University of Surrey

Test tubes in the lab Research in the ATI Dance Research

Surrogate-assisted Cooperative Swarm Optimization of High-dimensional Expensive Problems

Sun, C, Jin, Yaochu, Cheng, R, Ding, J and Zeng, J (2017) Surrogate-assisted Cooperative Swarm Optimization of High-dimensional Expensive Problems IEEE Transactions on Evolutionary Computation.

[img]
Preview
Text
FINALVERSION.pdf - Accepted version Manuscript
Available under License : See the attached licence file.

Download (1MB) | Preview
[img]
Preview
PDF (licence)
SRI_deposit_agreement.pdf
Available under License : See the attached licence file.

Download (33kB) | Preview

Abstract

Surrogate models have shown to be effective in assisting metaheuristic algorithms for solving computationally expensive complex optimization problems. The effectiveness of existing surrogate-assisted metaheuristic algorithms, however, has only been verified on low-dimensional optimization problems. In this paper, a surrogate-assisted cooperative swarm optimization algorithm is proposed, in which a surrogate-assisted particle swarm optimization algorithm and a surrogate-assisted social learning based particle swarm optimization algorithm cooperatively search for the global optimum. The cooperation between the particle swarm optimization and the social learning based particle swarm optimization consists of two aspects. First, they share promising solutions evaluated by the real fitness function. Second, the social learning based particle swarm optimization focuses on exploration while the particle swarm optimization concentrates on local search. Empirical studies on six 50-dimensional and six 100-dimensional benchmark problems demonstrate that the proposed algorithm is able to find high-quality solutions for high-dimensional problems on a limited computational budget.

Item Type: Article
Subjects : Computer Science
Divisions : Faculty of Engineering and Physical Sciences > Computing Science
Authors :
NameEmailORCID
Sun, CUNSPECIFIEDUNSPECIFIED
Jin, YaochuYaochu.Jin@surrey.ac.ukUNSPECIFIED
Cheng, RUNSPECIFIEDUNSPECIFIED
Ding, JUNSPECIFIEDUNSPECIFIED
Zeng, JUNSPECIFIEDUNSPECIFIED
Date : 1 March 2017
Identification Number : 10.1109/TEVC.2017.2675628
Copyright Disclaimer : (c) 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 : Surrogate models, computationally expensive problems, particle swarm optimization, radial-basis-function networks, fitness estimation strategy.
Related URLs :
Depositing User : Symplectic Elements
Date Deposited : 01 Mar 2017 16:18
Last Modified : 19 Jul 2017 11:33
URI: http://epubs.surrey.ac.uk/id/eprint/813661

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year


Information about this web site

© The University of Surrey, Guildford, Surrey, GU2 7XH, United Kingdom.
+44 (0)1483 300800