Evolutionary Many-Objective Algorithm Using Decomposition-Based Dominance Relationship

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

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Detail(s)

Original languageEnglish
Pages (from-to)4129-4139
Journal / PublicationIEEE Transactions on Cybernetics
Volume49
Issue number12
Online published10 Sep 2018
Publication statusPublished - Dec 2019

Abstract

Decomposition-based evolutionary algorithms have shown great potential in many-objective optimization. However, the lack of theoretical studies on decomposition methods has hindered their further development and application. In this paper, we first theoretically prove that weight sum, Tchebycheff, and penalty boundary intersection decomposition methods are essentially interconnected. Inspired by this, we further show that highly customized dominance relationship can be derived from decomposition for any given decomposition vector. A new evolutionary algorithm is then proposed by applying the customized dominance relationship with adaptive strategy to each subpopulation of multiobjective to multiobjective framework. Experiments are conducted to compare the proposed algorithm with five state-of-the-art decomposition-based evolutionary algorithms on a set of well-known scaled many-objective test problems with 5 to 15 objectives. Simulation results have shown that the proposed algorithm can make better use of the decomposition vectors to achieve better performance. Further investigations on unscaled many-objective test problems verify the robust and generality of the proposed algorithm.

Research Area(s)

  • Computer science, Convergence, Dominance relationship, evolutionary algorithm, Evolutionary computation, Machine-to-machine communications, many-objective, multiobjective to multiobjective (M2M) decomposition, Optimization, Sociology, Statistics

Citation Format(s)

Evolutionary Many-Objective Algorithm Using Decomposition-Based Dominance Relationship. / Chen, Lei; Liu, Hai-Lin; Tan, Kay Chen; Cheung, Yiu-Ming; Wang, Yuping.

In: IEEE Transactions on Cybernetics, Vol. 49, No. 12, 12.2019, p. 4129-4139.

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