摘要
通过对粒子群优化问题、小生境技术和多模态粒子群优化算法的深入研究,提出了一种自适应的多模态粒子群优化算法——ASPSO(adaptively species-based particle swarm optimization)。对ASPSO算法进行了综合测试,并与经典的多模态粒子群优化算法ANPSO和SPSO进行了比较。实验表明,ASPSO在处理低维测试函数与ANPSO和SPSO具有同样高的成功率和峰值覆盖率,并且ASPSO在处理高维复杂测试函数时,表现出的性能比其他已经存在的多模态粒子群优化算法更好。
The adaptively species-based particle swarm optimization（ASPSO） is proposed based on the analysis of particle swarm optimizer（PSO）,niching techniques and multimodal particle swarm optimization algorithms.The ASPSO is comprehensively tested and compared with ANPSO and SPSO.Experimental results show that ASPSO has a success rate as high as ANPSO and SPSO in solving low dimensional problems,and has better performance in solving high dimensional and difficult problems than other existing multimodal particle swarm optimization algorithms.
出处
《山东大学学报（理学版）》
CAS
CSCD
北大核心
2011年第5期91-96,122,共7页
Journal of Shandong University(Natural Science)
关键词
多模态函数
粒子群
小生境技术
优化算法
multimodal function； particle swarm； niching technique； optimization algorithm；
