计算机应用研究2017，Vol.34Issue(12)：3599-3602,4.DOI:10.3969/j.issn.1001-3695.2017.12.018
一种基于引导策略的自适应粒子群优化算法
Adaptive particle swarm optimization algorithm based on guiding strategy
摘要
Abstract
In order to solve the problems of blind search in the early stage and slow search speed as well as easily trapped in the local optimum in the later period,this paper proposed an adaptive particle swarm optimization algorithm based on guiding strategy(IPSO) by improving the particle updating way and inertia weight.The algorithm introduced four kinds of panicles in the population,which were the main panicles,double center particles,cooperative particles and chaos particles.The algorithm decreased the randomness and improved the search efficiency through guiding particle position updating.Moreover,the new algorithm introduced the focusing distance changing rate which adjusted the inertia weight dynamically by the size of the focusing distance changing rate to improve the convergence speed and accuracy.The combination of the both modes improved the effectiveness of the search for the global optimal solution greatly.The simulation experiments tested on the four benchmark functions.The resultsshow that IPSO has obviously higher convergence rate,convergence accuracy and success rate than the other two algorithms.关键词
粒子群优化算法/惯性权重/混合粒子Key words
panicle swarm optimization (PSO)/inertia weight/hybrid particles分类
信息技术与安全科学引用本文复制引用
姜凤利,张宇,王永刚..一种基于引导策略的自适应粒子群优化算法[J].计算机应用研究,2017,34(12):3599-3602,4.基金项目
辽宁省博士启动基金资助项目(201601106) （201601106）
国家自然科学基金资助项目(F030112) （F030112）
辽宁省教育厅科研项目(L2013260) （L2013260）
