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A surrogate model assisted evolutionary algorithm for computationally expensive design optimization problems with discrete variables

Liu, B and Sun, N and Zhang, Q and Grout, V and Gielen, G (2016) A surrogate model assisted evolutionary algorithm for computationally expensive design optimization problems with discrete variables. In: UNSPECIFIED, ? - ?.

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Abstract

© 2016 IEEE. 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 or Workshop Item (Paper)
Additional Information: Published proceedings: 2016 IEEE Congress on Evolutionary Computation, CEC 2016
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Science and Health > Computer Science and Electronic Engineering, School of
Depositing User: Jim Jamieson
Date Deposited: 14 Dec 2016 09:52
Last Modified: 05 Feb 2019 15:15
URI: http://repository.essex.ac.uk/id/eprint/18558

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