长春工程学院学报（自然科学版）Issue(4)：116-119,4.DOI:10.3969/j.issn.1009-8984.2014.04.027
一种改进的动态惯性权重粒子群优化算法
A modified optimization to dynamic inertia weight particles swarm
摘要
Abstract
Considering the problems of local optimum and difficulty in balancing the search capability of searching accuracy and extension caused by particle swarm optimization, this paper proposes a modified particle swarm optimization by using dynamic inertia weight. This algorithm considers the influence to opti‐mization both from the evolution velocity of particle swarm and the agglomeration degree. To improve the global searching capacity of this algorithm, and to increasethe inertia weight, when agglomeration of parti‐cles is high. In order to balance global and local optimization ability of this algorithm, local optimization a‐bility should be increased w hen algorithm has higher evolution velocity, so as not to miss a good location. The algorithm in this paper can be used in 4 classical testing functions, and the results show that the pro‐posed algorithm can not only balance the global and local search abilities, but also optimize the searching ef‐ficiency and accuracy.关键词
粒子群算法/集聚度/进化速度/惯性权重Key words
particle swarm optimization/agglomeration degree/evolution velocity/inertia weight分类
信息技术与安全科学引用本文复制引用
李艳,杨华芬..一种改进的动态惯性权重粒子群优化算法[J].长春工程学院学报（自然科学版）,2014,(4):116-119,4.基金项目
云南省自然科学基金 （）
