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A Surrogate-assisted Reference Vector Guided Evolutionary Algorithm for Computationally Expensive Many-objective Optimization

Chugh, T, Jin, Yaochu, Miettinen, K, Hakanen, J and Sindhya, K (2016) A Surrogate-assisted Reference Vector Guided Evolutionary Algorithm for Computationally Expensive Many-objective Optimization IEEE Transactions on Evolutionary Computation.

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Abstract

We propose a surrogate-assisted reference vector guided evolutionary algorithm for computationally expensive optimization problems with more than three objectives. The proposed algorithm is based on a recently developed evolutionary algorithm for many-objective optimization that relies on a set of adaptive reference vectors for selection. The proposed surrogateassisted evolutionary algorithm uses Kriging to approximate each objective function to reduce the computational cost. In managing the Kriging models, the algorithm focuses on the balance of diversity and convergence by making use of the uncertainty information in the approximated objective values given by the Kriging models, the distribution of the reference vectors as well as the location of the individuals. In addition, we design a strategy for choosing data for training the Kriging model to limit the computation time without impairing the approximation accuracy. Empirical results on comparing the new algorithm with the state-of-the-art surrogate-assisted evolutionary algorithms on a number of benchmark problems demonstrate the competitiveness of the proposed algorithm.

Item Type: Article
Subjects : Computing Science
Divisions : Faculty of Engineering and Physical Sciences > Computing Science
Authors :
NameEmailORCID
Chugh, TUNSPECIFIEDUNSPECIFIED
Jin, YaochuYaochu.Jin@surrey.ac.ukUNSPECIFIED
Miettinen, KUNSPECIFIEDUNSPECIFIED
Hakanen, JUNSPECIFIEDUNSPECIFIED
Sindhya, KUNSPECIFIEDUNSPECIFIED
Date : 27 October 2016
Identification Number : 10.1109/TEVC.2016.2622301
Copyright Disclaimer : © 2016 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 : multiobjective optimization, reference vectors, surrogate-assisted evolutionary algorithms, model management, Kriging, computational cost
Depositing User : Symplectic Elements
Date Deposited : 02 Nov 2016 09:03
Last Modified : 18 Jul 2017 14:42
URI: http://epubs.surrey.ac.uk/id/eprint/812700

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