多惯性权重的自适应粒子群优化算法  

Adaptive particle swarm optimization algorithm with multiple inertia weights

作　　者：杨博雯 钱伟懿[1] YANG Bowen;QIAN Weiyi(College of Mathematics and Physics,Bohai University,Jinzhou 121013,China)

机构地区：[1]渤海大学数学科学学院,辽宁锦州121013

出　　处：《渤海大学学报（自然科学版）》2021年第1期41-48,90,共9页Journal of Bohai University:Natural Science Edition

基　　金：国家自然科学基金项目(No:11371071)。

摘　　要：惯性权重是粒子群优化算法重要参数之一,它能够平衡算法的全局搜索能力和局部搜索能力.为了利用已知惯性权重解决某些问题的优点,提出一种多惯性权重的自适应粒子群优化算法.首先定义了K步进化度的概念,然后基于进化度,从惯性权重集中随机选择惯性权重,使得适合解决某一问题的惯性权重在迭代过程中能够多次被使用,从而提高算法性能,把该算法应用到典型测试函数中,并与其他算法进行比较分析,结果表明,所提出的算法是可行的、有效的.Inertia weight is one of the important parameters of the particle swarm optimization algorithm,which can balance the global search ability and local search ability of the algorithm.In order to use the advantages of known inertia weights in solving some problems,an adaptive particle swarm optimization algorithm with multiple inertia weights is proposed.Firstly,the concept of k-step evolution degree is defined,then,based on the evolution degree,the inertia weight is selected randomly from the inertia weight set,so that the inertia weight which is suitable for solving a problem can be used many times during the iterative process,thus,the performance of the proposed algorithm is improved.Finally,the proposed algorithm is applied to the standard benchmark functions,and compared with other algorithms.The numerical results show that the proposed algorithm is feasible and effective.

关 键 词：粒子群优化算法 惯性权重 自适应算法 统计分析 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置]