南京理工大学学报（自然科学版）2017，Vol.41Issue(3)：301-306,349,7.DOI:10.14177/j.cnki.32-1397n.2017.41.03.005
多策略自适应粒子群优化算法
Multi-strategy adaptive particle swarm optimization algorithm
摘要
Abstract
In order to improve the efficiency of particle swarm optimization(PSO)algorithm for searching for optimal solutions,a multi-strategy adaptive particle swarm optimization(MAPSO)algorithm is proposed.A diversity-measurement strategy is developed to evaluate the population distribution.A real-time alternating strategy is performed to determine predefined evolutionary states,exploration or exploitation.During iterative optimization,the inertia weight is dynamically controlled according to the diversity of particles.An elitist learning strategy is introduced to enhance population diversity and to prevent the population from possibly falling into local optimal solutions.Experimental results show that,compared with the adaptive particle swarm optimization(APSO),comprehensive learning particle swarm optimization(CLPSO)and perturbed particle swarm optimization(PPSO),the MAPSO can substantially enhance the ability of jumping out of the local optimal solutions and significantly improve the search efficiency and convergence speed.关键词
粒子群优化/多样性测试/实时交替策略/精英学习策略/种群多样性Key words
particle swarm optimization/diversity-measurement/real-time alternating strategy/elitist learning strategy/population diversity分类
信息技术与安全科学引用本文复制引用
汤可宗,丰建文,李芳,杨静宇..多策略自适应粒子群优化算法[J].南京理工大学学报（自然科学版）,2017,41(3):301-306,349,7.基金项目
国家自然科学基金(61662037) （61662037）
高维信息智能感知与系统教育部重点实验室开放课题资助课题(JYB201507) （JYB201507）
江西省科技计划项目(20161BAB212042) （20161BAB212042）
江西省教育厅科学技术研究项目(GJJ150927) （GJJ150927）
