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基于蚁群和粒子群优化的混合算法求解TSP问题 被引量：18

Solving Traveling Salesman Problems by an ACO-and-PSO-Based Hybrid Algorithm
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摘要 提出了一种基于蚁群优化和粒子群优化的混合算法求解TSP(Traveling Salesm an Prob lem)问题。在应用蚁群算法对TSP问题的求解过程中,利用粒子群算法对蚁群系统的参数进行优化,其目的是提高蚁群系统的优化性能,使蚁群系统的参数不必靠人工经验或反复试验选取,而是通过粒子搜索自适应选取。 A hybrid algorithm is presented to solve traveling salesman problems based on ACO （Ant Colony Optimization） and PSO （Particle Swarm Optimization）. In the proposed algorithm, the PSO is used to optimize the parameters in the ant colony system to improve the performance of the ACO, which makes the parameter selection for the ACO do not depend on artificial experience or repeating trials, but Ely on the self-adaptive search of the particles in the PSO.
作者 闵克学 葛宏伟 张毅 梁艳春
机构地区 通化师范学院教务处 吉林大学计算机科学与技术学院
出处 《吉林大学学报（信息科学版）》 CAS 2006年第4期402-405,共4页 Journal of Jilin University（Information Science Edition）
关键词 蚁群优化 粒子群优化 混合算法 TSP问题 ant colony optimization （ACO） particle swarm optimization （PSO） hybrid algorithm traveling salesman problem
分类号 TP18 [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10

  • 1DORIGO M, VITTORIO M, ALBERTO C. The Ant System: Optimization by a Colony of Cooperating Agents [J]. IEEE Transactions on Systems, Man, and Cybernetics, Part B, 1996, 26 ( 1 ) : 1-13.
  • 2DORIGO M, GAMBARDEUA L M. Ant Colony System: A Cooperative Learning Approach to the Traveling Salesman Problem [J]. IEEE Transactions on Evolutionary Computation, 1997, 1 (1): 53-66.
  • 3GOMEZ O, BANAN B. Reasons of ACO's Success in TSP [J]. Lecture Notes in Computer Science, 2004, 3172: 226-237.
  • 4SEUNG G L, TAE U J. An Effective Dynamic Weighted Rule for Ant Colony System Optimization [ C] //Proceedings of the IEEE Conference on Evolutionary Computation. Seoul: IEEE Press, 2001, 2: 1393-1397.
  • 5MERKLE D, MIDDENDORF M, SCHMECK H. Ant Colony Optimization for Resource-Constrained Project Scheduling [J ]. IEEE Transactions on Evolutionary Computation, 2002, 6 (4) : 333-346.
  • 6LEVINE J, DUCATELLE F. Ant Colony Optimization and Local Dearch for Bin Packing and Cutting Stock Problems [ J ] . Journal of the Operational Research Society, 2004, 55 (7) : 705-716.
  • 7LUO S, WANG C, WANG J. Ant Colony Optimization for Resource-Constrained Project Scheduling with Generalized Precedence Relations [ C ] // of the International Conference on Tools with Artificial Intelligence. Sacramento, CA, United States: IEEE Press, 2003: 284-289.
  • 8KENNEDY J, EBERHART R. Particle Swarm Optimization [ C ] //Proceedings of the IEEE International Conference on Neural Networks. [S. l. ] : IEEE Press, 1995 (4) : 1942-1948.
  • 9PRNH Y, PRNH X. Adaptive Parameter Calibration with Particle Swarm Optimization for Virtual Instrument [ C ] // Proceedings of the International Symposium on Test and Measurement. Shenzhen, China: International Academic Publishers Press, 2003, 6: 4687-4690.
  • 10THOMPSON B B, MARKS I I, ROBERT J, et al. Inversion of Neural Network Underwater Acoustic Model for Estimation of Bottom Parameters Using Modified Particle Swarm Optimizers [ C] //Proceedings of the International Joint Conference on Neural Networks. Portland, OR, United States: IEEE Press, 2003, 2: 1301-1306.

同被引文献226

引证文献18

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吉林大学学报（信息科学版）
2006年 第4期

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