Improving Multi-layer Particle Swarm Optimization Using Powell Method

被引：0

作者：

Sun, Fengyang ^{[1
]}

Wang, Lin ^{[1
]}

Yang, Bo ^{[1
]}

Chen, Zhenxiang ^{[1
]}

Zhou, Jin ^{[1
]}

Tang, Kun ^{[1
]}

Wu, Jinyan ^{[1
]}

机构：

[1] Univ Jinan, Shandong Prov Key Lab Network Based Intelligent C, Jinan 250022, Shandong, Peoples R China

来源：

ADVANCES IN SWARM INTELLIGENCE, ICSI 2017, PT I | 2017年 / 10385卷

基金：

中国国家自然科学基金;

关键词：

Multi-layer particle swarm optimization; Powell; Particle Swarm Optimization; Tournament; DYNAMIC EQUATIONS; OSCILLATION;

D O I：

10.1007/978-3-319-61824-1_18

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In recent years, multi-layer particle swarm optimization (MLPSO) has been applied in various global optimization problems for its superior performance. However, fast convergence speed leads the algorithm easy to converge to the local minimum. Therefore, MLPSO-Powell algorithm is proposed in this paper, selecting several swarm particles by the tournament operator in each generation to run Powell algorithm. MLPSO global searching performance with Powell local searching performance forces swarm particles to search more optima as much as possible, then it will rapidly converge as soon as it gets close to the global optimum. MLPSO-Powell enhances local search ability of PSO in dealing with multi-modal problems. The experimental results shows that the proposed approach improves performance and final results.

引用

页码：166 / 173

页数：8

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