Particle Swarm Optimization with pbest Crossover

被引：0
作者：
Chen, Stephen [1 ]
机构：
[1] York Univ, Sch Informat Technol, Toronto, ON M3J 2R7, Canada
来源：
2012 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC) | 2012年
关键词：
particle swarm optimization; crossover; multi-modal search spaces; exploration; exploitation; GLOBAL OPTIMIZATION; LOCUST SWARMS;
D O I：
暂无
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
Particle swarm optimization can be viewed as a system with two populations: a population of current positions and a population of personal best attractors. In genetic algorithms, crossover is applied after selection - the goal is to create a new offspring solution using components from the best available solutions. In a particle swarm, the best available solutions are in the population of personal best attractors. Compared to standard particle swarm optimization, a modified version which periodically creates particle positions by crossing the personal best positions can achieve large improvements. These improvements are most consistent on multi-modal search spaces where the new crossover solutions may help the search process escape from local optima.
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页数：6
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