摘要
针对粒子群算法搜索精度不高的问题,提出了一种改进的粒子群算法。该算法一方面通过跟踪个体极值、全局极值和周围极值来搜索解空间的最优值;另一方面通过引入3种非线性递减函数对惯性权重进行调整,仿真结果表明改进的粒子群算法具有更强的寻优能力及更高的搜索精度。
A modified particle swarm optimization is proposed to overcome the problems such as low precision that exist in the standard PSO algorithm. On one hand this algorithm searches for the extreme value by tracking three extreme values （individual extreme value, global extreme value, circumference extreme value）. On the other hand three non - linear functions are introduced to adjust the inertia weight of the particle swarm optimization algorithm. Simulations show that modified particle swarm optimization algorithm has more powerful optimizing ability and higher optimizing precision.
出处
《杭州电子科技大学学报（自然科学版）》
2008年第6期103-106,共4页
Journal of Hangzhou Dianzi University：Natural Sciences
基金
国家自然科学基金资助项目(60675043)
浙江省科技计划资助项目(C21051)
关键词
粒子群算法
极值
惯性权重
particle swarm optimization
extreme value
inertia weight
