Grey wolf optimizer
This work proposes a new meta-heuristic called Grey Wolf Optimizer (GWO) inspired by grey wolves (Canis lupus). The GWO algorithm mimics the leadership hierarchy and hunting mechanism of grey wolves in nature. Four types of grey wolves such as alpha, beta, delta, and omega are employed for simulatin...
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ftgriffithuniv:oai:research-repository.griffith.edu.au:10072/66188 2023-10-29T02:35:33+01:00 Grey wolf optimizer Mirjalili, Seyedali Mirjalili, Seyed Mohammad Lewis, Andrew 2014 http://hdl.handle.net/10072/66188 https://doi.org/10.1016/j.advengsoft.2013.12.007 English eng eng Pergamon Press Advances in Engineering Software http://hdl.handle.net/10072/66188 0965-9978 doi:10.1016/j.advengsoft.2013.12.007 http://creativecommons.org/licenses/by-nc-nd/4.0/ © 2014 Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Licence (http://creativecommons.org/licenses/by-nc-nd/4.0/) which permits unrestricted, non-commercial use, distribution and reproduction in any medium, providing that the work is properly cited. open access Optimisation Information and computing sciences Engineering Journal article 2014 ftgriffithuniv https://doi.org/10.1016/j.advengsoft.2013.12.007 2023-10-02T22:26:44Z This work proposes a new meta-heuristic called Grey Wolf Optimizer (GWO) inspired by grey wolves (Canis lupus). The GWO algorithm mimics the leadership hierarchy and hunting mechanism of grey wolves in nature. Four types of grey wolves such as alpha, beta, delta, and omega are employed for simulating the leadership hierarchy. In addition, the three main steps of hunting, searching for prey, encircling prey, and attacking prey, are implemented. The algorithm is then benchmarked on 29 well-known test functions, and the results are verified by a comparative study with Particle Swarm Optimization (PSO), Gravitational Search Algorithm (GSA), Differential Evolution (DE), Evolutionary Programming (EP), and Evolution Strategy (ES). The results show that the GWO algorithm is able to provide very competitive results compared to these well-known meta-heuristics. The paper also considers solving three classical engineering design problems (tension/compression spring, welded beam, and pressure vessel designs) and presents a real application of the proposed method in the field of optical engineering. The results of the classical engineering design problems and real application prove that the proposed algorithm is applicable to challenging problems with unknown search spaces. Griffith Sciences, School of Information and Communication Technology Full Text Article in Journal/Newspaper Canis lupus Griffith University: Griffith Research Online Advances in Engineering Software 69 46 61 |
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Griffith University: Griffith Research Online |
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English |
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Optimisation Information and computing sciences Engineering |
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Optimisation Information and computing sciences Engineering Mirjalili, Seyedali Mirjalili, Seyed Mohammad Lewis, Andrew Grey wolf optimizer |
topic_facet |
Optimisation Information and computing sciences Engineering |
description |
This work proposes a new meta-heuristic called Grey Wolf Optimizer (GWO) inspired by grey wolves (Canis lupus). The GWO algorithm mimics the leadership hierarchy and hunting mechanism of grey wolves in nature. Four types of grey wolves such as alpha, beta, delta, and omega are employed for simulating the leadership hierarchy. In addition, the three main steps of hunting, searching for prey, encircling prey, and attacking prey, are implemented. The algorithm is then benchmarked on 29 well-known test functions, and the results are verified by a comparative study with Particle Swarm Optimization (PSO), Gravitational Search Algorithm (GSA), Differential Evolution (DE), Evolutionary Programming (EP), and Evolution Strategy (ES). The results show that the GWO algorithm is able to provide very competitive results compared to these well-known meta-heuristics. The paper also considers solving three classical engineering design problems (tension/compression spring, welded beam, and pressure vessel designs) and presents a real application of the proposed method in the field of optical engineering. The results of the classical engineering design problems and real application prove that the proposed algorithm is applicable to challenging problems with unknown search spaces. Griffith Sciences, School of Information and Communication Technology Full Text |
format |
Article in Journal/Newspaper |
author |
Mirjalili, Seyedali Mirjalili, Seyed Mohammad Lewis, Andrew |
author_facet |
Mirjalili, Seyedali Mirjalili, Seyed Mohammad Lewis, Andrew |
author_sort |
Mirjalili, Seyedali |
title |
Grey wolf optimizer |
title_short |
Grey wolf optimizer |
title_full |
Grey wolf optimizer |
title_fullStr |
Grey wolf optimizer |
title_full_unstemmed |
Grey wolf optimizer |
title_sort |
grey wolf optimizer |
publisher |
Pergamon Press |
publishDate |
2014 |
url |
http://hdl.handle.net/10072/66188 https://doi.org/10.1016/j.advengsoft.2013.12.007 |
genre |
Canis lupus |
genre_facet |
Canis lupus |
op_relation |
Advances in Engineering Software http://hdl.handle.net/10072/66188 0965-9978 doi:10.1016/j.advengsoft.2013.12.007 |
op_rights |
http://creativecommons.org/licenses/by-nc-nd/4.0/ © 2014 Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Licence (http://creativecommons.org/licenses/by-nc-nd/4.0/) which permits unrestricted, non-commercial use, distribution and reproduction in any medium, providing that the work is properly cited. open access |
op_doi |
https://doi.org/10.1016/j.advengsoft.2013.12.007 |
container_title |
Advances in Engineering Software |
container_volume |
69 |
container_start_page |
46 |
op_container_end_page |
61 |
_version_ |
1781058818857238528 |