A hybrid evolutionary multiobjective optimization algorithm with adaptive multi-fitness assignment

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalNot applicablepeer-review

5 Scopus Citations
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Author(s)

Detail(s)

Original languageEnglish
Pages (from-to)3249-3259
Journal / PublicationSoft Computing
Volume19
Issue number11
Publication statusPublished - 24 Nov 2015
Externally publishedYes

Abstract

There are several studies on hybrid multi-operator recombination methods, while few works have been proposed in the area of combining different fitness assignment in a framework. On the other hand, it is known that fitness assignment has a marked impact on the performance of evolutionary multiobjective optimization algorithm (EMOA). In this paper, a hybrid EMOA is proposed, which divides the population into several smaller subpopulations according to their distribution in the objective space. Each subpopulation is evolved by an individual EMOA, and a hybrid performance measure estimates the performance of these EMOAs. We focus on the fitness assignment and assume that all EMOAs used in the subpopulations adopt the same recombination operator. To evaluate performance of the proposed algorithm, we compare it with MOEA/D-M2M, MOE-A/D, SMS-EMOA and NSGA-II on 16 test instances. Experimental results show that the proposed algorithm performs better than or similar to those compared EMOAs.

Research Area(s)

  • Decomposition, Evolutionary algorithm, Hybrid algorithm, Multiobjective optimization