摘要
惯性权重作为粒子群最重要的参数之一,对全局搜索能力和局部搜索能力有重要的影响。针对传统粒子群算法的局限性,本文对其惯性权重进行改进,提出自适应惯性权重优化的粒子群算法,与原始粒子群算法相比,现在惯性权重和迭代次数与每个粒子适应度有关。仿真结果表明:本文所提出的自适应粒子群算法在迭代次数上优于基本粒子群算法,平均适应度低于基本粒子群算法。
As one of the most important parameters of particle swarm,inertia weight has an important influence on global search ability and local search ability.Aiming at the limitation of traditional particle swarm optimization algorithm,the inertia weight is improved and an adaptive inertia weight particle swarm optimization algorithm is proposed.Compared with before,the inertia weight is related to the number of iterations and the fitness of each particle.The simulation results show that the proposed adaptive particle swarm optimization algorithm is superior to the basic particle swarm optimization algorithm in the number of iterations,and the average fitness is lower than the basic particle swarm optimization algorithm.
作者
张豪
王贤琳
ZHANG Hao;WANG Xianlin(School of Machinery and Automation,Wuhan University of Science and Technology,Wuhan 430081,China)
出处
《智能计算机与应用》
2023年第9期5-8,共4页
Intelligent Computer and Applications
基金
国家自然科学基金(51975432)。
关键词
自适应惯性权重
粒子群算法
迭代次数
adaptive inertia weight
particle swarm optimization(PSO)
iteration times
