Dynamic Multi-Swarm Particle Swarm Optimizer with Local Search for Large Scale Global Optimization

被引：187

作者：

Zhao, S. Z. ^{[2
]}

Liang, J. J. ^{[2
]}

Suganthan, P. N. ^{[2
]}

Tasgetiren, M. F. ^{[1
]}

机构：

[1] Dept Operat Management & Business Stat, Muscat, Oman

[2] Nanyang Technol Univ, Sch Elect & Elect Engn, Singapore 639798, Singapore

来源：

2008 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION, VOLS 1-8 | 2008年

关键词：

D O I：

10.1109/CEC.2008.4631320

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper, the performance of dynamic multi-swarm particle swarm optimizer (DMS-PSO) on the set of benchmark functions provided for the CEC2008 Special Session on Large Scale optimization is reported. Different from the existing multi-swarm PSOs and local versions of PSO, the sub-swarms are dynamic and the sub-swarms' size is very small. The whole population is divided into a large number sub-swarms, these sub-swarms are regrouped frequently by using various regrouping schedules and information is exchanged among the particles in the whole swarm. The Quasi-Newton method is combined to improve its local searching ability.

引用

页码：3845 / +

页数：2

共 11 条

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