Northern Goshawk Optimization: A New Swarm-Based Algorithm for Solving Optimization Problems
Optimization algorithms are one of the effective stochastic methods in solving optimization problems. In this paper, a new swarm-based algorithm called Northern Goshawk Optimization (NGO) algorithm is presented that simulates the behavior of northern goshawk during prey hunting. This hunting strateg...
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ftdoajarticles:oai:doaj.org/article:bb26aa6f10c54fbe89dd3a605e6e5846 2023-05-15T17:43:04+02:00 Northern Goshawk Optimization: A New Swarm-Based Algorithm for Solving Optimization Problems Mohammad Dehghani Stepan Hubalovsky Pavel Trojovsky 2021-01-01T00:00:00Z https://doi.org/10.1109/ACCESS.2021.3133286 https://doaj.org/article/bb26aa6f10c54fbe89dd3a605e6e5846 EN eng IEEE https://ieeexplore.ieee.org/document/9638618/ https://doaj.org/toc/2169-3536 2169-3536 doi:10.1109/ACCESS.2021.3133286 https://doaj.org/article/bb26aa6f10c54fbe89dd3a605e6e5846 IEEE Access, Vol 9, Pp 162059-162080 (2021) Exploitation exploration northern goshawk optimization optimization problem Electrical engineering. Electronics. Nuclear engineering TK1-9971 article 2021 ftdoajarticles https://doi.org/10.1109/ACCESS.2021.3133286 2022-12-31T15:16:19Z Optimization algorithms are one of the effective stochastic methods in solving optimization problems. In this paper, a new swarm-based algorithm called Northern Goshawk Optimization (NGO) algorithm is presented that simulates the behavior of northern goshawk during prey hunting. This hunting strategy includes two phases of prey identification and the tail and chase process. The various steps of the proposed NGO algorithm are described and then its mathematical modeling is presented for use in solving optimization problems. The ability of NGO to solve optimization problems is evaluated on sixty-eight different objective functions. To analyze the quality of the results, the proposed NGO algorithm is compared with eight well-known algorithms, particle swarm optimization, genetic algorithm, teaching-learning based optimization, gravitational search algorithm, grey wolf optimizer, whale optimization algorithm, tunicate swarm algorithm, and marine predators algorithm. In addition, for further analysis, the proposed algorithm is also employed to solve four engineering design problems. The results of simulations and experiments show that the proposed NGO algorithm, by creating a proper balance between exploration and exploitation, has an effective performance in solving optimization problems and is much more competitive than similar algorithms. Article in Journal/Newspaper Northern Goshawk Directory of Open Access Journals: DOAJ Articles IEEE Access 9 162059 162080 |
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Open Polar |
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Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
Exploitation exploration northern goshawk optimization optimization problem Electrical engineering. Electronics. Nuclear engineering TK1-9971 |
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Exploitation exploration northern goshawk optimization optimization problem Electrical engineering. Electronics. Nuclear engineering TK1-9971 Mohammad Dehghani Stepan Hubalovsky Pavel Trojovsky Northern Goshawk Optimization: A New Swarm-Based Algorithm for Solving Optimization Problems |
topic_facet |
Exploitation exploration northern goshawk optimization optimization problem Electrical engineering. Electronics. Nuclear engineering TK1-9971 |
description |
Optimization algorithms are one of the effective stochastic methods in solving optimization problems. In this paper, a new swarm-based algorithm called Northern Goshawk Optimization (NGO) algorithm is presented that simulates the behavior of northern goshawk during prey hunting. This hunting strategy includes two phases of prey identification and the tail and chase process. The various steps of the proposed NGO algorithm are described and then its mathematical modeling is presented for use in solving optimization problems. The ability of NGO to solve optimization problems is evaluated on sixty-eight different objective functions. To analyze the quality of the results, the proposed NGO algorithm is compared with eight well-known algorithms, particle swarm optimization, genetic algorithm, teaching-learning based optimization, gravitational search algorithm, grey wolf optimizer, whale optimization algorithm, tunicate swarm algorithm, and marine predators algorithm. In addition, for further analysis, the proposed algorithm is also employed to solve four engineering design problems. The results of simulations and experiments show that the proposed NGO algorithm, by creating a proper balance between exploration and exploitation, has an effective performance in solving optimization problems and is much more competitive than similar algorithms. |
format |
Article in Journal/Newspaper |
author |
Mohammad Dehghani Stepan Hubalovsky Pavel Trojovsky |
author_facet |
Mohammad Dehghani Stepan Hubalovsky Pavel Trojovsky |
author_sort |
Mohammad Dehghani |
title |
Northern Goshawk Optimization: A New Swarm-Based Algorithm for Solving Optimization Problems |
title_short |
Northern Goshawk Optimization: A New Swarm-Based Algorithm for Solving Optimization Problems |
title_full |
Northern Goshawk Optimization: A New Swarm-Based Algorithm for Solving Optimization Problems |
title_fullStr |
Northern Goshawk Optimization: A New Swarm-Based Algorithm for Solving Optimization Problems |
title_full_unstemmed |
Northern Goshawk Optimization: A New Swarm-Based Algorithm for Solving Optimization Problems |
title_sort |
northern goshawk optimization: a new swarm-based algorithm for solving optimization problems |
publisher |
IEEE |
publishDate |
2021 |
url |
https://doi.org/10.1109/ACCESS.2021.3133286 https://doaj.org/article/bb26aa6f10c54fbe89dd3a605e6e5846 |
genre |
Northern Goshawk |
genre_facet |
Northern Goshawk |
op_source |
IEEE Access, Vol 9, Pp 162059-162080 (2021) |
op_relation |
https://ieeexplore.ieee.org/document/9638618/ https://doaj.org/toc/2169-3536 2169-3536 doi:10.1109/ACCESS.2021.3133286 https://doaj.org/article/bb26aa6f10c54fbe89dd3a605e6e5846 |
op_doi |
https://doi.org/10.1109/ACCESS.2021.3133286 |
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IEEE Access |
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9 |
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162059 |
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162080 |
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1766145072735191040 |