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一种求解函数优化的混合蚁群算法 被引量：6

A Mixed Ant Colony Algorithm for Function Optimization
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摘要 将遗传算法与蚁群算法中的协同模型进行有机结合,在蚁群算法中引入交叉、变异、选择算子来改进基本蚁群算法,克服了蚁群算法不太适合求解连续空间优化问题的缺陷。通过测试函数表明该方法具有较好的收敛速度和稳定性,求解结果好于遗传算法。 The genetic algorithm is combined with the cooperated model of ant colony algorithm.The crossover, mutation and selection operator are propose d to improve the basic ant colony algorithm. The limitation that the algorithm doesn’t fit to solve continuous space optimization is overcome. The tested function shows that the method has the better convergence speed and the stability, the solution is better than genetic algorithm’s.
作者 熊伟清 陈烽 魏平
机构地区 宁波大学信息科学与工程学院
出处 《计算机应用研究》 CSCD 北大核心 2005年第7期51-53,共3页 Application Research of Computers
基金 国家自然科学基金资助项目(60272034)
关键词 模拟进化 蚁群算法 遗传算法 函数优化 Simulating Evolution Ant Colony Algorithm Genetic Algorithm Function Optimization
分类号 TP301.6 [自动化与计算机技术—计算机系统结构]
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参考文献6

  • 1Colorni A, Dorigo M, Maniezzo V. Distributed Optimization by Ant Colonies[C]. Elsevier: Proc. the 1st European Conf. Artificial Life,Pans, 1991. 134-142.
  • 2Dorigo M, Gambardella LM. Ant Colony Svstem A Cooperative Learning Approach to the Traveling Salesman Problem [J].IEEE Trans. on Evolutionary Computation, 1997,1(1):53-66.
  • 3Sutzle T, Hoos H. The Max-Min Ant System and Local Search for the Traveling Salesman Problem[C]. Proc. of ICEC'97, IEEE the 4th Int. Conf. on Evolutionary Computation, 1997.
  • 4ZbigniewMichalewicz.Genetic Algorithms + Data Structures = Evolution Programs[M].北京:科学出版社,2000..
  • 5熊伟清,余舜浩,赵杰煜.具有分工的蚁群算法及应用[J].模式识别与人工智能,2003,16(3):328-333. 被引量：14
  • 6陈崚,沈洁,秦玲.蚁群算法进行连续参数优化的新途径[J].系统工程理论与实践,2003,23(3):48-53. 被引量：37

二级参考文献20

  • 1[1]Dorigo M, Maniezzo V, Colorni A. Ant system: optimization by a colony of coorperating agents[J]. IEEE Trans on SMC, 1996, 26(1): 28-41.
  • 2[2]Dorigo M, Gambardella L M. Ant colony system: a cooperative learning approach to the traveling salesman problem[J]. IEEE Trans on Evolutionary Computing, 1997, 1(1):53-56.
  • 3[3]Colorni A, Dorigo M, Maniezzo V. Ant colony system for job-shop scheduling[J]. Belgian J of Operations Research Statistics and Computer Science, 1994, 34(1): 39-53.
  • 4[4]Maniezzo V. Exact and approximate nonditerministic tree search procedures for the quadratic assignment problem[J]. INFORMS J Comput, 1999, 11: 358-369.
  • 5[5]Maniezzo V, Carbonaro A. An ANTS heuristic for the frequency assignment problem[J]. Future Generation Computer Systems, 2000, 16:927-935.
  • 6[6]Gambardella L M, Dorigo M. HAS-SOP: An Hybrid Ant System for the Sequential Ordering Problem[R]. Tech Rep No IDSIA 97-11, IDSIA, Lugano, Switzerland, 1997.
  • 7[7]Gambardella, L M, Dorigo M. Ant-Q: A reinforcement learning approach to the traveling salesman problem[A]. Proceedings of the 11th International Conference on Evolutionary Computation[C]. IEEE Press, 1996, 616-621.
  • 8[8]Dorigo M, Luca M. A study of Ant-Q[A]. Proceedings of 4th International Conference on Parallel Problem from Nature[C]. Berlin: Springer Verlag, 1996. 656-665.
  • 9[9]Stutzle T, Hoos H H. Improvements on the Ant System: Introducting the MAX-MIN Ant System. Artificial Neural Networks and Genetic Algorithms[M]. New York: Springer Verlag, 1988. 245-249.
  • 10[10]Gambaradella L M, Dorigo M. HAS-SOP: Hybrid ant system for the sequential ordering problem[R]. Technical Report IDSIA, 1997.

共引文献49

同被引文献45

引证文献6

二级引证文献12

计算机应用研究
2005年 第7期

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