A Novel Hybrid Algorithm Based on Grey Wolf Optimizer and Fireworks Algorithm
Grey wolf optimizer (GWO) is a meta-heuristic algorithm inspired by the hierarchy of grey wolves (Canis lupus). Fireworks algorithm (FWA) is a nature-inspired optimization method mimicking the explosion process of fireworks for optimization problems. Both of them have a strong optimal search capabil...
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ftmdpi:oai:mdpi.com:/1424-8220/20/7/2147/ 2023-08-20T04:05:47+02:00 A Novel Hybrid Algorithm Based on Grey Wolf Optimizer and Fireworks Algorithm Zhihang Yue Sen Zhang Wendong Xiao 2020-04-10 application/pdf https://doi.org/10.3390/s20072147 EN eng Multidisciplinary Digital Publishing Institute Intelligent Sensors https://dx.doi.org/10.3390/s20072147 https://creativecommons.org/licenses/by/4.0/ Sensors; Volume 20; Issue 7; Pages: 2147 Grey Wolf Optimizer Fireworks Algorithm hybrid algorithm exploitation and exploration Text 2020 ftmdpi https://doi.org/10.3390/s20072147 2023-07-31T23:21:25Z Grey wolf optimizer (GWO) is a meta-heuristic algorithm inspired by the hierarchy of grey wolves (Canis lupus). Fireworks algorithm (FWA) is a nature-inspired optimization method mimicking the explosion process of fireworks for optimization problems. Both of them have a strong optimal search capability. However, in some cases, GWO converges to the local optimum and FWA converges slowly. In this paper, a new hybrid algorithm (named as FWGWO) is proposed, which fuses the advantages of these two algorithms to achieve global optima effectively. The proposed algorithm combines the exploration ability of the fireworks algorithm with the exploitation ability of the grey wolf optimizer (GWO) by setting a balance coefficient. In order to test the competence of the proposed hybrid FWGWO, 16 well-known benchmark functions having a wide range of dimensions and varied complexities are used in this paper. The results of the proposed FWGWO are compared to nine other algorithms, including the standard FWA, the native GWO, enhanced grey wolf optimizer (EGWO), and augmented grey wolf optimizer (AGWO). The experimental results show that the FWGWO effectively improves the global optimal search capability and convergence speed of the GWO and FWA. Text Canis lupus MDPI Open Access Publishing Sensors 20 7 2147 |
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topic |
Grey Wolf Optimizer Fireworks Algorithm hybrid algorithm exploitation and exploration |
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Grey Wolf Optimizer Fireworks Algorithm hybrid algorithm exploitation and exploration Zhihang Yue Sen Zhang Wendong Xiao A Novel Hybrid Algorithm Based on Grey Wolf Optimizer and Fireworks Algorithm |
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Grey Wolf Optimizer Fireworks Algorithm hybrid algorithm exploitation and exploration |
description |
Grey wolf optimizer (GWO) is a meta-heuristic algorithm inspired by the hierarchy of grey wolves (Canis lupus). Fireworks algorithm (FWA) is a nature-inspired optimization method mimicking the explosion process of fireworks for optimization problems. Both of them have a strong optimal search capability. However, in some cases, GWO converges to the local optimum and FWA converges slowly. In this paper, a new hybrid algorithm (named as FWGWO) is proposed, which fuses the advantages of these two algorithms to achieve global optima effectively. The proposed algorithm combines the exploration ability of the fireworks algorithm with the exploitation ability of the grey wolf optimizer (GWO) by setting a balance coefficient. In order to test the competence of the proposed hybrid FWGWO, 16 well-known benchmark functions having a wide range of dimensions and varied complexities are used in this paper. The results of the proposed FWGWO are compared to nine other algorithms, including the standard FWA, the native GWO, enhanced grey wolf optimizer (EGWO), and augmented grey wolf optimizer (AGWO). The experimental results show that the FWGWO effectively improves the global optimal search capability and convergence speed of the GWO and FWA. |
format |
Text |
author |
Zhihang Yue Sen Zhang Wendong Xiao |
author_facet |
Zhihang Yue Sen Zhang Wendong Xiao |
author_sort |
Zhihang Yue |
title |
A Novel Hybrid Algorithm Based on Grey Wolf Optimizer and Fireworks Algorithm |
title_short |
A Novel Hybrid Algorithm Based on Grey Wolf Optimizer and Fireworks Algorithm |
title_full |
A Novel Hybrid Algorithm Based on Grey Wolf Optimizer and Fireworks Algorithm |
title_fullStr |
A Novel Hybrid Algorithm Based on Grey Wolf Optimizer and Fireworks Algorithm |
title_full_unstemmed |
A Novel Hybrid Algorithm Based on Grey Wolf Optimizer and Fireworks Algorithm |
title_sort |
novel hybrid algorithm based on grey wolf optimizer and fireworks algorithm |
publisher |
Multidisciplinary Digital Publishing Institute |
publishDate |
2020 |
url |
https://doi.org/10.3390/s20072147 |
genre |
Canis lupus |
genre_facet |
Canis lupus |
op_source |
Sensors; Volume 20; Issue 7; Pages: 2147 |
op_relation |
Intelligent Sensors https://dx.doi.org/10.3390/s20072147 |
op_rights |
https://creativecommons.org/licenses/by/4.0/ |
op_doi |
https://doi.org/10.3390/s20072147 |
container_title |
Sensors |
container_volume |
20 |
container_issue |
7 |
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2147 |
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1774716521599205376 |