摘要
标准微粒群算法的种群多样性随进化变差是造成陷于局部最优的主要原因,本文提出了一种多样性监控的免疫微粒群算法.利用多样性函数对种群的多样性进行监控,并在多样性下降到一定程度时,引入免疫机制中的克隆选择算子和免疫记忆特性来对粒子进行更新,从而有效地克服了微粒群算法易陷于局部最优以及对多峰值函数搜索效果不佳的缺点.用经典benchmark测试函数对算法进行仿真实验,实验结果表明该算法比标准微粒群算法有着更好的收敛性能.
Particle swarm optimization is easy to trap into local optimum because the diversity of population becomes worse during the evolution. An immune particle swarm optimization with diversity monitoring （DIPSO） is proposed. The algorithm monitors diversity of population with diversity function. Clone selection operator and immune memory characteristic are introduced to update particles when diversity decline to some degree. The modified algorithm can avoid the local optimization and has better search performance for multi-peak functions. Testing over the benchmark problems,the experimental results show the modified algorithm has better convergence performance than original particle swarm optimization algorithm.
出处
《小型微型计算机系统》
CSCD
北大核心
2008年第2期304-307,共4页
Journal of Chinese Computer Systems
基金
国家自然科学基金项目(60674104)资助
关键词
微粒群优化算法
多样性函数
免疫机制
克隆选择
免疫记忆
particle swarm optimization algorithm
diversity function
mechanism of immune system
clone selection
immunememory
