| 基于蚁群和粒子群优化的混合算法求解TSP问题 |
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| 引用本文： | 闵克学,葛宏,张毅,梁艳春. 基于蚁群和粒子群优化的混合算法求解TSP问题[J]. 吉林大学学报(信息科学版), 2006, 24(4): 402-405 |
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| 作者姓名： | 闵克学 葛宏 张毅 梁艳春 |
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| 作者单位： | 通化师范学院,教务处,吉林,通化,134002;吉林大学,计算机科学与技术学院,长春,130012 |
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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。
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| 关 键 词： | 蚁群优化 粒子群优化 混合算法 TSP问题 |
| 文章编号： | 1671-5896(2006)04-0402-04 |
| 修稿时间： | 2005-08-23 |
Solving Traveling Salesman Problems by an ACO-and-PSO-Based Hybrid Algorithm |
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| MIN Ke-xue,GE Hong-wei,ZHANG Yi,LIANG Yan-chun. Solving Traveling Salesman Problems by an ACO-and-PSO-Based Hybrid Algorithm[J]. Journal of Jilin University:Information Sci Ed, 2006, 24(4): 402-405 |
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| Authors: | MIN Ke-xue GE Hong-wei ZHANG Yi LIANG Yan-chun |
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| Abstract: | 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 rely on the self-adaptive search of the particles in the PSO. |
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| Keywords: | ant colony optimization(ACO) particle swarm optimization(PSO) hybrid algorithm traveling salesman problem |
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