摘要
为克服粒子群算法易于陷入局部极值的缺点,通过引入聚群效应和扰动,设计了一种新的粒子群算法.通过对常用测试函数的数值试验,说明了新算法不仅能有效地进行全局搜索,而且具有更好的收敛精度.
To overcome the particle swarm optimization algorithm to the local optima,an improved new algorithm is proposed by introducing the swarm behavior and disturbances.To show the optimization performances of the new algorithm,several benchmark functions are tested.The experimental results show that the new algorithm not only effectively solves the premature convergence problem,but also significantly speeds up the convergence.
出处
《纺织高校基础科学学报》
CAS
2011年第3期428-431,共4页
Basic Sciences Journal of Textile Universities
基金
国家自然科学基金资助项目(60671063
10902062)
关键词
聚群
粒子群算法
扰动
惯性权重
swarm behavior
particle swarm optimization algorithm
disturbances
inertia weight
