自适应搜索区域的微粒群优化算法

基于基本微粒群优化算法搜索后期,众多微粒都拥挤在历史最优位置周围进行重复性无效搜索这一现象,提出一种改进的微粒群算法--自适应搜索区域的微粒群优化算法,其主要思想为:每当搜索进行到当前设定的一个最大迭代次数时(即,微粒在全局历史最优位置周围徘徊进行无效搜索时),在原搜索区域的基础上,重新构造一个较小的搜索区域,并重新初始化微粒,继续进行搜索,最终获得最优解.对3个常用标准测试函数进行优化计算,仿真结果表明,该算法具有比基本微粒群优化算法更好的优化性能.

Particle swarm optimization algorithm based on self-adaptive search area

Based on the phenomena that a lot of particles crowded around the best position and many particles repeated an ineffective search in search later period,an improved algorithm is proposed,which is called particle swarm optimization algorithm based on self-adaptive search area(SSAPSO).Its characteristic was that while a lot of particles crowded around the best position and repeated an ineffective search,there constructed a new less search area based on former one,initialized particles renewedly and continued ...