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An Indicator Based Multi-Objective Evolutionary Algorithm with Reference Point Adaptation for Better Versatility

Tian, Y, Cheng, R, Zhang, X, Cheng, F and Jin, Yaochu (2017) An Indicator Based Multi-Objective Evolutionary Algorithm with Reference Point Adaptation for Better Versatility IEEE Transactions on Evolutionary Computation, 22 (4). pp. 609-622.

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During the past two decades, a variety of multiobjective evolutionary algorithms (MOEAs) have been proposed in the literature. As pointed out in some recent studies, however, the performance of an MOEA can strongly depend on the Pareto front shape of the problem to be solved, whereas most existing MOEAs show poor versatility on problems with different shapes of Pareto fronts. To address this issue, we propose an MOEA based on an enhanced inverted generational distance indicator, in which an adaptation method is suggested to adjust a set of reference points based on the indicator contributions of candidate solutions in an external archive. Our experimental results demonstrate that the proposed algorithm is versatile for solving problems with various types of Pareto fronts, outperforming several state-of-the-art evolutionary algorithms for multi-objective and many-objective optimization.

Item Type: Article
Divisions : Faculty of Engineering and Physical Sciences > Computing Science
Authors :
Tian, Y
Cheng, R
Zhang, X
Cheng, F
Date : 7 September 2017
DOI : 10.1109/TEVC.2017.2749619
Copyright Disclaimer : Copyright 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, 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 components of this work in other works.
Uncontrolled Keywords : Evolutionary multi-objective optimization, many-objective optimization, indicator based selection, adaptive reference point
Depositing User : Melanie Hughes
Date Deposited : 07 Sep 2017 08:35
Last Modified : 18 Oct 2018 11:25

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