A novel improved accelerated particle swarm optimization algorithm for global numerical optimization

Wang, Gai-Ge, Hossein Gandomi, Amir, Yang, Xin-She ORCID: https://orcid.org/0000-0001-8231-5556 and Hossein Alavi, Amir (2014) A novel improved accelerated particle swarm optimization algorithm for global numerical optimization. Engineering Computations, 31 (7). pp. 1198-1220. ISSN 0264-4401 (doi:10.1108/EC-10-2012-0232)

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Abstract

Meta-heuristic algorithms are efficient in achieving the optimal solution for engineering problems. Hybridization of different algorithms may enhance the quality of the solutions and improve the efficiency of the algorithms. The purpose of this paper is to propose a novel, robust hybrid meta-heuristic optimization approach by adding differential evolution (DE) mutation operator to the accelerated particle swarm optimization (APSO) algorithm to solve numerical optimization problems. A novel hybrid method is proposed and used to optimize 51 functions. It is compared with other methods to show its effectiveness. The effect of the DPSO parameters on convergence and performance is also studied and analyzed by detailed parameter sensitivity studies.

Item Type: Article
Research Areas: A. > School of Science and Technology > Design Engineering and Mathematics
Item ID: 19601
Useful Links:
Depositing User: Xin-She Yang
Date Deposited: 27 Apr 2016 11:39
Last Modified: 10 Jun 2019 13:07
URI: https://eprints.mdx.ac.uk/id/eprint/19601

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