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Soft Computing
Published in: Soft Computing 20/2017

02-11-2016 | Focus

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

Authors: Hongfeng Wang, Yaping Fu, Min Huang, George Huang, Junwei Wang

Published in: Soft Computing | Issue 20/2017

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Abstract

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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Metadata
Title
A hybrid evolutionary algorithm with adaptive multi-population strategy for multi-objective optimization problems
Authors
Hongfeng Wang
Yaping Fu
Min Huang
George Huang
Junwei Wang
Publication date
02-11-2016
Publisher
Springer Berlin Heidelberg
Published in
Soft Computing / Issue 20/2017
Print ISSN: 1432-7643
Electronic ISSN: 1433-7479
DOI
https://doi.org/10.1007/s00500-016-2414-5

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