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Indoor Robot Path Planning Using an Improved Whale Optimization Algorithm
An improved whale optimization algorithm is proposed to solve the problems of the original algorithm in indoor robot path planning, which has slow convergence speed, poor path finding ability, low efficiency, and is easily prone to falling into the local shortest path problem. First, an improved log...
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/PMC10143162/ https://www.ncbi.nlm.nih.gov/pubmed/37112328 http://dx.doi.org/10.3390/s23083988 |
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author | Si, Qing Li, Changyong |
author_facet | Si, Qing Li, Changyong |
author_sort | Si, Qing |
collection | PubMed |
description | An improved whale optimization algorithm is proposed to solve the problems of the original algorithm in indoor robot path planning, which has slow convergence speed, poor path finding ability, low efficiency, and is easily prone to falling into the local shortest path problem. First, an improved logistic chaotic mapping is applied to enrich the initial population of whales and improve the global search capability of the algorithm. Second, a nonlinear convergence factor is introduced, and the equilibrium parameter A is changed to balance the global and local search capabilities of the algorithm and improve the search efficiency. Finally, the fused Corsi variance and weighting strategy perturbs the location of the whales to improve the path quality. The improved logical whale optimization algorithm (ILWOA) is compared with the WOA and four other improved whale optimization algorithms through eight test functions and three raster map environments for experiments. The results show that ILWOA has better convergence and merit-seeking ability in the test function. In the path planning experiments, the results are better than other algorithms when comparing three evaluation criteria, which verifies that the path quality, merit-seeking ability, and robustness of ILWOA in path planning are improved. |
format | Online Article Text |
id | pubmed-10143162 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-101431622023-04-29 Indoor Robot Path Planning Using an Improved Whale Optimization Algorithm Si, Qing Li, Changyong Sensors (Basel) Article An improved whale optimization algorithm is proposed to solve the problems of the original algorithm in indoor robot path planning, which has slow convergence speed, poor path finding ability, low efficiency, and is easily prone to falling into the local shortest path problem. First, an improved logistic chaotic mapping is applied to enrich the initial population of whales and improve the global search capability of the algorithm. Second, a nonlinear convergence factor is introduced, and the equilibrium parameter A is changed to balance the global and local search capabilities of the algorithm and improve the search efficiency. Finally, the fused Corsi variance and weighting strategy perturbs the location of the whales to improve the path quality. The improved logical whale optimization algorithm (ILWOA) is compared with the WOA and four other improved whale optimization algorithms through eight test functions and three raster map environments for experiments. The results show that ILWOA has better convergence and merit-seeking ability in the test function. In the path planning experiments, the results are better than other algorithms when comparing three evaluation criteria, which verifies that the path quality, merit-seeking ability, and robustness of ILWOA in path planning are improved. MDPI 2023-04-14 /pmc/articles/PMC10143162/ /pubmed/37112328 http://dx.doi.org/10.3390/s23083988 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 Si, Qing Li, Changyong Indoor Robot Path Planning Using an Improved Whale Optimization Algorithm |
title | Indoor Robot Path Planning Using an Improved Whale Optimization Algorithm |
title_full | Indoor Robot Path Planning Using an Improved Whale Optimization Algorithm |
title_fullStr | Indoor Robot Path Planning Using an Improved Whale Optimization Algorithm |
title_full_unstemmed | Indoor Robot Path Planning Using an Improved Whale Optimization Algorithm |
title_short | Indoor Robot Path Planning Using an Improved Whale Optimization Algorithm |
title_sort | indoor robot path planning using an improved whale optimization algorithm |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10143162/ https://www.ncbi.nlm.nih.gov/pubmed/37112328 http://dx.doi.org/10.3390/s23083988 |
work_keys_str_mv | AT siqing indoorrobotpathplanningusinganimprovedwhaleoptimizationalgorithm AT lichangyong indoorrobotpathplanningusinganimprovedwhaleoptimizationalgorithm |