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An adaptive reference vector guided evolutionary algorithm using growing neural gas for many-objective optimization of irregular problems

Liu, Qiqi, Jin, Yaochu, Heiderich, Martin, Rodemann, Tobias and Yu, Guo (2020) An adaptive reference vector guided evolutionary algorithm using growing neural gas for many-objective optimization of irregular problems IEEE Transactions on Cybernetics.

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Abstract

Most reference vector based decomposition algorithms for solving multi-objective optimization problems may not be well suited for solving problems with irregular Pareto fronts because the distribution of predefined reference vectors may not match well with the distribution of the Pareto optimal solutions. Thus, adaptation of the reference vectors is an intuitive way for decomposition based algorithms to deal with irregular Pareto fronts. However, most existing methods frequently change the reference vectors based on the activeness of the reference vectors within specific generations, slowing down the convergence of the search process. To address this issue, we propose a new method to learn the distribution of the reference vectors using the growing neural gas network to achieve automatic yet stable adaptation. To this end, an improved growing neural gas is designed for learning the topology of the Pareto fronts with the solutions generated during a period of the search process as the training data. We use the individuals in the current population as well as those in previous generations to train the growing neural gas to strike a balance between exploration and exploitation. Comparative studies conducted on popular benchmark problems and a realworld hybrid vehicle controller design problem with complex and irregular Pareto fronts show that the proposed method is very competitive.

Item Type: Article
Divisions : Faculty of Engineering and Physical Sciences > Computer Science
Authors :
NameEmailORCID
Liu, Qiqiqiqi.liu@surrey.ac.uk
Jin, YaochuYaochu.Jin@surrey.ac.uk
Heiderich, Martin
Rodemann, Tobias
Yu, Guoguo.yu@surrey.ac.uk
Date : 2020
Copyright Disclaimer : © 2020 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 : Evolutionary many-objective optimization; Reference vector, Irregular Pareto front; Growing neural gas
Related URLs :
Depositing User : Clive Harris
Date Deposited : 04 Sep 2020 06:53
Last Modified : 04 Sep 2020 06:53
URI: http://epubs.surrey.ac.uk/id/eprint/858540

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