计算机工程与应用2018，Vol.54Issue(9)：139-144,6.DOI:10.3778/j.issn.1002-8331.1612-0093
一种新的自适应惯性权重混沌PSO算法
New chaos particle swarm optimization based on adaptive inertia weight
摘要
Abstract
Particle Swarm Optimization(PSO)is easy to fall into the local optimal value.According to this disadvantage, a New Chaos Particle Swarm Optimization based on Adaptive Inertia Weight(CPSO-NAIW)is proposed.Firstly,the new inertia weight adaptive method is used to make a balance between the global and local search of the particles.It can reduce the probability of particles trap in local optimal.Then,when the algorithm falls into local optimal value,the strategy of chaos optimization is introduced to adjust the position of the population's extreme value so that the particles can search the new neighorhood and path.The probability of getting rid of the local extremum is increaseed.Finally,the experimental results show that the CPSO-NAIW algorithm can avoid the local optimal and improve the performance of the algorithm effectively.关键词
粒子群/自适应惯性权重/混沌/局部极值Key words
particle swarm optimization/adaptive inertia weight/chaos/local extreme value分类
信息技术与安全科学引用本文复制引用
李龙澍,张效见..一种新的自适应惯性权重混沌PSO算法[J].计算机工程与应用,2018,54(9):139-144,6.基金项目
青年科学基金项目(No.61402005). （No.61402005）
