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Committee-based Active Learning for Surrogate-Assisted Particle Swarm Optimization of Expensive Problems

Wang, Handing, Jin, Yaochu and Doherty, John (2017) Committee-based Active Learning for Surrogate-Assisted Particle Swarm Optimization of Expensive Problems IEEE Transactions on Cybernetics.

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Abstract

Function evaluations of many real-world optimization problems are time or resource consuming, posing a serious challenge to the application of evolutionary algorithms to solve these problems. To address this challenge, the research on surrogate-assisted evolutionary algorithms has attracted increasing attention from both academia and industry over the past decades. However, most existing surrogate-assisted evolutionary algorithms either still require thousands of expensive function evaluations to obtain acceptable solutions, or are only applied to very low-dimensional problems. In this paper, a novel surrogateassisted particle swarm optimization inspired from committeebased active learning is proposed. In the proposed algorithm, a global model management strategy inspired from committeebased active learning is developed, which searches for the best and most uncertain solutions according to a surrogate ensemble using a particle swarm optimization algorithm and evaluates these solutions using the expensive objective function. In addition, a local surrogate model is built around the best solution obtained so far. Then a particle swarm optimization algorithm searches on the local surrogate to find its optimum and evaluates it. The evolutionary search using the global model management strategy switches to the local search once no further improvement can be observed, and vice versa. This iterative search process continues until the computational budget is exhausted. Experimental results comparing the proposed algorithm with a few state-of-the-art surrogate-assisted evolutionary algorithms on both benchmark problems up to 30 decision variables as well as an airfoil design problem demonstrate that the proposed algorithm is able to achieve better or competitive solutions with a limited budget of hundreds of exact function evaluations.

Item Type: Article
Divisions : Faculty of Engineering and Physical Sciences > Computing Science
Authors :
NameEmailORCID
Wang, Handinghanding.wang@surrey.ac.ukUNSPECIFIED
Jin, YaochuYaochu.Jin@surrey.ac.ukUNSPECIFIED
Doherty, Johnjohn.doherty@surrey.ac.ukUNSPECIFIED
Date : 30 August 2017
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 : particle swarm optimization, expensive problems, surrogate, model management, active learning.
Depositing User : Melanie Hughes
Date Deposited : 30 May 2017 15:19
Last Modified : 16 Jun 2017 10:20
URI: http://epubs.surrey.ac.uk/id/eprint/841230

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