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Wild Geese Migration Optimization Algorithm: A New Meta-Heuristic Algorithm for Solving Inverse Kinematics of Robot

This paper proposes a new meta-heuristic algorithm, named wild geese migration optimization (GMO) algorithm. It is inspired by the social behavior of wild geese swarming in nature. They maintain a special formation for long-distance migration in small groups for survival and reproduction. The mathem...

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Detalles Bibliográficos
Autores principales: Wu, Honggang, Zhang, Xinming, Song, Linsen, Zhang, Yufei, Gu, Lidong, Zhao, Xiaonan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9630037/
https://www.ncbi.nlm.nih.gov/pubmed/36337271
http://dx.doi.org/10.1155/2022/5191758
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author Wu, Honggang
Zhang, Xinming
Song, Linsen
Zhang, Yufei
Gu, Lidong
Zhao, Xiaonan
author_facet Wu, Honggang
Zhang, Xinming
Song, Linsen
Zhang, Yufei
Gu, Lidong
Zhao, Xiaonan
author_sort Wu, Honggang
collection PubMed
description This paper proposes a new meta-heuristic algorithm, named wild geese migration optimization (GMO) algorithm. It is inspired by the social behavior of wild geese swarming in nature. They maintain a special formation for long-distance migration in small groups for survival and reproduction. The mathematical model is established based on these social behaviors to solve optimization problems. Meanwhile, the performance of the GMO algorithm is tested on the stable benchmark function of CEC2017, and its potential for dealing with practical problems is studied in five engineering design problems and the inverse kinematics solution of robot. The test results show that the GMO algorithm has excellent computational performance compared to other algorithms. The practical application results show that the GMO algorithm has strong applicability, more accurate optimization results, and more competitiveness in challenging problems with unknown search space, compared with well-known algorithms in the literature. The proposal of GMO algorithm enriches the team of swarm intelligence optimization algorithms and also provides a new solution for solving engineering design problems and inverse kinematics of robots.
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spelling pubmed-96300372022-11-04 Wild Geese Migration Optimization Algorithm: A New Meta-Heuristic Algorithm for Solving Inverse Kinematics of Robot Wu, Honggang Zhang, Xinming Song, Linsen Zhang, Yufei Gu, Lidong Zhao, Xiaonan Comput Intell Neurosci Research Article This paper proposes a new meta-heuristic algorithm, named wild geese migration optimization (GMO) algorithm. It is inspired by the social behavior of wild geese swarming in nature. They maintain a special formation for long-distance migration in small groups for survival and reproduction. The mathematical model is established based on these social behaviors to solve optimization problems. Meanwhile, the performance of the GMO algorithm is tested on the stable benchmark function of CEC2017, and its potential for dealing with practical problems is studied in five engineering design problems and the inverse kinematics solution of robot. The test results show that the GMO algorithm has excellent computational performance compared to other algorithms. The practical application results show that the GMO algorithm has strong applicability, more accurate optimization results, and more competitiveness in challenging problems with unknown search space, compared with well-known algorithms in the literature. The proposal of GMO algorithm enriches the team of swarm intelligence optimization algorithms and also provides a new solution for solving engineering design problems and inverse kinematics of robots. Hindawi 2022-09-27 /pmc/articles/PMC9630037/ /pubmed/36337271 http://dx.doi.org/10.1155/2022/5191758 Text en Copyright © 2022 Honggang Wu et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Wu, Honggang
Zhang, Xinming
Song, Linsen
Zhang, Yufei
Gu, Lidong
Zhao, Xiaonan
Wild Geese Migration Optimization Algorithm: A New Meta-Heuristic Algorithm for Solving Inverse Kinematics of Robot
title Wild Geese Migration Optimization Algorithm: A New Meta-Heuristic Algorithm for Solving Inverse Kinematics of Robot
title_full Wild Geese Migration Optimization Algorithm: A New Meta-Heuristic Algorithm for Solving Inverse Kinematics of Robot
title_fullStr Wild Geese Migration Optimization Algorithm: A New Meta-Heuristic Algorithm for Solving Inverse Kinematics of Robot
title_full_unstemmed Wild Geese Migration Optimization Algorithm: A New Meta-Heuristic Algorithm for Solving Inverse Kinematics of Robot
title_short Wild Geese Migration Optimization Algorithm: A New Meta-Heuristic Algorithm for Solving Inverse Kinematics of Robot
title_sort wild geese migration optimization algorithm: a new meta-heuristic algorithm for solving inverse kinematics of robot
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9630037/
https://www.ncbi.nlm.nih.gov/pubmed/36337271
http://dx.doi.org/10.1155/2022/5191758
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