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改进型蚁群算法及其在TSP中的应用 被引量：6

An improved model of ant colony algorithm and its application in TSP
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摘要 介绍了蚁群算法的基本原理,并对其优、缺点作了详细的分析.基于蚁群算法的缺点--需要较长的计算时间,收敛速度慢,提出了一种改进型的蚁群算法,可以有效提高收敛速度,并把该算法应用到TSP问题中,取得了很好的效果. The elemental principles of ant colony algorithm is introduced in this paper, with a detailed analysis of its advantages and disadvantages. And an improved model of ant colony algorithm is proposed, based on the disadvantages of the ant colony algorithm that needs much more time when being used and whose speed of convergence is slower. But the new method can improve the speed of convergence efficiently.
作者 田富鹏
机构地区 西北民族大学现代教育技术学院
出处 《兰州大学学报（自然科学版）》 CAS CSCD 北大核心 2005年第2期78-80,共3页 Journal of Lanzhou University(Natural Sciences)
关键词 蚁群算法 TSP 信息素 ant colony algorithm TSP pheromone
分类号 TP181 [自动化与计算机技术—控制理论与控制工程]
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参考文献5

  • 1段海滨,王道波.一种快速全局优化的改进蚁群算法及仿真[J].信息与控制,2004,33(2):241-244. 被引量：57
  • 2Dorigo M, Maniezzo V, Colorni A. The ant system:optimization by a colony of cooperating agents[J].IEEE Transactions on Systems, 1996, 26(1): 29-41.
  • 3Bentley J L. Fast algorithms for geometric traveling salesman problem[J]. ORSA Journalon Computing,1992, 4: 387-411.
  • 4Back T, Schwefel H P. An overview of evolutionary algorithms for parameter optimization[J]. Evolutionary Computation, 1993, 1(1): 1-23.
  • 5Dorigo M, Gambardella L M. Ant colony system: a cooperative learning approach to the traveling salesman problem[J].

二级参考文献17

  • 1Colorni A, Dorigo M, Maniezzo V, et al. Distributed optimization by ant colonies [ A]. Proceedings of ECAL91 ( European Conference on Artificial Life) [ C ]. Paris, France : 1991.134 - 142.
  • 2Dorigo M, Maniezzo V, Colomi A. The ant system:optimization by a colony of cooperating agents [ J]. IEEE Transactions on Systems, Man, and Cybernetics - Part B, 1996, 26( 1 ) : 29-41.
  • 3Verbeeck K, Nowe A. Colonies of learning automata [J]. IEEE Transactions on Systems, Man, and Cybernetics-Part B, 2002,32(6) : 772 -780.
  • 4Montgomery J, Randall M. Anti-pheromone as a tool for better exploration of search space [A]. Proceedings of Third International Workshop ANTS [C]. Brussels, Belgium:2002. 100 - 110.
  • 5Bonabeau E, Dorigo M, Theraulaz G. Inspiration for optimization from social insect behaviour [J]. Nature, 2000, 406(6) :39-42.
  • 6Dorigo M, Gambardella L M. Solving symmetric and asym-metric TSPs by ant colonies [ A ]. Proceedings of the IEEE Conference on Evolutionary Computation [ C ]. Nagoya, Japan : 1996. 622-627.
  • 7Thomas S, Holger H H. MAX - MIN ant system [J]. Future Generation Computer Systems, 2000,16 ( 8 ) : 889 - 914.
  • 8Bonabeau E, Dorigo M, Theraulaz G. Inspiration for optimization from social insect behaviour [J]. Nature, 2000, 406(6) :39 -42.
  • 9Dorigo M, Gambardella L M. Solving symmetric and asym-metric TSPs by ant colonies [ A]. Proceedings of the IEEE Conference on Evolutionary Computation [ C ]. Nagoya, Japan: 1996. 622 -627.
  • 10Thomas S, Holger H H. MAX- MIN ant system [ J]. Future Generation Computer Systems, 2000,16 (8): 889 - 914.

共引文献56

同被引文献56

引证文献6

二级引证文献17

兰州大学学报（自然科学版）
2005年 第2期

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