Benchmarking Heuristic Search and Optimisation Algorithms in Matlab

Luo, W. and Li, Y. (2016) Benchmarking Heuristic Search and Optimisation Algorithms in Matlab. In: 22nd International Conference on Automation and Computing (ICAC), 2016, University of Essex, Colchester, UK, 7-8 Sept 2016, pp. 250-255. ISBN 9781509028771 (doi: 10.1109/IConAC.2016.7604927)

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Abstract

With the proliferating development of heuristic methods, it has become challenging to choose the most suitable ones for an application at hand. This paper evaluates the performance of these algorithms available in Matlab, as it is problem dependent and parameter sensitive. Further, the paper attempts to address the challenge that there exists no satisfied benchmarks to evaluation all the algorithms at the same standard. The paper tests five heuristic algorithms in Matlab, the Nelder-Mead simplex search, the Genetic Algorithm, the Genetic Algorithm with elitism, Simulated Annealing and Particle Swarm Optimization, with four widely adopted benchmark problems. The Genetic Algorithm has an overall best performance at optimality and accuracy, while PSO has fast convergence speed when facing unimodal problem.

Item Type:Conference Proceedings
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Luo, Miss Wuqiao and Li, Professor Yun
Authors: Luo, W., and Li, Y.
College/School:College of Science and Engineering > School of Engineering > Systems Power and Energy
ISBN:9781509028771
Copyright Holders:Copyright © 2016 IEEE
First Published:First published in 22nd International Conference on Automation and Computing (ICAC), 2016: 250-255
Publisher Policy:Reproduced in accordance with the publisher copyright policy

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