A hybrid evolutionary algorithm with adaptive multi-population strategy for multi-objective optimization problems

被引：0
作者：
Hongfeng Wang
Yaping Fu
Min Huang
George Huang
Junwei Wang
机构：
[1] The University of Hong Kong,Department of Industrial and Manufacturing System Engineering
[2] Northeastern University,College of Information Science and Engineering
来源：
Soft Computing | 2017年 / 21卷
关键词：
Evolutionary multi-objective optimization; Hybrid evolutionary algorithm; Multi-objective optimization problem; Particle swarm optimization; Differential evolution; Multi-population;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
In this paper, a new multi-objective evolutionary algorithm (MOEA) named hybrid MOEA with adaptive multi-population strategy (HMOEA-AMP) is proposed for multi-objective optimization problems (MOPs).In the framework of HMOEA-AMP, the particle swarm optimization and differential evolution are hybridized to guide the exploitation of the Pareto optimal solutions and the exploration of the optimal distribution of the achieved solutions, respectively. Multiple subpopulations are constructed in an adaptive fashion according to a number of scalar subproblems, which are decomposed from a MOP through a set of predefined weight vectors. Comprehensive experiments using a set of benchmark are conducted to investigate the performance of HMOEA-AMP in comparison with several state-of-the-art MOEAs. The experimental results show the advantage of the proposed algorithm.
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页码：5975 / 5987
页数：12
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