A novel hybrid firefly algorithm for global optimization

Zhang, Lina, Liu, Liqiang, Yang, Xin-She ORCID logoORCID: https://orcid.org/0000-0001-8231-5556 and Dai, Yuntao (2016) A novel hybrid firefly algorithm for global optimization. PLoS ONE, 11 (9) , e0163230. pp. 1-17. ISSN 1932-6203 [Article] (doi:10.1371/journal.pone.0163230)

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Global optimization is challenging to solve due to its nonlinearity and multimodality. Traditional algorithms such as the gradient-based methods often struggle to deal with such problems and one of the current trends is to use metaheuristic algorithms. In this paper, a novel hybrid population-based global optimization algorithm, called hybrid firefly algorithm (HFA), is proposed by combining the advantages of both the firefly algorithm (FA) and differential evolution (DE). FA and DE are executed in parallel to promote information sharing among the population and thus enhance searching efficiency. In order to evaluate the performance and efficiency of the proposed algorithm, a diverse set of selected benchmark functions are employed and these functions fall into two groups: unimodal and multimodal. The experimental results show better performance of the proposed algorithm compared to the original version of the firefly algorithm (FA), differential evolution (DE) and particle swarm optimization (PSO) in the sense of avoiding local minima and increasing the convergence rate.

Item Type: Article
Research Areas: A. > School of Science and Technology > Design Engineering and Mathematics
Item ID: 20900
Notes on copyright: Copyright: © 2016 Zhang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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Depositing User: Xin-She Yang
Date Deposited: 03 Nov 2016 10:38
Last Modified: 29 Nov 2022 21:34
URI: https://eprints.mdx.ac.uk/id/eprint/20900

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