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

被引：182
|
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
收藏
页码：3845 / +
页数：2
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