Convergence analysis of particle swarm optimization and its improved algorithm based on chaos

被引：0
|
作者：
Liu, Hong-Bo [1 ]
Wang, Xiu-Kun [1 ]
Tan, Guo-Zhen [1 ]
机构：
[1] Department of Computer, Dalian University of Technology, Dalian 116023, China
来源：
Kongzhi yu Juece/Control and Decision | 2006年 / 21卷 / 06期
关键词：
Genetic algorithms - Optimization - Simulated annealing - Sustainable development - Velocity;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
The particle swarm optimization (PSO) algorithm is analyzed. Its premature convergence is due to the decrease of velocity of particles in search space that leads to a total implosion and ultimately fitness stagnation of the swarm. A chaotic particle swarm optimization (CPSO) algorithm is introduced to overcome the problem of premature convergence. CPSO uses the properties of ergodicity, stochastic property, and regularity of chaos to lead particles' exploration. This enable the swarm system to have the ability of sustainable development. Simulation results show that CPSO prevents premature convergence effectively and is better than PSO, genetic algorithm and simulated annealing on some benchmark function optimization problems.
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收藏
页码：636 / 640
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