A Multi-Objective Particle Swarm Optimization Algorithm Based on Gaussian Mutation and an Improved Learning Strategy

被引:28
|
作者
Sun, Ying [1 ]
Gao, Yuelin [1 ,2 ]
机构
[1] Hefei Univ Technol, Sch Comp Sci & Informat Engn, Hefei 230009, Anhui, Peoples R China
[2] North Minzu Univ, Ningxia Prov Key Lab Intelligent Informat & Data, Yinchuan 750021, Peoples R China
关键词
multi-objective optimization problems; particle swarm optimization (PSO); Gaussian mutation; improved learning strategy; EVOLUTIONARY ALGORITHMS;
D O I
10.3390/math7020148
中图分类号
O1 [数学];
学科分类号
0701 ; 070101 ;
摘要
Obtaining high convergence and uniform distributions remains a major challenge in most metaheuristic multi-objective optimization problems. In this article, a novel multi-objective particle swarm optimization (PSO) algorithm is proposed based on Gaussian mutation and an improved learning strategy. The approach adopts a Gaussian mutation strategy to improve the uniformity of external archives and current populations. To improve the global optimal solution, different learning strategies are proposed for non-dominated and dominated solutions. An indicator is presented to measure the distribution width of the non-dominated solution set, which is produced by various algorithms. Experiments were performed using eight benchmark test functions. The results illustrate that the multi-objective improved PSO algorithm (MOIPSO) yields better convergence and distributions than the other two algorithms, and the distance width indicator is reasonable and effective.
引用
收藏
页数:16
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