A novel hybrid firefly algorithm for global optimization
Article
Zhang, L., Liu, L., Yang, X. and Dai, Y. 2016. A novel hybrid firefly algorithm for global optimization. PLoS ONE. 11 (9), pp. 1-17. https://doi.org/10.1371/journal.pone.0163230
Type | Article |
---|---|
Title | A novel hybrid firefly algorithm for global optimization |
Authors | Zhang, L., Liu, L., Yang, X. and Dai, Y. |
Abstract | 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. |
Publisher | Public Library of Science |
Journal | PLoS ONE |
ISSN | |
Electronic | 1932-6203 |
Publication dates | |
29 Sep 2016 | |
Publication process dates | |
Deposited | 03 Nov 2016 |
Submitted | 22 Jun 2016 |
Accepted | 06 Sep 2016 |
Output status | Published |
Publisher's version | License |
Copyright Statement | 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. |
Digital Object Identifier (DOI) | https://doi.org/10.1371/journal.pone.0163230 |
Web of Science identifier | WOS:000384328500030 |
Language | English |
https://repository.mdx.ac.uk/item/86v45
Download files
67
total views20
total downloads1
views this month1
downloads this month