统计与决策2019，Vol.35Issue(8)：13-17,5.DOI:10.13546/j.cnki.tjyjc.2019.08.003
混合变异和时变惯量的混沌多目标粒子群优化
Optimization of Chaotic Multi-objective Particle Swarm With Hybrid Mutation and Time-varying Inertia
摘要
Abstract
To optimize the multiple objectives, this paper presents a hybrid method named HMOPSO, which is a chaotic particle swarm optimization based on hybrid mutation and time-varying inertia. Firstly the paper uses the global search capability of PSO to integrate chaos factors into the basic PSO, realizing the local search in the high-dimensional search space. In addition, the paper adopts the time-varying inertia weight and improves the ability of exploring space efficiently by changing its value iteratively. Finally hybrid variation is applied at different stages of the search, and the crowded distance mechanism is used to realize the global optimal selection and maintain the diversity of non-dominant solutions, ensuring fast convergence to Pareto optimal frontier. Compared with several typical multi-objective optimization algorithms, HMOPSO performs more effectively.关键词
多目标优化/混沌粒子群优化/混合变异/时变惯性/拥挤距离Key words
multi-objective optimization/chaotic particle swarm optimization/hybrid mutation/time-varying inertia/crowded distance分类
管理科学引用本文复制引用
朱沙,陈臣,田月娜..混合变异和时变惯量的混沌多目标粒子群优化[J].统计与决策,2019,35(8):13-17,5.基金项目
重庆市教委科技项目(KJ1500618) （KJ1500618）
重庆市教育科学规划重点项目(2015-GX-007) （2015-GX-007）
重庆工商大学博士项目(1701-670100190) （1701-670100190）
重庆工商大学青年博士基金项目(670100713) （670100713）
