一种含驻留粒子的改进粒子群算法  

An Improved Particle Swarm Optimization Algorithm Containing Resident Particles

作　　者：张宏伟 张向锋[1] 朱晨煊 

机构地区：[1]上海电机学院电气学院,上海201306

出　　处：《计算机与现代化》2017年第11期1-5,12,共6页Computer and Modernization

基　　金：国家自然科学基金青年科学基金资助项目(11302123);上海电机学院学科建设项目(16DFXK02)

摘　　要：为了避免粒子群算法过早收敛,提出一种包含局部驻留粒子的改进粒子群算法(CRPSO)。该算法将基本的粒子群算法的粒子称为主粒子,而当算法每找到一个新的全体最优点之后,将会在这个最优点附近产生几个称为驻留粒子的搜索粒子。2种粒子分工协作,主粒子负责全局搜索而驻留粒子负责局部搜索。驻留粒子帮助主粒子群避免过早收敛,提高整个粒子群多样性。仿真结果表明,该算法能有效地改善粒子群算法在非线性全局优化问题上的早熟现象,增强粒子群算法的全局搜索能力。An improved particle swarm optimization algorithm（CRPSO） is proposed to improve the premature convergence of particle swarm optimization algorithm（PSO）.The particles of basic PSO are called as main particles in the improved algorithm.When the improved algorithm finds a better globally optimal extreme value point,it produces several points named resident particles around the global optimization point.The two kinds of particles work in cooperation that main particles are responsible for global research and resident particles for local search.Resident particles will help main particles to avoid falling into local extremum easily and improve the diversity of the whole particle swarm.The simulation results have validated its feasibility and effectiveness.

关 键 词：粒子群算法 早熟现象 主粒子 驻留粒子 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]