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Multi-objective Infill Criterion Driven Gaussian Process Assisted Particle Swarm Optimization of High-dimensional Expensive Problems

Tian, Jie, Tan, Ying, Zeng, Jianchao, Sun, Chaoli and Jin, Yaochu (2018) Multi-objective Infill Criterion Driven Gaussian Process Assisted Particle Swarm Optimization of High-dimensional Expensive Problems IEEE Transactions on Evolutionary Computation.

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Abstract

Model management plays an essential role in surrogate-assisted evolutionary optimization of expensive problems, since the strategy for selecting individuals for fitness evaluation using the real objective function has substantial influences on the final performance. Among many others, infill criterion driven Gaussian process assisted evolutionary algorithms have been demonstrated competitive for optimization of problems with up to 50 decision variables. In this paper, a multi-objective infill criterion that considers the approximated fitness and the approximation uncertainty as two objectives is proposed for a Gaussian process assisted social learning particle swarm optimization algorithm. The multi-objective infill criterion uses non-dominated sorting for model management, thereby avoiding combining the approximated fitness and the approximation uncertainty into a scalar function, which is shown to be particularly important for high-dimensional problems, where the estimated uncertainty becomes less reliable. Empirical studies on 50- and 100-dimensional benchmark problems and a synthetic problem constructed from four real-world optimization problems demonstrate that the proposed multi-objective infill criterion is more effective than existing scalar infill criteria for Gaussian process assisted optimization given a limited computational budget.

Item Type: Article
Divisions : Faculty of Engineering and Physical Sciences > Computing Science
Authors :
NameEmailORCID
Tian, Jie
Tan, Ying
Zeng, Jianchao
Sun, Chaoli
Jin, YaochuYaochu.Jin@surrey.ac.uk
Date : 10 September 2018
DOI : 10.1109/TEVC.2018.2869247
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 : Expensive optimization, multi-objective infill criterion, Gaussian process, social learning particle swarm optimization.
Depositing User : Melanie Hughes
Date Deposited : 06 Sep 2018 10:41
Last Modified : 23 Nov 2018 17:46
URI: http://epubs.surrey.ac.uk/id/eprint/849231

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