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Sample records for ant colony algorithm

  1. GRID SCHEDULING USING ENHANCED ANT COLONY ALGORITHM

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    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.

  2. Improvement and Implementation of Best-worst Ant Colony Algorithm

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    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.

  3. An ant colony algorithm on continuous searching space

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    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.

  4. Ant Colony Algorithm for Solving QoS Routing Problem

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    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.

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

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    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.

  6. Introduction to Ant Colony Algorithm and Its Application in CIMS

    Institute of Scientific and Technical Information of China (English)

    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...

  7. Hybrid ant colony algorithm for traveling salesman problem

    Institute of Scientific and Technical Information of China (English)

    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.

  8. Core Business Selection Based on Ant Colony Clustering Algorithm

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    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.

  9. AN IMPROVED ANT COLONY ALGORITHM IN CONTINUOUS OPTIMIZATION

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    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.

  10. Ant Colony Search Algorithm for Solving Unit Commitment Problem

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    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.

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

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    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.

  12. Cooperation-based Ant Colony Algorithm in WSN

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    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.

  13. AN ANT COLONY ALGORITHM FOR MINIMUM UNSATISFIABLE CORE EXTRACTION

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    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.

  14. A Novel Algorithm for Manets using Ant Colony

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    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.

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

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    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.

  16. An ant colony algorithm for solving Max-cut problem

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    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.

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

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    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.

  18. Solution to the problem of ant being stuck by ant colony routing algorithm

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    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.

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

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    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

  20. Ant Colony Based Path Planning Algorithm for Autonomous Robotic Vehicles

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    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.

  1. Time-Based Dynamic Trust Model Using Ant Colony Algorithm

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    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.

  2. Multiple ant colony algorithm method for selecting tag SNPs.

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    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.

  3. Departure Traj ectory Design Based on Pareto Ant Colony Algorithm

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    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.

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

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    Ivan A. Mantilla-Gaviria

    2013-01-01

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

  5. Road Network Vulnerability Analysis Based on Improved Ant Colony Algorithm

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    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.

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

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    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.

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

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    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.

  8. Text clustering based on fusion of ant colony and genetic algorithms

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    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.

  9. Identification of Dynamic Parameters Based on Pseudo-Parallel Ant Colony Optimization Algorithm

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    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.

  10. A Hybrid Routing Algorithm Based on Ant Colony and ZHLS Routing Protocol for MANET

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    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.

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

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    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.

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

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    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.

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

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    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.

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

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    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.

  15. Adaptive Ant Colony Algorithm for the VRP Solution of Logistics Distribution

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    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.

  16. Assessment Guidelines for Ant Colony Algorithms when Solving Quadratic Assignment Problems

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    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.

  17. Application of ant colony algorithm in plant leaves classification based on infrared spectroscopy

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    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.

  18. Weapon target assignment problem satisfying expected damage probabilities based on ant colony algorithm

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    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.

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

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    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.

  20. 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.

  1. An Energy Consumption Optimized Clustering Algorithm for Radar Sensor Networks Based on an Ant Colony Algorithm

    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.

  2. Item Selection for the Development of Short Forms of Scales Using an Ant Colony Optimization Algorithm

    Science.gov (United States)

    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…

  3. 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.

  4. Three-dimensional Path Planning for Underwater Vehicles Based on an Improved Ant Colony Optimization Algorithm

    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.

  5. Edge Detection of Medical Images Using Modified Ant Colony Optimization Algorithm based on Weighted Heuristics

    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.

  6. Study on Increasing the Accuracy of Classification Based on Ant Colony algorithm

    Science.gov (United States)

    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

  7. Ant Colony Search Algorithm for Optimal Generators Startup during Power System Restoration

    OpenAIRE

    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...

  8. Ant Colony Clustering Algorithm and Improved Markov Random Fusion Algorithm in Image Segmentation of Brain 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.

  9. Application of the dynamic ant colony algorithm on the optimal operation of cascade reservoirs

    Science.gov (United States)

    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.

  10. 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.

  11. Coupling ant colony optimization and the extended great deluge algorithm for the discrete facility layout problem

    Science.gov (United States)

    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.

  12. A Novel Approach to the Convergence Proof of Ant Colony Algorithm and Its MATLAB GUI-Based Realization

    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.

  13. A new ant colony-based routing algorithm with unidirectional link in UV mesh communication wireless network

    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.

  14. Comparison Performance of Genetic Algorithm and Ant Colony Optimization in Course Scheduling Optimizing

    Directory of Open Access Journals (Sweden)

    Imam Ahmad Ashari

    2016-11-01

    Full Text Available Scheduling problems at the university is a complex type of scheduling problems. The scheduling process should be carried out at every turn of the semester's. The core of the problem of scheduling courses at the university is that the number of components that need to be considered in making the schedule, some of the components was made up of students, lecturers, time and a room with due regard to the limits and certain conditions so that no collision in the schedule such as mashed room, mashed lecturer and others. To resolve a scheduling problem most appropriate technique used is the technique of optimization. Optimization techniques can give the best results desired. Metaheuristic algorithm is an algorithm that has a lot of ways to solve the problems to the very limit the optimal solution. In this paper, we use a genetic algorithm and ant colony optimization algorithm is an algorithm metaheuristic to solve the problem of course scheduling. The two algorithm will be tested and compared to get performance is the best. The algorithm was tested using data schedule courses of the university in Semarang. From the experimental results we conclude that the genetic algorithm has better performance than the ant colony optimization  algorithm in solving the case of course scheduling.

  15. Parallel Machine Scheduling (PMS) in Manufacturing Systems Using the Ant Colonies Optimization Algorithmic Rule

    Science.gov (United States)

    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.

  16. 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.

  17. 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.

  18. 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.

  19. 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.

  20. Enhanced Clustering Techniques for Hyper Network Planning using Minimum Spanning Trees and Ant-Colony Algorithm

    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

  1. An ant colony optimization algorithm for phylogenetic estimation under the minimum evolution principle

    Directory of Open Access Journals (Sweden)

    Milinkovitch Michel C

    2007-11-01

    Full Text Available Abstract Background Distance matrix methods constitute a major family of phylogenetic estimation methods, and the minimum evolution (ME principle (aiming at recovering the phylogeny with shortest length is one of the most commonly used optimality criteria for estimating phylogenetic trees. The major difficulty for its application is that the number of possible phylogenies grows exponentially with the number of taxa analyzed and the minimum evolution principle is known to belong to the NP MathType@MTEF@5@5@+=feaafiart1ev1aaatCvAUfKttLearuWrP9MDH5MBPbIqV92AaeXatLxBI9gBaebbnrfifHhDYfgasaacPC6xNi=xH8viVGI8Gi=hEeeu0xXdbba9frFj0xb9qqpG0dXdb9aspeI8k8fiI+fsY=rqGqVepae9pg0db9vqaiVgFr0xfr=xfr=xc9adbaqaaeGacaGaaiaabeqaaeqabiWaaaGcbaWenfgDOvwBHrxAJfwnHbqeg0uy0HwzTfgDPnwy1aaceaGae8xdX7Kaeeiuaafaaa@3888@-hard class of problems. Results In this paper, we introduce an Ant Colony Optimization (ACO algorithm to estimate phylogenies under the minimum evolution principle. ACO is an optimization technique inspired from the foraging behavior of real ant colonies. This behavior is exploited in artificial ant colonies for the search of approximate solutions to discrete optimization problems. Conclusion We show that the ACO algorithm is potentially competitive in comparison with state-of-the-art algorithms for the minimum evolution principle. This is the first application of an ACO algorithm to the phylogenetic estimation problem.

  2. A survey paper on Ant Colony Optimization Routing algorithm for selecting Multiple Feasible Paths for Packet Switched Networks

    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.

  3. Ant colony optimization algorithm and its application to Neuro-Fuzzy controller design

    Institute of Scientific and Technical Information of China (English)

    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.

  4. 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.

  5. 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.

  6. 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.

  7. Ant Colony Optimization Algorithm For PAPR Reduction In Multicarrier Code Division Multiple Access System

    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.

  8. 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.

  9. 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.

  10. Flood risk zoning using a rule mining based on ant colony algorithm

    Science.gov (United States)

    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.

  11. Energy Efficient Ant Colony Algorithms for Data Aggregation in Wireless Sensor Networks

    CERN Document Server

    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...

  12. Optimizing Route for Hazardous Materials Logistics Based on Hybrid Ant Colony Algorithm

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    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.

  13. 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.

  14. Improving Regression Testing through Modified Ant Colony Algorithm on a Dependency Injected Test Pattern

    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.

  15. A multi-objective Pareto ant colony algorithm for the Multi-Depot Vehicle Routing problem with Backhauls

    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.

  16. 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.

  17. Gene order computation using Alzheimer's DNA microarray gene expression data and the Ant Colony Optimisation algorithm.

    Science.gov (United States)

    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.

  18. Using Data Mining to Find Patterns in Ant Colony Algorithm Solutions to the Travelling Salesman Problem

    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.

  19. 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.

  20. A Comparative Study of Geometric Hopfield Network and Ant Colony Algorithm to Solve Travelling Salesperson Problem

    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.

  1. 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.

  2. 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.

  3. 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%.

  4. Azcaxalli: A system based on Ant Colony Optimization algorithms, applied to fuel reloads design in a Boiling Water Reactor

    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.

  5. 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.

  6. Parallel ant colony algorithm and its application in the capacitated lot sizing problem for an agile supply chain

    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.

  7. Monte Carlo simulation using the PENELOPE code with an ant colony algorithm to study MOSFET detectors

    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.

  8. 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.

  9. Optimal management of substrates in anaerobic co-digestion: An ant colony algorithm approach.

    Science.gov (United States)

    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.

  10. Building optimal regression tree by ant colony system-genetic algorithm: Application to modeling of melting points

    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.

  11. Image Watermarking Algorithm Based on Multiobjective Ant Colony Optimization and Singular Value Decomposition in Wavelet Domain

    Directory of Open Access Journals (Sweden)

    Khaled Loukhaoukha

    2013-01-01

    Full Text Available We present a new optimal watermarking scheme based on discrete wavelet transform (DWT and singular value decomposition (SVD using multiobjective ant colony optimization (MOACO. A binary watermark is decomposed using a singular value decomposition. Then, the singular values are embedded in a detailed subband of host image. The trade-off between watermark transparency and robustness is controlled by multiple scaling factors (MSFs instead of a single scaling factor (SSF. Determining the optimal values of the multiple scaling factors (MSFs is a difficult problem. However, a multiobjective ant colony optimization is used to determine these values. Experimental results show much improved performances of the proposed scheme in terms of transparency and robustness compared to other watermarking schemes. Furthermore, it does not suffer from the problem of high probability of false positive detection of the watermarks.

  12. Ant colonies for the travelling salesman problem.

    Science.gov (United States)

    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.

  13. A universal optimization strategy for ant colony optimization algorithms based on the Physarum-inspired mathematical model.

    Science.gov (United States)

    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.

  14. Ant Colony Optimization for Control

    NARCIS (Netherlands)

    Van Ast, J.M.

    2010-01-01

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

  15. 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.

  16. Research on remote sensing image segmentation based on ant colony algorithm: take the land cover classification of middle Qinling Mountains for example

    Science.gov (United States)

    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.

  17. 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.

  18. 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.

  19. Research of the path optimization in agricultural water-saving irrigation and canal system water distribution in Ant colony algorithm

    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.

  20. 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.

  1. Solving the Traveling Salesman Problem Based on The Genetic Reactive Bone Route Algorithm whit Ant Colony System

    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.

  2. 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.

  3. A Global Approach to the Optimal Trajectory Based on an Improved Ant Colony Algorithm for Cold Spray

    Science.gov (United States)

    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.

  4. At-Least Version of the Generalized Minimum Spanning Tree Problem: Optimization Through Ant Colony System and Genetic Algorithms

    Science.gov (United States)

    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.

  5. Three-Dimensional Path Planning and Guidance of Leg Vascular Based on Improved Ant Colony Algorithm in Augmented Reality.

    Science.gov (United States)

    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.

  6. 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.

  7. 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.

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

    Science.gov (United States)

    Blum, Christian; Dorigo, Marco

    2004-04-01

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

  9. 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。

  10. 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.

  11. Optimization on Paddy Crops in Central Java (with Solver, SVD on Least Square and ACO (Ant Colony Algorithm))

    Science.gov (United States)

    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.

  12. Parallelizing Ant Colony Optimization via Area of Expertise Learning

    Science.gov (United States)

    2007-09-13

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

  13. Analysis of Ant Colony Optimization and Population-Based Evolutionary Algorithms on Dynamic Problems

    DEFF Research Database (Denmark)

    Lissovoi, Andrei

    This thesis presents new running time analyses of nature-inspired algorithms on various dynamic problems. It aims to identify and analyse the features of algorithms and problem classes which allow efficient optimization to occur in the presence of dynamic behaviour. We consider the following...

  14. Ant colony algorithm implementation in electron and photon Monte Carlo transport: Application to the commissioning of radiosurgery photon beams

    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.

  15. Optimal angle of polycrystalline silicon solar panels placed in a building using the ant colony optimization algorithm

    Science.gov (United States)

    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.

  16. Hybrid ANFIS with ant colony optimization algorithm for prediction of shear wave velocity from a carbonate reservoir in Iran

    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.

  17. Ant Colony Optimization for Train Scheduling: An Analysis

    OpenAIRE

    Sudip Kumar Sahana; Aruna Jain; Prabhat Kumar Mahanti

    2014-01-01

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

  18. 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.

  19. 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.

  20. HYBRIDIZATION OF MODIFIED ANT COLONY OPTIMIZATION AND INTELLIGENT WATER DROPS ALGORITHM FOR JOB SCHEDULING IN COMPUTATIONAL GRID

    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.

  1. Ant Colony Optimization Based Modified Termite Algorithm (MTA with Efficient Stagnation Avoidance Strategy for MANETs

    Directory of Open Access Journals (Sweden)

    Sharvani G S

    2012-10-01

    Full Text Available Designing an effective load balancing algorithm is difficult due to Dynamic topology of MANET. Toaddress the problem, a load balancing routing algorithm namely Modified Termite Algorithm (MTA hasbeen developed based on ant’s food foraging behavior. Stability of the link is determined based on nodestability factor ‘’. The stability factor “ “of the node is the ratio defined between the “hello sent” and“hello replied” by a node to its neighbors. This also indicates the link stability in relation to other pathstowards the destination. A higher ratio of “” indicates that the neighbor node is more stable. Using thisconcept pheromone evaporation for the stable node is fine tuned such that if the ratio “” is more, theevaporation is slow and if “” is less the evaporation is faster. This leads to decreasing of the pheromonecontent in an optimal path which may result in congestion. These paths can be avoided using efficientevaporation technique. The MTA developed by adopting efficient pheromone evaporation technique willaddress the load balancing problems and expected to enhance the performance of the network in terms ofthroughput, and reduces End-to-end delay and Routing overheads

  2. 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。

  3. Evolutional Ant Colony Method Using PSO

    Science.gov (United States)

    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.

  4. Ant Colony Optimization and Hypergraph Covering Problems

    CERN Document Server

    Pat, Ankit

    2011-01-01

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

  5. 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.

  6. 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

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

    Institute of Scientific and Technical Information of China (English)

    宋佩莉; 祁飞; 张鹏

    2012-01-01

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

  8. 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.

  9. 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.

  10. Solving a Closed-Loop Location-Inventory-Routing Problem with Mixed Quality Defects Returns in E-Commerce by Hybrid Ant Colony Optimization Algorithm

    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.

  11. Proposing an Optimum Multicasting Routing Algorithm Using Ant Colony for Improving QoS in Wireless Mesh Network

    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.

  12. 基于量子遗传算法的蚁群多目标优化研究%Research on Ant Colony Algorithm Based on Quantum Genetic Algorithm for Multi-objective Knapsack Problem

    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.

  13. 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.

  14. 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.

  15. 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.

  16. 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.

  17. 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...

  18. Applying Ant Colony Algorithm and Neural Network Model to Color Deviation Defect Detection in Liquid Crystal Displays

    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.

  19. 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。

  20. 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.

  1. Heterogeneous Multiple Colonies Ant Colony Algorithm Based on Survival of Fittest Rules%基于优胜劣汰规则的异类多种群蚁群算法

    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.

  2. 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。

  3. 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.

  4. Improving Emergency Management by Modeling Ant Colonies

    Science.gov (United States)

    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

  5. 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.

  6. 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.

  7. 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.

  8. 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.

  9. 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.

  10. 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.

  11. 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。

  12. 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.

  13. 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。

  14. Human Body Tracking Algorithm of Ant Colony Detection with Pigment Information under the Weak Light Environment%带色素信息蚁群检测的弱光环境人体跟踪算法

    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.

  15. 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.

  16. 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。

  17. 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.

  18. 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

  19. 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。

  20. 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.

  1. Optimization Design of Motor for Torpedo Integrated Motor Propulsor Based on Chaos Ant Colony Algorithm%基于混沌蚁群算法的鱼雷集成电机推进器电机优化设计

    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.

  2. 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.

  3. Ant colony optimization and constraint programming

    CERN Document Server

    Solnon, Christine

    2013-01-01

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

  4. Polyethism in a colony of artificial ants

    CERN Document Server

    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.

  5. Ant colony optimized planning for unmanned surface marine vehicles

    OpenAIRE

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

    2010-01-01

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

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

    OpenAIRE

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

    2015-01-01

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

  7. Military Logistics Distribution Vehicle Scheduling Based on Ant Colony Optimization Algorithm%基于蚁群算法的军事物流配送车辆调度优化问题研究

    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.

  8. 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

  9. 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.

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

    Science.gov (United States)

    Mohsen, Abdulqader M

    2016-01-01

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

  11. 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.

  12. 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。

  13. Application Research of Collision Detection Based on Optimization Ant Colony Algorithm%基于优化的蚁群算法在碰撞检测中的应用研究

    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.

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

    Science.gov (United States)

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

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

  15. Random walk models of worker sorting in ant colonies.

    Science.gov (United States)

    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.

  16. Research on the Military Logistics Delivery Routing Optimization Problem Bgsed on Ant Colony Algorithm%蚁群算法的军事物流配送路径优化

    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。

  17. 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.

  18. 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.

  19. 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.

  20. A review on the ant colony optimization metaheuristic: basis, models and new trends

    OpenAIRE

    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 ...

  1. 蚁群算法在磁测资料反演解释中的应用%THE APPLICATION OF ANT COLONY ALGORITHM TO THE INVERSION AND INTERPRETATION OF MAGNETIC DATA

    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.

  2. 改进遗传蚁群算法及其在电机结构优化中的研究%Improved genetic ant colony algorithm and research on motor structure optimization

    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。

  3. Mutation Ant Colony Algorithm of Milk-Run Vehicle Routing Problem with Fastest Completion Time Based on Dynamic Optimization

    Directory of Open Access Journals (Sweden)

    Jianhua Ma

    2013-01-01

    Full Text Available The objective of vehicle routing problem is usually to minimize the total traveling distance or cost. But in practice, there are a lot of problems needed to minimize the fastest completion time. The milk-run vehicle routing problem (MRVRP is widely used in milk-run distribution. The mutation ACO is given to solve MRVRP with fastest completion time in this paper. The milk-run VRP with fastest completion time is introduced first, and then the customer division method based on dynamic optimization and split algorithm is given to transform this problem into finding the optimal customer order. At last the mutation ACO is given and the numerical examples verify the effectiveness of the algorithm.

  4. Study on distribution model of clothing timeliness and modified ant colony algorithm%时效性服装配送模型与改进蚁群算法的研究

    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.

  5. 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.

  6. 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.

  7. 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.

  8. 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.

  9. 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.

  10. 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.

  11. 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.

  12. 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。

  13. 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.

  14. 传感器网络中基于模糊理论和蚁群的路由算法%ROUTING ALGORITHM IN WIRELESS SENSOR NETWORK BASED ON FUZZY THEORY AND ANT COLONY

    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。

  15. 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.

  16. 蚁群算法及其在求解旅行商问题中的应用%Ant Colony Algorithm and Its Application in Solving the Traveling Salesman Problem

    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.

  17. 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.

  18. Enhanced Bee Colony Algorithm for Complex Optimization Problems

    Directory of Open Access Journals (Sweden)

    S.Suriya

    2012-01-01

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

  19. 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.

  20. 多机器人Ad Hoc路由协议中蚁群算法的改进%Improvement on ant-colony algorithm in Ad Hoc routing protocol for multi-robot network

    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.

  1. Hybrid Ant Algorithm and Applications for Vehicle Routing Problem

    Science.gov (United States)

    Xiao, Zhang; Jiang-qing, Wang

    Ant colony optimization (ACO) is a metaheuristic method that inspired by the behavior of real ant colonies. ACO has been successfully applied to several combinatorial optimization problems, but it has some short-comings like its slow computing speed and local-convergence. For solving Vehicle Routing Problem, we proposed Hybrid Ant Algorithm (HAA) in order to improve both the performance of the algorithm and the quality of solutions. The proposed algorithm took the advantages of Nearest Neighbor (NN) heuristic and ACO for solving VRP, it also expanded the scope of solution space and improves the global ability of the algorithm through importing mutation operation, combining 2-opt heuristics and adjusting the configuration of parameters dynamically. Computational results indicate that the hybrid ant algorithm can get optimal resolution of VRP effectively.

  2. KohonAnts: A Self-Organizing Ant Algorithm for Clustering and Pattern Classification

    CERN Document Server

    Fernandes, C; Merelo, J J; Ramos, V; Laredo, J L J

    2008-01-01

    In this paper we introduce a new ant-based method that takes advantage of the cooperative self-organization of Ant Colony Systems to create a naturally inspired clustering and pattern recognition method. The approach considers each data item as an ant, which moves inside a grid changing the cells it goes through, in a fashion similar to Kohonen's Self-Organizing Maps. The resulting algorithm is conceptually more simple, takes less free parameters than other ant-based clustering algorithms, and, after some parameter tuning, yields very good results on some benchmark problems.

  3. The Multi-Robot Task Allocation Study Based on Improved Ant Colony Algorithm%基于改进蚁群算法的多机器人任务分配

    Institute of Scientific and Technical Information of China (English)

    曹宗华; 吴斌; 黄玉清; 邓春艳

    2013-01-01

    This paper researches the multi-robot system' s task allocating process, which is a NP problem. In order to solve the best task allocating problem of the multi-robot system, an improved Ant Colony Algorithm is used in allusion to the multi-robot system autonomously task allocating problem in a dynamic environment, considered the task restricting and robot ability. The improved algorithm achieving overall situation approximation optimal task allocation is verified by simulation based on the basic Ant Colony Algorithm and the improved algorithm.%研究了多机器人系统的任务分配方法,多机器人多任务分配是一个NP问题.针对多机器人系统在动态环境下自主任务分配问题,综合考虑任务约束及机器人的执行任务能力,采用了一种改进的蚁群算法,解决多机器人系统全局最优任务分配问题.通过对基本蚁群算法和改进的算法的仿真,验证了改进的算法实现了全局近似最优的任务分配.

  4. Ant Colony System Algorithm for Real-Time Globally Optimal Path Planning of Mobile Robots%基于ACS算法的移动机器人实时全局最优路径规划

    Institute of Scientific and Technical Information of China (English)

    谭冠政; 贺欢; SLOMAN Aaron

    2007-01-01

    A novel method for the real-time globally optimal path planning of mobile robots is proposed based on the ant colony system (ACS) algorithm. This method includes three steps: the first step is utilizing the MAKLINK graph theory to establish the free space model of the mobile robot, the second step is utilizing the Dijkstra algorithm to find a sub-optimal collision-free path,and the third step is utilizing the ACS algorithm to optimize the location of the sub-optimal path so as to generate the globally optimal path. The result of computer simulation experiment shows that the proposed method is effective and can be used in the real-time path planning of mobile robots. It has been verified that the proposed method has better performance in convergence speed, solution variation, dynamic convergence behavior, and computational efficiency than the path planning method based on the genetic algorithm with elitist model.

  5. 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.

  6. 蚁群算法在数据挖掘分类中的研究%Application Research on the classification of Data Mining Using Ant Colony Algorithm

    Institute of Scientific and Technical Information of China (English)

    熊斌; 熊娟

    2012-01-01

    Classification is an important task in data mining, using ant foraging theory in the database search to introduce the ant algorithm classification in rules discovery,to chose and optimize a group of rules which is produced random, until the database can be covered, thereby dig the implicit rules in the database, set up the optimal classification model.%对蚁群算法杂数据挖掘中的分类任务的应用进行了研究,算法实质上是利用蚁群觅食原理在数据库中进行搜索,对随机产生的一组规则进行选择优化,直到数据库能被该组规则覆盖,从而挖掘出隐含在数据库中的规则。

  7. 基于改进蚁群算法的无线Mesh网QoS路由算法研究%Research of QoS Routing Algorithm of Wireless Mesh Network Based on Improved Ant-colony Algorithm

    Institute of Scientific and Technical Information of China (English)

    王霄; 吴开军

    2011-01-01

    目前,无线Mesh网络正成为无线网络研究中的一个热点.Quality of service (QoS)是无线Mesh网络中的一个非常重要问题,而QoS路由技术是解决这一问题的关键技术之一.本文就蚁群算法进行研究和改进,并将改进后的算法应用于无线Mesh网络QoS路由问题,进而提出了无线Mesh网络QoS路由算法,通过实验证明该算法能够对QoS提供较好的支持.%Currently, the wireless Mesh network is a hot spot of wireless network research. In wireless Mesh network,QoS is an important issue, and QoS routing is the key technology to solve this problem. In this paper, we proposed a wireless Mesh network QoS routing algorithm on Improved ant colony algorithm, and our experimental results shows that it can provide better QoS support.

  8. 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.

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

    Science.gov (United States)

    Khichane, Madjid; Albert, Patrick; Solnon, Christine

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

  10. 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.

  11. 基于二进制蚁群模拟退火算法的认知引擎%Cognitive engine based on binary ant colony simulated annealing algorithm

    Institute of Scientific and Technical Information of China (English)

    夏龄; 冯文江

    2012-01-01

    在认知无线电系统中,认知引擎依据通信环境的变化和用户需求动态配置无线电工作参数.针对认知引擎中的智能优化问题,提出一种二进制蚁群模拟退火(BAC&SA)算法用于认知无线电参数优化.该算法在二进制蚁群优化(BACO)算法中引入模拟退火(SA)算法,融合了BACO的快速寻优能力和SA的概率突跳特性,能有效避免BACO容易陷入局部最优解的缺陷.仿真实验结果表明,与遗传算法(GA)和BACO算法相比,基于BAC&SA算法的认知引擎在全局搜索能力和平均适应度等方面具有明显的优势.%In cognitive radio system, cognitive engine can dynamically configure its working parameters according to the changes of communication environment and users' requirement. Intelligent optimization algorithm of cognitive engine had been studied, and a Binary Ant Colony Simulated Annealing ( BAC&SA) algorithm was proposed for parameters optimization of cognitive radio system. The new algorithm, which introduced the Simulated Annealing (SA) algorithm into the Binary Ant Colony Optimization ( BACO) algorithm, combined the rapid optimization ability of BACO with probability jumping property of SA, and effectively avoided the defect of falling into local optimization result of BACO. The simulation results show that cognitive engine based on BAC&SA algorithm has considerable advantage over GA and BACO algorithm in the global search ability and average fitness.

  12. Novel Degree Constrained Minimum Spanning Tree Algorithm Based on an Improved Multicolony Ant Algorithm

    Directory of Open Access Journals (Sweden)

    Xuemei Sun

    2015-01-01

    Full Text Available Degree constrained minimum spanning tree (DCMST refers to constructing a spanning tree of minimum weight in a complete graph with weights on edges while the degree of each node in the spanning tree is no more than d (d ≥ 2. The paper proposes an improved multicolony ant algorithm for degree constrained minimum spanning tree searching which enables independent search for optimal solutions among various colonies and achieving information exchanges between different colonies by information entropy. Local optimal algorithm is introduced to improve constructed spanning tree. Meanwhile, algorithm strategies in dynamic ant, random perturbations ant colony, and max-min ant system are adapted in this paper to optimize the proposed algorithm. Finally, multiple groups of experimental data show the superiority of the improved algorithm in solving the problems of degree constrained minimum spanning tree.

  13. 蚁群优化算法在物流配送车辆路径问题中的应用研究%Application Research of Ant Colony Optimization Algorithm for Vehicle Routing Problem in Logistic Distribution

    Institute of Scientific and Technical Information of China (English)

    席先杰

    2011-01-01

    Vehicle routing problem (VRP), which belongs to NP problems, is a typical combinational optimization problem widely utilized in logistic distribution. Therefore, this paper puts forward an improved ant colony optimization (ACO) algorithm to solve VRP. Thus, a VRP based on ACO algorithm is implemented. Results of the experiment prove the effectiveness of ACO algorithm in solving VRP.%车辆路径问题(VRP)是一类物流配送领域具有广泛应用的组合优化问题,属于NP难题。一种改进的蚁群优化算法可以用于求解VRP。实验结果表明,采用蚁群优化算法能有效求解VRP问题。

  14. 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.

  15. 蚁群算法在选煤厂产品结构优化中的应用%Application of Ant Colony Algorithm in Product Structure Optimization of Coal Preparation Plant

    Institute of Scientific and Technical Information of China (English)

    孙伟; 王宜雷; 王慧; 曾盈

    2012-01-01

    Mathematical model of product structure optimization was established according to characteristics of process of coal preparation plant, and steps of optimizing the product structure by ant colony algorithm were given. Nantun Coal Preparation Plant was used for example to simulate, and the best yields of products under various constraints were obtained. The simulation results showed the feasibility of ant colony algorithm for product structure optimization of coal preparation plant.%根据选煤厂生产流程特点建立了产品结构优化数学模型,给出了应用蚁群算法优化产品结构的步骤;并以南屯选煤厂为例进行仿真,得到了满足各种约束条件下的各产品的最佳产量.仿真结果说明了蚁群算法在选煤厂产品结构优化中应用的可行性.

  16. 基于蚁群算法的循环取货车辆路径优化%Problem on Vehicle Route Optimization of Milk-run Based on Ant Colony Algorithm

    Institute of Scientific and Technical Information of China (English)

    孙洋; 严伟

    2015-01-01

    In order to solve the problem on the vehicle routing optimization of milk-run to achieve the overall cost in the lowest In the process of milk-run.This paper constructs the model of vehicle routing optimization to solve the model by ant colony algorithm.The result shows that the ant colony algorithm can effectively solve the vehicle routing problem,and makes the cost of the whole process be the least.%文中以实现循环取货过程中整体费用最低为目标,通过构建车辆路径优化的模型,使用蚁群算法对模型进行求解,并与解决该类问题常用的遗传算法、粒子群算法进行比较分析。证明了蚁群算法能够有效的解决车辆路径优化问题,并使得循环取货过程的整体费用达到最低。

  17. Research on A Simplex Ant Colony Optimization Algorithm for VRP with Time Windows%单纯形蚁群算法对带时间窗车辆路径优化问题的研究

    Institute of Scientific and Technical Information of China (English)

    李永亮; 王玉富; 向长城

    2014-01-01

    This paper studied a complex vehicle routing problem with time window constraints by simplex ant colony algorithm.This article aims to research not only the shortest distance,but also the application of time as small as possible in the vehicle of transportation.We established function that time and distance influence on searching path,then,we used simplex ACO to solve this problem and get the optimal solu-tion.By describing the status of transportation,we proposed a mathematical model of a two-way transport and logistics simplex ant colony algorithm,and obtained the most economical transport routes.%研究了单纯形蚁群算法解决带时间窗约束条件的车辆路径问题,旨在突出研讨在运输中不仅距离最短,而且使应用的时间尽可能的少。首先建立时间、距离对搜索路径的影响函数,然后用单纯形蚁群算法解出最优路径。简单介绍了运输的现状,提出了物流双向运输的数学模型及单纯形蚁群算法,得出了物流运输最经济的合理路线结论。

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

    Institute of Scientific and Technical Information of China (English)

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

    2005-01-01

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

  19. Study of TSP Problem Solving Based on Improved Quantum Ant Colony Algorithm%基于改进量子蚁群算法的 TSP 求解问题研究

    Institute of Scientific and Technical Information of China (English)

    王启明; 李玮瑶

    2015-01-01

    TSP 问题是一个组合优化问题,该问题具有 NP 计算复杂性,运用量子蚁群算法求解该问题时易陷入局部最优和收敛速度慢的问题。因此提出一种基于博弈论的量子蚁群算法(GQA-CA),该算法采用重复博弈模型,在重复博弈中产生一个博弈序列,使得每次博弈都能够产生最大效益,并得到相应博弈过程的纳什均衡。把该算法应用于 TSP 求解,实验结果表明本文中 GQACA算法的收敛精度和稳定性均要优于其他量子蚁群算法。%TST,as a combinatorial optimization problem,is solved by Quantum ant colony algorithm which is easy to fall into the local optimum and slow convergence.The quantum ant colony algorithm, based on game theory (GQACA),is put forward in this paper.It uses the repeated game to create the repeated game sequence for maximum benefit in each game,and get the corresponding game process of Nash equilibrium.The typical test functions are used for GQACA algorithm optimization performance experiment testing.The experimental results show that the GQACA convergence precision and stability of the algorithm are better than QACA algorithm and ACA one.

  20. Research Status of Route Planning of UAV Based on Ant Colony Algorithm%基于蚁群算法的无人机航迹规划技术及研究现状

    Institute of Scientific and Technical Information of China (English)

    张斌; 钱正祥

    2012-01-01

    Appropriate algorithm is one of the key technologies which are used in the field of UAV route planning. The basic principle of UAV route planning technology which are based on ant colony algorithm is introduced, and demonstrates the general implemental steps of UAV 2-D path planning problem by using the grid figure modeling. Then through improving algorithm, changing models, multi-UAV route planning, dynamic path planning, the present research situation of UAV route planning is discussed based on ant colony algorithm, indicates that the direction of UAV route planning is more perfect algorithm and real-time path planning under complex conditions.%配置适当的算法是无人机航迹规划的关键技术之一。介绍了一种基于蚁群算法的无人机航迹规划技术的基本原理,并采用网格图建模,演示了无人机二维航迹规划问题的一般实现步骤。然后从改进算法、更换其它模型、多无人机航迹规划、动态航迹规划等四个方面探讨了基于蚁群算法的无人机航迹规划技术的研究现状,并指明了配置更完善的算法实现复杂条件下的实时航迹规划等问题是无人机航迹规划未来的主要研究方向。

  1. 复杂环境移动机器人路径规划的改进蚁群算法%Improved ant colony algorithm of path planning for mobile robot under complex environment.

    Institute of Scientific and Technical Information of China (English)

    刘锴; 游晓明; 刘升

    2016-01-01

    针对蚁群算法易陷入路径死锁的缺点,提出了一种复杂环境下移动机器人路径规划的改进蚁群算法.对机器人环境建立栅格模型,在传统转移规则中引入指向上一节点的数组,增强了算法的逃逸能力;在信息素更新中减去最差蚂蚁释放的信息量,有利于种群的进化.仿真分析了主要参数对算法性能的影响,实验结果表明,该算法在复杂地图中搜索到的路径优于传统算法.%For the shortcomings of easy to fall into the path deadlocks, an improved ant colony algorithm is proposed to plan the optimal collision-free path for a mobile robot in a complex environment. Firstly, grid model of the robot environ-ment is established, and an array of element point to the previous is employed to enhance the escaping capability of algo-rithm. It utilizes the pheromone released by the worst ant to update the pheromone, which is conducive to the evolution of the colony. The main parameters'influence on the performance of the algorithm is analyzed. Simulation results show that the optimal collision-free path on the complex map obtained by this algorithm is superior to the traditional algorithm.

  2. 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.

  3. 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.

  4. Aircraft technology portfolio optimization using ant colony optimization

    Science.gov (United States)

    Villeneuve, Frederic J.; Mavris, Dimitri N.

    2012-11-01

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

  5. Path Planning for Robot Introduction Parallel Ant Colony Algorithm Based on Division of Labor and Assessment%基于评估和分工合作并行蚁群机器人路径规划

    Institute of Scientific and Technical Information of China (English)

    吕凌; 曾碧

    2011-01-01

    In order to efficiently solve the problem of mobile robot' s path planning in complex dynamic environment, proposed a new method which implemented parallel ant colony optimization algorithm based on assessment and division-cooperation of labor. This method consists of control center and independent process unit. Each process unit uses division-cooperation of labor ant colony to optimize the ant search in local and global aspect, and then sends the result to the control center. Control center was responsible for coordinating the result which got from each independent process unit and used the assessment mechanism to make final decision. Simulation result shows that this algorithm is feasible and effective.%针对复杂环境中移动机器人的导航中存在的问题,提出了一种适用于机器人路径规划的并行蚁群分工合作算法.该方法由控制中心和独立的运算单元组成,每个运算单元中使用分工合作的蚁群进行计算从而从局部和全局两个方面优化蚁群的路径搜索,并将计算发送给处于控制中心的计算机,控制中心则负责处理每个运算单元发送的阶段性的路径搜索结果并利用评估机制对每个计算机得出的结果做最后的决策.从仿真结果可以看出该算法是有效且可行的.

  6. Solution of Multi-objective Order Allocation Problem Based on Binary Ant Colony Algorithm%基于二元蚁群算法的多目标订单分配问题求解

    Institute of Scientific and Technical Information of China (English)

    叶青; 熊伟清; 江宝钏

    2011-01-01

    为了在最小化综合成本的同时尽量均衡企业的生产负荷以及为水平型制造协作联盟(HMCA)订单分配的管理工作提供依据,设计多种群混合行为二元蚁群算法,用于求解 HMCA 订单分配的多目标模型.该方法在二元蚁群算法的堆础上引入区域划分、环境评价与奖励策略,以弥补二元蚁群算法难以同时寻找多个解的缺陷,通过引入中心扰动行为,进一步提高求解质量.实验结果表明,该算法可以保证分布性,且求解质量较高.%In order to provide the basis for the management of allocating orders in Horizontal Manufacturing Collaborative Alliance(HMCA), this paper designs an algorithm named Multi-population Binary Ant Colony Algorithm with Hybrid Behaviors(MPBAHB) to minimize the comprehensive cost and balance the production loads among the selected manufacturing enterprises.Based on Binary Ant Colony Algorithm (BACA), two strategies of zoning and environmental evaluation/reward are introduced to conquer the drawback of original BACA of difficult to get multiple solutions.And a searching behavior named "central disturbing" is introduced to BACA, so as to strengthen the searching ability.Experimental results prove that the algorithm can get better solutions while keeping the distribution of Pareto front.

  7. Research of Air Route Planning Optimization Method without Threat Based on Improved Ant Colony Algorithm%利用改进蚁群算法的可规避威胁源最优航线规划

    Institute of Scientific and Technical Information of China (English)

    柴毅哲; 杨任农; 马明杰; 刘孟强

    2015-01-01

    Aimed at air route planning problems in complex environment,a route planning optimization method without threat based on the fundamental ant colony algorithm is proposed.The use of this method enhances the descriptive ability of the real flight circumstance to improve the effectiveness of route plan-ning by reconstructing the route planning target function and comprehensively analyzing the information of terrain and threat in aircraft flight environment,including the factors such as route distance,time,fuel, cost and threat evasion ,etc.Still,the use of this method can save algorithm time and enhance efficiency of optimization by improving distance heuristic factor to introduce heuristic direction.The simulation results show that the use of this improved ant colony algorithm can save 10% of the optimization time and reduce 10��odd iterative times compared with that of the fundamental ant colony algorithm.%针对复杂环境中飞行器航线规划问题,在基本蚁群算法的基础上,提出一种可规避威胁源的航线规划方法。通过综合分析飞行器飞行环境中的地形信息和威胁信息,考虑航线距离、时耗、能耗、全程费用和威胁规避等因素,重构航线规划目标函数,加强了对飞行器实际飞行环境的描述,从而提高了航线规划的有效性;通过增加目标节点对下一节点的影响来改进状态转移概率,促使蚂蚁向目标方向前进,以节省计算时间,提高优化效率。仿真结果显示,与基本蚁群算法相比,改进算法可以节省10%的优化时间且缩短10多次迭代次数。

  8. 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.

  9. 基于空间蚁群算法的混流装配线组批排序方法%Mixed-Model Assembly Line Batch Sequencing Method Based on Space Ant Colony Algorithm

    Institute of Scientific and Technical Information of China (English)

    2016-01-01

    A mathematical sequence optimization model with constraints of components kitting and batch production based on shop calendar,aiming at minimize total completion time, total earliness/tardiness time and overtime is established.The proposition that the optimal job sequence considering shortest total completion time also has a characteristic of "V" is proved.A space ant colony algorithm with a new weight determination mechanism is designed to solve this problem,in which ant searched solutions along a randomly generated weight direction in its sub-space,thus improving the global searching capability.Besides,ants are assigned to the corresponding sub-space according to the distribution of current Pareto solutions,this efficiently avoids algorithm falling into local optimum.Compared to the results of the algorithm in literature,the space ant colony algorithm has better optimizing performance.A case study is also given to validate the proposed sequencing method.%针对混流装配线按工作日历调度过程中,组批生产导致订单准时交付率差、加班时间长,以及缺料等干扰因素导致完工周期延长等问题,以零部件配套、按工作日历组批为约束,最小化完工周期、提前/拖期时间以及加班时间为多目标,建立工件排序数学优化模型.验证以完工周期最短为目标的流水线工件最优排序亦具有V型特征.提出空间蚁群权重设计方法,将蚂蚁沿不同的权重向量寻优,提高算法的全局搜索能力;并根据当前Pareto解在各权重子空间的分布情况动态调整各子空间的蚂蚁数量,避免陷入局部最优.通过与文献算法对比,验证空间蚁群算法具有良好的优化性能,并通过实例验证了排序方法的有效性.

  10. Improved Ant Algorithms for Software Testing Cases Generation

    Directory of Open Access Journals (Sweden)

    Shunkun Yang

    2014-01-01

    Full Text Available Existing ant colony optimization (ACO for software testing cases generation is a very popular domain in software testing engineering. However, the traditional ACO has flaws, as early search pheromone is relatively scarce, search efficiency is low, search model is too simple, positive feedback mechanism is easy to porduce the phenomenon of stagnation and precocity. This paper introduces improved ACO for software testing cases generation: improved local pheromone update strategy for ant colony optimization, improved pheromone volatilization coefficient for ant colony optimization (IPVACO, and improved the global path pheromone update strategy for ant colony optimization (IGPACO. At last, we put forward a comprehensive improved ant colony optimization (ACIACO, which is based on all the above three methods. The proposed technique will be compared with random algorithm (RND and genetic algorithm (GA in terms of both efficiency and coverage. The results indicate that the improved method can effectively improve the search efficiency, restrain precocity, promote case coverage, and reduce the number of iterations.

  11. 基于改进蚁群算法的数据仓库多连接查询优化%Multi-join Query Optimization of Data Warehouse Based on Improved Ant Colony Algorithm

    Institute of Scientific and Technical Information of China (English)

    赵鹏; 王守军; 龚云

    2012-01-01

    传统蚁群算法在解决数据仓库查询优化问题时存在过早收敛、收敛速度慢的缺点.为此,对传统蚁群算法进行改进,将伪随机状态转移规则引入最大最小蚁群系统,在每次迭代结束后进行迭代局部搜索.实验结果表明,改进算法在多表连接查询优化中具有较快的收敛速度,能提高最优解的质量.%Traditional Ant Colony Algorithm(ACA) is applied to solve the query optimization problem of Data Warehouse(DW), it has some shortcomings such as premature convergence and slowly convergence. This paper improves the traditional ACA to address these issues. The pseudo-random proportion rule is introduced to the Max-Min Ant System(MMAS), and the Iterated Local Search(ILS) strategy is performed after each iteration. Experimental results show that the improved algorithm accelerates the convergence rate of the algorithm and improves the quality of the optimal solution in solving multi-join query optimization.

  12. Using ant colony algorithm to calculate the chemical equilibrium of complex system%利用蚁群算法计算复杂体系化学平衡

    Institute of Scientific and Technical Information of China (English)

    胡元; 李尚勇; 谢刚

    2012-01-01

    基于Gibbs自由能最小原理,本文利用蚁群算法构建了一个复杂体系化学平衡计算的数值计算模型.该模型嵌入局部搜索算法以提高计算精度,通过蚁群移动以获取全局最优解.通过算法验证计算表明,该模型能够作为一种复杂体系化学平衡计算的方法.%Based on the Gibbs free energy minimization method, we use ant colony algorithm to build a numerical calculation model of chemical equilibrium calculations of complex system. The model is embedded into the local search algorithm to improve precision in calculation and through the colony moved to obtain global optimal solution. Algorithm validation results show that the model can be used as a method of chemical equilibrium calculations of complex system.

  13. 改进蚁群算法在SVM参数优化研究中的应用%Application of improved ant colony algorithm in SVM parameter optimization selection

    Institute of Scientific and Technical Information of China (English)

    高雷阜; 张秀丽; 王飞

    2015-01-01

    SVM parameter selection determines SVM classification accuracy and generalization ability, and its lack of theoretical guidance parameter optimization, ACO-SVM model is proposed, it predicts the SVM classification accuracy as the objective function, and improves the ant colony algorithm, with the introduction of search and updates the pheromone based on time-varying function update policy, uses the ant colony algorithm parallelism, positive feedback mechanism and strong robustness, in order to achieve optimal goals and get the optimal combination of parameters of SVM. The results of numerical value experiments show that the improved Ant Colony Optimization algorithm for SVM parameters selection has better optimization performance and higher classification accuracy. This method has the better parallelism and strong global optimization ability.%支持向量机参数的选择决定着支持向量机的分类精度和泛化能力,而其参数优化缺乏理论指导,在此背景下提出了ACO-SVM模型。该模型将SVM分类预测准确率作为目标函数,对蚁群算法进行改进,引入有向搜索和基于时变函数更新的信息素更新原则,利用蚁群算法的并行性、正反馈机制和较强的鲁棒性,以求得最优目标并得到SVM的最优参数组合。数值实验结果表明,改进蚁群算法在SVM参数优化选取中具有更好的寻优性能,具有较高的分类准确率;该方法具有较好的并行性和较强的全局寻优能力。

  14. Research of Improved AODV Routing Protocol Based on Ant Colony Algorithm%基于蚁群算法改进的 AODV 路由协议研究

    Institute of Scientific and Technical Information of China (English)

    周德荣

    2014-01-01

    AODV protocol is one of the classic routing protocols in wireless Ad hoc network .Aimed at the deficiency of AODV protocol ,an improved AODV routing protocol based on ant colony algorithm has been proposed in this paper .Combined ant colony algorithm with characteristics of Ad hoc network ,applied ant colony algorithm to AODV protocol ,and considered the node load ,path hop ,path delay and other fac‐tors ,the routing construction and routing maintenance policies of AODV protocolhave been improved . Through setting up different network loads and node movement speed in NS 2 platform , the improved AODV protocol has beensimulated .The simulation results show that this protocol has certain advantages compared with AODV protocol in packet delivery fraction ,average end‐to‐end delay ,and normalized rou‐ting overhead and other respects .In the meantime ,the network robustness and anti‐destroying ability are enhanced .%AODV 协议是 Ad hoc 无线自组网中经典路由协议之一;针对 AODV 协议的缺点,提出一种基于蚁群算法改进的 AODV 路由协议;结合蚁群算法与 Ad Hoc 网络的特点,将蚁群算法应用于 AODV 协议,考虑节点负载、路径跳数、路径时延等因素,对 AODV 的路由组建和路由维护策略进行改进;通过在 NS2平台中设置不同的网络负载和不同的节点移动速度,对改进后的 AODV 协议进行模拟,仿真结果表明,该路由协议在分组投递率、平均端到端延时和归一化路由开销等性能上比 AODV 协议具有一定的优势,网络的健壮性和抗毁性得到增强。

  15. 蚁群算法在无人驾驶智能车中的应用及改进%The application and improvement for ant colony algorithm of the unmanned intelligent car

    Institute of Scientific and Technical Information of China (English)

    谭宝成; 宋洁

    2012-01-01

    无人驾驶智能车的最优路径问题是路径规划的核心问题,而算法的选择是其关键.选用的是模拟仿生类蚁群算法,针对传统的蚁群算法在搜索时间和运算速度上还有待提高,我们从信息素的更新方式及局部搜索策略方面进行了改进,并且将虚拟路径这一概念应用于动态路径规划中.在考虑了多种状态参数后,我们得出结论是实际路径最短的不一定就是最优路径,还需要取决于各状态参数的取值,这样的改进满足了车载系统的一些实时性和可行性要求.%The optimal path problem of the unmanned intelligent car is the core issues of path planning,and how to select the algorithm is the key. We selected the simulation of bionic type of ant colony algorithm in this article. There is still need to improve the search time and operation speed based on the traditional ant colony algorithm. We improved from the pheromone update method and local search strategy,and applied the concept of the virtual path to the dynamic path planning. After considering a number of parameters, we concluded that the actual shortest path is not necessarily optimal shortest paths.it also need to depend on the status parameter value. Such improvement meet some of the feasibility and real-time requirements of the on-board system.

  16. 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.

  17. Effective ANT based Routing Algorithm for Data Replication in MANETs

    Directory of Open Access Journals (Sweden)

    N.J. Nithya Nandhini

    2013-12-01

    Full Text Available In mobile ad hoc network, the nodes often move and keep on change its topology. Data packets can be forwarded from one node to another on demand. To increase the data accessibility data are replicated at nodes and made as sharable to other nodes. Assuming that all mobile host cooperative to share their memory and allow forwarding the data packets. But in reality, all nodes do not share the resources for the benefits of others. These nodes may act selfishly to share memory and to forward the data packets. This paper focuses on selfishness of mobile nodes in replica allocation and routing protocol based on Ant colony algorithm to improve the efficiency. The Ant colony algorithm is used to reduce the overhead in the mobile network, so that it is more efficient to access the data than with other routing protocols. This result shows the efficiency of ant based routing algorithm in the replication allocation.

  18. 量子蚁群模糊聚类算法在图像分割中的应用%Image Segmentation Based on Quantum Ant Colony Fuzzy Clustering Algorithm

    Institute of Scientific and Technical Information of China (English)

    李积英; 党建武

    2013-01-01

    Fuzzy C-Means algorithm is dependent on the initial value, resulting in easy to fall into the disadvantage of the local optimum value. A combination of quantum ant colony algorithm and FCM clustering algorithm is put forward. Firstly, the original center and numbers of cluster of the image are determined by using global type, robustness and advantages of fast convergence of quantum ant colony algorithm. Secondly, the obtained results are taken as the initial parameters for FCM clustering algorithm, and then the medical image is divided by using FCM clustering algorithm. It is proved that the method has reduced the dependence of FCM clustering algorithm on initial parameters effectively, overcome the shortcomings of easy falling into the local minimum of both algorithms,and greatly improved dividing speed and accuracy, which is simulated by real experiment.%  针对模糊C-均值算法对初始值的依赖,容易陷入局部最优值的缺点,本文提出将量子蚁群算法与FCM聚类算法结合,首先利用量子蚁群算法的全局性和鲁棒性以及快速收敛的优点确定图像的初始聚类中心和聚类个数,再将所得结果作为FCM聚类算法的初始参数,然后用FCM聚类算法对医学图像进行分割。实验结果表明,该方法有效解决了FCM算法对初始参数的依赖,克服了FCM算法及蚁群算法容易陷入局部极值的的缺点,而且在分割速度和精度上得到了较大提高。

  19. 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.

  20. 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.

  1. 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.

  2. Scalable Clustering of High-Dimensional Data Technique Using SPCM with Ant Colony Optimization Intelligence

    Directory of Open Access Journals (Sweden)

    Thenmozhi Srinivasan

    2015-01-01

    Full Text Available Clusters of high-dimensional data techniques are emerging, according to data noisy and poor quality challenges. This paper has been developed to cluster data using high-dimensional similarity based PCM (SPCM, with ant colony optimization intelligence which is effective in clustering nonspatial data without getting knowledge about cluster number from the user. The PCM becomes similarity based by using mountain method with it. Though this is efficient clustering, it is checked for optimization using ant colony algorithm with swarm intelligence. Thus the scalable clustering technique is obtained and the evaluation results are checked with synthetic datasets.

  3. Solving Dual Resource Constrained Job-Shop Scheduling Problem (DRCJSP) Based on Hybrid Ant Colony Algorithm with Self-Adaptive Parameters%基于自适应参数混合蚁群算法的双资源约束作业车间调度

    Institute of Scientific and Technical Information of China (English)

    李兢尧; 孙树栋; 黄媛; 王宁

    2011-01-01

    文章针对以生产成本最小为目标,考虑差异性工人的双资源约束作业车间调度问题,提出参数按算法迭代结果自适应调整,基于蚂蚁流量自适应控制路径选择的混合蚁群算法,在算法前期扩大解搜索空间,后期加快算法收敛,实现算法性能的分阶段性能优化.通过对仿真实验结果的分析,该混合蚁群算法能有效求解双资源约束车间调度问题,且能够在保证得到较优调度结果的同时,具备优秀的收敛性能.%Keeping in mind the minimization of production cost and that workers differ greatly in degrees of training, we propose a hybrid ant colony algorithm for solving the DRCJSP.Sections 1 and 2 of the full paper explain our proposed algorithm.The core of section 1 is that we describe the DRCJSP and establish its mathematical model.The core of section 2 is that we design the self-adaptive ant colony algorithm which adjusts parameters with the iteration outcome and self-adaptively controls the route selection with the ant flow; the hybrid ant colony algorithm expands the search space at the beginning stage of iteration and accelerates its convergence at the later stage of iteration, thus optimizing its performance through different stages.Section 3 simulates our ant colony algorithm; the simulation results, given in Tables 2 and 3, and their analysis show preliminarily that our hybrid ant colony algorithm can effectively solve the DRCJSP and optimize the convergence performance.

  4. Parallelization Strategies for Ant Colony Optimisation on GPUs

    CERN Document Server

    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...

  5. Ant colony optimization for solving university facility layout problem

    Science.gov (United States)

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

    2013-04-01

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

  6. 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.

  7. 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...

  8. 证据理论融合蚁群神经网络的短期负荷组合预测%Short-term Load Combination Forecasting Based on Combination of Evidential Theory with Ant Colony Algorithm-Neural Network

    Institute of Scientific and Technical Information of China (English)

    华静; 艾莉; 程加堂

    2013-01-01

    In order to improve the accuracy of short-term load forecasting, combination prediction method is proposed by combining evidence theory with ant colony algorithm-neural network. According to the actual load data of Chongqing City, ant colony algorithm-neural network as single model is used to its initial forecast. Then the BP neural networks is selected to get the credibility of each model by forecasting errors and the main environmental influencing factors. And the evidence theory was applied to obtain the combination weight. So, short-term load forecast was fulfilled. Examples show that the fitting error of the method is small with high prediction accuracy and it has a certain application value.%为提高短期负荷预测的精度,引入了证据理论融合蚁群神经网络的组合预测方法,根据重庆市负荷的实际数据,采用蚁群神经网络作为单一模型对其进行初步预测,由BP神经网络对预测误差及主要外界影响因素进行分析建模,获得了每个模型的可信度,并用证据理论对可信度进行合成得到组合权值,进而实现对短期电力负荷的组合预测.结果表明,该方法拟合误差小、预测精度高,具有一定的应用价值.

  9. Study of variable options based on modified ant colony algorithm in QSAR of Alkyl phenol compounds%新蚁群算法在烷基酚类化合物的QSAR中变量选择的研究

    Institute of Scientific and Technical Information of China (English)

    费红琳; 张永清

    2009-01-01

    采用最子化学从头算方法在HF/6-311+G(d)水平上计算8种烷基酚类化合物的分子结构描述符,选用修正过的CP统计量为目标函和新蚁群优化算法,于烷基酚类化合物的定量结构--活性相关研究中的变量选择,建立烷基酚类化合物的生物降解速率常数与其量化参数之间的QSAR模型.结果表明,新蚁群优化算法用于定量构效中的变量选择比较简单,而且需要调节的参数少,是变量选择的有用方法,且应用量子化学结构参数建模的相关系数R=0.994,与文献中R=0.982相比相关性更好.%The quantum chemical descriptors of Alkylphenols were obtained at the HF/6-311 + G (d) level with the ab initio method of quantum chemistry. The modified Cp statistics was chose to be the objective function, QSAR models were obtained for the biodegrada-tion rate constant of Alkylphenols based on improved ant colony algorithm. The results showed that the modified ant colony method was a useful for variable selection. The regulative parameter was less and the algorithm was simpler. The correlation coefficient of the QSAR model based on some quantum chemical descriptors was 0. 994, which is better than the correlation coefficient of 0. 982 mentioned in the literature.

  10. Design of the Logistics Distribution Optimization System Based on Ant Colony Algorithm%基于蚁群算法的物流配送优化系统设计

    Institute of Scientific and Technical Information of China (English)

    符志强; 刘磊安

    2014-01-01

    随着物流业的快速发展,配送路径优化成为研究热点,而路径优化是NP难问题,传统的算法不能在有限的时间内给出最优解。使用蚁群算法并对其参数进行优化,从而解决车辆配送路径优化问题,使得配送路径实时最优化,并应用在物流配送系统中,降低物流配送成本和企业经营成本。基于蚁群算法的物流配送系统采用模块化设计,实现对物流管理、数据统计、货物配送、实时生成最优路径的功能。系统具有结构清晰,易于扩展的优点。%With the rapid development of the logistics industry, distribution optimization has become a research hotspot. Path optimization is a NP hard problem and traditional algorithms can not be given an optimal solution in finite time. Uses ant colony optimization and adjusts the parameters to solve the optimization of vehicle routing problem. It can reduce logistics cost and the operation cost of enterprises. Designs logistics distribution system based on ant colony algorithm based on modular. The system can give the service of logistics management, data statistics, goods distribution, real-time generation of optimal path. The system has the advantages of clear structure and easy to ex-pand.

  11. Colony life history and lifetime reproductive success of red harvester ant colonies.

    Science.gov (United States)

    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

  12. Research on Solving the Problem of Task Scheduling in Grid Environment by Ant Colony Algorithm%蚁群算法解决网格环境下任务调度问题的研究

    Institute of Scientific and Technical Information of China (English)

    赵飞; 吴航; 龚跃

    2013-01-01

    Task scheduling in grid environment is a typical NP-hard combinatorial optimization problem,and has been the focus which scholars study intensely in recent years.The traditional Min-Min algorithm has the defects such as long task completing time and poor load balance performs,therefore we propose using ant colony algorithm to solve the problem.According to the nature by which that ants can always frnd the shortest path from the cave to the food source,one task allocation process is abstracted as a path finding procedure of ants allocation and experimental simulation results wene obtained.%网格环境下的任务调度是典型的NP难组合优化问题,是近些年来学者们争相研究的热点.传统的Min-Min算法具有任务完成时间长,负载平衡性差等缺点,因此,本文提出了一种应用蚁群算法解决该问题的方法.利用蚂蚁总能从蚁穴到食物源之间找到最短路径这一自然特性,将任务的一次分配过程抽象为蚂蚁的一次探路过程,最终得到较优的分配结果,并进行了实验仿真模拟,取得了不错的效果.

  13. Nestmate and kin recognition in interspecific mixed colonies of ants.

    Science.gov (United States)

    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.

  14. ACOustic: A Nature-Inspired Exploration Indicator for Ant Colony Optimization.

    Science.gov (United States)

    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.

  15. ACOustic: A Nature-Inspired Exploration Indicator for Ant Colony Optimization

    Directory of Open Access Journals (Sweden)

    Rafid Sagban

    2015-01-01

    Full Text Available A statistical machine learning indicator, ACOustic, is proposed to evaluate the exploration behavior in the iterations of ant colony optimization algorithms. This idea is inspired by the behavior of some parasites in their mimicry to the queens’ acoustics of their ant hosts. The parasites’ reaction results from their ability to indicate the state of penetration. The proposed indicator solves the problem of robustness that results from the difference of magnitudes in the distance’s matrix, especially when combinatorial optimization problems with rugged fitness landscape are applied. The performance of the proposed indicator is evaluated against the existing indicators in six variants of ant colony optimization algorithms. Instances for travelling salesman problem and quadratic assignment problem are used in the experimental evaluation. The analytical results showed that the proposed indicator is more informative and more robust.

  16. Bio-inspired Ant Algorithms: A review

    Directory of Open Access Journals (Sweden)

    Sangita Roy

    2013-05-01

    Full Text Available Ant Algorithms are techniques for optimizing which were coined in the early 1990’s by M. Dorigo. The techniques were inspired by the foraging behavior of real ants in the nature. The focus of ant algorithms is to find approximate optimized problem solutions using artificial ants and their indirect decentralized communications using synthetic pheromones. In this paper, at first ant algorithms are described in details, then transforms to computational optimization techniques: the ACO metaheuristics and developed ACO algorithms. A comparative study of ant algorithms also carried out, followed by past and present trends in AAs applications. Future prospect in AAs also covered in this paper. Finally a comparison between AAs with well-established machine learning techniques were focused, so that combining with machine learning techniques hybrid, robust, novel algorithms could be produces for outstanding result in future.

  17. Ant Colony Algorithm Combined with Survey Propagation for Satisfiability Problem%调查传播算法和蚁群算法相结合求解可满足性问题

    Institute of Scientific and Technical Information of China (English)

    王芙; 周育人; 叶立

    2012-01-01

    布尔可满足性问题(Boolean Satisfiability Problem,SAT)是逻辑学的一个基本问题,也是NP-hard问题.调查传播算法(Survey Propagation,SP)是求解SAT的一种非常高效的算法,但SP在难解区域极易不收敛,或者出现错误赋值.将SP算法与蚁群算法结合,把SP算法得到的消息值应用到蚁群算法中来求解3-SAT问题,使用这些消息值引导蚁群算法求解,并在算法中加入高效的局部搜索.新算法对于SP算法不收敛的一些实例也能很快找到解.%Satisfiability problem is a basic problem in logic,and also is NP-hard. Survey propagation(SP) is a very effective algorithm for this problem. However, SP tends not to converge in hard region, or gives wrong assignments to the variables. An algorithm combined with SP and ant colony optimization(ACO) was proposed The messages calculated in SP were used in ACO as guidance to help ACO find a solution. And local search was conducted in the new algorithm. The new algorithm can quickly find solutions for some instances that SP doesn't work.

  18. 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.

  19. 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.

  20. Ant-cuckoo colony optimization for feature selection in digital mammogram.

    Science.gov (United States)

    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.

  1. 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.

  2. A Novel Parser Design Algorithm Based on Artificial Ants

    CERN Document Server

    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 ...

  3. 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

  4. Resources search strategy based on ant colony algorithm in unstructured P2P networks%基于蚁群算法的非结构化P2P网络资源搜索策略

    Institute of Scientific and Technical Information of China (English)

    李春秀; 刘方爱

    2012-01-01

    针对非结构化P2P网络资源搜索算法中冗余消息数过多、搜索效率低等问题,提出一种基于蚁群算法的非结构化P2P网络资源搜索策略,该策略利用蚂蚁信息素的正反馈原理,同时综合考虑邻居节点度和邻居-邻居节点信息,选择下一条邻居节点路径转发查询消息,有效地指导资源搜索路径的生成.实验结果表明,该算法在一定程度上减少了大量的冗余查询消息,提高了资源搜索的成功率,是一种有效的非结构化P2P网络资源搜索策略.%For the resources search algorithm's too many redundant messages and low efficiency issues in unstructured P2P networks, it proposes a resources search strategy based on ant colony algorithm. The strategy uses ant pheromone' s positive feedback principle, meanwhile considers comprehensively of neighbor nodes'degrees and neighbor-neighbor node's informations to choose the next neighbor nodes. Experimental results show that this algorithm can reduce the number of redundant query messages and improve resource search success rate, it is an effective search strategy in unstructured P2P networks.

  5. 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.

  6. Ant colony optimization analysis on overall stability of high arch dam basis of field monitoring.

    Science.gov (United States)

    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.

  7. On Analytical Comparative Study for Performance Evaluation of Three Psycho-Learning Experimental Results versus Searching for Optimal Algorithmic Solution of Travelling Sales' Man Problem Using Smart Ant Colony System

    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

  8. 基于All-In-Roulette选择算法的GPU并行加速蚁群优化算法%Parallel Ant Colony Optimization Algorithm with GPU-Acceleration Based on All-In-Roulette Selection

    Institute of Scientific and Technical Information of China (English)

    付杰; 周国华

    2011-01-01

    蚁群优化算法应用于复杂问题的求解是非常耗时的.文章在MATLAB环境下实现了一个基于GPU+CPU的并行MAX-MIN蚁群系统,并将其应用于旅行商问题的求解.让全部蚂蚁共享一个伪随机数矩阵,一个信息素矩阵,一个禁忌矩阵和一个概率矩阵,并运用了一个全新的基于这些矩阵的随机选择算法-AIR(All-In-Roulette).文章还介绍了如何使用这些矩阵来构造并行蚁群优化算法,并与相应串行算法进行了比较.计算结果表明新的并行算法比相应串行算法要高效很多.%Ant colony optimization is computationally expensive when it comes to complex problems. This paper presents and implements a parallel MAX-MIN Ant System(MMAS) based on a GPU+CPU hardware platform under the MATLAB environment solve Traveling Salesman Problem(TSP). The key idea is to let all ants share only one pseudorandom number matrix, one pheromone matrix, one taboo matrix, and one probability matrix. A new selection approach based on those matrices, named AIR(All-In-Roulette) has been used. The main contribution of this paper is the description of how to design parallel MMAS based on those ideas and the comparison to the relevant sequential version. The computational results show that our parallel algorithm is much more efficient than the sequential version.

  9. Plant defense, herbivory, and the growth of Cordia alliodora trees and their symbiotic Azteca ant colonies.

    Science.gov (United States)

    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.

  10. Multi-Robot Dynamic Task Allocation Using Modified Ant Colony System

    Science.gov (United States)

    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.

  11. 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.

  12. 蚁群算法在超高压输电线路故障测距的应用%Fault Location for EHV Transmission Line Based on Ant Colony Algorithm

    Institute of Scientific and Technical Information of China (English)

    刘迅; 黄纯

    2012-01-01

    Through analyzing the existing fault location methods and the effect of optimization for transmission line, a method of fault location based on ant colony algorithm is presented. Based on the distributing parameter transmission line model, the fault location function is educed according to the principle that the amplitude of fault point's voltage calculated from the two ends of a line is equal. The ant colony algorithm is introduced to resolve the fault location function optimization problems. To eliminate the effect of untransposed conductors and unbalanced transmission line impedances, phase components are transformed to model components. At last, the simulation based on a 750 kV transmission system model is presented to demonstrate that the algorithm is of high accuracy and not affected by fault type, system impedance, fault resistance, unsynchronized angle. The method has high practical value.%通过对现有输电线路故障测距方法的探讨以及优化算法测距效果的对比分析,提出了一种基于蚁群算法的故障测距方法.该方法基于线路分布参数模型,依据从线路两端分别推算出的故障点电压的幅值相等的原理,列出故障测距方程.引入蚁群算法来求解故障测距方程,并通过相模变换来减少实际线路的不换位和参数不平衡的影响.最后以750 kV超高压输电线路故障测距为例进行仿真,结果表明此算法测距精度高,不需要选择故障类型,不受系统阻抗、过渡电阻、不同步角的影响,有很强的实用价值.

  13. 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.

  14. 动态自适应加权多态蚁群算法求解差异工件单机批调度问题%Dynamic adaptive weighted polymorphic ant colony algorithm for scheduling single batch-processing machine with non-identical job sizes

    Institute of Scientific and Technical Information of China (English)

    李菲; 王书锋; 冯冬青

    2011-01-01

    Dynamic adaptive weighted polymorphic ant colony algorithm was applied to minimize the makespan on a single batch-processing machine with non-identical job sizes. The algorithm introduced the different types of ant colonies, each colony had a different updating mechanism, the transition probabilities and the pheromone value update of ant colony was redesigned for the problem. The algorithm was more accordant with the ants' information processing mechanism, which combined the local search with the global search to improve its convergence and searching ability. In the experiment, different levels of instances are simulated and the results show the efficiency of dynamic adaptive weighted polymorphic ant colony algorithm.%针对差异工件的单机批调度问题,提出了动态自适应加权多态蚁群算法对最大完工时间进行优化,该算法引入了不同种类的蚁群,每种蚁群都有不同的信息素调控机制,并根据批调度问题对不同种类的蚁群状态转移概率和信息素更新机制进行了改进,同时将局域搜索与全局搜索相结合,从而更符合蚁群的真实信息处理机制.对不同规模的算例进行了仿真,结果验证了该算法的有效性和可行性.

  15. 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.

  16. A Partheno-genetic Hybrid Ant Colony Algorithm for Solving Multi-objective Vehicle Routing Problem with Time Window%多目标带时间窗的车辆路径问题的 单亲遗传混合蚁群算法

    Institute of Scientific and Technical Information of China (English)

    2016-01-01

    Considering the vehicle routing problem which has the restriction of maximum vehicle waiting time, maximum vehicle transport time and time windows, a mathematical model for the shortest length of vehicle travel and the minimum number of the using vehicles as the multi-objective is established. Then, 2 partheno-genetic hybrid ant colony algorithms for solving the problem are proposed by combining partheno-genetic algorithm with basic ant colony algorithm to have their complementary advantages and the features of partheno-genetic algorithm, which are monogene partheno-genetic hybrid ant colony algorithm and polygenic partheno-genetic hybrid ant colony algorithm. The result of the test case shows that the partheno-genetic hybrid ant colony algorithm has the advantages of better computational efficiency and convergence, and especially monogene partheno-genetic hybrid ant colony algorithm is more stable and has better computational performance.%考虑具有最大等待时间、 最大运输时间限制且带时间窗的车辆路径问题,建立了以车辆行驶路径最短和使用车辆数最小为目标的数学模型.将单亲遗传算法和基本蚁群算法相结合,使其优势互补,并利用单亲遗传算法的特点,构建出两种求解该问题的单亲遗传混合蚁群算法,分别为:单点单亲遗传混合蚁群算法和多点单亲遗传混合蚁群算法.测试算例的结果表明:求解多目标带时间窗的车辆路径问题时,与基本蚁群算法相比,单亲遗传混合蚁群算法具有计算效率高、 收敛性好等优点,尤其单点单亲遗传混合蚁群算法不仅具有较好的计算性能,而且具有较高的稳定性.

  17. The distribution of military vehicle scheduling optimization based on ant colony algorithm%基于蚁群算法的军用物资车辆调度优化配置问题研究

    Institute of Scientific and Technical Information of China (English)

    余建平

    2015-01-01

    In order to improve the complex military material military material allocation optimization problem,and the core of military physical distribution vehicle scheduling problem is.Therefore,in the analysis of military material reasonable vehicle scheduling problem characteristics and based on the model,the ant colony algorithm is introduced to solve the problem of the.Experiments show that,in the vehicle routing problem with time windows,the algorithm can effectively improve the convergence speed and accuracy,better use of the vehicle dispatching.%为提高复杂军用物资军用物质优化配置问题,而军用物质配送的核心是车辆调度问题。为此,在合理分析军用物质车辆调度问题的特性和模型基础上,将蚁群算法引入到其中解决该问题。实验表明,在带有时间窗的车辆路径问题上,该算法能够有效地提高解决收敛速度与精确度,更好地实现车辆调度的使用性。

  18. Multi-logistics Transfer Stations Allocation Based on Improved Ant Colony Optimization Algorithm%基于改进蚁群算法的多物流中转站选址规划

    Institute of Scientific and Technical Information of China (English)

    王勇; 张永; 毛海军; 杭文

    2011-01-01

    根据多物流中转站选址问题的特点,应用遗传算法和分配算法将大规模客户点划分为不同的配送单元,建立了包含配送中心和中转站的运营成本以及配送中心和中转站的大小车维护费用的数学模型,其中,运营成本包括车辆的运输成本和中转站的建造成本.提出了一种解决多物流中转站选址问题的改进蚁群算法,由于该算法在评价函数中隐含加入了约束条件,并将相应遗传操作加入算法中,设计了配送中心和中转站选择普通配送单元的编码方式,以运营成本最低为标准来确定中转站的选取方案,因此具有较高的全局搜索能力和局部搜索能力.对多中转站选址进行仿真试验,试验结果表明本算法优于蚁群算法和遗传算法,且适合大规模客户点的中转站选址和配送中心选址,并具有较强的灵活性.%According to the characters of multi-logistics transfer stations allocation, genetic algorithm (CA) and assignment algorithm were applied to divide large-scale customers into differents distribution units. The mathematical models containing the operating cost (including vehicle transport cost and transfer stations construction cost) and vehicle maintenance cost of distribution center and transfer stations were established. An improved ant colony optimization algorithm for multi-logistics transfer stations allocation was proposed. Because the constraints were implicitly added in the evaluation function and the corresponding genetic manipulations were added in algorithm, the coding style for distribution center and transfer stations choosing general distribution units was designed, and the scheme of transfer stations allocation was determined by the lowest operating cost, therefore, it has higher global and local search capability. A detailed logistics transfer station allocation based on improved genetic algorithm was experimented by simulation, which show that the new algorithm is

  19. 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.

  20. Evaluation on Power Customer Value Based on Ants Colony Clustering Algorithm Optimized by Genetic Algorithm%基于遗传改进蚁群聚类算法的电力客户价值评价

    Institute of Scientific and Technical Information of China (English)

    李泓泽; 郭森; 王宝

    2012-01-01

    It is an important procedure for service resource allocation of power supply enterprises to evaluate power consumer value. Based on the analysis on ant colony clustering algorithm (ACCA) and in allusion to the blindness in the setup of parametric combination of ACCA, its low convergence speed and easily falling into local convergence, a new method to evaluate power customer value, in which ACCA is improved by genetic algorithm (GA), is proposed. In the proposed method, the parameters of ACCA are optimized by GA, and then the clustering evaluation of power customer value is performed. Results of case study show that the clustering performance of the proposed method is evidently enhanced and the convergence is speeded up and local convergence can be avoided, in addition, the subjective factor during the evaluation is decreased. The proposed method is applied to evaluate ten industrial customers of a certain urban power supply company, and the evaluation results show that the proposed method is accurate, efficient and practicable. The features of various types of power customers are summarized and some suggestions on optimal allocation of service resources of power supply enterprises are put forward.%对电力客户价值进行评价是供电企业优化服务资源配置的重要步骤.分析了蚁群聚类算法,并针对蚁群聚类算法进行评价时参数组合设置盲目性、收敛速度慢、容易陷入局部收敛的缺点,提出了运用遗传算法改进蚁群聚类算法评价电力客户价值的新方法.该新方法利用遗传算法对蚁群聚类算法的参数进行优化,进而再对电力客户价值进行聚类评价.通过实例验证表明,该新方法聚类性能有较大的提升,能够提升收敛速度和避免陷入局部收敛,并且减少了聚类评价时的主观因素,其具有准确、高效、实用等优点.最后,运用该新方法对某市供电公司的10个工业客户进行了评价,总结了不同类别电力客户的特

  1. 基于蚁群算法特征选择的语音情感识别%Feature Selection of Speech Emotional Recognition Based on Ant Colony Optimization Algorithm

    Institute of Scientific and Technical Information of China (English)

    杨鸿章

    2013-01-01

    情感特征提取是语音情感准确识别的关键,传统方法采用单一特征或者简单组合特征提取方法,单一特征无法全面反映语音情感变化,简单组合特征会使特征间产生大量冗余特征,影响识别正确结果.为了提高语音情感识别率,提了一种蚁群算法的语音情感智能识别方法.首先采用语音识别正确率和特征子集维数加权作为目标函数,然后利用蚁群算法找到最优语音特征子集,消除特征冗余信息.通过汉话和丹麦语两种情感语音库进行仿真测试,仿真结果表明,改进方法不仅消除了冗余、无用特征,降低了特征维数,而且提高了语音情感识别率,是一种有效的语音情感智能识别方法.%Speech emotion information has the characteristics of high dimension and redundancy, in order to improve the accuracy of speech emotion recognition, this paper put forward a speech emotion recognition model to select features based on ant colony optimization algorithm. The classification accuracy of KNN and the selected feature dimension form the fitness function, and the ant colony optimization algorithm provides good global searching capability and multiple sub - optimal solutions. A local refinement searching scheme was designed to exclude the redundant features and improve the convergence rate. The performance of method was tested by Chinese emotional speech database and a Danish Emotional Speech. The simulation results show that the proposed method can not only eliminate redundant and useless features to reduce the dimension of features, but also improve the speech emotion recognition rate, therefore the proposed model is an effective speech emotion recognition method.

  2. Three Dimensional Path Planning of Fruit and Vegetable Picking Robot Based on Improved Ant Colony Algorithm%基于改进蚁群算法的果蔬采摘机器人三维路径规划

    Institute of Scientific and Technical Information of China (English)

    黄玲; 胡蔚蔚

    2016-01-01

    In recent years, the"shortage of farmer"problem plays more more strong in our country,and a large number of young workers migrant workers, rural land fallow more and more.China's population aging is serious, the agricultural population is reduced, the labor gap is too large, which leads to the demand of agricultural robot very urgent.With the rapid development of agricultural machinery and automation technology , agricultural robots are constantly developing , which can better adapt to the development of biotechnology, the past the traditional picking methods will be greatly changed, the focus of farmers planting is about to improve.Based on the improved ant colony algorithm, this paper de-signs and plans the 3D path of fruit picking robot walking, and increases the adaptive adjustment function in the process of advancing.Experimental simulation results show that the improved ant colony algorithm based 3D space path planning of fruit and vegetable picking robot is the best way to meet the requirements of the picking robot.%近年来,我国“农民荒”问题越演越烈,大量年轻劳动力外出务工,农村土地荒置越来越多。我国人口高龄化严重,农业人口的减少,劳动力缺口过大,导致对农业机器人的需求极为迫切。随着农业机械和自动化技术的快速发展,农业机器人也在不断发展,其可以更好地适应生物技术种植产业发展,过去传统的采摘方式将会有很大改变,农民种植的侧重点即将改善。为此,基于改进蚁群算法,设计和规划了果蔬采摘机器人行走的三维路径,并增加在前进过程中的自适应调整功能。实验仿真结果表明:基于改进蚁群算法的果蔬采摘移动机器人三维空间路径规划在路径和转弯个数上都做到了最小化,能够很好地满足采摘机器人运行需求。

  3. 基于改进蚁群算法的农业运输车辆路径优化研究%Research on Path Optimization of Agricultural Transport Vehicles Based on Improved Ant Colony Algorithm

    Institute of Scientific and Technical Information of China (English)

    赵晓侠; 鞠成恩

    2016-01-01

    In view of the problems of agricultural products in transportation, such as long time and easy to go bad, the distribution path of fruit and vegetable transport vehicles is reasonably planned.Based on the basic ant colony algorithm, an improved algorithm is proposed, which is suitable for solving path planning .The adaptive scheme is proposed to improve the ability of avoiding local optimal solution and the global conver-gence of the algorithm.The simulation results show that the improved algorithm is feasible and efficient .It can achieve the purpose of optimizing the route of transport vehicles, and provide theoretical basis for improving the efficiency of agricultural products transportation, reducing costs and improving income.%针对农产品在运输过程中运输时间长易变质等问题,合理规划果蔬运输车辆的配送路径。在基本蚁群算法的基础上,提出适合求解路径规划的改进型算法,同时提出了自适应调整的方案,提高跳出局部优解的能力以及算法的全局收敛性。仿真试验结果验证了改进型算法的可行性和高效性,从而达到运输车辆路径优化的目的,为提高农产品的运输效率、降低成本、提高收益提供了理论依据。

  4. 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...

  5. 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.

  6. Automated Software Testing Using Metahurestic Technique Based on An Ant Colony Optimization

    CERN Document Server

    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

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

    Science.gov (United States)

    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.

  8. 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.

  9. 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.

  10. Rationality in collective decision-making by ant colonies.

    Science.gov (United States)

    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.

  11. Optimization of task scheduling in virtual enterprises based on ant colony algorithm%基于蚁群算法的虚拟企业生产任务调度优化研究

    Institute of Scientific and Technical Information of China (English)

    赵强; 周敏

    2011-01-01

    描述了虚拟企业生产任务调度的层次框架,该调度框架包括虚拟企业全局调度和合作伙伴局部调度两个层次.针对虚拟企业调度层的优化问题,综合考虑虚拟企业生产任务的时序逻辑关系、作业时间和生产任务集等影响因素,建立了以任务总作业时间最小化为目标的数学模型,并基于蚁群算法对上述优化模型进行了求解.应用实例与算法比较验证了优化模型与求解算法的有效性.%A two-level framework of production task scheduling in virtual enterprises (VE) is proposed, which includes global scheduling in VE layer and local scheduling in partner layer. As global scheduling optimization of production tasks is a key process in VE, a mathematical model for production task scheduling in VE layer aimed at minimizing the duration of production is established with the logical relationship between tasks, the production task and production activity of each partner, the operation time of each subtask, and other factors considered. Ant colony algorithm is employed for the model optimization, and practical examples and algorithm comparison have confirmed the validity of the optimization model and the algorithm employed.

  12. Novel method based on ant colony optimization for solving ill-conditioned linear systems of equations

    Institute of Scientific and Technical Information of China (English)

    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.

  13. Route Planning of Unmanned Target Drone Based on Cellular-Ant Colony Algorithm%基于元胞蚂蚁算法的无人靶机航路规划设计

    Institute of Scientific and Technical Information of China (English)

    刘志强; 陈景彬

    2013-01-01

      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。本文通过元胞蚂蚁算法对某型无人靶机飞行航路优化设计进行了研究,分析了实现航路优化应突出解决的问题,并通过仿真实验验证该方法的可行性。%  Route planning of unmanned target drone is the basis of its efficient control and ensuring its high-performance of completing target mission. The route planning of an unmanned target drone is studied based on cellular-ant colony algorithm. The main problem of the optimum of the route planning is analyzed. The possibility of this method has been tested by simulation experiments.

  14. 基于蚁群算法的石英λ/4消色差复合波片优化设计%Design of quartz λ/4 achromatic composite wave plate based on the ant colony algorithm

    Institute of Scientific and Technical Information of China (English)

    刘彩彩; 宋连科; 刘前

    2013-01-01

    为了设计消色差性能优良的三元复合λ/4波片,根据复合波片原理,利用蚁群算法对三元复合波片进行了优化设计,即通过改变三波片的厚度和精确调整复合角,使消色差范围拓宽到500nm,相位延迟偏差在0.81%左右.这种优化设计对于拓宽消色差范围,提高延迟精度具有实用价值.%According to the composite wave - plate theory, I use ant colony algorithm to calculate composite Angle and three wave plate thickness then through matlab do simulation experiment and analysis the error . experimental result shows that the retardation deviation of this device is within 0. 81% covering the spectrum scope of 500nm. This paper supplies considerable theoretical premise to the design of composite achromatic wave - plate, and provides theoretical basis for wider achromatic spectral range and higher precision. So our work has very important pratical reference value. in order to design some high - quality infrared composite achromatic wave - plates.

  15. Path Planning for UAV Based on Ant Colony Algorithm and Dynamic Simulation%基于蚁群算法的无人机航迹规划及其动态仿真

    Institute of Scientific and Technical Information of China (English)

    王绪芝; 姚敏; 赵敏; 胡中华

    2012-01-01

    为实现无人机航迹规划的实时性和交互性,建立了无人机动态仿真系统.以“捕食者”无人机模型为应用背景,结合MAK仿真技术的相关理论,利用蚁群算法计算了无人机航迹规划,并实现了Matlab平台下的实例仿真.基于构建的仿真系统,利用MAK软件实现了二维和三维飞行过程的显示,仿真效果理想,为无人机航迹规划结果的交互验证提供了一种有效的手段.%In order to realize real-time and interaction on UAV route planning, dynamic simulation system is established. Taking the "Predator" unmanned aerial vehicle model as the background, and combining with the theory of MAK simulation technology, UAV route planning is calculated by using ant colony algorithm, and simulation on Matlab platform is realized. By MAK software, the system also reproduces the whole process of the two and three-dimensional flight, the actual results are obvious and satisfied, and also provides an effective measure to verify interaction on the planning result.

  16. 改进蚁群算法在采购供应链组建中的应用%The Application of Improved Ant Colony Algorithm in Purchasing Supplier Chain

    Institute of Scientific and Technical Information of China (English)

    李婷; 刘艳斌

    2011-01-01

    Taking the elevator enterprises as the background, it researches multi - suppliers selection problem in purchasing supplier chain, and establishes multi - objective purchasing optimization model. These objectives contain nine evaluating indicators such as cost, quality, delivery and after- sale service. Based on the multi -objective programming method which uses analytic hierarchy process with an improved ant colony algorithm, it solves the mathematical model to obtain the best supplier choice. Finally it verifes the validity of this method with an example.%以电梯企业为背景,研究了采购供应链中多供应商选择问题,建立了以成本、质量、交货及售后服务等9项评价指标为目标的多目标采购优化数学模型,并采用层次分析法和改进的蚁群算法相结合的多目标规划方法对该模型进行求解,得到一组最优的供应商组合.最后,通过一个算例验证了该方法的有效性.

  17. Using pleometrosis (multiple queens) and pupae transplantation to boost weaver ant (Oecophylla smaragdina) colony growth in ant nurseries

    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...

  18. An Improved Heuristic Ant-Clustering Algorithm

    Institute of Scientific and Technical Information of China (English)

    Yunfei Chen; Yushu Liu; Jihai Zhao

    2004-01-01

    An improved heuristic ant-clustering algorithm(HAC)is presented in this paper. A device of 'memory bank' is proposed,which can bring forth heuristic knowledge guiding ant to move in the bi-dimension grid space.The device experiments on real data sets and synthetic data sets.The results demonstrate that HAC has superiority in misclassification error rate and runtime over the classical algorithm.

  19. Dealing with water deficit in Atta ant colonies: large ants scout for water while small ants transport it

    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.

  20. Vehicle Routing Planning Based on the Ant Colony Algorithm for Book Distribution%基于蚁群算法的图书物流中心配送路径规划

    Institute of Scientific and Technical Information of China (English)

    计三有; 王星

    2011-01-01

    Taking the vehicle routing problem of book distribution center as the research object and considering the characteristics of the book distribution and the limited capacity of the distribution vehicles,a vehicle path planning model based on less-than-truck-load transport strategy is established in this paper.In view of the deficiency of traditional research on vehicle routing problem,the distribution distance is redefined by GPS vehicle navigation system.An ant colony algorithm is developed and used to improve the model.The optimization result demonstrated in an instance proves the availability of the model and the algorithm.%以图书物流中心车辆路径规划问题为研究对象,结合图书配送多品种小批量的特点,以配送路线最短为目标,在考虑车辆容量限制的条件下,建立基于零担运输策略的图书物流中心车辆路径规划模型;针对传统路径规划问题研究的不足,运用GPS导航系统重新定义了配送距离.用蚁群算法对所建模型进行求解与仿真,并结合实际案例给出优化结果,验证了模型及算法的有效性.

  1. A Stochastic Inversion Method for Potential Field Data: Ant Colony Optimization

    Science.gov (United States)

    Liu, Shuang; Hu, Xiangyun; Liu, Tianyou

    2014-07-01

    Simulating natural ants' foraging behavior, the ant colony optimization (ACO) algorithm performs excellently in combinational optimization problems, for example the traveling salesman problem and the quadratic assignment problem. However, the ACO is seldom used to inverted for gravitational and magnetic data. On the basis of the continuous and multi-dimensional objective function for potential field data optimization inversion, we present the node partition strategy ACO (NP-ACO) algorithm for inversion of model variables of fixed shape and recovery of physical property distributions of complicated shape models. We divide the continuous variables into discrete nodes and ants directionally tour the nodes by use of transition probabilities. We update the pheromone trails by use of Gaussian mapping between the objective function value and the quantity of pheromone. It can analyze the search results in real time and promote the rate of convergence and precision of inversion. Traditional mapping, including the ant-cycle system, weaken the differences between ant individuals and lead to premature convergence. We tested our method by use of synthetic data and real data from scenarios involving gravity and magnetic anomalies. The inverted model variables and recovered physical property distributions were in good agreement with the true values. The ACO algorithm for binary representation imaging and full imaging can recover sharper physical property distributions than traditional linear inversion methods. The ACO has good optimization capability and some excellent characteristics, for example robustness, parallel implementation, and portability, compared with other stochastic metaheuristics.

  2. 改进蚁群算法优化PID控制器参数及其应用%Improved ant colony algorithm to parameters optimization of PID controller and its application

    Institute of Scientific and Technical Information of China (English)

    吴剑威; 孔慧芳; 唐立新

    2012-01-01

    Most of the industrial process control still uses PID (Proportion Integration Differentiation) controller.However,because the traditional PID controller is time-varying nonlinear system and can't acquire accurate model easily,it is difficult to obtain good performance and can't meet the precise control requirements.An improved ant colony algorithm (IACA) is proposed for PID parameters optimization in order to attain ideal control effect in this paper.The proposed algorithm uses an adaptive tuning mechanism and a dynamic updating strategy of pheromone intensity and pheromone evaporation coefficient to accelerate algorithm convergence.The control system has better dynamic performance,robustness and more advantages in many aspects such as settling time (ts),rise time (tr) and peak time (tp) by using IACA.Moreover,the feasibility and superiority of the algorithm is further validated.IACA is simple and easy to find the optimal solution.Secondly,it can improve optimization efficiency and stability.Simulation results show it can reduce the overshoot,ts,tr and tp of the PID control systems,compared with other tuning algorithms.%绝大多数工业工程控制仍然使用PID控制器,但由于它不易获得精确的数学模型和其非线性时变系统的性质,传统PID控制难以获得良好的控制品质、难以满足精确的控制要求.为了使PID控制器达到理想的控制效果,提出了一种基于改进蚁群算法的PID参数优化整定算法.该算法采用了信息素挥发系数和信息素强度自适应调整机制和动态更新策略,用以加速优化算法的收敛.该算法简单易行,更容易找到全局最优解,优化效率和性能明显提高.仿真实验结果表明,同现有的优化算法整定的结果比较,被控系统的超调量、调整时间等明显减少,动态特性、鲁棒性和稳定性等明显提高,进而验证了所设计算法的可行性和优越性.

  3. Detection Of Ventricular Late Potentials Using Wavelet Transform And ANT Colony Optimization

    Science.gov (United States)

    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.

  4. 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.

  5. 基于多目标鱼群-蚁群算法的水资源优化配置%Optimal Allocation of Water Resources Based on the Multi-Objective Fish-Ant Colony Algorithm

    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

  6. 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.

  7. 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 .

  8. Hybrid strategy with ant colony and simulated annealing algorithm and its improvement in target assignment%目标分配的蚁群-模拟退火算法及其改进

    Institute of Scientific and Technical Information of China (English)

    麻士东; 龚光红; 韩亮; 宋晓

    2011-01-01

    In the process of air-to-ground attacking by helicopter formations, target assignment plays an important role in completing military tasks. The integrated interest function and the principle of helicopter target assignment are made. Target assignment in helicopter formation' s air-to-ground attacking is realized by using hybrid strategy with ant colony algorithm and simulated annealing algorithm. Aimed at the defect because of deciding current best solution by integrated interest function, the hybrid strategy are improved, that is, to decide current best solution by amount of pheromone. So the best solution can be determined both by integrated interest function and amount of pheromone, and bad individual interests and slow convergence will be avoided in order to get maximal interests in process of assignment. A test is given to validate the improved hybrid algorithm, and the results indicate that the improved algorithm has a better performance in finding optimal solution and more quick convergence than before, and has a more reasonable assignment results.%直升机编队的对地攻击过程中,目标分配是实现作战任务的重要条件.确定了直升机目标分配的优势度计算方法以及目标分配的原则.利用蚁群-模拟退火算法实现了直升机对地攻击的目标分配过程,并针对分配过程中,采用综合优势度最大来确定最优路径所出现的不足对算法进行了改进,即根据信息素的积累量来确定最优路径,能够综合考虑信息素浓度与优势度的影响,避免了为达到全局最大优势度而出现的个体分配效益不好以及收敛缓慢的情况.实验结果表明,改进的算法效率更高,收敛的速度较之前更快,分配结果更趋合理.

  9. Resource Service Chain Construction for Networked Manufacturing Based on FAHP and Improved Ant Colony Algorithm%基于FAHP和改进蚁群算法的网络化制造资源链构建研究

    Institute of Scientific and Technical Information of China (English)

    王正成; 谢先文

    2012-01-01

    网络化制造资源集成共享与优化配置主要包括协同制造总任务的分解、单任务驱动的制造资源的评价选择、时序约束关联单任务链驱动制造资源链的构建三个阶段。首先根据协同制造总任务分解的特点,提出了跨组织协同制造任务分解过程模型和相应的分解算法。然后提出了单任务约束驱动网络化制造资源评价指标体系与评价算法,在此基础上对单任务驱动检索的候选资源集,提出了基于改进蚁群算法的时序约束关联单任务链驱动网络化制造资源服务链构建模型与算法。最后通过仿真算例说明了研究成果的有效性。%The nature of networked manufacturing resource integration and optimized configuration is a complex process.In this process,total collaborative manufacturing tasks can be decomposed to construct a single-task-driven chain according to time and sequence constraints in cross-organizational.The process mainly included three stages:the decomposition of the collaborative manufacturing tasks;the evaluation and selection of the manufacturing resources and the construction of the single-task-driven manufacturing resource chain.Firstly,according to the features of the collaborative task decomposition,a process model for cross-organizational collaborative manufacturing and corresponding decomposition algorithm were proposed.Secondly it also put forward an index system and evaluation algorithm for the networked manufacturing resources evaluation.Then the networked manufacturing resource service chain model and the algorithm which was based on improved ant colony algorithm with single task-driven related timing constrains were proposed.Finally,a simulation was taken to verify the feasibility of this research.

  10. 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.

  11. Microsatellites reveal high genetic diversity within colonies of Camponotus ants.

    Science.gov (United States)

    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.

  12. 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.

  13. Routing and spectrum assignment based on ant colony optimization of minimum consecutiveness loss in elastic optical networks

    Science.gov (United States)

    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.

  14. Ant System Algorithm Research and Its Applications

    Institute of Scientific and Technical Information of China (English)

    2002-01-01

    In this paper, systematic review on Ant System (AS) algorithm research and application is made, and the authors works of introducing As algorithm into continuous space application are summarized. Then the applicability characters of AS in continuous space optimization problems are also discussed.

  15. Emergency Supplies Distribution of Sudden Water Pollution based on Ant Colony Algorithm%基于蚁群算法的突发性水污染应急物资调配

    Institute of Scientific and Technical Information of China (English)

    王彦贺; 匡正

    2014-01-01

    随着工业现代化的发展,带来了经济的增长,但同时也在不断破坏着环境,突发性水污染事件频发,如2014年兰州苯污染、2013年山西苯胺污染、2012年龙江镉污染;《国家环境保护“十二五”规划(2011-2015)》提出要完善应急决策、指挥调度系统。本文主要通过对突发性水污染的特性,及应急物流的特点进行分析,提出了数学模型,通过改进的蚁群算法进行求解。最后通过实例仿真,验证其有效性。%While the development of modern industry has brought economic growth,the environment is constantly de-stroyed,which caused frequent incidents of sudden water pollution,such as benzene pollution in Lanzhou in 2014,2013, Shanxi aniline contamination,2012 Long Jiang cadmium pollution;"National Environmental Protection"Twelfth five Year Plan "(2011 -2015)"proposed to improve emergency decisions,command and Control System.In this paper,through the characteristics analysis of sudden water pollution,and the characteristics of emergency logistics ,a mathematical model is solved by improved ant colony algorithm.After that,an example simulation in done to verify its validity.

  16. 基于蚁群算法的停车场车位引导问题研究%Research on Parking Guidance Based on Improved Ant Colony Algorithm

    Institute of Scientific and Technical Information of China (English)

    黄小珂

    2012-01-01

    In order to solve the problem of parking guidance in modern large scale parking lot more efficiently,a parking lot structure model is built in this paper;the concept of node busy factor is introduced,a constrained mathematical model of the optimal path is mentioned,and the problem of parking guidance to be translated into solving for the optimal path based on the actual situation.Moreover,improved the heuristic function and pheromone update rule of ant colony algorithm,which is used to get the optimal path of the parking guidance.Finally,the best parking space and the guiding paths out of parking lot are given out by the simulation.This solution provides guidance for the parking,and the efficiency of the parking lot usage is improved.%根据停车场实际情况建立了停车场结构模型,引入了节点繁忙因子的概念,提出了带约束的最优路径数学模型,将车位引导问题转化为对网络中最优路径的求解,并对基本蚁群算法的启发函数、信息素更新规则进行改进,将改进后的算法用于停车场车位引导问题中最优路径的求解。最后通过仿真实验找出了最优车位及存取车路线,为进出停车场的车辆提供引导,提高了停车场的使用效率。

  17. A dynamic path planning model based on the optimal ant colony algorithm%基于优化蚁群算法的动态路径规划问题研究

    Institute of Scientific and Technical Information of China (English)

    李露蓉; 王蕾; 高应波; 何川

    2013-01-01

    为解决传统的交通流分配模型在处理突发的交通事故、交通拥堵等实时交通信息时,无法对其作出合理及时的处理的问题.将动态交通网中实时变化的交通信息加入到路径规划模型当中,构建了基于优化蚁群算法的动态路径规划模型,通过与传统动态路径规划模型的比较,证明该模型收敛速度更快,且能有效避免局部收敛现象,实现了交通网络中车流量的合理分配,在大规模动态交通网络中表现更为明显.%The traditional traffic assignment model can not solve the problem of traffic accidents, traffic congestion and other real-time traffic information reasonably and timely. In order to solve these problems, real-time changes in road network information are added to the dynamic traffic assignment model,a dynamic module for dynamic traffic assignment model based on the optimal ant colony algorithms. Compared with the traditional dynamic path planning model, the dynamic traffic assignment model converges faster and can effectively avoid local convergence. A reasonable distribution of traffic flow in traffic network is achieved and it is more effective in the large-scale dynamic traffic network.

  18. Control research of construction project acceleration measures based on ant colony algorithm%基于蚁群算法的建设项目赶工措施控制研究

    Institute of Scientific and Technical Information of China (English)

    赵平; 张向伟

    2014-01-01

    通过讨论资源受限项目调度问题的约束条件、工序实施模式的函数表达及工序工期与工序成本之间的关系,建立了以项目工期-成本最优化为目标的多工序交叉作业赶工措施(MCAM)控制模型;运用引入精英策略的蚁群算法(ACA)结合串行进度生成机制,以某项目标准层施工为例,最终得到可行项目进度计划,并通过与其他算法比较,证明了ACA求解MCAM模型结果合理,算法高效。%According to the discussion of resource-constrained project scheduling problem constraint conditions, process implementation mode function expression and the relationship between process duration and process cost, the Multi-process Cross-operation Acceleration Measures(MCAM)model is built whose objective is the project duration-cost optimization. Then the feasible project schedule based on some engineering example can be achieved by solving this model with Ant Colony Algorithm(ACA)containing elite strategy and serial schedule generation scheme. The results prove that this cal-culation method for MCAM is reasonable and efficient compared with other method.

  19. 基于蚁群算法的应急物资运输路径优化%Transport path optimization for emergency material based on ant colony algorithm

    Institute of Scientific and Technical Information of China (English)

    杨菊花; 朱昌锋; 田志强

    2012-01-01

    由于各种自然灾害和公共卫生事件频发,世界各国加大了对应急物资的采购、存储和调运方案的研究,旨在建立高效的救援物流系统.结合以往文献中有关应急状态下物资运输的模型和非常规物流中车辆运输的特殊性,运用多式联运和路网的脆弱性理论,建立应急物资全程调拨时运输方式和路径选择问题的综合模型,设计改进的蚁群算法,结合算例说明当运输路径及其流量发生改变时应急物资运输路径的变化情况.%Because of frequent occurrence of natural disasters and public health events, study on the purchasing, storage and allocation plan for emergency materials was enforced all over the world, which aims at establishing high efficient rescue logistic system. Referring to material transportation models in emergent state and the specialty of vehicle transportation in irregular logistics which were proposed in many papers, multimodal transportation theory and network vulnerability theory were applied to establish compositive model of transport mode and path selection for the whole course allocation of emergency materials. The modified ant colony algorithm was designed which combines with an example to explain the change conditions according to the change of transport path and its flow.

  20. Ant Colony Algorithm based on the ASRank and MMAS for the Aircraft Assigning Problem%基于ASRank和MMAS的蚁群算法求解飞机指派问题

    Institute of Scientific and Technical Information of China (English)

    张涛; 胡佳研; 李福娟; 张玥杰

    2012-01-01

    of large scale NP-hard combinatorial optimization problem which is difficult to be solved by the accurate algorithms effectively. As a meta-heuristics algorithm, ant colony optimization algorithm (ACO) has strong global search ability and the ability to find a better solution. Thus, we select the ACO as the algorithm for the AAP problem.Considering the link time constraints and link airport constraints between two consecutive flight strings, and considering the total flying time constraints of the aircrafts, we propose the concept of virtual flight string and construct a mixed integer programming model. To solve this model, we propose an ACO algorithm according to the characteristics of the problem. In this algorithm we adopt the pheromone updating strategy of rank-based version of ant system (ASRank) and MAX-MIN ant system ( MMAS). The ASRank can make the search space more close to the optimal solution while the MMAS make the algorithm avoid stagnation in the process of searching.For testing the validity of our model and algorithm, we use the practical data of one airline to do the experiments and test some important parameters of the algorithm. The numerical results show that the best goal value obtained by our method is 3.75% less than the best goal value obtained by manual method, the utilization rate of the aircrafts is improved and the total link time is effectively decreased.

  1. 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.

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

    Science.gov (United States)

    Dorigo, M; Maniezzo, V; Colorni, A

    1996-01-01

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

  3. 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.

  4. Combination of Pareto ant colony algorithm with remote sensing for optimal allocation of water resources%Pareto蚁群算法与遥感技术耦合的水资源优化配置

    Institute of Scientific and Technical Information of China (English)

    侯景伟; 孔云峰; 孙九林

    2012-01-01

    为了尝试用Pareto蚁群算法(PACA)和遥感技术(RS)来求解复杂的水资源优化配置问题,建立了以经济、社会和生态环境综合效益最大为目标,以供水、需水、水质等为约束条件的基于像元的水资源优化配置模型.通过局部信息素强度限制、全局信息素动态更新、Pareto解集过滤器构建等策略,使蚂蚁向信息素浓度大的优化边界移动,以提高PACA的全局搜索能力和收敛速度.以中原地区某县为仿真对象,借助RS获取其土地利用类型,利用PACA在栅格地图上求解水资源优化配置模型,并得到水资源最优配置方案.最后PACA与遗传算法(GA)和BP神经网络算法(BP-ANN)进行了比较.结果表明,PACA能有效地求解大范围、多目标水资源优化配置模型,并提高了算法的全局搜索能力、收敛速度和计算结果的精度.%To solve the optimal allocation problem of water resources with Pareto ant colony algorithm (PACA) and remote sensing (RS), we develop an optimization model in pixel scales. This model produces the largest social, economic, and environmental benefits under constraints on water supply, water demand and water quality. By limiting the local pheromone scope, dynamically updating the global pheromone and filtering the Pareto solution set, we improve the PACA to make ants move towards the optimal border with higher pheromone density, and enhance the global search capability and raise the convergence rate. To validate the feasibility and effectiveness of the PACA, a county in central China is selected as the simulation object, from which the data of the land-use pattern is obtained by using the RS technology. By solving the multi-objective model, we obtain the optimal allocation scheme for water resources with the aid of PACA on a raster map. Performance and convergence of the PACA are compared with those of the genetic algorithm (GA) and BP neural network algorithm (BP-ANN); results show that PACA can

  5. Population and colony structure of the carpenter ant Camponotus floridanus.

    Science.gov (United States)

    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.

  6. An energy efficient routing algorithm based on clustering and ant colony optimization for wireless sensor networks%一种基于分簇蚁群策略的无线传感器网络路由算法

    Institute of Scientific and Technical Information of China (English)

    刘逵; 刘三阳; 冯海林; 焦合华

    2012-01-01

    如何最大化地延长网络的生存时间是无线传感器(WSN)网络研究的核心问题.基于分簇策略,提出一种能量有效的路由算法(EEA).该算法利用分簇原理减少了参与寻找最优路径的节点数,从而降低了系统的能耗.同时设计一种改进的最优路径评价标准,该标准兼顾了传输路径上各节点的剩余能量和最优路径上总的能量消耗.仿真结果表明,与其他蚁群策略的路由算法(如:基于蚁群算法的路由算法(ARA)和EEAWSN)相比,该算法能在寻找最优路径时避开剩余能量少的节点,使最优路径上各节点的能量呈整体性衰落,从而沿长了网络的寿命.%How to make efficient use of the limited energy of nodes so as to prolong the lifetime of the wireless sensor network(WSN) is an important problem.An energy efficient routing algorithm(EEA) based on clustering is presented.This algorithm uses clustering to reduce the number of nodes which join in researching route,which can reduce consumer energy.The improve route optimal degree is presented to evaluate the performance of the chosen route.Simulation results show that,compared with other algorithms,like ant colony optimization(ACO)-based routing algorithm(ARA) and EEAWSN,the proposed approach is able to keep away from the node with less residual energy,which can improve the life of networks.

  7. Multi-tenant Service Customization Algorithm Based on MapReduce and Multi-objective Ant Colony Optimization%基于MapReduce和多目标蚁群算法的多租户服务定制算法

    Institute of Scientific and Technical Information of China (English)

    王会颖; 倪志伟; 伍章俊

    2014-01-01

    Multi_tenant service customization is one of the key technologies to facilitate the agile SaaS multi_tenant architecture, and it can meet the ever_changing personalized demands from customers as well. The hierarchical graph and the customization process of multi_tenant service customization are employed in this paper, and a customization algorithm based on MapReduce and multi_objective ant colony optimization ( MSCMA) is proposed. The most suitable business process and the optimized service composition can be found out from various business processes and massive services according to the non_functionality requirement of the tenant, and the optimization tasks can be fulfilled in distributed cloud computing environment in parallel by MSCMA. The results of the simulated experiment demostrate that MSCMA shows favorable convergence and scalability in solving multi_tenant service customization and the proposed algorithm has good ability in processing massive data and solving large scale problems.%多租户服务定制能满足租户不断变化的个性化服务需求,是实现灵活的SaaS多租户软件体系结构的核心技术之一。文中给出多租户服务定制的层次结构图和定制流程,并提出基于MapReduce和多目标蚁群算法的多租户服务定制算法( MSCMA)。 MSCMA从众多业务流程和海量服务中为租户定制出最适合的业务流程和优化的服务组合,并设计多目标蚁群算法,应用MapReduce云计算技术,在云计算环境中分布式并行地运行优化任务,并采用优良解保持策略和解多样性保持策略。实验表明,MSCMA在求解多租户个性化服务定制问题时表现出良好的收敛性和扩展性,具有处理海量数据和大规模问题的能力。

  8. Forward Kinematics of 3-RPS Parallel Mechanism Based on a Continuous Ant Colony Algorithm%基于连续蚁群算法的3-RPS并联机构正解

    Institute of Scientific and Technical Information of China (English)

    谢志江; 梁欢; 宋代平

    2015-01-01

    为了避免传统数值方法求解并联机构正解问题的弊端,提出了一种将并联机构正解问题转化为目标函数优化问题的求解方法。并联机构正解的核心问题是求解一组多元耦合非线性方程组,以此为依据建立了并联机构正解的目标函数优化模型,并提出了一种简单的连续蚁群算法来求解该优化模型。以求解3-RPS并联机构正解为例进行了仿真分析。结果表明,该算法具有良好的全局寻优功能,能够避免初始值和局部极小值对计算结果的影响,不用计算雅可比矩阵及其逆阵,且计算精度满足并联机构正解的要求。%In order to avoid the drawbacks of traditional numerical methods for solving the problem of parallel mechanism forward kinematics,this paper proposed a method that translated the problem of solving parallel mechanism forward kinematics into obj ective function optimization problems.The central issue of solving the problem of parallel mechanism forward kinematics was to solve a set of multiple coupled nonlinear equations,thus obj ective function optimization model of parallel mechanism forward kinematics was established,and a kind of simple continuous ant colony algorithm was put for-ward to solve the above optimization model.Taking the 3-RPS parallel mechanism for example,some simulation analyses were completed.The results show that the algorithm has a good global optimiza-tion function and can avoid initial values and the local minimum effect on the calculation results with-out calculating Jacobian matrix and its inverse matrix.The accuracy of calculation meets the require-ments of parallel mechanism of forward kinematics.

  9. 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.

  10. Power Efficient Resource Allocation for Clouds Using Ant Colony Framework

    CERN Document Server

    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.

  11. Colony location algorithm for assignment problems

    Institute of Scientific and Technical Information of China (English)

    Dingwei WANG

    2004-01-01

    A novel algorithm called Colony Location Algorithm (CLA) is proposed. It mimics the phenomena in biotic conmunity that colonies of species could be located in the places most suitable to their growth. The factors working on the species location such as the nutrient of soil, resource competition between species, growth and decline process, and effect on environment were considered in CLA via the nutrient function, growth and decline rates, environment evaluation and fertilization strategy.CLA was applied to solve the classical assignment problems. The computation results show that CLA can achieve the optimal solution with higher possibility and shorter running time.

  12. Nest site and weather affect the personality of harvester ant colonies.

    Science.gov (United States)

    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.

  13. Evaluating projection pursuit model of irrigation district based on ant colony optimization algorithm for continuous domains%基于连续蚁群优化的投影寻踪灌区评价模型

    Institute of Scientific and Technical Information of China (English)

    费良军; 孙洁; 任长江; 谢芳

    2014-01-01

    针对连续蚁群算法(CACA)中模型参数必须限定在[0,1]区间,以及不能搜索到节点之间可能存在的最佳函数值的缺点,对其做出2点改进:①修改蚂蚁路径解码公式,将参数域扩展到整个实数域;②将蚂蚁寻优路径二维表T(m ×d+m+1,10)改为三维搜索表T(m,d+2,10),比二维表减少了10×m-10个节点城市.得到改进的连续蚁群算法所需的节点少,减少了无效搜索路径,大大提高了运行速度和精度;引入到投影寻踪模型中,得到各指标的权重值,并以陕西关中交口抽渭灌区为例进行了实证研究.评价结果显示:交口抽渭灌区在1999,2005,2008,2011年的综合投影值分别为1.3531,2.0747,2.5022,2.4039,运行状况所属级别为中等、中等、较好、较好.2011年的运行状况较之前的发展趋势有所下降,评价结果与灌区实际情况相符.%There exist two limitations in ant colony optimization algorithm for continuous domains, namely the model parameters must be in the range of 0 to 1 and the algorithm fails to search a potential optimal function value between nodes.Therefore two updates are proposed to remove the limitations, (1 )modify the decoding formula to expand the parameter domain into the whole real number domain, (2)use a 3D table-T(m ×d+m+1 ,1 0)to search an optimum function,and 1 0 ×m-1 0 node ci-ties are reduced compared with the original 2D table -T(m,d+2,1 0).In the improved algorithm, the number of nodes has been reduced and invalid search paths have been removed,causing greatly shortened computational time and improved accuracy.In addition,the improved algorithm is intro-duced into the projection pursuit model and the weight coefficients of various indices are obtained based on Jiaokou Irrigation District of River Wei in Guanzhong Plan of Shanxi Province.It was turned out that the comprehensive projections are 1.353 1 (1 999 ),2.074 7 (2005

  14. Research and application based on ant colony algorithm for heating furnace scheduling%基于蚁群算法的加热炉作业计划研究与应用

    Institute of Scientific and Technical Information of China (English)

    陈友文; 柴天佑

    2011-01-01

    The scheduling of heating furnace has a direct impact on steel productivity in the steel rolling process.Because furnace is a complex industrial process, manual operations is still widely used in the furnace scheduling.For the manual scheduling of heat furnace in steel enterprises, a method for scheduling is proposed for furnace plan control.The scheduling system is established, which is suitable for normal and abnormal operating conditions.The system consists of some modules,including the production plan, the intelligence pre-handled process data, the tracking inside the furnace, the available furnace capacity parameter, the shortest possible date of operation plan and earliest due date of operation plan, the ant colony algorithm, the operation plan made in both the normal operating conditions and the abnornal operating conditions.Finally,heating rate and billet lot number are yielded by scheduling model for control on line.The proposed scheduling method is successfully applied to some steel plant and significant result is obtained.%加热炉的作业效率直接影响企业的最终效益.由于加热炉的复杂性,其作业计划仍然处于手动操作状态.针对加热炉这样一个复杂工业过程,提出一个优化作业计划与调度的编排方法,用于加热炉在正常和异常生产的优化排产.利用最短时间和最早缴期算法进行加热炉正常作业排序.采用蚁群算法搜索异常情况下的最优解,通过跟踪形成作业计划闭环控制,从而实现加热炉作业优化的实时控制.该方法在某钢铁公司进行了实际应用,效果良好.

  15. Research on Trajectory Planning of Six-freedom-degree Picking Robot Based on Ant Colony Algorithm%基于蚁群算法的六自由度采摘机器人轨迹规划研究

    Institute of Scientific and Technical Information of China (English)

    黄轶文; 张梅

    2017-01-01

    In modern agriculture planting picking, picking fruits and vegetables operating in a complex and arduous, picking robot in the process often need to experience thousands of fruit and vegetable picking point, the face of such a huge workload, picking mobile robot path planning is very important. The to picking robot trajectory as the research ob-ject, takes the shortest length of the motion trajectory as the research target, for each robot joint mechanism motion speed changes, combined with robot motion characteristics, the basic ant colony algorithm of six degree of freedom picking robot path planning. The experimental results show that: the design of the picking robot trajectory optimization technology not only has strong ability of path optimization, smooth motion trajectory, but also has the advantages of strong reliability and good stability.%在现代农业生产中,果蔬采摘作业复杂而繁重,采摘机器人在作业过程中常常需要经历成千上万个果蔬采摘点,面对这样巨大的工作量,采摘机器人移动路径规划显得非常重要。为此,以采摘机器人运动轨迹为研究对象,以其运动轨迹总长最短为研究目标,针对机器人各关节机构运动速度变化情况及机器人运动特性,利用基本蚁群原理对六自由度采摘机器人的路径进行规划。实验结果表明:所设计的采摘机器人轨迹优化技术不但路径优化能力强、运动轨迹平滑,还具有可靠性强及稳定性好的优点。

  16. A load distribution algorithm based on an ant colony for multi-source multicast networks%基于蚁群算法的多源组播流量均衡的研究

    Institute of Scientific and Technical Information of China (English)

    王另秀; 曹叶文

    2011-01-01

    针对现有组播路由技术因路由单一而导致的不能满足多源组播网络中流量均衡的问题,基于蚁群算法提出了一种组播流量均衡的方法——LDA(load distribution algorithm)。LDA主要包括选择候选路由和组播调度两个模块,通过与常用的特定源组播路由协议(PIM-SSM)相结合,从整体上考虑均衡网络负载的同时,一方面减小了组播数据包传递的时延,另一方面减小了丢包率。仿真实验结果表明,在PIM-SSM的基础上,该方法能有效提高网络资源的利用率,降低组播数据传输时因排队造成的过大的时延和丢包率。%Due to the problem that IP multicast protocols tended to construct a single minimum spanning tree for a multicast source(i.e.,group),which can not balance the resource allocation of multicast networks,an ant colony-based load balancing algorithm for multicast networks called the load distribution algorithm(LDA) was proposed.The proposed LDA mainly consisted of two parts: the Selecting Candidate Path and Multicast Scheduling.PIM-SSM(Protocol-Independent Multicast Single-Source Multicast) with the LDA can balance network traffic distribution and meanwhile maintain less packet loss and average delay in the case of co-existing multiple multicast sources.Simulation comparisons between PIM-SSM with the LDA and the original PIM-SSM,showed that higher network utilization was achieved in PIM-SSM with the proposed LDA,while maintaining less average end to end delay where there were bottleneck effects.

  17. Applying Ant Colony Optimization to the Problem of Cell Planning in Mobile Telephone System Radio Network

    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.

  18. 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.

  19. Yeasts associated with the infrabuccal pocket and colonies of the carpenter ant Camponotus vicinus.

    Science.gov (United States)

    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.

  20. Ant colony clustering with fitness perception and pheromone diffusion for community detection in complex networks

    Science.gov (United States)

    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.

  1. Colony variation in the collective regulation of foraging by harvester ants.

    Science.gov (United States)

    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.

  2. 蚁群算法在单级多时段多资源约束的生产批量问题中的应用研究%Application of an Improved Ant Colony Optimization Algorithm for Solving Single-level Multi-period Capacitated Dynamic Lot-sizing Problem

    Institute of Scientific and Technical Information of China (English)

    李英俊; 陈志祥

    2012-01-01

    One algorithm structure of an ant colony optimization(ACO) for solving multi -periods continuous and mixed integer programming problem was first designed herein and then its application in the single-level multi -period capacitated dynamic lot-sizing problem(CLSP) was introduced. The algorithm was based on the model characteristics of CLSP and improvements of traditional ant colony algorithm. Compared with other algorithms of other literature, the algorithm presented herein performs better than that the traditional genetic algorithm and the hybrid of simulated annealing penalty and genetic algorithm do;it has higher ability of obtaining optimal value. The application results show the method is feasible and effective for solving this kind problem.%设计了一个用于求解具有多时段连续与整数混合规划问题的算法结构,并以单级多时段多资源约束的生产批量问题(CLSP)的模型为背景进行了应用研究,根据此类问题的特点设计了新颖的蚁群算法,阐明了算法的具体实现过程.通过对其他文献中的例子进行计算和结果比较,表明提出的改进蚁群算法在寻优方面比退火惩罚混合遗传算法和传统的遗传算法要好,验证了所提算法对解决此类问题的可行性和适用性.

  3. AntStar: Enhancing Optimization Problems by Integrating an Ant System and A⁎ Algorithm

    Directory of Open Access Journals (Sweden)

    Mohammed Faisal

    2016-01-01

    Full Text Available Recently, nature-inspired techniques have become valuable to many intelligent systems in different fields of technology and science. Among these techniques, Ant Systems (AS have become a valuable technique for intelligent systems in different fields. AS is a computational system inspired by the foraging behavior of ants and intended to solve practical optimization problems. In this paper, we introduce the AntStar algorithm, which is swarm intelligence based. AntStar enhances the optimization and performance of an AS by integrating the AS and A⁎ algorithm. Applying the AntStar algorithm to the single-source shortest-path problem has been done to ensure the efficiency of the proposed AntStar algorithm. The experimental result of the proposed algorithm illustrated the robustness and accuracy of the AntStar algorithm.

  4. 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.

  5. An ant colony optimization heuristic for an integrated production and distribution scheduling problem

    Science.gov (United States)

    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.

  6. 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.

  7. 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)

  8. Information cascade, Kirman's ant colony model, and kinetic Ising model

    CERN Document Server

    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...

  9. Enhanced ant colony optimization for inventory routing problem

    Science.gov (United States)

    Wong, Lily; Moin, Noor Hasnah

    2015-10-01

    The inventory routing problem (IRP) integrates and coordinates two important components of supply chain management which are transportation and inventory management. We consider a one-to-many IRP network for a finite planning horizon. The demand for each product is deterministic and time varying as well as a fleet of capacitated homogeneous vehicles, housed at a depot/warehouse, delivers the products from the warehouse to meet the demand specified by the customers in each period. The inventory holding cost is product specific and is incurred at the customer sites. The objective is to determine the amount of inventory and to construct a delivery routing that minimizes both the total transportation and inventory holding cost while ensuring each customer's demand is met over the planning horizon. The problem is formulated as a mixed integer programming problem and is solved using CPLEX 12.4 to get the lower and upper bound (best integer) for each instance considered. We propose an enhanced ant colony optimization (ACO) to solve the problem and the built route is improved by using local search. The computational experiments demonstrating the effectiveness of our approach is presented.

  10. 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.

  11. 基于遥感和蚁群算法的多目标种植结构优化%Multi-objective optimization of crop planting structure based on remote sensing and ant colony algorithm

    Institute of Scientific and Technical Information of China (English)

    张智韬; 刘俊民; 陈俊英; 汪志农; 李援农

    2011-01-01

    A water-saving and multi-objective planting structure optimization model was put forward against the mismatching between regional planting structure and water resources.Based on the matching degree between water requirement at different crop-growing stages and the regional precipitation as well as the crop planting structure obtained by remote sensing, a planting structure optimization model with multiple objectives such as water saving, economic and ecological benefits in the irrigated area was established.With Wuquan irrigation district in Baojixia as an example, the model was optimized with ant colony algorithm.The total water requirements for irrigation district of plan 1 and plan 2 is respectively 85.4% and 83.4% of the present situation.The income of plan 1 is lower 5.4% and the plan 2 is higher 7.1% than common years.Meanwhile the income of plan 1 is lower 5.9% and the project 2 is higher 7.3% than the droughty years.The coupling degree of precipitation of plan 1 and plan 2 is respectively higher 12.6% and 15.6% than common years while higher 17.5% and 28.6% than droughty years.The comparison between the two plans of controlled optimization shows that the second one is the better readjustment plan because, without changing the planting area, the structure optimization helps not only maintain the sustainable development of the entironment but also improve the economic income dramatically.It is practically and theoretically for agricultural water saving planning.%针对农业种植结构与水资源不匹配的问题,以主要作物不同生育期需水特点和区域降水特点吻合性为基础,建立了考虑灌区节水效益、经济效益和生态效益的多目标种植结构优化模型,并以宝鸡峡五泉灌区为例,利用遥感快速获取灌区种植结构信息,并以不同口粮面积约束为条件建立2种优化方案,采用蚁群算法对模型在不同约束下的2种优化方案进行优化求解.结果表明:方案1

  12. Specialization and group size: brain and behavioural correlates of colony size in ants lacking morphological castes.

    Science.gov (United States)

    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.

  13. Queen movement during colony emigration in the facultatively polygynous ant Pachycondyla obscuricornis

    Science.gov (United States)

    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.

  14. Displacement back analysis for underground engineering based on immunized continuous ant colony optimization

    Science.gov (United States)

    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.

  15. 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®.

  16. Abnormality detection in retinal images using ant colony optimization and artificial neural networks - biomed 2010.

    Science.gov (United States)

    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.

  17. Polygynous supercolonies of the acacia-ant Pseudomyrmex peperi, an inferior colony founder.

    Science.gov (United States)

    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.

  18. A Deterministic Model for Analyzing the Dynamics of Ant System Algorithm and Performance Amelioration through a New Pheromone Deposition Approach

    CERN Document Server

    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...

  19. Optimization of placing-in and taking-out wagons on branch-shaped railway lines based on genetic and ant colony algorithm%基于遗传蚁群算法的树枝型铁路取送车问题优化

    Institute of Scientific and Technical Information of China (English)

    雷友诚; 涂祖耀; 桂卫华; 吴志飞; 闫福全

    2011-01-01

    Aiming at the distribution of railway line and a combinatorial mode of placing-in and taking-out wagons at an enterprise railway freight station, a mathematical model of optimal operation for placing-in and taking-out wagons in the branch-shaped private line was established, which is deduced as a typical traveling salesman problem(TSP). Meanwhile, a combination of genetic algorithm and ant colony algorithm called GACA was presented to resolve the large-scale combinatorial optimization problem. The genetic algorithm was adopted to generate pheromone to distribute. And the ant colony algorithm was used to find an accurate solution. As a result, the searching efficiency and the time efficiency of the combinatorial algorithm are both greatly improved. Combined with an example, the optimal solution of the placing-in and taking-out wagons problem is found.%针对企业铁路货运站的铁路线分布特点和“连送带取”的作业方式,建立树枝型专用线取送车的数学模型,将其归纳为一个典型的旅行商问题.同时提出一种融合遗传算法和蚁群算法特点的遗传蚁群算法(GACA)来解决这种大规模组合优化问题;采用遗传算法生成信息素分布,利用蚁群算法求精确解,有效提高算法的时间效率和求解效率.结合实例计算求得了企业取送车作业问题的最优解.

  20. Research on Time-cost Trade-off of Project Portfolio Based on Improved Ant Colony Algorithm%基于改进蚁群算法的项目组合工期——成本优化的研究

    Institute of Scientific and Technical Information of China (English)

    白礼彪; 白思俊; 郭云涛

    2012-01-01

    基于企业战略导向的项目组合工期——成本优化问题是企业进行多项目管理时需要解决的重要问题,对企业资源效益最大化发挥起到关键作用,它从本质上属于多目标优化问题.本文将蚁群算法引入项目组合工期——成本优化问题的求解,并针对蚁群算法存在的早熟、停止、局部最优的缺点,提出与混沌结合的改进蚁群算法,引进确定和不确定性搜索规则.实验结果表明,改进的蚁群算法能够有效地提高蚁群算法的全局寻优能力,对工期——成本优化问题的求解能够得出比较好的结果.%The time-cost trade-off based on the strategic orientation is one of the most crucial aspects of enterprise project portfolio planning that plays a key role in enterprise resources benefit maximization, which in fact is a multi-objective optimization problem. A new evolutionary algorithm-ant colony optimization ( ACO) algorithm is employed to solve the time-cost trade-off problem. According to the ant colony algorithm existing precocious, stagnation, local optimal shortcomings, adopting certainty and uncertainty search rules and combining with chaos, an improved ant colony algorithm is proposed. Experimental results indicate that join chaos and search rules, the developed ACO can effectively improve global optimization ability, can draw better results in solving time-cost trade-off of project portfolio.

  1. Pupae transplantation to boost early colony growth in the weaver ant Oecophylla longinoda Latreille (Hymenoptera: Formicidae)

    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...

  2. 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...

  3. 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.

  4. Energy Aware Simple Ant Routing Algorithm for Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Sohail Jabbar

    2015-01-01

    Full Text Available Network lifetime is one of the most prominent barriers in deploying wireless sensor networks for large-scale applications because these networks employ sensors with nonrenewable scarce energy resources. Sensor nodes dissipate most of their energy in complex routing mechanisms. To cope with limited energy problem, we present EASARA, an energy aware simple ant routing algorithm based on ant colony optimization. Unlike most algorithms, EASARA strives to avoid low energy routes and optimizes the routing process through selection of least hop count path with more energy. It consists of three phases, that is, route discovery, forwarding node, and route selection. We have improved the route discovery procedure and mainly concentrate on energy efficient forwarding node and route selection, so that the network lifetime can be prolonged. The four possible cases of forwarding node and route selection are presented. The performance of EASARA is validated through simulation. Simulation results demonstrate the performance supremacy of EASARA over contemporary scheme in terms of various metrics.

  5. 基于蚁群优化的超声波电动机系统动态模糊辨识建模%Dynamic Fuzzy Modeling of Ultrasonic Motor Using Ant Colony Algorithm

    Institute of Scientific and Technical Information of China (English)

    吕琳; 史敬灼

    2011-01-01

    The model of ultrasonic motor system is an important premise of designing motor motion controller. Fuzzy modeling method of ultrasonic motor system was given. Ant colony optimization and least square method were used to obtain the unknown parameters of membership functions and fuzzy rules, respectively. The two-input and one-output Takagi-Sugeno model was established,and the model can well show the nonlinear dynamic relationship of ultrasonic motor system.%超声波电动机系统的模型是设计电机运动控制器的重要前提.给出了超声波电动机系统的模糊建模方法,分别采用蚁群算法和最小二乘方法获取隶属函数及模糊规则的待定参数,建立了能够表征超声波电动机系统非线性动态关系的二输入单输出Takagi-Sugeno模糊模型.

  6. 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.

  7. 3D sensor placement strategy using the full-range pheromone ant colony system

    Science.gov (United States)

    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.

  8. The rewards of restraint in the collective regulation of foraging by harvester ant colonies.

    Science.gov (United States)

    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.

  9. A cuckoo-like parasitic moth leads African weaver ant colonies to their ruin.

    Science.gov (United States)

    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.

  10. A multiple classifier system based on Ant-Colony Optimization for Hyperspectral image classification

    Science.gov (United States)

    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.

  11. The Role of Non-Foraging Nests in Polydomous Wood Ant Colonies.

    Science.gov (United States)

    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.

  12. Breeding system, colony and population structure in the weaver ant Oecophylla smaragdina.

    Science.gov (United States)

    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.

  13. 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...

  14. 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

  15. 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.

  16. 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.

  17. Eggs of Mallada desjardinsi (Neuroptera: Chrysopidae) are protected by ants: the role of egg stalks in ant-tended aphid colonies.

    Science.gov (United States)

    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.

  18. 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

  19. An ant colony based resilience approach to cascading failures in cluster supply network

    Science.gov (United States)

    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.

  20. Blending of heritable recognition cues among ant nestmates creates distinct colony gestalt odours but prevents within-colony nepotism

    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....

  1. A novel speech emotion recognition algorithm based on combination of emotion data field and ant colony search strategy%一种新的结合情感数据场和蚁群策略的语音情感识别算法

    Institute of Scientific and Technical Information of China (English)

    查诚; 陶华伟; 张昕然; 周琳; 赵力; 杨平

    2016-01-01

    In order to effectively conduct emotion recognition from spontaneous, non-prototypical and unsegmented speech so as to create a more natural human-machine interaction; a novel speech emotion recognition algorithm based on the combination of the emotional data field ( EDF ) and the ant colony search ( ACS ) strategy, called the EDF-ACS algorithm, is proposed. More specifically, the inter-relationship among the turn-based acoustic feature vectors of different labels are established by using the potential function in the EDF. To perform the spontaneous speech emotion recognition, the artificial colony is used to mimic the turn-based acoustic feature vectors. Then, the canonical ACS strategy is used to investigate the movement direction of each artificial ant in the EDF, which is regarded as the emotional label of the corresponding turn-based acoustic feature vector. The proposed EDF-ACS algorithm is evaluated on the continueous audio/visual emotion challenge ( AVEC ) 2012 dataset, which contains the spontaneous, non-prototypical and unsegmented speech emotion data. The experimental results show that the proposed EDF-ACS algorithm outperforms the existing state-of-the-art algorithm in turn-based speech emotion recognition.%为了有效识别自发、非典型及未分割语音的情感以建立更自然的人机交互界面,提出了一种新的结合情感数据场和蚁群策略的语音情感识别算法。用情感数据场中势函数建立基于块的声学特征向量之间的内在联系。为识别自发语音情感,用人工蚁群模拟基于块的声学特征向量,然后用典型的蚁群策略研究每个人工蚂蚁在情感数据场的运动轨迹,并把该蚂蚁的运动轨迹作为对应的声学特征向量的情感标签。利用2012年连续音视频情感挑战赛中的语音数据对所提算法进行测试。实验结果表明:该算法较已有算法能更好地对基于块的语音情感进行识别。

  2. 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.

  3. 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.

  4. An ant colony optimization based feature selection for web page classification.

    Science.gov (United States)

    Saraç, Esra; Özel, Selma Ayşe

    2014-01-01

    The increased popularity of the web has caused the inclusion of huge amount of information to the web, and as a result of this explosive information growth, automated web page classification systems are needed to improve search engines' performance. Web pages have a large number of features such as HTML/XML tags, URLs, hyperlinks, and text contents that should be considered during an automated classification process. The aim of this study is to reduce the number of features to be used to improve runtime and accuracy of the classification of web pages. In this study, we used an ant colony optimization (ACO) algorithm to select the best features, and then we applied the well-known C4.5, naive Bayes, and k nearest neighbor classifiers to assign class labels to web pages. We used the WebKB and Conference datasets in our experiments, and we showed that using the ACO for feature selection improves both accuracy and runtime performance of classification. We also showed that the proposed ACO based algorithm can select better features with respect to the well-known information gain and chi square feature selection methods.

  5. Ant colony optimization as a descriptor selection in QSPR modeling for prediction of λmax of azo dyes

    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.

  6. 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...

  7. Utilisation of multiple queens and pupae transplantation to boost early colony growth of weaver ants Oecophylla smaragdina

    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...

  8. Disease in the Society: Infectious Cadavers Result in Collapse of Ant Sub-Colonies

    Science.gov (United States)

    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

  9. 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...

  10. Biomantling and bioturbation by colonies of the Florida harvester ant, Pogonomyrmex badius.

    Science.gov (United States)

    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.

  11. 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.

  12. Colony growth of two species of Solenopsis fire ants(Hymenoptera: Formicidae) reared with crickets and beef liver

    Science.gov (United States)

    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...

  13. A Multi-Attribute Pheromone Ant Secure Routing Algorithm Based on Reputation Value for Sensor Networks

    Directory of Open Access Journals (Sweden)

    Lin Zhang

    2017-03-01

    Full Text Available With the development of wireless sensor networks, certain network problems have become more prominent, such as limited node resources, low data transmission security, and short network life cycles. To solve these problems effectively, it is important to design an efficient and trusted secure routing algorithm for wireless sensor networks. Traditional ant-colony optimization algorithms exhibit only local convergence, without considering the residual energy of the nodes and many other problems. This paper introduces a multi-attribute pheromone ant secure routing algorithm based on reputation value (MPASR. This algorithm can reduce the energy consumption of a network and improve the reliability of the nodes’ reputations by filtering nodes with higher coincidence rates and improving the method used to update the nodes’ communication behaviors. At the same time, the node reputation value, the residual node energy and the transmission delay are combined to formulate a synthetic pheromone that is used in the formula for calculating the random proportion rule in traditional ant-colony optimization to select the optimal data transmission path. Simulation results show that the improved algorithm can increase both the security of data transmission and the quality of routing service.

  14. A Multi-Attribute Pheromone Ant Secure Routing Algorithm Based on Reputation Value for Sensor Networks.

    Science.gov (United States)

    Zhang, Lin; Yin, Na; Fu, Xiong; Lin, Qiaomin; Wang, Ruchuan

    2017-03-08

    With the development of wireless sensor networks, certain network problems have become more prominent, such as limited node resources, low data transmission security, and short network life cycles. To solve these problems effectively, it is important to design an efficient and trusted secure routing algorithm for wireless sensor networks. Traditional ant-colony optimization algorithms exhibit only local convergence, without considering the residual energy of the nodes and many other problems. This paper introduces a multi-attribute pheromone ant secure routing algorithm based on reputation value (MPASR). This algorithm can reduce the energy consumption of a network and improve the reliability of the nodes' reputations by filtering nodes with higher coincidence rates and improving the method used to update the nodes' communication behaviors. At the same time, the node reputation value, the residual node energy and the transmission delay are combined to formulate a synthetic pheromone that is used in the formula for calculating the random proportion rule in traditional ant-colony optimization to select the optimal data transmission path. Simulation results show that the improved algorithm can increase both the security of data transmission and the quality of routing service.

  15. A Multi-Attribute Pheromone Ant Secure Routing Algorithm Based on Reputation Value for Sensor Networks

    Science.gov (United States)

    Zhang, Lin; Yin, Na; Fu, Xiong; Lin, Qiaomin; Wang, Ruchuan

    2017-01-01

    With the development of wireless sensor networks, certain network problems have become more prominent, such as limited node resources, low data transmission security, and short network life cycles. To solve these problems effectively, it is important to design an efficient and trusted secure routing algorithm for wireless sensor networks. Traditional ant-colony optimization algorithms exhibit only local convergence, without considering the residual energy of the nodes and many other problems. This paper introduces a multi-attribute pheromone ant secure routing algorithm based on reputation value (MPASR). This algorithm can reduce the energy consumption of a network and improve the reliability of the nodes’ reputations by filtering nodes with higher coincidence rates and improving the method used to update the nodes’ communication behaviors. At the same time, the node reputation value, the residual node energy and the transmission delay are combined to formulate a synthetic pheromone that is used in the formula for calculating the random proportion rule in traditional ant-colony optimization to select the optimal data transmission path. Simulation results show that the improved algorithm can increase both the security of data transmission and the quality of routing service. PMID:28282894

  16. Swarm Intelligence Based Ant Colony Optimization (ACO Approach for Maximizing the Lifetime of Heterogeneous Wireless Sensor Networks

    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

  17. Harvester ant colony variation in foraging activity and response to humidity.

    Science.gov (United States)

    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.

  18. A Method for Multi-constraint Location Decision of Distribution Center Based on Refined Ant Colony Algorithm and GIS%一种基于改进蚁群算法与GIS的多约束配送中心选址方法

    Institute of Scientific and Technical Information of China (English)

    赵仁辉; 杨丽娜; 邵静

    2015-01-01

    针对单一指派约束和容量约束的设施选址问题(Single Source Capacitated Facility Location Problem, SSC-FLP),建立了一种基于改进蚁群算法与GIS的配送中心选址方法。构建了以总成本费用最小为目标的配送中心选址模型;提出了适合求解SSCFLP问题的改进双层蚁群算法,将求解过程划分为彼此关联的设施选择层和需求指派层2层蚁群,采用改进的全局信息素更新策略加强双层蚁群交流,并对迭代最优解的指派关系进行局部优化;将方法应用于汽车配送中心的选址,利用GIS工具构建选址空间。实验结果表明,该选址方法能找到质量较好的选址及指派结果,对于求解同类问题具有较强的借鉴意义。%Location decision of any logistics distribution center meets multiple constraints, such as the specific spatial environment, the single assignment constraint, the capacity of warehouses and the minimum cost of capi-tal. This paper proposed a model based on refined ant colony algorithm and GIS tools to solve Single Source Ca-pacitated Facility Location Problem (SSCFLP). Firstly, a location selection model was established, which met the target of minimizing the total cost. Secondly, by combining ant colony algorithm and local search, the refined bi-level ant colony optimization to solve the SSCFLP problem was proposed. The solving process was divided in-to two layers:the layer of choosing facilities and the layer of assigning demands. These two layers were associat-ed with each other. In each iteration, the ants would generate solutions by selecting new sets of facility locations from the candidate sites according to the capacity constraint, and establish the assignment of each customer to a selected facility location using pseudorandom search. The iteration-best solution was optimized and memorized using local search. Then the global optimal solution could be attained through conducting multiple iterations

  19. Application of a Novel Ant Algorithm Termed Continuous Gridded in Aidded Drug Design%Application of a Novel Ant Algorithm Termed Continuous Gridded in Aidded Drug Design

    Institute of Scientific and Technical Information of China (English)

    陈国华; 陆瑶

    2011-01-01

    Aimed at solving continuous optimum parameter problems effectively in added drug design, this paper develops a novel ant algorithm termed continuous gridded ant colony (CGAC), where the spy ants are utilized to search the latent optimum grid in the domain completely and effectively. In order to test the effect, the CGAC algorithm was success in finding the best values of C and y, when the support vector machine (SVM) was used to fit the nonlinear relationship between the numerical representation of the chemical structure and IC50. The genetic algorithm (GA) was also used to obtain the appropriate feature subset simultaneously, because feature subset selection influences the appropriate kernel parameters and vice versa. The obtained results illustrate that GA-CGAC-SVM models have satisfactory prediction accuracy. The best quantitative modeling results in thirteen-descriptors model based on GA-CGAC-SVMr with mean-square errors 0.397, a predicted correlation coefficient (R2) 0.842, and a cross-validated correlation coefficient (Q^2) 0.756. The best classification result was found using SVM: the percentage (%) of correct prediction based on 7-fold cross-validation was 90.6%. The results demonstrate that the proposed CGAC algorithm provides a new and effective method to find the optimum parameters when the SVM tool is used.

  20. Aplicación de un algoritmo ACO al problema de taller de flujo de permutación con tiempos de preparación dependientes de la secuencia y minimización de makespan An ant colony algorithm for the permutation flowshop with sequence dependent setup times and makespan minimization

    Directory of Open Access Journals (Sweden)

    Eduardo Salazar Hornig

    2011-08-01

    Full Text Available En este trabajo se estudió el problema de secuenciamiento de trabajos en el taller de flujo de permutación con tiempos de preparación dependientes de la secuencia y minimización de makespan. Para ello se propuso un algoritmo de optimización mediante colonia de hormigas (ACO, llevando el problema original a una estructura semejante al problema del vendedor viajero TSP (Traveling Salesman Problem asimétrico, utilizado para su evaluación problemas propuestos en la literatura y se compara con una adaptación de la heurística NEH (Nawaz-Enscore-Ham. Posteriormente se aplica una búsqueda en vecindad a la solución obtenida tanto por ACO como NEH.This paper studied the permutation flowshop with sequence dependent setup times and makespan minimization. An ant colony algorithm which turns the original problem into an asymmetric TSP (Traveling Salesman Problem structure is presented, and applied to problems proposed in the literature and is compared with an adaptation of the NEH heuristic. Subsequently a neighborhood search was applied to the solution obtained by the ACO algorithm and the NEH heuristic.

  1. A Multipath Routing Protocol Based on Clustering and Ant Colony Optimization for Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Jing Yang

    2010-05-01

    Full Text Available For monitoring burst events in a kind of reactive wireless sensor networks (WSNs, a multipath routing protocol (MRP based on dynamic clustering and ant colony optimization (ACO is proposed.. Such an approach can maximize the network lifetime and reduce the energy consumption. An important attribute of WSNs is their limited power supply, and therefore some metrics (such as energy consumption of communication among nodes, residual energy, path length were considered as very important criteria while designing routing in the MRP. Firstly, a cluster head (CH is selected among nodes located in the event area according to some parameters, such as residual energy. Secondly, an improved ACO algorithm is applied in the search for multiple paths between the CH and sink node. Finally, the CH dynamically chooses a route to transmit data with a probability that depends on many path metrics, such as energy consumption. The simulation results show that MRP can prolong the network lifetime, as well as balance of energy consumption among nodes and reduce the average energy consumption effectively.

  2. Evaluation of Anaerobic Biofilm Reactor Kinetic Parameters Using Ant Colony Optimization.

    Science.gov (United States)

    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.

  3. 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.

  4. Integrating geological uncertainty in long-term open pit mine production planning by ant colony optimization

    Science.gov (United States)

    Gilani, Seyed-Omid; Sattarvand, Javad

    2016-02-01

    Meeting production targets in terms of ore quantity and quality is critical for a successful mining operation. In-situ grade uncertainty causes both deviations from production targets and general financial deficits. A new stochastic optimization algorithm based on ant colony optimization (ACO) approach is developed herein to integrate geological uncertainty described through a series of the simulated ore bodies. Two different strategies were developed based on a single predefined probability value (Prob) and multiple probability values (Pro bnt), respectively in order to improve the initial solutions that created by deterministic ACO procedure. Application at the Sungun copper mine in the northwest of Iran demonstrate the abilities of the stochastic approach to create a single schedule and control the risk of deviating from production targets over time and also increase the project value. A comparison between two strategies and traditional approach illustrates that the multiple probability strategy is able to produce better schedules, however, the single predefined probability is more practical in projects requiring of high flexibility degree.

  5. A Multipath Routing Protocol Based on Clustering and Ant Colony Optimization for Wireless Sensor Networks

    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.

  6. 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

  7. 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。

  8. Ant Colony Optimization for Route Allocation in Transportation Networks

    Science.gov (United States)

    Zamfirescu, Constantin-Bǎlǎ; Negulescu, Sorin; Oprean, Constantin; Banciu, Dorin

    2009-04-01

    The paper introduces a bio-inspired approach to solve the route allocation problem (RAP) in the transportation networks. The approach extends a well-known meta-heuristics algorithm with the real life constraints that are dealt with in the scheduling process (i.e. the uniform distribution of routes diversity for vehicles, the average distance travelled in a month, the driver's rest between subsequent trips etc.). The paper is focusing on the engineering aspects of employing bio-inspired algorithms (which proved to have near-optimal results for toy-like problems) to a real-life application domain. The approach proved to be capable of preserving the software components (agents) to the complexity and dynamics of the situation when the RAP requires incremental extensions of constraints to reflect the traffic conditions in the transportation network.

  9. High recombination frequency creates genotypic diversity in colonies of the leaf-cutting ant Acromyrmex echinatior.

    Science.gov (United States)

    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.

  10. Mathematical Modeling of Cooperative E-Learning Performance in Face to Face Tutoring (Ant Colony System Approach

    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.

  11. Ant Colony Optimization Combined With Immunosuppression and Parameters Switching Strategy for Solving Path Planning Problem of Landfill Inspection Robots

    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.

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

    Science.gov (United States)

    Prasanth, Rajanampalli Seshasai Srinivasa; Hans Raj, Kandikonda

    2016-06-01

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

  13. Simultaneous feature selection and parameter optimisation using an artificial ant colony: case study of melting point prediction

    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.

  14. Ant colonies optimization of an energy generation site; Optimisation par colonies de fourmis d'un site de generation d'energie

    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)

  15. Colony insularity through queen control on worker social motivation in ants.

    Science.gov (United States)

    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.

  16. 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.

  17. Specialization and group size: brain and behavioural correlates of colony size in ants lacking morphological castes

    OpenAIRE

    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...

  18. 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.

  19. Co-evolutionary design of discrete structures based on the ant colony optimization

    Institute of Scientific and Technical Information of China (English)

    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.

  20. Scheduling Multi-Mode Projects under Uncertainty to Optimize Cash Flows: A Monte Carlo Ant Colony System Approach

    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.

  1. An Improved Ant Algorithm for Grid Task Scheduling Strategy

    Science.gov (United States)

    Wei, Laizhi; Zhang, Xiaobin; Li, Yun; Li, Yujie

    Task scheduling is an important factor that directly influences the performance and efficiency of the system. Grid resources are usually distributed in different geographic locations, belonging to different organizations and resources' properties are vastly different, in order to complete efficiently, intelligently task scheduling, the choice of scheduling strategy is essential. This paper proposes an improved ant algorithm for grid task scheduling strategy, by introducing a new type pheromone and a new node redistribution selection rule. On the one hand, the algorithm can track performances of resources and tag it. On the other hand, add algorithm to deal with task scheduling unsuccessful situations that improve the algorithm's robustness and the successful probability of task allocation and reduce unnecessary overhead of system, shortening the total time to complete tasks. The data obtained from simulation experiment shows that use this algorithm to resolve schedule problem better than traditional ant algorithm.

  2. 基于改进蚁群算法的一类运输能力约束的生产-运输批量问题求解%An Improved Ant Colony Algorithm for Solving Production Transportation Lot-Sizing Problem

    Institute of Scientific and Technical Information of China (English)

    李英俊; 陈志祥

    2012-01-01

    针对生产与运输两个过程的联合决策,通过分析一类生产-运输批量优化问题,建立的混合0-1整数规划模型整合了多产品多阶段能力约束批量生产和产品运输.其中运输成本由运输工具使用数量决定,当企业内部运输能力不能满足运输需求时可将运输外包,但需支付更高的运输成本.根据此问题的特点,构造改进蚁群算法求解,令其信息素和启发信息都存在0和1两种状态下的不同取值,通过转移概率确定0-1生产准备矩阵,进一步得到生产矩阵和运输计划.仿真实验结果表明在生产批量决策的同时考虑运输,可以减少运输成本,令总费用最小,通过将实验结果与其他优化算法比较,所构造的蚁群算法寻优概率是100%,平均进化10代,平均耗时小于l s,稳定性和求解效率均高于其他算法,是求解这类问题一种有效与适用的算法.%Aiming at the implementation of joint decision of production and transportation, production-transportation lot-sizing problem is discussed, which is a multi-item-and-multi-period capacitated lot-sizing and transportation problem. This problem is then formulated as a 0-1 mixed integer programming problem. In this model, the transportation cost is decided by the numbers of containers. However, if demands ex-ceed the transportation capacity, it can be outsourced, but with higher freight rate. After analyzing the properties of the model, an improved ant colony algorithm (ANT) is proposed. By this algorithm, different value of pheromone and heuristic information is set as 0-state or 1 -state. Then, the 0-1 setup matrix, production matrix, and transportation plan can be obtained accordingly. A numerical example shows that integrated production and transportation can effectively reduce the procurement cost and further reduce the total cost. Comparison with other methods shows that the searching optimization probability of the proposed ANT is 100% , the average

  3. Breeding system, colony structure, and genetic differentiation in the Camponotus festinatus species complex of carpenter ants.

    Science.gov (United States)

    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.

  4. Multipath Data Transmission with minimization of Congestion Using Ant Colony Optimization for MTSP and Total Queue Length

    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.

  5. 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

  6. Arboreal ant colonies as 'hot-points' of cryptic diversity for myrmecophiles: the weaver ant Camponotus sp. aff. textor and its interaction network with its associates.

    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

  7. 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.

  8. Optimization of Placing-in and Taking-out Wagons on Branch-shaped Railway Lines Based on Enhanced Ant Colony Algorithm%改进的蚁群算法在放射形专用线取送车优化中的应用

    Institute of Scientific and Technical Information of China (English)

    雷友诚; 吴志飞

    2012-01-01

    Delivery operation is an important part of enterprise railway freight station. Its efficiency is directly related to vehicle flow and the speed of goods delivery, which affecting the competitiveness, the production and management of enterprises. Reasonable arrangements to the order of the delivery vehicles are very significant in reducing vehicle running time and improving operation efficiency. This paper establishes a mathematical model of delivery vehicles of radial special - purpose lines and puts forward a solution of the ant colony algorithm on the basis of genetic algorithm pheromone update strategy. Satisfying results have been achieved through case simulation. Thus a better solution to delivery vehicles has been discovered.%取送车工作是货运站的一项重要工作,它的效率高低直接关系到车辆周转和货物送达的快慢,影响到企业的竞争力和生产经营.针对放射形专用线,建立了放射形专用线取送车数学模型,提出一种基于遗传算法信息素更新策略的改进蚁群算法进行求解.通过实例计算仿真,取得了满意的结果,得到了较优的取送车方案.该算法对于合理安排取送车顺序,压缩机车运行时间、提高机车运行效率具有重要意义.

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

    Directory of Open Access Journals (Sweden)

    Zhigang Lu

    2014-01-01

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

  10. Trust Ant Colony Self-Organization Routing Algorithm in Delay Tolerant Network%容迟网络中基于信任蚁群的自组织路由算法

    Institute of Scientific and Technical Information of China (English)

    胡海峰; 刘兴贵

    2014-01-01

    由于移动节点间的相遇机会的不确定性,容迟网络采用机会转发机制完成分组的转发.这一机制要求节点以自愿合作的方式来完成消息转发.然而,在现实中,绝大多数的节点表现出自私行为.针对节点的自私行为,提出了基于信任蚁群的自组织路由算法TrACO(Trust Ant Clone Optimization).该算法利用蚁群算法基于群空间的搜索能力和快速的自适应学习特性,能够适应容迟网络动态复杂多变的网络环境.最后对TrACO进行性能仿真分析,仿真结果表明TrACO能够在较低的消息冗余度和丢弃数下获得较高的分组转发率和较低的消息传输时延,表现出较强的挫败节点自私行为的能力.

  11. Energy Efficiency Performance Improvements for Ant-Based Routing Algorithm in Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Adamu Murtala Zungeru

    2013-01-01

    Full Text Available The main problem for event gathering in wireless sensor networks (WSNs is the restricted communication range for each node. Due to the restricted communication range and high network density, event forwarding in WSNs is very challenging and requires multihop data forwarding. Currently, the energy-efficient ant based routing (EEABR algorithm, based on the ant colony optimization (ACO metaheuristic, is one of the state-of-the-art energy-aware routing protocols. In this paper, we propose three improvements to the EEABR algorithm to further improve its energy efficiency. The improvements to the original EEABR are based on the following: (1 a new scheme to intelligently initialize the routing tables giving priority to neighboring nodes that simultaneously could be the destination, (2 intelligent update of routing tables in case of a node or link failure, and (3 reducing the flooding ability of ants for congestion control. The energy efficiency improvements are significant particularly for dynamic routing environments. Experimental results using the RMASE simulation environment show that the proposed method increases the energy efficiency by up to 9% and 64% in converge-cast and target-tracking scenarios, respectively, over the original EEABR without incurring a significant increase in complexity. The method is also compared and found to also outperform other swarm-based routing protocols such as sensor-driven and cost-aware ant routing (SC and Beesensor.

  12. A cross-layer design and ant-colony optimization based load-balancing routing protocol for ad-hoc networks

    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.

  13. 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

  14. Optimization of NIR Spectroscopy Based on Ant Colony Optimization and Genetic Algorithm for the Anthocyanin Content in Scented Tea%蚁群和遗传算法优化花茶花青素近红外光谱预测模型的比较

    Institute of Scientific and Technical Information of China (English)

    李艳肖; 黄晓玮; 邹小波; 赵杰文; 石吉勇; 张小磊

    2015-01-01

    Optimization of Near infrared (NIR) spectroscopy for quantitative analysis of the anthocyanin content in scented tea was discussed by selecting the optimal spectra intervals from the whole NIR spectroscopy using two variable models: Ant colony optimization interval partial least squares (ACO-iPLS) and Genetic Algorithm interval partial least squares (GA-iPLS). The ACO-iPLS full-spectrum was split into 12 intervals. The optimal intervals selected were the 1st interval, 9th interval and 10th interval. The calibration and prediction correlation coefficient of ACO-iPLS model were 0.901 3 and 0.864 2, in which the root mean square error of cross validation (RMSECV) of 0.160 0 mg/g and the root mean square error of prediction (RMSEP) of 0.206 0 mg/g were achieved.As in the GA-iPLS model, the data set was split into 15 intervals for optimization where 1st and 5th intervals were selected. The calibration and prediction correlation coefficient of GA-iPLS model were 0.901 3 and 0.864 2, and the RMSECV and RMSEP of GA-iPLS models based on these intervals were 0.156 0 mg/g and 0.206 0 mg/g, respectively. The results showed that both ACO-iPLS and GA-iPLS models could efficiently select spectrum intervals for quantitative analysis of anthocyanin in scented tea. The optimal GA-iPLS model had better performance with higher accuracy.%以建立花茶花青素含量的最优近红外光谱模型为目标,对比研究了蚁群算法(Ant Colony Optimization, ACO)和遗传算法(Genetic Algorithm, GA)优化近红外光谱谱区的效果。ACO-iPLS将全光谱划分为12个子区间时,优选出第1、9、10共3个子区间,所建的校正集和预测集相关系数分别为0.9013和0.8642;交互验证均方根误差(RMSECV)和预测均方根误差(RMSEP)分别为0.1600 mg/g和0.2020 mg/g;GA-iPLS将全光谱划分为15个子区间时,优选出第1、5共2个子区间,所建模型的校正集和预测集相关系数分别为0.9063和0.8793,交互验证均

  15. Research on Hand Gesture Recognition of Remote Control Picking Robot Based on Potential Field and Ant Colony Algorithm%远程控制采摘机器人手势识别研究-基于势场蚁群算法

    Institute of Scientific and Technical Information of China (English)

    袁路路; 张娓娓

    2017-01-01

    When the robot is unable to cross the obstacle in the operation , or when the dangerous area can not be manu-ally picked up , it needs to use the remote method for real-time control , so that it can successfully cross the obstacles , and in the high risk environment to carry out the picking operation effectively .In order to optimize the robot remote con-trol system , a remote control scheme based on gesture recognition is proposed , and the potential field ant colony algorithm is introduced to improve the accuracy and efficiency of the robot .In the remote control scheme , the gesture recognition combined with remote control robot arm based on the depth camera gesture image and extract the gesture features into ma -nipulator servo control commands , and through the wireless network to the picking robot control unit , to realize the re-mote control of the robot visual gestures .Finally , the robot is tested .The recognition system based on ant colony algo-rithm can effectively track the different dynamic gestures , and can accurately identify the meaning of gestures .This meth-od can not only realize the function of obstacle avoidance , but also can be used in the area of the valley , the swamp and so on.%采摘机器人在作业时遇到通过自主导航无法越过的障碍物时,或者在危险的地带无法进行人工采摘作业时,需要借助远程方式进行实时控制,使其成功越过障碍物,并在高危环境中有效地展开采摘作业。为了优化采摘机器人远程控制系统,提出了一种基于手势识别的远程控制方案,并引入了势场蚁群算法,提高了机器人的控制的准确性和高效性。在远程控制方案中,将基于视觉的手势识别与远程控制机械手相结合,通过深度相机采集手势图像并提取手势特征,转换为机械手舵机的控制命令,并通过无线网络发送至采摘机器人控制单元,实现视觉手势对机器人的远程控制。对采摘

  16. Automatic Plating of Single-line Diagrams for Power Transmission Network Online Theoretical Line Loss Analysis Based on Ant Colony Algorithm%基于蚁群的在线理论线损分析用输电网单线图自动布局

    Institute of Scientific and Technical Information of China (English)

    卢志刚; 李学平

    2011-01-01

    输电网在线理论线损分析有时需要根据公共信息模型自动生成电网单线图,此时必须实现电网布局的自动生成;对于大电网,需要较短的求解时间。自动布局一般存在容易陷入局部最优解和求解时间长2种问题。文中将输电网单线图布局转化为二次分配问题,并且采用蚁群算法和3-opt优化,解决了以上问题。考虑可能的并行计算扩展,算法忽略各蚂蚁间的信息素更新,选择局部最优解和全局最优解更新信息素。仿真结果布局清晰,求解时间短,能够满足输电网在线理论线损分析要求。%To meet occasional needs for automatic generation of power network single-line diagrams from the common information model(CIM) in power transmission network online theoretical line loss analysis,it is necessary for the big power network to automatically generate its network layout and ask for rather short solving-time.In view of the problems with automatic plating,namely,local optimum and the long solving time,the plating of single-line diagrams for power transmission networks is transformed into a quadratic assignment problem using the ant colony algorithm with 3-opt optimization.By taking into consideration potential parallel computation,local optimum and global optimum are chosen to update the pheromone while ignoring pheromone updating between ants.Simulation results show that the requirement of the power transmission network online theoretical line loss analysis is met by clear layout and short solving time. This work is supported by National Natural Science Foundation of China(No.61071201) and Natural Science Foundation of Hebei Province(No.F2010001319).

  17. Scheduling Problem of Aircraft Refueling Vehicle Based on Ant Colony Algorithm%基于蚁群算法的飞机加油车辆调度问题研究

    Institute of Scientific and Technical Information of China (English)

    刘长有; 王一飞

    2014-01-01

    Aircraft refueling is an important part of ground security operations. Reasonable scheduling of aircraft refueling vehicle is the key factor for aircraft to complete the refueling operations on time and quantity, so the research of aircraft refueling vehicle scheduling is very meaningful. This paper firstly introduces the types, functions, driving process and considerations of aircraft refueling vehicle, then specifically describes the model of aircraft refueling vehicle scheduling according to the basic model of vehicle scheduling, finally carries on simulation calculation for the driving situations of airport tank refueling vehicle within three hours in an airport and provides a viable optimization algorithms for aircraft refueling vehicle operation.%飞机加油是停机坪地面保障作业的重要环节,能否对飞机按时按量的完成加油作业的关键因素在于对飞机加油车辆的合理调度,因此对飞机加油车辆调度的研究是十分有意义的。本文首先对飞机加油车辆的类型、功能、行驶过程及其注意事项做了简单的介绍,然后根据车辆调度的基本模型对飞机加油车辆调度的模型进行了比较具体的描述,文章最后用蚁群算法对某机场3个小时内飞机油罐加油车辆行驶情况进行仿真计算,希望通过对该模型的仿真优化计算,为机场加油车辆运行提供一种可行优化算法。

  18. Toxicity Profiles and Colony Effects of Liquid Baits on Tawny Crazy Ants (plus an update on their U.S. distribution)

    Science.gov (United States)

    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...

  19. Role of relative humidity in colony founding and queen survivorship in two carpenter ant species.

    Science.gov (United States)

    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.

  20. 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.

  1. 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)

  2. 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.

  3. Satellite Constellation Design with Adaptively Continuous Ant System Algorithm

    Institute of Scientific and Technical Information of China (English)

    He Quan; Han Chao

    2007-01-01

    The ant system algorithm (ASA) has proved to be a novel meta-heuristic algorithm to solve many multivariable problems. In this paper, the earth coverage of satellite constellation is analyzed and a (n + 1)-fold coverage rate is put forward to evaluate the coverage performance of a satellite constellation. An optimization model of constellation parameters is established on the basis of the coverage performance. As a newly developed method, ASA can be applied to optimize the constellation parameters. In order to improve the ASA,a rule for adaptive number of ants is proposed, by which the search range is obviously enlarged and the convergence speed increased.Simulation results have shown that the ASA is more quick and efficient than other methods.

  4. 基于附加虚拟阻抗和蚁群优化算法的动态等效模型在线修正方法%An Online Adjustment Method of Dynamic Equivalent Model Based on Additional Fictitious Impedances and Ant Colony Optimization Algorithm

    Institute of Scientific and Technical Information of China (English)

    周海强; 鞠平; 宋忠鹏; 金宇清; 孙国强

    2011-01-01

    A novel method of online adjustment of dynamic equivalent model was proposed in this paper. Firstly, it was pointed out that the unreasonable aggregation algorithm and constant hypothesis of the time-varying system were main sources of equivalent error. Then, online adjustment was put forward to overcome the error caused by the time-varying characteristics of the system. Parameters in the equivalent model were too many to adjust directly. And the additional fictitious impedances were introduced to overcome this difficulty. These impedances were connected to equivalent generator and equivalent motor buses. Injecting power match at boundary nodes has been achieved by adjustment of fictitious impedances with ant colony optimization (ACO) algorithm. Online adjustment can be further realized when the dynamic equivalent model is modified timely according to real time information provided by the wide area measurement system (WAMS). Finally, simulation results of the IEEE 10-generator and 39-bus test system showed that both the static and transient precisions can be enhanced largely with this method, and the robustness of the equivalent model can also be improved.%提出基于虚拟阻抗的动态等效模型在线修正方法。首先,指出等值过程中不合理的聚类算法、对时变系统的定常化假设是误差主要来源。接着,提出通过等效模型的在线修正以克服系统时变性所导致的误差。由于等效模型可调参数过多,难以对所有参数进行调整。为此,在等效发电机、等效电动机节点引入附加虚拟阻抗,应用蚁群优化算法进行调节,以实现边界点最佳功率匹配,并利用广域测量系统(wide area measurement system,WAMS)提供的实测数据对动态等效模型进行定时修正。最后,IEEE 10机39母线系统的等值计算结果表明:算法较好地改进了动态等效模型的静态精度与暂态精度,提高了模型的强壮性。

  5. 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.

  6. Multi-Sensor Data Fusion Using a Relevance Vector Machine Based on an Ant Colony for Gearbox Fault Detection

    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.

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

    Science.gov (United States)

    Chen, Wei

    2015-07-01

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

  8. PENGEMBANGAN MODEL PEMILIHAN RUTE JALAN RAYA BERDASARKAN PERILAKU PENGGUNA MENGGUNAKAN ANT-COLONY OPTIMIZATION (ACO

    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.

  9. Design of FIR Filters with Discrete Coefficients using Ant Colony Optimization

    Science.gov (United States)

    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.

  10. 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.

  11. 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)

  12. Global optimal path planning for mobile robot based on improved Dijkstra algorithm and ant system algorithm

    Institute of Scientific and Technical Information of China (English)

    2006-01-01

    A novel method of global optimal path planning for mobile robot was proposed based on the improved Dijkstra algorithm and ant system algorithm. This method includes three steps: the first step is adopting the MAKLINK graph theory to establish the free space model of the mobile robot, the second step is adopting the improved Dijkstra algorithm to find out a sub-optimal collision-free path, and the third step is using the ant system algorithm to adjust and optimize the location of the sub-optimal path so as to generate the global optimal path for the mobile robot. The computer simulation experiment was carried out and the results show that this method is correct and effective. The comparison of the results confirms that the proposed method is better than the hybrid genetic algorithm in the global optimal path planning.

  13. Ant Colonyhybrid Algorithm Research for PTN Network Managemnet%用于PTN网管的蚁群混合算法改进

    Institute of Scientific and Technical Information of China (English)

    李庆; 师敏; 杨永康

    2013-01-01

    针对目前PTN网管多约束条件下的路由计算中常用的蚁群混合算法存在的复杂度高、杂乱搜索、局部最优等问题,提出了基于蚁群算法的改进算法.该算法借鉴A*算法的思想克服了蚁群混合算法中杂乱搜索的缺陷,同时采用全局更新机制避免了混合算法中局部最优问题的出现.并采用改进后的美国Salama博士的Waxman随机网络拓扑生成器进行实验仿真,验证了改进后的算法的有效性.%In order to resolve problems such as high complexity,messy search,local optimum etc which exist in ant colony hybrid algorithm in PTN network management system with multiple constraints,this paper presents improved algorithm based on ant colony algorithm.The algorithm draws on the thinking of A * algorithm to overcome the messy search defect while using the global update mechanism to avoid local optimum problem in the ant colony hybrid algorithm.Finally,this paper uses improved Dr Salama Waxman random network topology generator to verify the validity of the improved algorithm.

  14. Bus Stops Location and Bus Route Planning Using Mean Shift Clustering and Ant Colony in West Jakarta

    Science.gov (United States)

    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.

  15. An efficient technique to solve combined economic and emission dispatch problem using modified Ant colony optimization

    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.

  16. Facilitating the 3D Indoor Search and Rescue Problem: An Overview of the Problem and an Ant Colony Solution Approach

    Science.gov (United States)

    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.

  17. FACILITATING THE 3D INDOOR SEARCH AND RESCUE PROBLEM: AN OVERVIEW OF THE PROBLEM AND AN ANT COLONY SOLUTION APPROACH

    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.

  18. 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.

  19. Using ant colony optimization on the quadratic assignment problem to achieve low energy cost in geo-distributed data centers

    Science.gov (United States)

    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

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

    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.

  1. Identifying robustness in the regulation of collective foraging of ant colonies using an interaction-based model with backward bifurcation.

    Science.gov (United States)

    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.

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

    Science.gov (United States)

    Chen, Wei

    2014-01-01

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

  3. 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。

  4. 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.

  5. 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...

  6. Graph Theory and ANT Colony Optimization Approach for Forest Patch Connectivity Analysis

    Science.gov (United States)

    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.

  7. MATLAB Simulation of UPQC for Power Quality Mitigation Using an Ant Colony Based Fuzzy Control Technique

    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.

  8. A modified ant colony optimization to solve multi products inventory routing problem

    Science.gov (United States)

    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.

  9. MATLAB Simulation of UPQC for Power Quality Mitigation Using an Ant Colony Based Fuzzy Control Technique.

    Science.gov (United States)

    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.

  10. MATLAB Simulation of UPQC for Power Quality Mitigation Using an Ant Colony Based Fuzzy Control Technique

    Science.gov (United States)

    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

  11. 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.

  12. QoS Multicast Routing Based on Ant Algorithm in Internet

    Institute of Scientific and Technical Information of China (English)

    2000-01-01

    In this paper, based on ant-algorithm, we propose a QoS multicast routing scheme in Internet. We first describe ant-algorithm model and give ant-network model, then present an approach using ant-algorithm to optimize the multicast routes with QoS constaints. Finally, simulations has been made to show the efficiency of the approach in the environment of OPNET simulation software, and the simulation results show that the proposed approach can find the best optimal multicast routes which can satisfy the delay-bounded requirement and avoid congested nodes as soon as possible.

  13. Intelligent Routing using Ant Algorithms for Wireless Ad Hoc Networks

    Directory of Open Access Journals (Sweden)

    S. Menaka

    2013-08-01

    Full Text Available Wireless network is one of the niche areas and has been a growing interest owing to their ability to control the physical environment even from remote locations. Intelligent routing, bandwidth allocation and power control techniques are the known critical factors for this network communication. It is customary to find a feasible path between the communication end point which is a challenging task in this type of network. The present study proposes an Ant Mobility Model (AMM, an on-demand, multi-path routing algorithm that exercises power control and coordinate the nodes to communicate with one another in wireless network. The main goal of this protocol is to reduce the overhead, congestion, and stagnation, while increasing the throughput of the network. It can be realized from the simulation results that AMM proves to be a promising solution for the mobility pattern in wireless networks like MANETs.

  14. Ant colony method to control variance reduction techniques in the Monte Carlo simulation of clinical electron linear accelerators

    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.

  15. A Multiple-Dimensional Tree Routing Protocol for Multisink Wireless Sensor Networks Based on Ant Colony Optimization

    OpenAIRE

    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...

  16. 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。

  17. 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.

  18. Ant Colony Optimization detects anomalous aerosol variations associated with the Chile earthquake of 27 February 2010

    Science.gov (United States)

    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.

  19. A MODIFIED ANT-BASED TEXT CLUSTERING ALGORITHM WITH SEMANTIC SIMILARITY MEASURE

    Institute of Scientific and Technical Information of China (English)

    Haoxiang XIA; Shuguang WANG; Taketoshi YOSHIDA

    2006-01-01

    Ant-based text clustering is a promising technique that has attracted great research attention. This paper attempts to improve the standard ant-based text-clustering algorithm in two dimensions. On one hand, the ontology-based semantic similarity measure is used in conjunction with the traditional vector-space-model-based measure to provide more accurate assessment of the similarity between documents. On the other, the ant behavior model is modified to pursue better algorithmic performance.Especially, the ant movement rule is adjusted so as to direct a laden ant toward a dense area of the same type of items as the ant's carrying item, and to direct an unladen ant toward an area that contains an item dissimilar with the surrounding items within its Moore neighborhood. Using WordNet as the base ontology for assessing the semantic similarity between documents, the proposed algorithm is tested with a sample set of documents excerpted from the Reuters-21578 corpus and the experiment results partly indicate that the proposed algorithm perform better than the standard ant-based text-clustering algorithm and the k-means algorithm.

  20. 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。

  1. Ant colony optimization as a descriptor selection in QSPR modeling: Estimation of the λmax of anthraquinones-based dyes

    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.

  2. Multi-objective Numeric Association Rules Mining via Ant Colony Optimization for Continuous Domains without Specifying Minimum Support and Minimum Confidence

    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.

  3. The effect of symbiotic ant colonies on plant growth: a test using an Azteca-Cecropia system.

    Science.gov (United States)

    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.

  4. 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.

  5. The Effect of Symbiotic Ant Colonies on Plant Growth: A Test Using an Azteca-Cecropia System

    Science.gov (United States)

    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

  6. Cloud Package Selection for Academic Requirements using Multi Criteria Decision Making based Modified Ant Colony Optimization Technique

    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.

  7. Performance evaluation of ant colony optimization-based solution strategies on the mixed-model assembly line balancing problem

    Science.gov (United States)

    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.

  8. Lévy flight artificial bee colony algorithm

    Science.gov (United States)

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

    2016-08-01

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

  9. Optimising social information by game theory and ant colony method to enhance routing protocol in opportunistic networks

    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.

  10. A Model-Based Approach to Predicting Predator-Prey & Friend-Foe Relationships in Ant Colonies

    CERN Document Server

    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.

  11. High recombination frequency creates genotypic diversity in colonies of the leaf-cutting ant Acromyrmex echinatior

    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...

  12. Strict monandry in the ponerine army ant genus Simopelta suggests that colony size and complexity drive mating system evolution in social insects

    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...

  13. Escape-Route Planning of Underground Coal Mine Based on Improved Ant Algorithm

    Directory of Open Access Journals (Sweden)

    Guangwei Yan

    2013-01-01

    Full Text Available When a mine disaster occurs, to lessen disaster losses and improve survival chances of the trapped miners, good escape routes need to be found and used. Based on the improved ant algorithm, we proposed a new escape-route planning method of underground mines. At first, six factors which influence escape difficulty are evaluated and a weight calculation model is built to form a weighted graph of the underground tunnels. Then an improved ant algorithm is designed and used to find good escape routes. We proposed a tunnel network zoning method to improve the searching efficiency of the ant algorithm. We use max-min ant system method to optimize the meeting strategy of ants and improve the performance of the ant algorithm. In addition, when a small part of the mine tunnel network changes, the system may fix the optimal routes and avoid starting a new processing procedure. Experiments show that the proposed method can find good escape routes efficiently and can be used in the escape-route planning of large and medium underground coal mines.

  14. 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.

  15. 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 perf