A novel improved accelerated particle swarm optimization algorithm for global numerical optimization
Article
Wang, G., Hossein Gandomi, A., Yang, X. and Hossein Alavi, A. 2014. A novel improved accelerated particle swarm optimization algorithm for global numerical optimization. Engineering Computations. 31 (7), pp. 1198-1220. https://doi.org/10.1108/EC-10-2012-0232
Type | Article |
---|---|
Title | A novel improved accelerated particle swarm optimization algorithm for global numerical optimization |
Authors | Wang, G., Hossein Gandomi, A., Yang, X. and Hossein Alavi, A. |
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. |
Keywords | Global optimization problem; Accelerated particle swarm optimization (APSO); Differential evolution (DE); Multimodal function |
Publisher | Emerald |
Journal | Engineering Computations |
ISSN | 0264-4401 |
Electronic | 1758-7077 |
Publication dates | |
30 Sep 2014 | |
Publication process dates | |
Deposited | 27 Apr 2016 |
Accepted | 22 Jan 2014 |
Output status | Published |
Digital Object Identifier (DOI) | https://doi.org/10.1108/EC-10-2012-0232 |
Web of Science identifier | WOS:000343322300004 |
Language | English |
https://repository.mdx.ac.uk/item/8652w
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