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《Computer Engineering and Design》 2007-02
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Self-learning particle swarm optimization algorithm for traveling salesman problem

CAI Rong-ying,LI Li-shan,LIN Xiao-yu,ZHONG Yi-wen(College of Computer and Information,Fujian Agriculture and Forestry University,Fuzhou 350002,China)  
A particle swarm optimization algorithm with self-learning operator is designed to tackle the traveling salesman problem.Based on the characteristics of the traveling salesman problem and the operations of discrete variables,particle's position,velocity and their operation rules are redefined.In order to restrain premature stagnation,a mutation velocity is designed to keep the diversity of particle swarm,and a self-learning operator is defined to improve the algorithm's intensification ability.Using those operators,the pro-posed algorithm can get good balance between exploration and exploitation.The simulation results comparing with typical algorithms from the literature show that it can produce good results.
【Fund】: 福建省自然科学基金项目(A0540006);; 福建省青年人才科技创新基金项目(2006F3013)
【CateGory Index】: TP301.6
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