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

Liu, Bo, Sun, Nan and Grout, Vic (2016) A Surrogate Model Assisted Evolutionary Algorithm for Computationally Expensive Design Optimization Problems with Discrete Variables. In: IEEE World Congress on Computational Intelligence (IEEE Congress on Evolutionary Computation), 24-29 July 2016, Vancouver, Canada.

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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 or Workshop Item (Paper)
Divisions: Applied Science, Computing and Engineering
Depositing User: Users 1048 not found.
Date Deposited: 23 Aug 2017 08:46
Last Modified: 19 Dec 2017 14:33
URI: http://glyndwr.collections.crest.ac.uk/id/eprint/16044

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