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An Improved Grey Wolf Optimizer and Its Application in Robot Path Planning
This paper discusses a hybrid grey wolf optimizer utilizing a clone selection algorithm (pGWO-CSA) to overcome the disadvantages of a standard grey wolf optimizer (GWO), such as slow convergence speed, low accuracy in the single-peak function, and easily falling into local optimum in the multi-peak...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9944837/ https://www.ncbi.nlm.nih.gov/pubmed/36810415 http://dx.doi.org/10.3390/biomimetics8010084 |
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author | Ou, Yun Yin, Pengfei Mo, Liping |
author_facet | Ou, Yun Yin, Pengfei Mo, Liping |
author_sort | Ou, Yun |
collection | PubMed |
description | This paper discusses a hybrid grey wolf optimizer utilizing a clone selection algorithm (pGWO-CSA) to overcome the disadvantages of a standard grey wolf optimizer (GWO), such as slow convergence speed, low accuracy in the single-peak function, and easily falling into local optimum in the multi-peak function and complex problems. The modifications of the proposed pGWO-CSA could be classified into the following three aspects. Firstly, a nonlinear function is used instead of a linear function for adjusting the iterative attenuation of the convergence factor to balance exploitation and exploration automatically. Then, an optimal α wolf is designed which will not be affected by the wolves β and δ with poor fitness in the position updating strategy; the second-best β wolf is designed, which will be affected by the low fitness value of the δ wolf. Finally, the cloning and super-mutation of the clonal selection algorithm (CSA) are introduced into GWO to enhance the ability to jump out of the local optimum. In the experimental part, 15 benchmark functions are selected to perform the function optimization tasks to reveal the performance of pGWO-CSA further. Due to the statistical analysis of the obtained experimental data, the pGWO-CSA is superior to these classical swarm intelligence algorithms, GWO, and related variants. Furthermore, in order to verify the applicability of the algorithm, it was applied to the robot path-planning problem and obtained excellent results. |
format | Online Article Text |
id | pubmed-9944837 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-99448372023-02-23 An Improved Grey Wolf Optimizer and Its Application in Robot Path Planning Ou, Yun Yin, Pengfei Mo, Liping Biomimetics (Basel) Article This paper discusses a hybrid grey wolf optimizer utilizing a clone selection algorithm (pGWO-CSA) to overcome the disadvantages of a standard grey wolf optimizer (GWO), such as slow convergence speed, low accuracy in the single-peak function, and easily falling into local optimum in the multi-peak function and complex problems. The modifications of the proposed pGWO-CSA could be classified into the following three aspects. Firstly, a nonlinear function is used instead of a linear function for adjusting the iterative attenuation of the convergence factor to balance exploitation and exploration automatically. Then, an optimal α wolf is designed which will not be affected by the wolves β and δ with poor fitness in the position updating strategy; the second-best β wolf is designed, which will be affected by the low fitness value of the δ wolf. Finally, the cloning and super-mutation of the clonal selection algorithm (CSA) are introduced into GWO to enhance the ability to jump out of the local optimum. In the experimental part, 15 benchmark functions are selected to perform the function optimization tasks to reveal the performance of pGWO-CSA further. Due to the statistical analysis of the obtained experimental data, the pGWO-CSA is superior to these classical swarm intelligence algorithms, GWO, and related variants. Furthermore, in order to verify the applicability of the algorithm, it was applied to the robot path-planning problem and obtained excellent results. MDPI 2023-02-16 /pmc/articles/PMC9944837/ /pubmed/36810415 http://dx.doi.org/10.3390/biomimetics8010084 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Ou, Yun Yin, Pengfei Mo, Liping An Improved Grey Wolf Optimizer and Its Application in Robot Path Planning |
title | An Improved Grey Wolf Optimizer and Its Application in Robot Path Planning |
title_full | An Improved Grey Wolf Optimizer and Its Application in Robot Path Planning |
title_fullStr | An Improved Grey Wolf Optimizer and Its Application in Robot Path Planning |
title_full_unstemmed | An Improved Grey Wolf Optimizer and Its Application in Robot Path Planning |
title_short | An Improved Grey Wolf Optimizer and Its Application in Robot Path Planning |
title_sort | improved grey wolf optimizer and its application in robot path planning |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9944837/ https://www.ncbi.nlm.nih.gov/pubmed/36810415 http://dx.doi.org/10.3390/biomimetics8010084 |
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