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Guiding Evolutionary Multi-Objective Optimization with Generic Front Modeling

Tian, Ye, Zhang, Xingyi, Cheng, Ran, He, Cheng and Jin, Yaochu (2018) Guiding Evolutionary Multi-Objective Optimization with Generic Front Modeling IEEE Transactions on Cybernetics.

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Abstract

In evolutionary multi-objective optimization, the Pareto front is approximated using a set of representative candidate solutions with good convergence and diversity. However, most existing multi-objective evolutionary algorithms have general difficulty in the approximation of Pareto fronts with complicated geometries. To address this issue, we propose a generic front modeling method for evolutionary multi-objective optimization, where the shape of the nondominated front is estimated by training a generalized simplex model. On the basis of the estimated front, we further develop a multi-objective evolutionary algorithm, where both the mating selection and environmental selection are driven by the approximate non-dominated fronts modeled during the optimization process. For performance assessment, the proposed algorithm is compared with several state-of-the-art evolutionary algorithms on a wide range of benchmark problems with various types of Pareto fronts and different numbers of objectives. Experimental results demonstrate that the proposed algorithm performs consistently on a variety of multi-objective optimization problems.

Item Type: Article
Divisions : Faculty of Engineering and Physical Sciences > Computing Science
Authors :
NameEmailORCID
Tian, Ye
Zhang, Xingyi
Cheng, Ran
He, Cheng
Jin, YaochuYaochu.Jin@surrey.ac.uk
Date : 2018
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 : Evolutionary algorithm; Multi- and many- objective optimization; Front modeling; Fitness function
Related URLs :
Depositing User : Clive Harris
Date Deposited : 27 Nov 2018 15:22
Last Modified : 27 Nov 2018 15:22
URI: http://epubs.surrey.ac.uk/id/eprint/849944

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