Solve traveling salesman problem using particle swarm optimization algorithm

被引：0
|
作者：
Yan, Xuesong [1 ]
Zhang, Can [1 ]
Luo, Wenjing [1 ]
Li, Wei [1 ]
Chen, Wei [1 ]
Liu, Hanmin [2 ]
机构：
[1] School of Computer Science, China University of Geosciences, Wuhan, Hubei 430074, China
[2] Wuhan Institute of Ship Building Technology, Wuhan, Hubei 430050, China
来源：
International Journal of Computer Science Issues | 2012年 / 9卷 / 6 6-2期
关键词：
Combinatorial optimization - Particle swarm optimization (PSO) - Genetic algorithms - Problem solving;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
The traveling salesman problem (TSP) is one of the most widely studied NP-hard combinatorial optimization problems and traditional genetic algorithm trapped into the local minimum easily for solving this problem. Particle Swarm Optimization (PSO) algorithm was developed under the inspiration of behavior laws of bird flocks, fish schools and human communities. Compared with the genetic algorithm, PSO algorithm has high convergence speed. In this paper, aim at the disadvantages of genetic algorithm like being trapped easily into a local optimum, we use the PSO algorithm to solve the TSP and the experiment results show the new algorithm is effective for the this problem. copy; 2012 International Journal of Computer Science Issues.
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页码：264 / 271
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