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A Strengthened Dominance Relation Considering Convergence and Diversity for Evolutionary Many-Objective Optimization

Tian, Ye, Cheng, Ran, Zhang, Xingyi, Su, Yansen and Jin, Yaochu (2018) A Strengthened Dominance Relation Considering Convergence and Diversity for Evolutionary Many-Objective Optimization IEEE Transactions on Evolutionary Computation.

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Abstract

Both convergence and diversity are crucial to evolutionary many-objective optimization, whereas most existing dominance relations show poor performance in balancing them, thus easily leading to a set of solutions concentrating on a small region of the Pareto fronts. In this paper, a novel dominance relation is proposed to better balance convergence and diversity for evolutionary manyobjective optimization. In the proposed dominance relation, an adaptive niching technique is developed based on the angles between the candidate solutions, where only the best converged candidate solution is identified to be nondominated in each niche. Experimental results demonstrate that the proposed dominance relation outperforms existing dominance relations in balancing convergence and diversity. A modified NSGA-II is suggested based on the proposed dominance relation, which shows competitiveness against the state-of-the-art algorithms in solving many-objective optimization problems. The effectiveness of the proposed dominance relation is also verified on several other existing multi- and many-objective evolutionary algorithms.

Item Type: Article
Divisions : Faculty of Engineering and Physical Sciences > Computing Science
Authors :
NameEmailORCID
Tian, Ye
Cheng, Ran
Zhang, Xingyi
Su, Yansen
Jin, YaochuYaochu.Jin@surrey.ac.uk
Date : 24 August 2018
Funders : EPSRC
DOI : 10.1109/TEVC.2018.2866854
Copyright Disclaimer : © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Uncontrolled Keywords : Many-objective optimization, evolutionary algorithm, convergence, diversity, Pareto dominance
Depositing User : Melanie Hughes
Date Deposited : 22 Aug 2018 11:31
Last Modified : 11 Dec 2018 11:24
URI: http://epubs.surrey.ac.uk/id/eprint/849061

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