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Surrogate-assisted Hierarchical Particle Swarm Optimization

Yu, Haibo, Tan, Ying, Zeng, Jianchao, Sun, Chaoli and Jin, Yaochu (2018) Surrogate-assisted Hierarchical Particle Swarm Optimization Information Sciences, 454. pp. 59-72.

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Meta-heuristic algorithms, which require a large number of fitness evaluations before locating the global optimum, are often prevented from being applied to computationally expensive real-world problems where one fitness evaluation may take from minutes to hours, or even days. Although many surrogate-assisted meta-heuristic optimization algorithms have been proposed, most of them were developed for solving expensive problems up to 30 dimensions. In this paper, we propose a surrogate-assisted hierarchical particle swarm optimizer for high-dimensional problems consisting of a standard particle swarm optimization (PSO) algorithm and a social learning particle swarm optimization algorithm (SL-PSO), where the PSO and SL-PSO work together to explore and exploit the search space, and simultaneously enhance the global and local performance of the surrogate model. Our experimental results on seven benchmark functions of dimensions 30, 50 and 100 demonstrate that the proposed method is competitive compared with the state-of-the-art algorithms under a limited computational budget.

Item Type: Article
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering
Authors :
Yu, Haibo
Tan, Ying
Zeng, Jianchao
Sun, Chaoli
Date : 24 April 2018
DOI : 10.1016/j.ins.2018.04.062
Copyright Disclaimer : © 2018. This manuscript version is made available under the CC-BY-NC-ND 4.0 license
Uncontrolled Keywords : Computationally expensive problems; Surrogate model; Radial basis function; Particle swarm optimization
Related URLs :
Depositing User : Clive Harris
Date Deposited : 24 Apr 2018 07:59
Last Modified : 25 Apr 2019 02:08

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