A Reinforced Self-Escape Discrete Particle Swarm Optimization for TSP

被引：3

作者：

Li, Liaoliao ^{[1
]}

Zhu, Zhongkui ^{[2
]}

Wang, Wenfeng ^{[3
]}

机构：

[1] Neijiang Normal Univ, Dept Comp Sci, Neijiang 641112, Sichuan, Peoples R China

[2] Soochow Univ, Sch Mech & Elect Engn, Suzhou 215021, Peoples R China

[3] Inner Mongolia MengDian HuaNeng Thermal Power Cor, Xilin, Peoples R China

来源：

SECOND INTERNATIONAL CONFERENCE ON GENETIC AND EVOLUTIONARY COMPUTING: WGEC 2008, PROCEEDINGS | 2008年

关键词：

DPSO; TSP; 5-nearest neighbor method; 5-relative nearest neighbor method;

D O I：

10.1109/WGEC.2008.120

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

To deal with the problem of premature convergence and slow search speed of PSO, inspired by the classical 5-nearest neighbor method, a reinforced self-escape discrete particle swarm optimization algorithm (RSEDPSO) is proposed in this paper. The modified method of selecting candidate edges can enhance the performance of RSEDPSO to explore the global minimum thoroughly. The 5-relative nearest neighbor method introduced in this paper can produce candidate edges list more efficiently than the classical way, 5-nearest neighbor method. Experimental simulations indicate that RSEDPSO can not only significantly speed up the convergence, but also effectively solve the premature convergence problem.

引用

页码：467 / +

页数：2