小生境微粒群优化算法  

Niche Particle Swarm Optimization Algorithm

作　　者：滕居特[1] 顾幸生[1] 

机构地区：[1]华东理工大学自动化研究所

出　　处：《华东理工大学学报（自然科学版）》2007年第1期133-136,共4页Journal of East China University of Science and Technology

基　　金：国家自然科学基金项目(60274043);国家高技术研究发展计划项目(2002AA412610)

摘　　要：将小生境技术引入到微粒群优化算法之中,设计出一种小生境微粒群优化算法。该算法除了始终赋予微粒生命力,还将位置重叠的差适应值微粒在搜索空间重新启动。通过对4个常用测试函数进行优化计算,仿真结果表明小生境微粒群优化算法比基本微粒群优化算法具有更好的优化性能。An improved particle swarm optimization algorithm——niche particle swarm optimization algorithm （NichePSO） is proposed in this paper. The new algorithm can ensure that the particles have enough energy to move during optimizing. Besides, the particle with poor fitness will be restarted in search space if its position is very close to the position of another particle with better fitness. Both NichePSO and PSO are used to solve four test functions＇ optimization problems. Results show that NichePSO has better optimization performance than PSO.

关 键 词：小生境 微粒群优化算法 优化 仿真 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置]