A Surrogate Model Assisted Evolutionary Algorithm for Computationally Expensive Design Optimization Problems With Discrete Variables

Liu, B. , Zhang, Q., Grout, V. and Gielen, G. (2016) A Surrogate Model Assisted Evolutionary Algorithm for Computationally Expensive Design Optimization Problems With Discrete Variables. In: 2016 IEEE Congress on Evolutionary Computation (CEC), Vancouver, BC, Canada, 24-29 Jul 2016, pp. 1650-1657. ISBN 9781509006236 (doi: 10.1109/CEC.2016.7743986)

Abstract

Real-world computationally expensive design optimization problems with discrete variables pose challenges to surrogate-based optimization methods in terms of both efficiency and search ability. In this paper, a new method is introduced, called surrogate model-aware differential evolution with neighbourhood exploration, which has two phases. The first phase adopts a surrogate-based optimization method based on efficient surrogate model-aware search framework, the goal of which is to reach at least the neighbourhood of the global optimum. In the second phase, a neighbourhood exploration method for discrete variables is developed and collaborates with the first phase to further improve the obtained solutions. Empirical studies on various benchmark problems and a real-world network-on-chip design optimization problem show the combined advantages in terms of efficiency and search ability: when only a very limited number of exact evaluations are allowed, the proposed method is not slower than one of the most efficient methods for the targeted problem; when more evaluations are allowed, the proposed method can obtain results with comparable quality compared to standard differential evolution, but it requires only 1% to 30% of exact function evaluations.

• Item Type: Conference Proceedings
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Liu, Professor Bo
• Authors: Liu, B., Zhang, Q., Grout, V., and Gielen, G.
• College/School: College of Science and Engineering > School of Engineering
• ISBN: 9781509006236
• Published Online: 21 November 2016
• Copyright Holders: Copyright © 2016 IEEE
• First Published: First published in 2016 IEEE Congress on Evolutionary Computation (CEC): 1650-1657
• Publisher Policy: Reproduced in accordance with the publisher copyright policy