Hybrid particle swarm optimization with simulated annealing

被引：0
|
作者：
Wang, XH [1 ]
Li, JJ [1 ]
机构：
[1] Shanghai Maritime Univ, Dept Elect & Automat, Shanghai 200135, Peoples R China
来源：
PROCEEDINGS OF THE 2004 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-7 | 2004年
关键词：
particle swarm optimization; simulated annealing; function optimization;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Particle swarm optimization is a recently invented intelligent optimizer with several highly desirable attributes. In this paper, a hybrid particle swarm optimization is proposed. This method integrates the particle swarm optimization with the simulated annealing. The method is applied to six test functions' optimization and the simulation shows that the performance of this algorithm is better than that of the adaptive particle swarm optimization and the genetic chaos optimization.
引用
收藏
页码：2402 / 2405
页数：4
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