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基于多信息素的蚁群算法 被引量：2

An Ant Colony Algorithm Based on Multi-Pheromones
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摘要 针对传统增强型蚁群算法容易出现早熟和停滞现象的缺陷,提出一种多信息素的蚁群算法(MPAS),并以TSPLIB的数据为例对该算法进行实验测试.MPAS算法将信息素分为局部和全局两种不同的信息素,在搜索过程中,对局部和全局信息素采用不同的更新策略和动态的路径选择概率,使得在搜索的中后期能更有效地发现全局最优解.在中大型问题上MPAS算法有着更好的发现最优解的能力. This paper improves an ant colony problem existed in classical augment ant algorithm based on multi-pheromones and solves the colony algorithm. The basic idea is to divide the pheromone into local pheromone and global pheromone. Then their pheromones are updated using different strategies during searching optimal path. Many experiments based on the data of TSPLIB show the advantages of this algorithm in sweeping problems.
作者 区云鹏 韦兆文 蒋慧超
机构地区 广西大学计算机与电子信息学院
出处 《广西科学院学报》 2008年第3期240-242,共3页 Journal of Guangxi Academy of Sciences
基金 广西自然科学基金项目(桂科自0640026)资助
关键词 蚁群算法 信息素 旅行商问题 ant colony algorithm, pheromones, traveling salesman problem
分类号 TP301.6 [自动化与计算机技术—计算机系统结构]
  • 相关文献

参考文献8

  • 1王晓东.计算机算法分析与设计[M].北京:电子工业出版社,2001:114-146.
  • 2Dorigo M,Maniezzo V,Colorni A. Positive feedback as a search strategy[R]. Technical Report, 1991 : 91-016.
  • 3Dorigo M,Gambardella L M. Ant colony system:a cooperative learning approach to the traveling salesman problem[J]. IEEE Trans on Evolutionary, 1997,1 (1) : 53-66.
  • 4陈宏建,陈崚,徐晓华,屠莉.改进的增强型蚁群算法[J].计算机工程,2005,31(2):176-178. 被引量：24
  • 5Stutzlet T, Hoos H. Improvements on the ant system: introducing MAX-MIN ant System:proceedings of the International Conference on Artificial Neural Networks and Genetic Algorithms [C]. Wien: Springer Verlag, 1997:245-249.
  • 6Cordon O,Fernandez I,Herrera F. A new ACO model integrating evolutionary computation concepts :the best- worst ant system:abstract proceedings of ANTS2000- From Ant Colonies to Artificial Ants: A Series of International Workshops on Ant Algorithms [C]. [S. l. ] :[ s. n. ],2000:22-29.
  • 7丁建立,陈增强,袁著祉.遗传算法与蚂蚁算法的融合[J].计算机研究与发展,2003,40(9):1351-1356. 被引量：287
  • 8Gerhard Reinelt. TSPLIB [EB/OL]. [2007-09-01]. http://www. iwr. uni-heidelberg. de/groups/eomopt/ software/TSPLIB95.

二级参考文献23

  • 1Marco Dorigo, Gambardella, Luca Maria. Ant colonies for the traveling salesman problem. Biosystems, 1997, 43(2): 73～81.
  • 2Marco Dorigo, Gambardelh, Luca Maria. Ant colony system: A cooperative learning approach to the traveling salesaum problem. IEEE Trans on Evolutionary Computation, 1997, 1(1) : 53～66.
  • 3Marco Dorigo, Eric Bonabeau, Theranlaz Guy. Ant algorithms and stigmergy. Future Generation Computer System, 2000, 16(8) : 851～871.
  • 4Thomas Stutzle, Holger H Hoos et al. MAX-MIN ant system. Future Generation Computer System, 2000, 16(8) : 889～914.
  • 5Marcus Randall, Andrew Lewis. A parallel implementation of ant colony optimization. Journal of Parallel and Distributed Computing, 2002, 62(9): 1421～1432.
  • 6Dorigo M, Maniezzo V,Colorni A.Ant System: Optimization by a Colony of Coorperating Agents. IEEE Transactions on SMC, 1996,26(1): 8-41
  • 7Dorigo M, Gambardella L M. Ant Colony System: a Cooperative Learning Approach to the Traveling Salesman Problem. IEEE Transactions on Evolutionary Computing, 1997,1 (1):53-56
  • 8Colorni A, Dorigo M, Maniezzo V. Ant Colony System for Job-shop Scheduling. Belgian Journal of Operations Research Statistics and Computer Science, 1994,34(1):39-53
  • 9Maniezzo V. Exact and Approximate Nonditerministic Tree Search Procedures for the Quadratic Assignment Problem. Informs Journal of Computer, 1999,11 (4):358-369
  • 10Maniezzo V, Carbonaro A. An ANTS Heuristic for the Frequency Assignment Problem. Future Generation Computer Systems, 2000, 16(8):927-935

共引文献310

同被引文献22

引证文献2

二级引证文献35

广西科学院学报
2008年 第3期

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