An Improved PSO-GWO Algorithm With Chaos and Adaptive Inertial Weight for Robot Path Planning

The traditional particle swarm optimization (PSO) path planning algorithm represents each particle as a path and evolves the particles to find an optimal path. However, there are problems in premature convergence, poor global search ability, and to the ease in which particles fall into the local opt...

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Autores principales:	Xuezhen Cheng, Jiming Li, Caiyun Zheng, Jianhui Zhang, Meng Zhao
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	path planning improved particle swarm optimization robot gray wolf algorithm adaptive inertia weight chaos Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
Acceso en línea:	https://doaj.org/article/51820d964ab24753abdc0e7cbc69bbe7
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id	oai:doaj.org-article:51820d964ab24753abdc0e7cbc69bbe7
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spelling	oai:doaj.org-article:51820d964ab24753abdc0e7cbc69bbe72021-11-05T14:43:22ZAn Improved PSO-GWO Algorithm With Chaos and Adaptive Inertial Weight for Robot Path Planning1662-521810.3389/fnbot.2021.770361https://doaj.org/article/51820d964ab24753abdc0e7cbc69bbe72021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fnbot.2021.770361/fullhttps://doaj.org/toc/1662-5218The traditional particle swarm optimization (PSO) path planning algorithm represents each particle as a path and evolves the particles to find an optimal path. However, there are problems in premature convergence, poor global search ability, and to the ease in which particles fall into the local optimum, which could lead to the failure of fast optimal path obtainment. In order to solve these problems, this paper proposes an improved PSO combined gray wolf optimization (IPSO-GWO) algorithm with chaos and a new adaptive inertial weight. The gray wolf optimizer can sort the particles during evolution to find the particles with optimal fitness value, and lead other particles to search for the position of the particle with the optimal fitness value, which gives the PSO algorithm higher global search capability. The chaos can be used to initialize the speed and position of the particles, which can reduce the prematurity and increase the diversity of the particles. The new adaptive inertial weight is designed to improve the global search capability and convergence speed. In addition, when the algorithm falls into a local optimum, the position of the particle with the historical best fitness can be found through the chaotic sequence, which can randomly replace a particle to make it jump out of the local optimum. The proposed IPSO-GWO algorithm is first tested by function optimization using ten benchmark functions and then applied for optimal robot path planning in a simulated environment. Simulation results show that the proposed IPSO-GWO is able to find an optimal path much faster than traditional PSO-GWO based methods.Xuezhen ChengJiming LiCaiyun ZhengJianhui ZhangMeng ZhaoFrontiers Media S.A.articlepath planningimproved particle swarm optimizationrobotgray wolf algorithmadaptive inertia weightchaosNeurosciences. Biological psychiatry. NeuropsychiatryRC321-571ENFrontiers in Neurorobotics, Vol 15 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	path planning improved particle swarm optimization robot gray wolf algorithm adaptive inertia weight chaos Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
spellingShingle	path planning improved particle swarm optimization robot gray wolf algorithm adaptive inertia weight chaos Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571 Xuezhen Cheng Jiming Li Caiyun Zheng Jianhui Zhang Meng Zhao An Improved PSO-GWO Algorithm With Chaos and Adaptive Inertial Weight for Robot Path Planning
description	The traditional particle swarm optimization (PSO) path planning algorithm represents each particle as a path and evolves the particles to find an optimal path. However, there are problems in premature convergence, poor global search ability, and to the ease in which particles fall into the local optimum, which could lead to the failure of fast optimal path obtainment. In order to solve these problems, this paper proposes an improved PSO combined gray wolf optimization (IPSO-GWO) algorithm with chaos and a new adaptive inertial weight. The gray wolf optimizer can sort the particles during evolution to find the particles with optimal fitness value, and lead other particles to search for the position of the particle with the optimal fitness value, which gives the PSO algorithm higher global search capability. The chaos can be used to initialize the speed and position of the particles, which can reduce the prematurity and increase the diversity of the particles. The new adaptive inertial weight is designed to improve the global search capability and convergence speed. In addition, when the algorithm falls into a local optimum, the position of the particle with the historical best fitness can be found through the chaotic sequence, which can randomly replace a particle to make it jump out of the local optimum. The proposed IPSO-GWO algorithm is first tested by function optimization using ten benchmark functions and then applied for optimal robot path planning in a simulated environment. Simulation results show that the proposed IPSO-GWO is able to find an optimal path much faster than traditional PSO-GWO based methods.
format	article
author	Xuezhen Cheng Jiming Li Caiyun Zheng Jianhui Zhang Meng Zhao
author_facet	Xuezhen Cheng Jiming Li Caiyun Zheng Jianhui Zhang Meng Zhao
author_sort	Xuezhen Cheng
title	An Improved PSO-GWO Algorithm With Chaos and Adaptive Inertial Weight for Robot Path Planning
title_short	An Improved PSO-GWO Algorithm With Chaos and Adaptive Inertial Weight for Robot Path Planning
title_full	An Improved PSO-GWO Algorithm With Chaos and Adaptive Inertial Weight for Robot Path Planning
title_fullStr	An Improved PSO-GWO Algorithm With Chaos and Adaptive Inertial Weight for Robot Path Planning
title_full_unstemmed	An Improved PSO-GWO Algorithm With Chaos and Adaptive Inertial Weight for Robot Path Planning
title_sort	improved pso-gwo algorithm with chaos and adaptive inertial weight for robot path planning
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/51820d964ab24753abdc0e7cbc69bbe7
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An Improved PSO-GWO Algorithm With Chaos and Adaptive Inertial Weight for Robot Path Planning

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