithm is presented to tackle the Quadratic Assignment Problem(QAP).The simulated annealing method is introduced to the ant colony algorithm.By setting the temperature which changes with the iterative,after each turn of circuit,the solution set got by the colony is taken as the candidate set,the update set is gotten by accepting the solutions in the candidate set with the probability determined by the temperature.The candidate set is used to update the trail information matrix.In each turn of updating the tail information,the best solution is used to enhance the tail information.The tail information matrix is reset when the algorithm is in stagnation.The computer experiments demonstrate this algorithm has high calculation stability and converging speed.%提出了一种求解二次分配问题的模拟退火蚁群算法.将模拟退火机制引入蚁群算法,在算法中设定随迭代变化的温度,将蚁群根据信息素矩阵搜索得到的解集作为候选集,根据当前温度按照模拟退火机制由候选集生成更新集,利用更新集更新信息素矩阵,并利用当前最优解对信息素矩阵进行强化.当算法出现停滞对信息素矩阵进行重置.实验表明,该算法有着高的稳定性与收敛速度.
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.
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.
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.
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.
Institute of Scientific and Technical Information of China (English)
万智萍
2013-01-01
针对在弱光环境下的人体跟踪问题，本文提出一种带色素信息蚁群检测的弱光环境人体跟踪算法。通过利用蚁群优化算法检测监控帧图像里的物体边缘，并在初始帧图像里为聚集在边缘的蚁群身上标记色素信息，采用带色素的蚁群检测带信息素的蚁群并向其聚拢的性质来实现对处于弱光环境下的运动人体进行跟踪的目的。实验数据分析及仿真结果表明，本文的人体跟踪算法对处于弱光环境下的运动人体具有跟踪实效性，对存在噪声的图像也具有一定的鲁棒性。%As to the human body tracking problem under the weak light environment, human body tracking algorithm of ant colony detection with pigment information is presented. The character of ant colony optimization algorithm was used to detect the monitoring initial frame image object edge, and in the initial frame image to gather on the edge of the ant colony body, pigment information is marked. By using pigments ant colony detected with pheromone ant colony and toward pheromone ant colony gather, the purpose of tracking for human movement at night is achieved. The experimental data analysis and simulation results show that the human tracking algorithms for human movement under the low light environment is effective, and also has certain robustness for an image with noise.
Acharya, Ayan; Konar, Amit; Janarthanan, Ramadoss
2008-01-01
Ant Colony Optimization (ACO) is a metaheuristic for solving difficult discrete optimization problems. This paper presents a deterministic model based on differential equation to analyze the dynamics of basic Ant System algorithm. Traditionally, the deposition of pheromone on different parts of the tour of a particular ant is always kept unvarying. Thus the pheromone concentration remains uniform throughout the entire path of an ant. This article introduces an exponentially increasing pheromone deposition approach by artificial ants to improve the performance of basic Ant System algorithm. The idea here is to introduce an additional attracting force to guide the ants towards destination more easily by constructing an artificial potential field identified by increasing pheromone concentration towards the goal. Apart from carrying out analysis of Ant System dynamics with both traditional and the newly proposed deposition rules, the paper presents an exhaustive set of experiments performed to find out suitable p...
基于元胞蚂蚁算法的防空靶机航路规划研究%Route Planning of Anti-Air Target Drone Based on Cellular-Ant Colony Algorithm
Institute of Scientific and Technical Information of China (English)
刘志强; 雷宇曜; 阳再清
2014-01-01
防空靶机飞行航路设计是实现靶机有效控制，确保高效完成供靶任务的保障。通过对靶机三维航路规划模型进行分析，给出了元胞蚂蚁算法的航路规划模型的求解方法及算法实现的具体流程，并分别应用蚁群算法和元胞蚂蚁算法进行仿真实验。结果表明：元胞蚂蚁算法克服了蚁群算法收敛速度慢、陷于局部最小值的缺陷，可得到较优的航路。%The design of the flight airway of anti-air target is essential to the effective target control and the high effective completion of target supply task. Through the analysis of the three-dimensional airway design model, the solution method and corresponding algorithm flow of the cellular-ant colony algorithm is provided in this paper. The simulation experiment of the ant colony and cellular-ant colony algorithms is carried out, which shows that the cellular ant algorithm over comes the ant colony algorithm disadvantages of the slow convergence and local optima, and it is able to obtain optimal airway.
Ingram, Krista K; Gordon, Deborah M; Friedman, Daniel A; Greene, Michael; Kahler, John; Peteru, Swetha
2016-08-31
Task allocation among social insect workers is an ideal framework for studying the molecular mechanisms underlying behavioural plasticity because workers of similar genotype adopt different behavioural phenotypes. Elegant laboratory studies have pioneered this effort, but field studies involving the genetic regulation of task allocation are rare. Here, we investigate the expression of the foraging gene in harvester ant workers from five age- and task-related groups in a natural population, and we experimentally test how exposure to light affects foraging expression in brood workers and foragers. Results from our field study show that the regulation of the foraging gene in harvester ants occurs at two time scales: levels of foraging mRNA are associated with ontogenetic changes over weeks in worker age, location and task, and there are significant daily oscillations in foraging expression in foragers. The temporal dissection of foraging expression reveals that gene expression changes in foragers occur across a scale of hours and the level of expression is predicted by activity rhythms: foragers have high levels of foraging mRNA during daylight hours when they are most active outside the nests. In the experimental study, we find complex interactions in foraging expression between task behaviour and light exposure. Oscillations occur in foragers following experimental exposure to 13 L : 11 D (LD) conditions, but not in brood workers under similar conditions. No significant differences were seen in foraging expression over time in either task in 24 h dark (DD) conditions. Interestingly, the expression of foraging in both undisturbed field and experimentally treated foragers is also significantly correlated with the expression of the circadian clock gene, cycle Our results provide evidence that the regulation of this gene is context-dependent and associated with both ontogenetic and daily behavioural plasticity in field colonies of harvester ants. Our results underscore
Hierarchical interactive ant colony optimization algorithm and its application%分层交互式蚁群优化算法及其应用
Institute of Scientific and Technical Information of China (English)
黄永青; 郝国生; 张俊岭; 王剑
2012-01-01
Conventional ant colony optimization algorithm cannot effectively solve the systems whose optimization performance indices are difficult to be quantifiable. In order to overcome this weakness, a novel Hierarchical Interactive Ant Colony Optimization (HIACO) that the objective function values of the potential solutions are determined by subjective human evaluation is proposed. The structure of a primal Interactive Ant Colony Optimization (IACO) model is designed. Appropriate pheromone update rule and the characters of pheromone in IACO are presented. The ideal of hierarchy, the chance to hierarchy and the method of hierarchy are given. The evaluation way of user is so simple that he or she only needs selecting a mostly interesting individual of current generation and not evaluating quantization of every solution. So user fatigue is reduced efficiently. IACO and HIACO are applied to car styling design. The experimental results demonstrate that the proposed algorithm has good performance.%传统蚁群优化算法在求解优化性能指标难以数量化的定性系统问题时无能为力,为此提出一种利用人对问题解进行评价的分层交互式蚁群优化算法.设计了一个基本交互式蚁群优化模型结构,讨论了信息素的更新策略和性质.给出分层的思想、分层的时机和分层的具体实现方法.算法用户参与评价时,只需指出每一代中最感兴趣的解,而不必给出每个解的具体数量值,可以极大降低用户评价疲劳.将算法应用于汽车造型设计,实验结果表明所提出算法具有较高运行性能.
Directory of Open Access Journals (Sweden)
Hassan M. H. Mustafa
2016-10-01
Full Text Available This research work introduces an interesting comparative analytical study associated with performance evaluation of two diverse intelligent learning paradigms. Both paradigms are described in brief as follows. Firstly, the paradigm concerned with practically obtained psycho-learning experimental results after Pavlov’s and Thorndike’s work. In addition to the obtained experimental results while performing optimal solution of reconstruction problem by a mouse’s movement inside a figure of eight (8 maze. Secondly, considering the paradigm associated with observed application results of a bio-Inspired clever algorithm after searching for an optimal solution of Traveling Sales-man Problem (TSP. The adopted bio-inspired clever algorithm originally based on observed Ant Colony System (ACS performance. The comparative study for both paradigms' results in agreement of their performances with a learning process convergence which based on Least Mean Square (LMS error. That's derived after training of an Artificial Neural Network (ANN model namely Single Layer Perceptron .By the end of this manuscript, more performance analysis of two types of learning models have been introduced in comparative with the previously suggested two paradigms. Namely, these two models are related to parallel genetic algorithmic programming, and modified Hebbian learning (Oja's rule
Directory of Open Access Journals (Sweden)
Maida Bárbara Reyes‐Rodríguez
2014-05-01
Full Text Available Los procesos de transferencia de calor sonuno de los problemas más importantes a resolver en el campo de la Ingeniería. Entre los equipos más usados en la industria para realizar la transferencia de calor están los intercambiadores de calor de tubo y coraza. En el presente trabajo se desarrolla el procedimiento para la optimización del diseño de estos equipos utilizando el método de Kern y aplicando el algoritmo de la colonia de hormigas. Se aplica el mismo a tres ejemplos concretos y los resultados obtenidos se comparan con los obtenidos aplicando otros métodos de la inteligencia artificial. Se optimizan los principales parámetros geométricos de los intercambiadores de calor de tubo y coraza para lograr un menor costo de los mismos. Se demuestra la eficacia del nuevo procedimiento MACO (Mixed Ant Colony Optimization, en el proceso de optimización desde el punto de vista económico utilizando diferentes casos de estudios.Palabras claves: intercambiadores de calor, colonia de hormigas, método de Kern.______________________________________________________________________________AbstractHeat transfer processes are one of the most important problems to be solved in the field of Engineering. Among the most widely used equipment for heat transfer in the industry are the shell and tube heat exchangers. This paper develops the procedure for optimizing the design of shell and tube heat exchangers using the method of Kern and applying the ant colony algorithm. The procedure has been applied to three specific examples and the results obtained are compared with those obtained by applying other methods of artificial intelligence. The main geometric parameters of shell and tube heat exchangers are optimized, to achieve a lower cost of the exchanger. The efficacy of the new procedure MACO (Mixed Ant Colony Optimization for the optimization process from economically point of view was demonstrated, using different case studies.Key words: heat
Energy Technology Data Exchange (ETDEWEB)
Esquivel E, J. [Universidad Autonoma del Estado de Mexico, Facultad de Ingenieria, Cerro de Coatepec s/n, Ciudad Universitaria, 50110 Toluca, Estado de Mexico (Mexico); Ortiz S, J. J. [ININ, Carretera Mexico-Toluca s/n, 52750 Ocoyoacac, Estado de Mexico (Mexico)], e-mail: jaime.es.jaime@gmail.com
2009-10-15
In this work some results obtained during the development of optimization systems are presented, which are employees for the fuel reload design in a BWR. The systems use the ant colony optimization technique. As first instance, a system is developed that was adapted at travel salesman problem applied for the 32 state capitals of Mexican Republic. The purpose of this implementation is that a similarity exists with the design of fuel reload, since the two problems are of combinatorial optimization with decision variables that have similarity between both. The system was coupled to simulator SIMULATE-3, obtaining good results when being applied to an operation cycle in equilibrium for reactors of nuclear power plant of Laguna Verde. (Author)
Directory of Open Access Journals (Sweden)
Junichi Ochiai
2014-02-01
Full Text Available This paper presents a solution to real-world delive ry problems for home delivery services where a large number of roads exist in cities and the tra ffic on the roads rapidly changes with time. The methodology for finding the shortest-travel-tim e tour includes a hybrid meta-heuristic that combines ant colony optimization with Dijkstra’s al gorithm, a search technique that uses both real-time traffic and predicted traffic, and a way to use a real-world road map and measured traffic in Japan. Experimental results using a map of central Tokyo and historical traffic data indicate that the proposed method can find a better solution than conventional methods.
Multi Target of Community Detection Algorithm Based on Ant Colony Optimization%基于蚁群优化的多目标社区检测算法
Institute of Scientific and Technical Information of China (English)
杨楠; 吕红娟; 陈婷
2015-01-01
As a single obj ective optimization problem,usually the solution of ant colony optimization algorithm is re-stricted to a specific range,since there is only one obj ective function.When the optimization target is not appropriate,the al-gorithm may fall into failure,such as resolution limitation.This paper combined the concept of multi-obj ective optimization with ant colony optimization algorithm for the traditional community detection,which increased the number of obj ective function.The algorithm introduced multi goal strategy,proposed multi-obj ective ACO algorithm,and generated a set of Pa-reto optimal solutions in a single run process.And the three real world networks show the effectiveness and accuracy of the algorithm.%蚁群优化算法作为单目标优化问题，由于只有一个目标函数，通常会将解限制到特定的范围内。当优化的目标不恰当时，算法可能失效，比如分辨率限制问题。我们将多目标优化的思想与传统的用于社区检测的蚁群优化算法相结合，增加了目标函数个数，即增加了解的评价指标数目。该算法引入多目标策略，提出多目标 ACO算法，该算法在一次运行过程中会产生一组Pareto最优解。并在三个真实世界网络证明该算法的有效性和准确性。
Service composition optimization approach based on affection ant colony algorithm%满足情感蚁群的服务组合优化方法
Institute of Scientific and Technical Information of China (English)
马洪江; 周相兵
2012-01-01
In the service computing mode, affection behaviour was employed to improve the efficiency of service composition. Firstly, an affection space was built to meet behaviour demands, and cognition was defined to reason state change of affection. In the change processing, mapping was done between affection and cognition, and emotion decay and emotion update were defined to maintain the stability of affective change. Secondly, affective mechanism was put into ant colony algorithm, which formed an affection ant colony algorithm, and the algorithm was applied to Web Service Modeling Ontology (WSMO) service composition. Finally, the paper adopted a Virtual Travel Agency ( VTA) example under WSMO to show this approach was effective and feasible.%在服务计算模式下,通过引入情感来改进服务组合效率.首先,建立一种满足行为分析的情感感知空间,并定义认知来推理情感变化情况,使情感与认知能有效的映射.同时,定义了情感衰减和更新机制来保持情感变化稳定性.其次,将所建立的情感机制引入蚁群算法中,形成一种满足情感变化的蚁群算法,并将该算法应用到服务组合中实现优化.最后在Web服务建模本体(WSMO)下提供的VTA中实验表明,该方法有效且可行.
一种基于蚁群优化的图像分类算法%AN IMAGE CLASSIFICATION ALGORITHM BASED ON ANT COLONY OPTIMISATION
Institute of Scientific and Technical Information of China (English)
屠莉; 杨立志
2015-01-01
现有图像降维方法中特征信息被过多压缩，从而影响图像分类效果。提出IC-ACO算法，利用蚁群算法来解决图像分类问题。算法充分提取并保留图像的各种形态特征。利用蚁群优化算法在特征集中自动挖掘有效特征和特征值，构建各类分类规则，从而实现图像的分类识别。在真实的车标图像数据集上的实验结果表明，IC-ACO算法比其他类似算法具有更高的分类识别率。%Feature information in current image dimension reduction methods has been excessively compressed,which impacts the efficiency of image classification.In this paper we present the IC-ACO algorithm,it employs ant colony optimisation to solve image classification problem.The algorithm fully extracts various morphological features of image and retains them.The ant colony optimisation is used to automatically mine effective features and feature values from feature sets,the algorithm then constructs the classification rules of every type,thus realises image’s classified recognition.Experimental results on actual vehicle-logo image data sets show that the IC-ACO algorithm outperforms other similar algorithms in terms of the classified recognition accuracy.
Study on cost optimization based on ant colony genetic algorithm%基于蚁群遗传算法的费用优化研究
Institute of Scientific and Technical Information of China (English)
任列艳; 卢才武; 郭梨
2011-01-01
For the duration of construction projects cost optimization,advanced ant colony algorithm genetic algorithm is proposed. The algorithm is based on the rapid global search capability convergence of genetic algorithms and positive feedback mechanism of ant colony algorithm.Beginning the process of genetic algorithm generate pheromone distribution.It overcomes the shortcomings effectively of ACA falling into local optimum and degradation easily.Later positive feedback mechanism of ACA is used to seek exact solutions. Finally,the simulation results of practical example show that the advanced algorithm has very good convergence speed and the ability to search the global optimal solution.%针对建筑工程项目费用优化问题,基于遗传算法的快速全局搜索能力和蚁群算法的正反馈收敛机制提出了改进的蚁群遗传算法。初期采用遗传算法过程,生成信息素分布,有效地解决了蚁群算法易陷入局部最优和易退化的缺点;后期利用蚁群算法的正反馈机制求精确解。最后通过实际算例的仿真实验表明该算法具有非常好的收敛速度和搜索全局最优解的能力。
Institute of Scientific and Technical Information of China (English)
黄虎; 谢洪波
2012-01-01
Objective To improve the classification performance of the surface electromyography (Semg) -based prosthesis and reduce the dimensions of features extracted from the Semg signals, a modified ant colony optimization (ACO) was employed to select the best feature subset. Methods The relationship between features and target classes was calculated as the heuristic function and the best feature subset was selected by ACO, and the trained artificial nerve net was utilized to verify the classification performance. Results Ten healthy subjects participated in the experiment on classification of hand and wrist motion using Semg signals. Compared to the principle component analysis (PCA) -based feature subsets, the ACO-reduced feature subsets not only improved the classification accuracy but greatly reduced the number of features in the original feature set, which subsequently simplified the structure of the classifier and reduced the computational cost. Conclusions The proposed method exhibits a great potential in the real-time applications, such as Semg-based prosthesis control.%目的为提高假肢系统对动作信号的识别速度,设计了基于优化蚁群算法(ant colony optimization,ACO)的特征选择法,对表面肌电信号(surface electromyography,sEMG)高维特征向量降维以减少计算负担.方法 以特征与目标类型之间互信息关系作为启发函数,通过蚁群算法选出最佳特征子集,最后用已训练好的人工神经网络检验其分类性能.结果 对10名健康受试者进行了手腕部动作的肌电信号模式分类实验.与传统主成分分析法(principle component analysis,PCA)相比,该算法选出的特征子集提高了识别准确率,并显著降低了原始特征集的特征维数,进而简化分类器的结构,减少计算开销.结论 本方法在实时性要求高的肌电控制假肢等系统中具有良好的应用前景.
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.
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.
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.
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.
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.
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.
Directory of Open Access Journals (Sweden)
Morteza Atabati
2016-09-01
Full Text Available Quantitative structure–property relationship (QSPR studies based on ant colony optimization (ACO were carried out for the prediction of λmax of 9,10-anthraquinone derivatives. ACO is a meta-heuristic algorithm, which is derived from the observation of real ants and proposed to feature selection. After optimization of 3D geometry of structures by the semi-empirical quantum-chemical calculation at AM1 level, different descriptors were calculated by the HyperChem and Dragon softwares (1514 descriptors. A major problem of QSPR is the high dimensionality of the descriptor space; therefore, descriptor selection is the most important step. In this paper, an ACO algorithm was used to select the best descriptors. Then selected descriptors were applied for model development using multiple linear regression. The average absolute relative deviation and correlation coefficient for the calibration set were obtained as 3.3% and 0.9591, respectively, while the average absolute relative deviation and correlation coefficient for the prediction set were obtained as 5.0% and 0.9526, respectively. The results showed that the applied procedure is suitable for prediction of λmax of 9,10-anthraquinone derivatives.
Multi-Agent Task Allocation Based on Ant Colony Algorithm%基于蚁群算法的多Agent任务分配方法
Institute of Scientific and Technical Information of China (English)
文志强; 何宇晨
2012-01-01
In view of multi-agent task allocation problems,a task allocation model based on graph is presented,and based on ant colony algorithm a multi-agent task allocation method is proposed.Through experiments,it is compared with three classic methods,and the influence of ants number on the solution is discussed.The experimental result shows that the proposed method is effective.%针对多Agent任务分配问题,结合蚁群算法的思想,设计了基于图的任务分配数学模型,提出了基于蚁群算法的多Agent任务分配方法,并通过实验与3个经典方法进行比较和分析,探讨了蚂蚁数对求解结果的影响。实验结果表明,所提出的算法是有效的。
一种基于无相交搜索策略的蚁群算法%An Ant Colony Algorithm Based No Intersection Search Strategy
Institute of Scientific and Technical Information of China (English)
王越; 黄丽丰
2011-01-01
Ant colony algorithm is a simulation of foraging behavior of ants. Combined with human factors to solve complex combinatorial optimization problem of the intelligent algorithm, it could avoid premature convergence and stagnation, and it is a non-intersection algorithm (NIAS). The algorithm could determine the information of path intersection, and adjust the pheromone evaporation factor ρ,and dynamically optimize the iterative cost so as to improve the optimal solution. It could make the ability of local optimization algorithm be more rapidly, and enhance the diversity of searching optimal solution, as well as effectively control the algorithm in problem of premature convergence, therefore it strengthened the optimization performance of algorithm. By means of the example of TSPLIB, and comparing with ant system algorithm by simulation experiments, the results show that the algorithm is significantly improved.%针对蚁群算法容易过早收敛和停滞的现象,通过判断路径相交信息,并调整信息素的挥发系数P,动态地对迭代最优解进行优化改进,从而使算法局部优化能力更迅速,同时提高最优解搜索的多样性,有效地控制算法过早收敛的问题,增强了算法的寻优性能.通过使用TSPLIB中的范例,与蚂蚁系统算法进行仿真实验比较.结果表明,该算法改进效果明显.
一种改进的蚁群算法及其在复杂TSP问题上的应用%An Improved Ant Colony Algorithm and Its Application on Complex TSP
Institute of Scientific and Technical Information of China (English)
朱旭燕; 李原洲
2011-01-01
以简单TSP问题为例描述了传统蚁群算法过程,提出了其存在的问题及解决该问题的方法.提出了复杂TSP问题的定义,结合改进后的蚁群算法提出了解决复杂TSP问题的方法.通过实验表明,改进后的蚁群算法能够用于解决复杂TSP问题.%This paper describes the process of traditional ant colony algorithm, and proposes an existing problem and its solution of traditional ant colony algorithm, taking simple traveling salesman problem as example. The definition of complex traveling salesman problem has been defined. Combining the improved ant colony algorithm, a method to solve the complex traveling salesman problem has been proposed too. The improved ant colony algorithm can be used to solve complex traveling salesman problem, which is indicated by experiments.
Organization model for Mobile Wireless Sensor Networks inspired in Artificial Bee Colony
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).
Directory of Open Access Journals (Sweden)
Kotymán László
2015-06-01
, and to open boxes. However, fledging rate in the same years was lower for both open boxes and older nest-boxes. We conclude that artificial colonies are an important and successful tool in Red-footed Falcon conservation, and that the breeding parameters measured in artificial colonies depend on nest-box design. We present correlative evidence that closed boxes have a significant positive species specific effect on reproduction, probably due to their protection against weather. We also show that birds may have a preference for a certain nest-box design, and that the breeding success in the less favoured box type may be similar to that in open nests. We recommend that future studies incorporate nest-type and nest-box design effects in all comparisons made on reproductive performance in case of Red-footed Falcons and Kestrels.
Dejean, Alain; Petitclerc, Frédéric; Roux, Olivier; Orivel, Jérôme; Leroy, Céline
2012-03-01
In the mutualisms involving the myrmecophyte Cecropia obtusa and Azteca ovaticeps or A. alfari, both predatory, the ants defend their host trees from enemies and provide them with nutrients (myrmecotrophy). A. ovaticeps provisioned with prey and then (15)N-enriched food produced more individuals than did control colonies (not artificially provisioned). This was not true for A. alfari colonies, possibly due to differences in the degree of maturity of the colonies for the chosen range of host tree sizes (less than 3m in height). Myrmecotrophy was demonstrated for both Azteca species as provisioning the ants with (15)N-enriched food translated into higher δ(15)N values in host plant tissues, indicating that nitrogen passed from the food to the plant. Thus, the predatory activity of their guest ants benefits the Cecropia trees not only because the ants protect them from defoliators since most prey are phytophagous insects but also because the plant absorbs nutrients.
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.
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。
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.
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.
Improved Ant Colony Algorithm for Period Vehicle Routing Problem%改进蚁群算法优化周期性车辆路径问题
Institute of Scientific and Technical Information of China (English)
蔡婉君; 王晨宇; 于滨; 杨忠振; 姚宝珍
2014-01-01
周期性车辆路径问题（PVRP）是标准车辆路径问题（VRP）的扩展，PVRP将配送期由单一配送期延伸到T（ T＞1）期，因此，PVRP需要优化每个配送期的顾客组合和配送路径。由于PVRP是一个内嵌VRP的问题，其比标准VRP问题更加复杂，难于求解。本文采用蚁群算法对PVRP进行求解，并提出采用两种改进措施---多维信息素的运用和基于扫描法的局部优化方法来提高算法的性能。最后，通过9个经典PVRP算例对该算法进行了数据实验，结果表明本文提出的改进蚁群算法求解PVRP问题是可行有效的，同时也表明两种改进措施可以显著提高算法的性能。%Period Vehicle Routing Problem(PVRP)is a generalized classic vehicle routing problem (VRP), in which the planning period is extended to a t-day period .Therefore , custom group and route should be optimized every day in PVRP.Embedded in VRP, PVRP is too complicated to be solved .As many route operations are formulated in a certain period, PVRP is very popular in practice.In recent years, distribution centers have paid much attention to the problem of vehicle delivery with the growing fuel cost and fierce supply chain competition . Especially in some situations , vehicles have to serve some fixed customers in a given period , and besides , the service times of each customer are settled in advance .Optimization of the repetitive operations will significantly save cost .Many researches have shown that the heuristic method based on the simulation of biological is very suitable for solving large-scale combinatorial optimization problem .Ant colony optimization ( ACO) is founded on the behavior of ant colony foraging in nature , which has been proved to be feasibility for solving VRP problems in lots of research .Therefore , this paper presents an improved ant colony optimization ( IACO ) , in which a multi-dimension pheromone matrix and a local optimization strategy based on
Institute of Scientific and Technical Information of China (English)
邹北骥; 孟志刚; 向遥; 曾羽
2011-01-01
This paper proposed a real-time bidirectional crowd search (RBCS) approach based on real time bidirectional search and crowd collaboration. The collaboration mechanism is inspired by the foraging behaviors of an ant colony. We present an ant colony foraging model based on RBCS and it consists of a finite state machine (FSM) and a serial of rules defining the interactions between ants and environment. The model has the ability of adapting to dynamically changing environment to find the shortest route. Experi-mental results demonstrate the power of our approach; ant colony behaves reasonably in different environments. Extension to human group moving between fixed positions time and again acquires approving result And comparison with traditional ant colony foraging model based on using pheromone shows the superiority of this method.%基于双向搜索和群组协作的研究,提出一种新颖的搜索算法一实时双向群组搜索(real-time bidirectional crowd search,简称RBCS).基于这个搜索算法所提出的蚁群觅食模型包含了有限状态机和一系列表示蚂蚁和环境交互的规则,具有在复杂动态环境下找到食物和巢穴之间最短路径的能力.2D/3D实验结果表明,算法的搜索能力具有可信性,将其扩展到人群在固定点之间的来回往复运动也获得了满意的效果;和传统基于信息素的蚁群觅食模型的仿真实验对比表明了算法的优越性.
一种基于优质边求解TSP的蚁群算法%Ant colony algorithm based on quality edge to solve TSP
Institute of Scientific and Technical Information of China (English)
胡银厚; 王世卿
2013-01-01
On the research of ant colony algorithm to the Traveling Salesman Problem shows that it can easily fall into the local optimal solution, leading to lower ability to explore better solutions. This paper proposes a solving approach which based on the quality edge to solve this problem. Choose the quality edge according to the information from the algorithm. While the algo-rithm is in stagnation, adjust the pheromone on quality edge, it will enhance the ability of algorithm to explore better solutions. At the same time, improved routing rules will limit the ant to choose the quality edge as much as possible, thereby improving the quality of solution. The experiment results show that the improved solution strategy is reasonable and effective.% 应用蚁群算法求解旅行商问题时发现，算法易陷入局部最优解而停滞，并导致其探索新解能力的降低。提出了一种基于优质边的求解方法，根据算法运行过程中的相关信息选取优质边，在停滞时调整优质边上的信息素；使用改进的选路规则将蚂蚁的路径选择尽可能限制在优质边中，从而改进蚂蚁构造解的质量以增强算法的探索能力。实验结果表明，改进的策略是合理有效的。
基于蚁群算法的聚类新算法%A new algorithm of cluster based on the algorithm of ant colonies
Institute of Scientific and Technical Information of China (English)
许国根; 徐昊; 王幸运
2012-01-01
The article puts forward a new clustering algorithm based on ant colony optimization. According to classified sample number N and category number p of the classification a city with N+l floors is designed. Each floor contains p cities except the first floor. The ant finishes classifying all the samples at a time when walking from the first floor to the last floor. The visit choice is influenced by combined action of both path pheromone and sample pheromone. The path pheromone need to be renewed after finishing the visit the cities on the same floor. Similarly, when a cycle is done, the path pheromone and sample pheromone should be renewed respectively. Through the analysis of living examples, we can receive a satisfying consequence.%提出了一种基于蚁群算法的聚类新算法.按分类的样本数N和类别数p,设计N+1层城市,除第1层城市外,其余城市均有p个城市.蚂蚁每次从第1层城市开始到最后一层城市的移动,就完成对所有样本的分类.访问城市的选择受路径信息素和样品类信息素的共同作用,每次完成层间城市的访问,需要对路径信息素更新；完成一次循环,分别对路径信息素和样本类信息素更新.通过实例分析,该算法能够得到较为满意的结果.
An Improved Ant Colony Clustering Method for Intrusion Detection%一种改进蚁群聚类的入侵检测方法
Institute of Scientific and Technical Information of China (English)
姜参; 王大伟
2013-01-01
Intrusion detection is an important aspect of the network information safety. For the disadvantage that the existing intrusion de-tection method is not comprehensive of various kinds of attack and has lower detection rate and the higher fault detection rate,an im-proved ant colony clustering method for intrusion detection is proposed. The convergence rate of ant colony cluster algorithm is improved. In the optimization process,the information entropy is introduced to prevent into local optimal,and thus the method can adjust automati-cally the pheromone updating and improve the clustering speed. And follow on,the intrusion detection system is designed. The experimen-tal results show that the method not only improves the detection rate,but reduces the fault detection rate,and can detect precisely the vari-ous kinds of attacks.%入侵检测是网络信息安全的一个重要方面。针对现有的入侵检测对各类攻击不全面以及在检测率低误检率高的缺点，文中提出了一种改进的蚁群聚类的入侵检测方法。该方法对蚁群聚类算法的收敛速度方面和易陷入局部最优问题进行了改进，在优化过程中引进K-means算法以及信息熵，从而使其能够对信息素的更新进行自动的调整，提高了聚类速度和效果。进而设计了网络入侵检测系统。实验结果表明，该方法不仅提高了检测率，而且降低了误检率，对于各大类攻击都能够进行精确的检测。
间歇自由基聚合反应器的Pareto蚁群优化%Pareto ant colony optimization to batch free-radical polymerization reactors
Institute of Scientific and Technical Information of China (English)
郭相坤; 王晓静; 许德平; 王晓玲
2009-01-01
Optimization technologies are widely used in chemical industry for chemical engineers to select the "best" manipulating profile out of a given set of technological conditions. The engineers must consider multiple objectives and can improve their opportunities by determining and exploring the solution space of all efficient solutions interactively with little a priori preference information available. However, the enumerative method only works on small instances and the underlying complex optimization problems become increasingly demanding as the number of projects grow. The recently developed meta-heuristics, Ant Colony Optimization Algorithms, provide a useful compromise between the computation time and the quality of the approximated solution space. In this study, a Pareto Ant Colony Optimization Algorithm was applied to a real world batch polymerization process of multi-objective optimization. The results indicate that the algorithm is robust and useful for the chemical process optimization.%优化技术广泛用于化工生产中"最佳"工艺条件的确定,工程师常需在无先验信息情况下,从若干工艺条件中确定同时能满足多方需求的最佳方案,实现效益最大化.枚举法只能在较简单的情况下使用,随着生产实际复杂程度的增加,枚举法显得无能为力.近来提出的元启发式蚁群优化算法无论计算时间,还是优化质量,都能满足复杂体系的优化.本研究采用Pareto蚁群算法,对间歇自由基聚合反应器进行了多目标优化,结果表明,该算法具有较强的鲁棒性,可用于间歇自由基聚合反应器的设计.
AntNet: Distributed Stigmergetic Control for Communications Networks
Di Caro, G
2011-01-01
This paper introduces AntNet, a novel approach to the adaptive learning of routing tables in communications networks. AntNet is a distributed, mobile agents based Monte Carlo system that was inspired by recent work on the ant colony metaphor for solving optimization problems. AntNet's agents concurrently explore the network and exchange collected information. The communication among the agents is indirect and asynchronous, mediated by the network itself. This form of communication is typical of social insects and is called stigmergy. We compare our algorithm with six state-of-the-art routing algorithms coming from the telecommunications and machine learning fields. The algorithms' performance is evaluated over a set of realistic testbeds. We run many experiments over real and artificial IP datagram networks with increasing number of nodes and under several paradigmatic spatial and temporal traffic distributions. Results are very encouraging. AntNet showed superior performance under all the experimental condit...
Fire localization strategy based on modified Ant Colony Algorithm%基于改进蚁群算法的火源定位策略研究
Institute of Scientific and Technical Information of China (English)
康一梅; 杨恩博; 杨鑫凯
2012-01-01
为了达到多机器人系统能够模仿蚁群寻找食物源的行为来定位搜索火源目标,对基本蚁群算法和禁忌搜索算法进行融合和修正,形成一种新的目标搜索策略.修正的蚁群算法包括:全局随机搜索、局部遍历搜索和信息素更新三个部分.在搜索过程中,通过设定信息素的有效作用范围来实现对多个火源目标的定位.仿真结果表明,局部遍历搜索能够保证机器人逐步靠近火源目标,而融合了禁忌搜索的蚁群算法在搜索效率上大大提高.%For robots searching for the fire sources by foraging behavior of art colony, a multi-robots search strategy is proposed by combing and modifying Ant Colony Algorithm (ACA) and Tabu Search algorithm (TS). The modified ACA includes three parts, which are global random search, local traversal search and pheromone update. In the search process, an effective range of pheromone is set to localize multiple fire sources. Simulation results show that the local traversal search can enable robots to move towards the fire gradually, and search efficiency is improved.
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.
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.
VRP Solution of Logistics Distribution Based on Ant Colony Algorithm%一种基于蚁群算法的物流配送VRP解决方案
Institute of Scientific and Technical Information of China (English)
薛戈丽; 王建平
2012-01-01
物流配送是目前物流发展的新趋势,在物流配送中,配送路径规划对于顾客的满意度以及经营总成本有相当大的影响.通过应用蚁群算法,实现了物流配送VRP的优化过程,建立的算法能在短时间内找到最佳车辆数及对应的最佳配送路径.通过数据测试,发现该算法收敛性较好,在较高服务水平的基础上,明显降低了配送成本.%At present, logistics distribution is the new trends of logistics, in logistics distribution, the distribution path planning is the main reason for the customer satisfaction and the total Operating costs. Using the ant colony optimization algorithm, we realize the optimization process of VRP problem for logistics distribution, the algorithm can find the best vehicle numbers and the relation path in a short time. By testing the algorithm, we find that the Convergence of the algorithm is good, when it reaches the high level of the service, the cost logistics distribution can reduce quickly.
Research on Train Set Scheduling Based on Ant Colony Algorithm%基于蚁群算法的动车组周转计划研究
Institute of Scientific and Technical Information of China (English)
王文宪; 柏伟
2012-01-01
Through analyzing the situation of train sets, an assignment model under the condition of uncertain railroad section was constructed and an ant colony algorithm to solve the combinatorial optimization problem and quantitative formulas of the high-speed passenger trains were proposed. Finally, taking Wuhan-Guangzhou high-speed railway for an example, an optimal program of using high-speed passenger trains was calculated and aworking diagram of the trainsets at a station was drawn. Through verification, this model and algorithm are feasible.%通过对高速铁路动车组运用现状进行分析，建立了高速铁路动车组在不固定区段使用条件下周转优化的指派模型，并提出了解决该组合优化问题的蚁群算法，以及动车组使用数量的公式。最后以武广客专为算例，计算出动车组优化运用方案，并铺画了一个车站相关的动车组周转图。通过验证，本文模型和算法具有可行性。
Bionic cutter of cutter suction dredger with ant colony optimization%绞吸式挖泥船仿生绞刀刀齿的蚁群优化
Institute of Scientific and Technical Information of China (English)
穆乃超; 许焕敏; 邬同舟
2014-01-01
为了提高绞吸式挖泥船仿生绞刀减粘降阻的效果，获得更高的生产效率和经济效益，应用蚁群算法对仿生绞刀的凸包的几何尺寸及其分布进行优化，得到对于不同类型的土壤和在一定的压力范围内所需的凸包形仿生绞刀的结构尺寸。%In order to reduce the adhesion and friction of soil, the effect of the adhesion and resistance, and gain higher productivity and economic efficiency. The application of ant colony algorithm for bionic cutter geometry and distribution of the convex hull is optimized to obtain the desired convex hull-shaped cutter biomimetic structure within different types of soil and a certain pressure range.
Institute of Scientific and Technical Information of China (English)
Morteza Atabati
2012-01-01
A quantitative structure-property relationship （QSPR） study was suggested for the prediction of infinite dilution activity coefficients of halogenated hydrocarbons, γ∞ , in water at 298.15 K. After optimization of 3D geometry of the halogenated hydrocarbons with semi-empirical quantum chemical calculations at the AM1 level, different descriptors （1514 descriptors） were calculated by the HyperChem and Dragon softwares. A major problem of QSPR is the high dimensionality of the descriptor space; therefore, descriptor selection is the most important step. In this paper, an ant colony optimization （ACO） algorithm was proposed to select the best descriptors. Then the selected descriptors were applied for model development using multiple linear regression. The average absolute relative deviation and correlation coefficient for the training set were obtained as 4.36% and 0.951, respectively, while the corresponding values for the test set were 5.96% and 0.929, respectively. The results showed that the applied procedure is suitable for the prediction of γ∞ of halogenated hydrocarbons in water.
蚁群算法在作业调度中应用研究%On the Application of Ant Colony Algorithm to Job Scheduling
Institute of Scientific and Technical Information of China (English)
朱福珍; 朱凌
2012-01-01
Reasonable allocation of resources in the workshop scheduling can improve the production equipment utilization and the productivity,and reduce the production cost.This paper proposes the ant colony algorithm to solve mixed flow assembly scheduling of jobs,which is demonstrated from updating element information and the state transition probability.Through the calculation of objective function and comparison with target follow method,genetic algorithm,and simulated annealing algorithm,the result shows that this algorithm can optimize the job scheduling.%合理配置车间作业调度中的各种资源可提高生产设备利用率与生产效率,降低生产成本。本文提出了一种求解混流装配线作业调度的蚁群算法,从信息素更新、状态转移概率论证该算法。通过计算目标函数与目标追随法、遗传算法、模拟退火算法比较,结果证明该算法对作业调度能够起到优化作用。
Directory of Open Access Journals (Sweden)
Hao Jin
2015-01-01
Full Text Available Steel-spring floating slab tracks are one of the most effective methods to reduce vibrations from underground railways, which has drawn more and more attention in scientific communities. In this paper, the steel-spring floating slab track located in Track Vibration Abatement and Control Laboratory was modeled with four-pole parameter method. The influences of the fastener damping ratio, the fastener stiffness, the steel-spring damping ratio, and the steel-spring stiffness were researched for the rail displacement and the foundation acceleration. Results show that the rail displacement and the foundation acceleration will decrease with the increase of the fastener stiffness or the steel-spring damping ratio. However, the rail displacement and the foundation acceleration have the opposite variation tendency for the fastener damping ratio and the steel-spring stiffness. In order to optimize the rail displacement and the foundation acceleration affected by the fastener damping ratio and the steel-spring stiffness at the same time, a multiobjective ant colony optimization (ACO was employed. Eventually, Pareto optimal frontier of the rail displacement and the foundation acceleration was derived. Furthermore, the desirable values of the fastener damping ratio and the steel-spring stiffness can be obtained according to the corresponding Pareto optimal solution set.
Directory of Open Access Journals (Sweden)
Weiqiang Dong
2016-09-01
Full Text Available A distributed power system with renewable energy sources is very popular in recent years due to the rapid depletion of conventional sources of energy. Reasonable sizing for such power systems could improve the power supply reliability and reduce the annual system cost. The goal of this work is to optimize the size of a stand-alone hybrid photovoltaic (PV/wind turbine (WT/battery (B/hydrogen system (a hybrid system based on battery and hydrogen (HS-BH for reliable and economic supply. Two objectives that take the minimum annual system cost and maximum system reliability described as the loss of power supply probability (LPSP have been addressed for sizing HS-BH from a more comprehensive perspective, considering the basic demand of load, the profit from hydrogen, which is produced by HS-BH, and an effective energy storage strategy. An improved ant colony optimization (ACO algorithm has been presented to solve the sizing problem of HS-BH. Finally, a simulation experiment has been done to demonstrate the developed results, in which some comparisons have been done to emphasize the advantage of HS-BH with the aid of data from an island of Zhejiang, China.
论“蚁群效应”与公共组织绩效管理%On“Ant Colony Effect”and Public Organization Performance Management
Institute of Scientific and Technical Information of China (English)
许英凤
2013-01-01
“蚁群效应”在现代管理学中运用广泛，它的优势集中体现为严密和谐的组织体系、互助合作的团队精神和处事灵活、责任感强的成员，让组织、团队、员工三者共同有效地协同发挥作用，为公共组织绩效管理提供了很好的借鉴。因此，要提高公共组织绩效管理，必须加强公共组织的团队建设，形成良好的团队合作精神。%“Ant colony effect is”widely applied in modern management and its advantages are characterized by tight and harmonious organization system,spirit of mutual cooperation,flexible and responsible mem-bers.It enables organization,team and employees to play a cooperative role in work,which provides much reference to performance management of the public organization.To improve performance management of public organizations,it is necessary to strengthen team construction in public organizations to form a good team spirit.
Research on An Improved Ant Colony Algorithm For Solving OAP%针对QAP问题的改进型蚁群优化算法研究
Institute of Scientific and Technical Information of China (English)
项前; 黄波; 李红旮
2010-01-01
本文结合二次分配问题(quadratic assignment problem,QAP)的特点,通过分析传统妈K算法在解决QAP问题时收数过快,精度不高的缺点,提出一种以ACS(ant colony system)为基础的改进蚁群算法--信息素迭代东积ACS(ACS with accumulated pheromone by iteration,ACS_API).新方法通过对定义启发式信息和信息素更新规则的改进,扩大了搜索空间,从而进免过早收数,陷入局部最优解中.该算法已应用于QAP标准浏试数据,并通过与另外两种先前提出的改进蚂蚁算法(HAS_QAP,ACO_GIS)的比较分析得出了它在算法精度和执行时间上的优势.
基于蚁群算法在机器人足球比赛中的应用%The Application of Ant Colony Algorithm in Robot Soccer Competition
Institute of Scientific and Technical Information of China (English)
贾翠玲; 李卫国; 郭连考; 陈杰
2012-01-01
To solve the problems of the efficiency of football robot searching path and multi-agents collaboration,this paper studies the algorithm and strategy issues for AS-MF09 robot soccer.Ant colony algorithm is applied to the competition. The actual match proved that the algorithm in the robot soccer can improve offensive and defensive performance and can get good results.%针对机器人足球中寻路效率和多智能体协作问题,提出相应的解决方案,文章主要研究在机器人足球比赛中算法和策略问题.将蚁群算法应用到该比赛中,经实际比赛证明该算法在机器人足球比赛中对于提高机器人寻找足球和攻防性能方面都能得到很好的结果.
Directory of Open Access Journals (Sweden)
Parisa Moslehi
2011-09-01
Full Text Available Currently, all search algorithms which use discretization of numeric attributes for numeric association rule mining, work in the way that the original distribution of the numeric attributes will be lost. This issue leads to loss of information, so that the association rules which are generated through this process are not precise and accurate. Based on this fact, algorithms which can natively handle numeric attributes would be interesting. Since association rule mining can be considered as a multi-objective problem, rather than a single objective one, a new multi-objective algorithm for numeric association rule mining is presented in this paper, using Ant Colony Optimization for Continuous domains (ACOR. This algorithm mines numeric association rules without any need to specify minimum support and minimum confidence, in one step. In order to do this we modified ACOR for generating rules. The results show that we have more precise and accurate rules after applying this algorithm and the number of rules is more than the ones resulted from previous works.
基于蚁群算法的四旋翼航迹规划%Four-rotor route planning based on the ant colony algorithm
Institute of Scientific and Technical Information of China (English)
莫宏伟; 马靖雯
2016-01-01
Given a four⁃rotor unmanned aerial vehicle’ s characteristics and complex flight environment, as well as the accuracy of the global positioning system in the environment model, the establishment of a 3D environment mod⁃el based on elevation maps has reduced the probability of encountering obstacles. In terms of planning algorithms, most of the existing path planning algorithms can only plan 2D paths. Numerous 3D planning algorithms have com⁃plex computations and require much storage space. A global path is also difficult to plan. The advantages of the ant colony algorithm include distributed computing and swarm intelligence. Moreover, this algorithm has great potential in path planning. However, when the fundamental ant colony algorithm is used in a 3D track search, the two direct⁃ly connected planes easily track straight through obstacles. The track then includes more nodes, and the fitness val⁃ue becomes too large. The algorithm was improved to address these issues by proposing the strategy of converting the main direction to search and the simplified track strategy. Ant simulation results showed that the improved algorithm could avoid obstacles, reduce path length, and improve search efficiency.%由于四旋翼无人机（ UAV）自身的特点和其复杂的飞行环境，考虑到全球定位系统（ GPS）定位的精度，在环境模型方面，建立了一个基于高程图的三维环境模型，减小了碰到障碍物的概率。在规划算法方面，大部分现有的路径规划算法只能规划二维平面路径，而一般的三维规划算法，大多数运算算法复杂，需要很大的存储空间，同时难以进行全局路径规划。该蚁群算法具有分布式计算、群体智能等优势，在路径规划上有很大潜力。但在应用基本三维蚁群算法进行航迹搜索时，两平面直接相连容易使航迹直接穿过障碍物，并且搜索出的航迹节点较多，适应度值过大。针对这两个
Institute of Scientific and Technical Information of China (English)
陈军; 智军; 王毓龙
2015-01-01
文中分析了物流配送车辆优化调度问题的分类，建立了蚁群算法的多目标优化模型，设计了基于多目标优化蚁群算法的车辆调度问题求解的流程，实例分析验证了算法的可行性。%It analyzes the classification of distribution vehicle scheduling problem and establishes the multi -objective optimization model of ant colony algorithm,designs the process of solving vehicle scheduling problem based on multi-objective optimization ant colony algorithm.Instances analysis verify the feasibility of the algorithm.
Institute of Scientific and Technical Information of China (English)
苏涛; 王庆斌; 孙聪; 李文强
2012-01-01
On the basis of the analysis on the general VRP problem, a mathematical model of military logistics delivery routing optimization problem was built, and the ant colony algorithm was used to simulate Txperimental results showed that ant colony algorithm is a fast and effective method of solving military logistics delivery routing optimization problem.%在对一般VRP问题分析的基础上，建立了军事物流配送路径优化问题的数学模型，运用蚁群算法进行了仿真实验，实验结果表明，蚁群算法可以快速有效地解决军事物流配送的路径优化问题。
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。
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.
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.
Institute of Scientific and Technical Information of China (English)
刘双; 刘天佑; 冯杰; 高文利; 邱礼泉
2013-01-01
Simulating the behavior of ant colony searching for food, the Ant Colony Algorithm is an emulated and optimized algorithm, which demonstrates excellent performance in such combinatorial optimization problems as Traveling Salesman. However, it has not been widely applied to the inversion and interpretation of magnetic data. Based on the characteristics of magnetic data inversion and interpretation, this paper improves the mapping mechanism from objective function value to pheromone and summarizes Ant Colony Algorithm optimizing for continuous and multiple objective function using nodes partition strategy. Satisfactory results were achieved when Ant Colony Algorithm was simulated to inverse the parameters in the synthetic model experiment of magnetic data and was applied to prospect the banded iron formation according to low-altitude aeromagnetic data surveyed at Iron Mount mining area, southern Australia.%蚁群算法是模拟蚂蚁群体觅食行为的仿真优化算法,在旅行商等组合优化问题中展现出优异的性能,其在磁测资料反演解释中的应用却很少.基于磁测资料反演解释的特点,本文改善了目标函数值与信息素的映射机制,总结出节点划分策略的连续域多变量目标函数优化蚁群算法.并对磁测资料的模型参数反演进行理论模拟,最后应用于澳大利亚南部Iron Mount矿区低空航磁资料的条带状铁矿构建勘查,取得良好应用效果.
Institute of Scientific and Technical Information of China (English)
焦亚萌; 黄建国; 侯云山
2011-01-01
针对最大似然(maximum likelihood,ML)方位估计方法多维非线性搜索计算量大的问题,将连续空间蚁群算法与最大似然算法相结合,提出基于蚁群算法的最大似然(ant colony optimization based maximum likelihood,ACOML)估计新方法.该方法将传统蚁群算法中的信息量留存过程拓展为连续空间的信息量高斯核概率密度函数,得到最大似然方位估计的非线性全局最优解.仿真结果表明,ACOML方法保持了原最大似然方位估计方法算法的优良估计性能,而计算量只是最大似然方法的1/15.%A new maximum likelihood direction of arrival (DOA) estimator based on ant colony optimization (ACOML) is proposed to reduce the computational complexity of multi-dimensional nonlinear existing in maximum likelihood (ML) DOA estimator. By extending the pheromone remaining process in the traditional ant colony optimization into a pheromone Gaussian kernel probability distribution function in continuous space, ant colony optimization is combined with maximum likelihood method to lighten computation burden. The simulations show that ACOML provides a similar performance to that achieved by the original ML method, but its computational cost is only 1/15 of ML.
Institute of Scientific and Technical Information of China (English)
陈莉芝
2012-01-01
传统的蚁群算法具有搜索时间长的缺点，在实际应用中受到限制。故该文提出了基于信息素扩散模型的蚁群算法，简化了信息素扩散，并改进了基本蚁群算法的信息素更新方式。最后将该改进算法应用在碰撞检测当中，通过手术中手术器械与人体的碰撞反映的仿真验算，验证了基于信息素扩散模型的蚁群算法在碰撞检测中能提高碰撞的效率和精确度，为实际的应用提供理论依据与指导。% The traditionnal ant colony algorithm has shortcomings of needing too long time when searching, restricted in practi⁃cal application. So the paper puts forward ant colony algorithm based on the pheromone diffusion model, simplifing the phero⁃mone diffusion, and improving the update mode of basic ant colony algorithm pheromone. Finally, this improved algorithm is ap⁃plied in collision detection. Through simulation of the surgical instruments and human collision in surgery, the paper verifies the ant colony algorithm based on the pheromone diffusion model can improve the efficiency of collision and accuracy in collision detection , providing theoretical basis and guidance in actual application.
Institute of Scientific and Technical Information of China (English)
王鼎; 谢顺依; 连军强
2011-01-01
针对集成电机推进器(IMP)一体化结构要求电机功率密度大,效率高的特点,采用将混沌优化算法嵌入蚁群算法的混合智能优化算法——昆沌蚁群算法(CACA)对集成电机推进器特种电机设计方案进行优化,该方法充分利用以上2种优化算法的优点,即蚁群算法的高精度和混沌算法的快速性.优化结果显示,优化后的设计方案使电机效率提高了1.6％,有效体积缩小了5.6％,使电机能更好地适应高启动场合要求,优化效果良好,可为鱼雷IMP特种电机的设计提供参考.%To enhance power density and work efficiency of the special brushless DC motor for the integrated motor propulsor (IMP) of a torpedo, a hybrid optimum intelligent algorithm, named chaos ant colony algorithm (CACA), is presented. This method embeds a chaos optimization algorithm into ant colony algorithm. The special brushless DC motor is optimized by making use of the high accuracy of the ant colony algorithm and the rapidness of the chaos optimization algorithm. The optimization results of the motor show that the efficiency is enhanced by 1.6% and the volume is reduced by 5.6%. The proposed chaos ant colony algorithm may become a tool for the design of special brushless DC motor of torpedo IMP.
基于改进蚁群算法的车辆路径优化问题研究%Study on VRP based on improved ant colony optimization
Institute of Scientific and Technical Information of China (English)
陈迎欣
2012-01-01
物流活动中需要找出各个配货节点之间的最短路径,用以指导物流车辆调度,进而节约物流成本.提出解决车辆路径优化问题的方法,针对蚁群算法的缺点,分别对信息素更新策略、启发因子进行改进,并引入搜索热区机制,有效解决了蚁群算法的缺陷.最后,以哈尔滨市局部地图为原型,应用MATLAB软件对改进蚁群算法求解车辆路径优化问题的性能进行仿真,并与基本蚁群算法对比分析,验证了改进蚁群算法的有效性和可行性.%Logistics activities need to find different distribution node of the shortest path, to instruct the logistics vehicle scheduling, and then save the logistics cost. This paper proposed the solution of vehicle routing optimization problem. In order to conquer the defects and improve the basic ant colony optimization, it improved pheromones updating strategy, stimulating factor and the introduction of search hotspots, solved the defects of ant colony optimization effectively. With the help of Harbin city map as the prototype and the MATLAB software, it carried out simulation to check the improved ant colony optimization. The result verifies the feasibility and effectiveness of the improved ant colony optimization.
Institute of Scientific and Technical Information of China (English)
张澎; 王鲁达; 胡丹
2013-01-01
针对蚁群算法解决一些复杂多维问题的能力不强,容易陷入局部最优,造成算法早熟的情况.为解决上述问题,提出了一种用量子衍生方法的多目标蚁群算法,可用量子遗传算法的全局搜索和蚁群算法的群体智能机制,将蚁群优化与量子遗传算法相结合,用于多维0-1背包问题的求解.与同类算法进行对比分析,实验证明改进算法不仅能更快更精确地逼近Pareto最优前端,并能够维持Pareto最优解分布的均匀性,有效的提高了计算效率和搜索效率,且能弥补蚁群算法的不足.%Ant colony algorithm is not expert in solving some complex mulli - dimensional problems, and tends to sink into partial optimum result leading to precocity of algorithm. This paper provided a new ant colony algorithm for solving knapsack problem. Based on global search of quantum genetic algorithm and swarm intelligence, this algorithm combines ant colony optimization with quantum genetic algorithm. Utilizing this method to solve multi ?dimensional 0 - 1 knapsack problem, compared with similar algorithms, the results indicate that the algorithm can approach Pareto optimal front faster and more exactly, meanwhile sustain balance of Pareto optimum solution distribution. The experiment proves that the algorithm improves efficiency of computing and searching, thus the algorithm can remedy the deficiency of ant colony algorithm.
Ant colony algorithm based on genetic method for continuous optimization problem%基于遗传机制的蚁群算法求解连续优化问题
Institute of Scientific and Technical Information of China (English)
朱经纬; 蒙陪生; 王乘
2007-01-01
A new algorithm is presented by using the ant colony algorithm based on genetic method (ACG) to solve the continuous optimization problem. Each component has a seed set. The seed in the set has the value of component, trail information and fitness. The ant chooses a seed from the seed set with the possibility determined by trail information and fitness of the seed. The genetic method is used to form new solutions from the solutions got by the ants. Best solutions are selected to update the seeds in the sets and trail information of the seeds. In updating the trail information, a diffusion function is used to achieve the diffuseness of trail information. The new algorithm is tested with 8 different benchmark functions.
Flexible job-shop scheduling based on multiple ant colony algo-rithm%基于多种群蚁群算法的柔性作业车间调度研究
Institute of Scientific and Technical Information of China (English)
薛宏全; 魏生民; 张鹏; 杨琳
2013-01-01
To the characteristics of flexible job-shop scheduling, this paper designs the disjunctive graph model of the flexible job-shop scheduling and presents the solution of the multiple ant colony algorithm for the competitive rule. According to the labor mode of ant colony, different colonies are located in different processing nodes in the algorithm. By the command of core colony, all types of ant colonies with pheromone updating mechanism and searching traits have mutual compensation of advantages as well as mutual competitive exclusion so that they can potentially cooperate smoothly, and fulfill the scheduling requirements of flexible job-shop scheduling. Through the analysis of the simulating experiment results prove the feasibility and effectiveness of the algorithm.%针对柔性作业车间调度的特点，设计了柔性作业车间调度析取图模型，结合蚁群分工组织的工作方式，给出了基于竞争规则的多种群蚁群算法求解方法。算法中不同种群的蚂蚁被放置在析取图中不同的工序节点上，通过核心种群的引导，充分发挥蚁群协作竞争的并行高效特点，满足柔性作业车间调度的要求。仿真实验表明该算法求解柔性作业车间调度具有可行性和有效性。
Institute of Scientific and Technical Information of China (English)
华铨平; 王昕
2012-01-01
This paper theoretically completed the research on the optimal route distribution of clothing timeliness. First, according to the features of clothing distribution timeliness, the clothing timelines fuzzy math model is built. With the model, the model of vehicle routing problem ( VRP) with the clothing timeliness is discussed. And then, on the basis the conventional ant colony algorithm is analyzed, and a modified ant colony algorithm ( MACA) which is fit for clothing VRP model is studied. The optimal attributes of the MACA, utilizing the variables of clothing timeliness as the updating function of the weighting, updates the function of information element at all clothing distribution points. The results indicate that compared with the existing ant colony algorithm, the MACA proposed by the paper can enhance the ability to find the solution of the clothing VRP with timeliness. The feasibility and rationality of the models and the algorithm are verified by practical examples.%从理论角度对具有时效性服装最优路径的配送进行研究.首先确定服装具有时效性等特点,建立了服装时效性的模糊数学模型,通过对服装配送问题的变量研究,结合服装时效性的模糊数学模型,研究了具有时效性的服装车辆路径问题(vehicle routing problem,VRP)模型；在此基础上,通过对传统的蚁群算法(ant colony algorithm)分析,研究了与服装VRP模型相匹配的改进型蚁群算法(modified ant colony algorithm,MACA)；MACA具有的最优属性是以服装时效性变量为权重的更新函数作为各配送点信息素更新函数.结果表明,改进的蚁群算法,对具有时效性服装的VRP模型的最优路径的求解更优,证明了MACA对具有时效性服装VRP模型求解的合理性与有效性.
Institute of Scientific and Technical Information of China (English)
谢颖; 李吉兴; 杨忠学; 张岩
2015-01-01
To optimize the structural parameters of the 4-pole 7. 5 kW line-start permanent magnet synchro-nous motor, an improved binary genetic ant colony algorithm which combined the advantages of genetic al-gorithm with ant colony algorithm and solved the problem of continuous space optimization was used. The basic idea of binary genetic ant colony algorithm and its features were presented, and the specific imple-mentation method of binary genetic ant colony algorithm in motor optimization design was mainly discussed. Language was used to realize the algorithm and results of simulation and calculation were obtained to prove its global convergence property. Finite element method was used to simulate the optimized electromagnetic design. The result slows that the optimization design of motor based on improved binary genetic ant colony algorithm may effectively improve the starting and running performance of the motor.%针对电机的优化设计问题，采用一种改进的二进制遗传蚁群算法，对一台4极7．5 kW的自起动永磁同步电动机的结构参数进行优化，该算法结合遗传算法和蚁群算法各自的优点，并且能解决连续空间优化问题。介绍了改进二进制遗传蚁群算法的基本思想及其特点，重点论述该算法在电机优化设计中的具体实现方法。采用编程语言实现该算法，通过大量的仿真计算验证算法的全局收敛能力。利用有限元方法对优化后的电磁设计方案进行仿真，结果表明该算法可以使自起动永磁同步电动机得到较好的优化，有可能提高电机的起动性能和运行性能。
Job Shop Scheduling Based on Improved Ant Colony Algorithm%基于改进蚁群算法的作业车间调度
Institute of Scientific and Technical Information of China (English)
王硕; 顾幸生
2012-01-01
提出了一种改进的蚁群算法,应用于经典的作业车间调度问题.编码采用基于机器的编码可以控制冗余解的数量,但同时会产生不可行解.本研究提出了控制不可行解产生的策略,同时对已出现的不可行解问题,在尽量保留种群基因的前提下,改变解的形式加以利用.在丰富了种群的多样性的同时解决了不可行解的问题.采用自适应参数法则,使参数的变化顺应种群发展过程各个阶段的需要.在一定代数的迭代后,通过改变某些参数跳出局部最优,从而达到了较好的搜索效果.%The improved ant colony algorithm is proposed and applied to solving the job shop scheduling problem. Using machine based coding, redundancy solution is perfectly limited. However, infeasible solutions can be generated by such coding method. In this paper, strategies to limit the infeasible solutions are put forward and the infeasible solution is transformed into feasible solution at the same time. Such strategies not only preserve the population in rich diversity, but also solved the problem of infeasible solutions. Adaptive parameter laws are issued to make the parameters changing every moment, which met the demands of population at all stages of evolution. After certain iterations, the algorithm may get out of local optimal value by merely changing some parameters. Finally, better searching results have been achieved.
Arenas, Andrés; Roces, Flavio
2017-01-01
Plants initially accepted by foraging leaf-cutting ants are later avoided if they prove unsuitable for their symbiotic fungus. Plant avoidance is mediated by the waste produced in the fungus garden soon after the incorporation of the unsuitable leaves, as foragers can learn plant odors and cues from the damaged fungus that are both present in the recently produced waste particles. We asked whether avoidance learning of plants unsuitable for the symbiotic fungus can take place entirely at the colony dump. In order to investigate whether cues available in the waste chamber induce plant avoidance in naïve subcolonies, we exchanged the waste produced by subcolonies fed either fungicide-treated privet leaves or untreated leaves and measured the acceptance of untreated privet leaves before and after the exchange of waste. Second, we evaluated whether foragers could perceive the avoidance cues directly at the dump by quantifying the visits of labeled foragers to the waste chamber. Finally, we asked whether foragers learn to specifically avoid untreated leaves of a plant after a confinement over 3 hours in the dump of subcolonies that were previously fed fungicide-treated leaves of that species. After the exchange of the waste chambers, workers from subcolonies that had access to waste from fungicide-treated privet leaves learned to avoid that plant. One-third of the labeled foragers visited the dump. Furthermore, naïve foragers learned to avoid a specific, previously unsuitable plant if exposed solely to cues of the dump during confinement. We suggest that cues at the dump enable foragers to predict the unsuitable effects of plants even if they had never been experienced in the fungus garden.
带有征税算子的改进蚁群优化方法%Improved ant colony algorithm with tax operator
Institute of Scientific and Technical Information of China (English)
郑松; 李春富; 王春林; 葛铭; 薛安克
2011-01-01
Aiming at the disadvantage(premature convergence) of Ant Colony Algorithm(ACA),edified from the role of tax mechanisms of human society, the tax operator is presented to strengthen its global search ability. Tax operator restrains the rapid expansion of difference between pheromone in order to improve the solution. The preferences and the convergence of the tax operator is discussed in the paper. In the end,an example of Traveling Salesman Problem(TSP) is given in the paper,which is simulated by using basic ACA and improved ACA. The simulation results show that the tax operator has excellent global optimization properties,it can avoid premature convergence of ACO.%针对蚁群算法存在停滞现象的缺点,借鉴人类社会税收机制的作用,提出了能够强化其全局搜索能力的征税算子.征税算子通过抑止信息素差异急剧膨胀,以提高所得解的全局性.并对征税算子的参数设置以及收敛性问题进行讨论研究,最后将添加征税算子的蚁群算法与传统蚁群算法分别应用于旅行商问题(TSP)进行仿真实验.仿真结果表明,征税算子具有优良的全局优化性能,可抑制算法过早收敛于次优解,有效防止了停滞现象.
无线传感器网络中蚁群路由算法的改进%Perfection of Ant Colony Routing Algorithm in Wireless Sensor Network
Institute of Scientific and Technical Information of China (English)
陶志勇; 蒋守凤
2014-01-01
在无线传感器节点能量有限的条件下，如何使网络寿命最大化是无线传感器网络研究的重点，均衡网络能量消耗是延长网络寿命的一个有效方法。在蚁群算法的基础上引入了模糊理论的概念，提出了一种ACO for Fuzzy Theory算法，根据节点剩余能量、通信距离、邻居节点数目和信息素等因素采用模糊综合评判法进行下一跳节点的选择。仿真实验表明，与基于能量有效蚁群算法（EEABR）进行比较，相同条件下AFT算法有效地减少了网络平均能量消耗，增强了网络节点的存活率。%How to maximize the life span of the network under the wireless sensor nodes with limited energy conditions is a research focus, Balaced the energy distribution of network is a effective way.A routing protocol of WSN is raised based on Ant Colony Algorithm and Fuzzy Theory(AFT),it selects next nodes according to node’s en-ergy,communication distance,the number of neighbor nodes and pheromones. The simulation results with MATLAB show that the AFT can reduce the average energy comsuption effectively and increase node survival compared to EE-ABR.
Aalizadeh, Reza; von der Ohe, Peter C; Thomaidis, Nikolaos S
2017-02-24
According to the European REACH Directive, the acute toxicity towards Daphnia magna should be assessed for any industrial chemical with a market volume of more than 1 t/a. Therefore, it is highly recommended to determine the toxicity at a certain confidence level, either experimentally or by applying reliable prediction models. To this end, a large dataset was compiled, with the experimental acute toxicity values (pLC50) of 1353 compounds in Daphnia magna after 48 h of exposure. A novel quantitative structure-toxicity relationship (QSTR) model was developed, using Ant Colony Optimization (ACO) to select the most relevant set of molecular descriptors, and Support Vector Machine (SVM) to correlate the selected descriptors with the toxicity data. The proposed model showed high performance (QLOO(2) = 0.695, Rfitting(2) = 0.920 and Rtest(2) = 0.831) with low root mean square errors of 0.498 and 0.707 for the training and test set, respectively. It was found that, in addition to hydrophobicity, polarizability and summation of solute-hydrogen bond basicity affected toxicity positively, while minimum atom-type E-state of -OH influenced toxicity values in Daphnia magna inversely. The applicability domain of the proposed model was carefully studied, considering the effect of chemical structure and prediction error in terms of leverage values and standardized residuals. In addition, a new method was proposed to define the chemical space failure for a compound with unknown toxicity to avoid using these prediction results. The resulting ACO-SVM model was successfully applied on an additional evaluation set and the prediction results were found to be very accurate for those compounds that fall inside the defined applicability domain. In fact, compounds commonly found to be difficult to predict, such as quaternary ammonium compounds or organotin compounds were outside the applicability domain, while five representative homologues of LAS (non-ionic surfactants) were, on average
Research on the Global Update Rule of Ant Colony Algorithm%蚁群算法全局更新规则的研究
Institute of Scientific and Technical Information of China (English)
陈烨
2002-01-01
@@ 1引言 通过考察和研究蚂蚁寻找事物的方法,意大利学者Macro Dorigo等人于1991年提出了蚂蚁系统.该算法具有较好的性能.随后,Macro,Gambardella 又提出了蚁群系统(ACS,Ant Colony System).该算法的性能较蚂蚁系统又有所提高,但是这种改进算法仍有搜索解的速度慢、容易陷入局部最优等缺点.虽然如此,这种算法仍可较好地解决各种组合优化问题,如TSP问题、QAP问题等.并且已经有人将这种算法用于解决网络路由问题以及电路设计中的元件以及线路布局的问题,取得了很好的结果.然而,蚁群算法求解速度慢、容易陷入局部最优的缺点成为限制它应用范围的瓶颈.因此,不断有人提出改进算法.本文将在简单介绍蚁群算法的基础上,分析这种算法在全局更新规则上的不足,并提出一种新的改进算法.
Soft computing in artificial intelligence
Matson, Eric
2014-01-01
This book explores the concept of artificial intelligence based on knowledge-based algorithms. Given the current hardware and software technologies and artificial intelligence theories, we can think of how efficient to provide a solution, how best to implement a model and how successful to achieve it. This edition provides readers with the most recent progress and novel solutions in artificial intelligence. This book aims at presenting the research results and solutions of applications in relevance with artificial intelligence technologies. We propose to researchers and practitioners some methods to advance the intelligent systems and apply artificial intelligence to specific or general purpose. This book consists of 13 contributions that feature fuzzy (r, s)-minimal pre- and β-open sets, handling big coocurrence matrices, Xie-Beni-type fuzzy cluster validation, fuzzy c-regression models, combination of genetic algorithm and ant colony optimization, building expert system, fuzzy logic and neural network, ind...
Distributed nestmate recognition in ants.
Esponda, Fernando; Gordon, Deborah M
2015-05-07
We propose a distributed model of nestmate recognition, analogous to the one used by the vertebrate immune system, in which colony response results from the diverse reactions of many ants. The model describes how individual behaviour produces colony response to non-nestmates. No single ant knows the odour identity of the colony. Instead, colony identity is defined collectively by all the ants in the colony. Each ant responds to the odour of other ants by reference to its own unique decision boundary, which is a result of its experience of encounters with other ants. Each ant thus recognizes a particular set of chemical profiles as being those of non-nestmates. This model predicts, as experimental results have shown, that the outcome of behavioural assays is likely to be variable, that it depends on the number of ants tested, that response to non-nestmates changes over time and that it changes in response to the experience of individual ants. A distributed system allows a colony to identify non-nestmates without requiring that all individuals have the same complete information and helps to facilitate the tracking of changes in cuticular hydrocarbon profiles, because only a subset of ants must respond to provide an adequate response.
Task Scheduling in Cloud Computing by Using Improved Ant Colony Algorithm%基于改进蚁群算法的云计算任务调度研究
Institute of Scientific and Technical Information of China (English)
张海玉
2016-01-01
In order to find the optimal scheduling scheme for cloud computing tasks ,and reduce completion time of tasks ,a task scheduling model in cloud computing by using improved ant colony algorithm is proposed in this paper , Firstly ,mathematical model of cloud computing task scheduling is established ,and secondly ant colony algorithm is used to simulate the process of ants searching for food to obtain the solution which And the local and global information depth updating method is introduced to speed up search speed , finally , performance is tested on cloudsim simulation platform . Results show that improved ant colony algorithm not only greatly reduces task execution time of cloud computing to solve the problem of unbalanced load of resources ,and it is very good to achieve optimal scheduling of cloud computing tasks .%为了找到最优的云计算任务调度方案，减少任务的完成时间，提出了基于改进蚁群算法的云计算任务调度算法。首先建立云计算任务调度的目标函数，然后采用蚁群算法模拟蚂蚁搜索食物过程对目标函数进行求解，并引入局部、全局信息深度更新方式进行改进，加快搜索速度，最后在CloudSim仿真平台进行性能测试实验。结果表明，改进蚁群算法不仅大幅度减少了云计算任务执行时间，而且解决了资源负载不均衡难题，很好地实现了云计算任务的最优调度。
Institute of Scientific and Technical Information of China (English)
侯景伟; 孔云峰; 孙九林
2011-01-01
To resolve complex problems on optimal allocation of water resources with intelligent optimal methods, a multi-objective optimization model was built and the multi-objective fish-ant colony algorithm （MFACA） was designed. This model, based on principles of efficiency, fairness, and harmoniousness, is aimed at producing the largest economic, social, and environmental benefits. The objective of economic benefit is the largest direct economic benefit produced by regional water supply. The objective of social benefit is referred to as the smallest regional water deficit. The objective of environmental benefit is to ensure the smallest discharge of major contaminants. Constraints included water supply, water demand, water settings, economic development, and its harmony. In this model, constraints of water supply include possible water yield and ground water yield. Constraints of water demand include living, industrial, agricultural, and environmental water. Constraints of water settings include overall merit index and water quality. The optimal allocation model had the characteristics of large-scale system, multiple objectives, multiple constraints, multiple levels, and multiple associations. To solve this complicated model, the multi-objective fish-ant colony algorithm was established in accordance with the integration of pheromone positive feedback of the ant colony optimization （ACO） and fast track change and jumping out of local extremum of the artificial fish-swarm algorithm （AFA）. A swarm degree in the AFA was used to avoid possible premature problems at the initial stage of ACO. It was not strict for MFACA to set parameters and initial values of a mathematical model. The objective functions and constraints were not necessarily continuous and differentiable. This algorithm has a faster convergence rate and a higher optimization power. In order to validate the feasibility and effectiveness of the MFACA, surveys were done in Zhenping County, Henan
Institute of Scientific and Technical Information of China (English)
陶志勇; 蒋守凤
2015-01-01
Based on ant colony algorithm we introduced the concept of fuzzy theory, and proposed a routing algorithm which is based on fuzzy theory and ant colony.In the algorithm, the forward ant selects the next hop node using fuzzy comprehensive evaluation method when exploring the path.In the update process of pheromone, the backward ants whom successfully reach the aggregation node and converted will enhance the path pheromones according to the network information carried by the forward ants, while whom failed to reach have to weaken the pheromone.In data transmission the algorithm uses low energy nodes dormant working mechanism to achieve the goal of balancing the energy consumption of network nodes.Simulation results showed that comparing with energy-efficient ant-based routing ( EEABR) algorithm, this algorithm effectively reduced the average energy consumption of the network and enhanced the survival of network nodes under the same condi-tions.%在蚁群算法基础上引入模糊理论的概念，提出基于模糊理论和蚁群BFTAC（ Based on Fuzzy Theory and Ant Colony）的路由算法。 BFTAC算法前向蚂蚁在路径探索中，通过模糊综合评判法选择下一跳节点；信息素更新过程中，成功到达汇聚节点转化的后向蚂蚁根据对应的前向蚂蚁携带的网络信息增强路径信息素，而未成功到达的要削弱信息素；数据传输时，采用低能量节点休眠工作机制，以此达到均衡网络节点的能耗的目的。仿真实验表明，与基于能量有效蚁群算法（ EEABR ）进行比较，相同条件下BFTAC算法有效地减少了网络平均能量消耗，增强了网络节点的存活率。
Application of Ant Colony Algorithm Based on LEACH Routing Protocol%蚁群算法在LEACH路由协议中的应用
Institute of Scientific and Technical Information of China (English)
段军; 张清磊
2014-01-01
It is an important problem that reduce energy consumption and prolong the life of wireless sensor network. LEACH is a low en-ergy adaptive clustering hierarchy algorithm for wireless sensor networks. However,it has many disadvantages such as the cluster head of LEACH spending more energy and the clusters distribution is not uniform. An improved routing algorithm is proposed according to the disadvantage of LEACH routing algorithm. The routing algorithm focuses on the building of clusters and routing topology. The radius of the cluster that was far away from the sink node was smaller than the radius of the cluster that was close to the sink node. The ant colony algorithm is chose for the routing topology of cluster headers. The experiment,from the network node number survived and the average energy consumption,evaluates the results of simulation,the simulation results show that the improved algorithm's network life time in-creased by 15%,higher than that of traditional result,the node average energy consumption reduced by 20%. The improved algorithm can effectively reduce the network consumption,balance the network load.%减少网络能量损失，增加网络的生成时间是无线传感网络的重要研究内容。 LEACH是针对无线传感网络设计的低功耗自适应的路由算法。但是传统LEACH路由算法存在簇首开销过大、簇规模分布不均匀等问题。针对LEACH算法存在的缺点，从成簇方式和簇头路由拓扑提出改进方案，成簇半径随着距离Sink节点的增加而减小，簇首间采用蚁群算法进行路由优化。实验从网络节点存活的节点数目和节点的平均耗能两个指标对仿真结果进行评价，仿真结果显示改进算法网络的生存时间比传统结果提高了15%，节点平均能耗降低20%。改进算法可有效减少网络的总能量消耗，均衡网络的负载。
基于蚁群优化的AUV全局路径规划研究%Research on global path planning based on ant colony optimization for AUV
Institute of Scientific and Technical Information of China (English)
王宏健; 熊伟
2009-01-01
路径规划是自主式水下潜器(AUV)导航研究的重要课题,AUV可用于未知环境如海洋空间探测.在大范围海洋环境中,应用蚁群优化原理对自主式水下潜器的全局路径规划问题进行了研究.引入栅格建模方法建立了蚁群可视图模型,设计了蚁群信息素更新规则;给出了蚁群全局路径规划的操作步骤;针对蚁群规划路径不平滑问题,设计了切割算予和插点算子.仿真实验结果表明,蚁群全局规划算法非常适合于求解复杂环境中的规划问题,规划时间短、路径平滑,其原型系统可应用于非结构化无人环境监测.%Path planning is an important issue for autonomous underwater vehicles (AUVs) traversing an unknown environment such as a sea floor, a jungle, or the outer celestial planets. For this paper, global path planning using large-scale chart data was studied, and the principles of ant colony optimization (ACO) were applied. This paper introduced the idea of a visibility graph based on the grid workspace model. It also brought a series of pheromone updating rules for the ACO planning algorithm. The operational steps of the ACO algorithm are proposed as a model for a global path planning method for AUV. To mimic the process of smoothing a planned path, a cutting operator and an insertion-point operator were designed. Simulation results demonstrated that the ACO algorithm is suitable for global path planning. The system has many advantages, including that the operating path of the AUV can be quickly optimized, and it is shorter, safer, and smoother. The prototype system successfully demonstrated the feasibility of the concept, proving it can be applied to surveys of unstructured unmanned environments.
求解FJSP的混合遗传一蚁群算法%Hybrid genetic algorithm-ant colony optimization for FJSP solution
Institute of Scientific and Technical Information of China (English)
董蓉; 何卫平
2012-01-01
为更有效地求解柔性作业车间调度问题，综合考虑其中的机器分配与工序排序问题，建立了相关析取图模型，提出一种混合遗传一蚁群算法。该算法首先通过遗传算法获取问题的较优解，据此给出蚁群算法的信息素初始分布；之后充分利用蚁群算法的正反馈性进行求解，采用精英策略对蚁群的信息素进行局部更新；最后借鉴遗传算法交叉算子的邻域搜索特性扩大蚁群算法解的搜索空间，从而改善解的质量。通过3个经典算例的实验仿真，以及与其他算法的比较，验证了所提算法的可行性与有效性。%To solve Flexible Job-Shop Scheduling Problem(FJSP)more effectively, a related disjunctive graph model was built and a hybrid Genetic Algorithm(GA)-Ant Colony Optimization( ACO) was proposed by considering equip- ments arrangement and operation sequencing. In this algorithm, a better solution to the problem was obtained by ge- netic algorithm, and pheromones initial distribution of ACO was provided on this basis. The positive feedback of ACO was used to solve the problem, and the local update of the pheromones were conducted by elitist strategy. The neighborhood searching feature of crossover operator in GA was used to increase the search space of ACO, thus the quality of solution was improved. Through the experimental simulation of 3 classical examples, the feasibility and effectiveness of proposed algorithm were verified.
Institute of Scientific and Technical Information of China (English)
曹洁; 王思乐; 帅立国
2012-01-01
Aimed at the problems of lower stability and reliability of the routing in multi-robot Ad Hoc routing protocols, an ant-colony algorithm was introduced and its thorough analysis was made. By means of improving the state transition strategy and pheromone update strategy in the ant-colony algorithm, the global searching ability was improved and the falling-into local optimum solution of the algorithm was a-voided. Therefore, the design of multi-robots Ad Hoc routing protocol was realized based on this improved ant-colony algorithm. Simulation result showed that, compared with classic AODV (Ad Hoc on-demand distance vector) protocol, this routing protocol has improved the stability of the network performances and communication efficiency more effectively.%针对多机器人Ad Hoc网络路由协议中路由稳定性和可靠性低的问题,引入蚁群算法并对其进行深入分析.通过对蚁群算法状态转移策略和信息素更新策略的改进,提高全局搜索能力,避免算法陷入局部最优解,实现基于改进蚁群算法多机器人Ad Hoc路由协议的设计.仿真结果表明,与经典的AODV(Ad Hoc on-denand distance vector)协议相比,该路由协议有效地提高了网络的稳定性和通信效率.
Energy Technology Data Exchange (ETDEWEB)
Ortiz S, J.J. [Depto. de Sistemas Nucleares, ININ, 52045 Ocoyoacac, Estado de Mexico (Mexico); Requena R, I. [Universidad de Granada, 18071 Granada (Spain)]. e-mail: jjortiz@nuclear.inin.mx
2003-07-01
In this work the AZCATL-PBC system based on a technique of ants colonies for the search of control rods patterns of those reactors of the Nuclear Power station of Laguna Verde (CNLV) is presented. The technique was applied to a transition cycle and one of balance. For both cycles they were compared the k{sub ef} values obtained with a Haling calculation and the control rods pattern proposed by AZCATL-PBC for a burnt one fixed. It was found that the methodology is able to extend the length of the cycle with respect to the Haling prediction, maintaining sure to the reactor. (Author)
基于蚁群算法的农业节水灌溉路径优化部署%Study on Pheromone-based Ant Colony Algorithm For Optimal Proliferation
Institute of Scientific and Technical Information of China (English)
邓蕾蕾; 于合龙; 于亚洲; 张献
2012-01-01
In order to realize the water-saving irrigation of field plots path pipeline deployment management and control, ant colony algorithm with optimized pheromone was designed. Based on the defects of the existing ant colony algorithm combinatorial optimization, with the coordinate of field plots as data source, an improved ant colony algorithm for field plots wiring path was designed to improve the ability to update the optimal solution of ant colony algorithm in an iterative process and finally in the same number of iterations to find the rules of a shorter path and of less cost, to solve the problem of agricultural water-saving irrigation pipeline path optimization and to verify the actual problems in the VC + + program validation path optimization. The test results show that, under the same climatic conditions, path optimization design results can provide reference basis and data support for water saving irrigation pipeline layout management.%为实现节水灌溉田间地块路径管线部署的管理和控制,采用信息素优化的改进蚁群算法进行设计研究.在现有蚁群算法组合优化的现实缺陷基础上,以田间地块坐标作为数据源,采用改进的蚁群算法对田间地块布线路径进行设计,从而提高蚁群算法在迭代过程中更新最优解的能力,最终在相同的迭代次数内找到路径更短、代价更小的规则,解决农业节水灌溉管线路径部署优化问题,并在VC++程序中验证路径优化的实际问题.测试结果表明:在相同的气候条件下,路径优化部署设计结果可以为节水灌溉的管道布局管理提供参考依据和数据支持.
Energy Technology Data Exchange (ETDEWEB)
Esquivel E, J.; Ordonez A, A. [Universidad Autonoma del Estado de Mexico, Facultad de Ingenieria, Cerro de Coatepec s/n, Toluca, Estado de Mexico (Mexico); Ortiz S, J. J. [Departamento de Sistemas Nucleares, ININ, Carretera Mexico-Toluca s/n, Ocoyoacac 52750, Estado de Mexico (Mexico)
2008-07-01
In this paper we present some results obtained during the development of optimization systems that can be used to design refueling and patterns of control rods in a BWR. These systems use ant colonies and Greedy search. The first phase of this project is to be familiar with these optimization techniques applied to the problem of travel salesman problem (TSP). The utility of TSP study is that, like the refueling design and pattern design of control rods are problems of combinative optimization. Even, the similarity with the problem of the refueling design is remarkable. It is presented some results for the TSP with the 32 state capitals of Mexico country. (Author)
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.
Institute of Scientific and Technical Information of China (English)
贾翠玲; 王利利; 徐明娜
2012-01-01
提出一种适用于灭火机器人避障路径规划的改进蚁群优化算法，采用自适应更新策略的方法规划最佳避障路径，建立了简洁、严谨的蚁群优化算法函数，以达到对灭火机器人避障路径的优化。这种方法能够使灭火机器人在未知环境寻找火源时有效避开障碍物并且使机器人所走路径最短，所用时间最少。经实验证明了该方法的可行性和有效性。%This paper put forward a kind of improved ant colony algorithm used in fire fighting robot path planning of obstacle avoidance. The algorithm using the adaptive updating strategy planed the best obstacle avoidance path. The method established a concise, rigorous ant colony optimization function, which could optimize the path of the robot obstacle avoidance. This method not only made fire -fighting robots in unknown environment when looking for fire avoiding obstacles effectively, but also made the robot have walked shortest path. The simulation and experiment results indicate the feasibility and validity of this algorithm.
Institute of Scientific and Technical Information of China (English)
米永强
2014-01-01
蚁群算法是一种求解组合优化问题较好的方法。在蚁群算法的基本原理基础上，以旅行商问题为例，介绍了该算法求解TSP的数学模型及具体步骤，并通过仿真实验与粒子群优化算法等方法比较分析，表明了该算法在求解组合优化问题方面具有良好的性能。%Ant colony algorithm is a kind of good methods to solve the combinatorial optimization problems. Based on the basic principle of ant colony algorithm, for traveling salesman problem as an example this paper, this paper introduced the mathematical model of the algorithm to solve TSP and specific steps, through the simulation experiments and comparative analysis with particle swarm optimization algorithm and others, it showed the algorithm has good performance in solving combinatorial optimization problems.