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蚂蚁算法的基本原理及其研究发展现状

乐群星, 魏法杰

乐群星, 魏法杰. 蚂蚁算法的基本原理及其研究发展现状[J]. 北京航空航天大学学报社会科学版, 2005, 18(4): 5-8.
引用本文: 乐群星, 魏法杰. 蚂蚁算法的基本原理及其研究发展现状[J]. 北京航空航天大学学报社会科学版, 2005, 18(4): 5-8.
shu
YUE Qun-xing, WEI Fa-jie. New Stochastic Optimization Algorithm——Ant System[J]. Journal of Beijing University of Aeronautics and Astronautics Social Sciences Edition, 2005, 18(4): 5-8.
Citation: YUE Qun-xing, WEI Fa-jie. New Stochastic Optimization Algorithm——Ant System[J]. Journal of Beijing University of Aeronautics and Astronautics Social Sciences Edition, 2005, 18(4): 5-8.
shu

蚂蚁算法的基本原理及其研究发展现状

  • 北京航空航天大学 经济管理学院,北京 100083

详细信息

  • 中图分类号: TP18

New Stochastic Optimization Algorithm——Ant System

  • School of Economics and Management, Beijing University of Aeronautic s and Astronautics, Beijing 100083, China

摘要

    摘要
  • 摘要: 蚂蚁算法是一种新的随机优化算法，它利用人工蚂蚁在其途经路上释放信息素寻优，体现了正反馈、分布式、多agent协同性和并行性等特点。文章详述了蚂蚁算法原理、蚂蚁算法的原型——TSP问题的蚂蚁算法以及蚂蚁算法在应用和理论方面的研究进展，明确指出了其极为广泛的应用前景。
    Abstract: Ant system (AS) is a new Stochastic optimization algorithm achieved by artificialants' releasing pheromone on the path,characterized with a positive feedback,distributed computing, multiagent synergy and parallel algorithm. This paper introduces the theory of AS, AS for TSP, its applications and its recent theoretical development. 
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    • 参考文献(12)
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    [2] Dorigo M, Maniezzo V, Colorni A. Ant System: Optimization by a Colony of Cooperating Agents[J]. IEEE Transactions on Systems, Man and Cybernetics, part B, 1996, 26(1): 29-41.
    [3] Gambardella L M, Taillard E D, Dorigo M. Ant Colonies for the Quadratic A ssignment Problem[J]. Journal of the Operational Research Society, 1999, 50(2) : 167-176.
    [4] Bullnheimer B, Hartl R F, Strauss C. Applying the Ant System to the Vehic le Routing Problem . Voss S, Martello S, Osman I H, Roucairol C. Meta-heuri stics: Advances and Trends in Local Search Paradigms for Optimization . Bost on: Kluwer, 1998. 109-120.
    [5] Costa D, Hertz A. Ants can Color Graphs[J]. Journal of the Operational Research Society, 1997, 48: 295-305.
    [6] Parmee I C, Vekeria H, Bilchev G. Role of Evolutionary and Adaptive Searc h during Whole System, Constrained and Detailed Design Optimization[J]. Engine ering Optimization, 1997, 29(1-4): 151-176.
    [7] Wang Lei, Wu Qidi. Ant System Algorithm for Optimization in Continuous Sp ace . Proceedings of the 2001 IEEE International Conference on Control Appli cations . New York: IEEE Press, 2001. 395-400.
    [8] Chen Ling, Sheng Jie, Qin Ling, Chen Hongjian. A Method for Solving Optim ization Problems in Continuous Space Using Ant Colony Algorithm . Ant System : 3rd International Workshop . Berlin: Springer Verlag, 2002. 288-289.
    [9] Gambardella L M, Dorigo M. Ant-Q: A Reinforcement Learning Approach to t he Traveling Salesman Problem . Proceedings of the Twelfth International Con ference on Machine Learning . Palo Alto, CA: Morgan Kauffman, 1995. 252-260 .
    [10] Stützle T, Hoos H. MAX-MIN Ant System and Local Search for the Traveling Salesman Problem . Proceedings of 1997 IEEE International Con ference on Evolutionary Computation . New York: IEEE Press, 1997. 309-314.
    [11] Bullnheimer B, Hartl R F, Strauss C. A New Rank-based Version of the An t System: A Computational Study[J]. Central European Journal for Operations Re search and Economics, 1999, 7(1): 25-38.
    [12] Middendorf, Martin. Multi Colony Ant Algorithms[J]. Journal of Heurist ics, 2002, (5): 305-320.
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出版历程
  • 收稿日期:  2003-12-08
  • 发布日期:  2005-12-24

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