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An improved harris hawks optimization algorithm based on chaotic sequence and opposite elite learning mechanism

The Harris hawks optimization (HHO) algorithm is a new swarm-based natural heuristic algorithm that has previously shown excellent performance. However, HHO still has some shortcomings, which are premature convergence and falling into local optima due to an imbalance of the exploration and exploitat...

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Autores principales: Yang, Ting, Fang, Jie, Jia, Chaochuan, Liu, Zhengyu, Liu, Yu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9946268/
https://www.ncbi.nlm.nih.gov/pubmed/36812174
http://dx.doi.org/10.1371/journal.pone.0281636
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author Yang, Ting
Fang, Jie
Jia, Chaochuan
Liu, Zhengyu
Liu, Yu
author_facet Yang, Ting
Fang, Jie
Jia, Chaochuan
Liu, Zhengyu
Liu, Yu
author_sort Yang, Ting
collection PubMed
description The Harris hawks optimization (HHO) algorithm is a new swarm-based natural heuristic algorithm that has previously shown excellent performance. However, HHO still has some shortcomings, which are premature convergence and falling into local optima due to an imbalance of the exploration and exploitation capabilities. To overcome these shortcomings, a new HHO variant algorithm based on a chaotic sequence and an opposite elite learning mechanism (HHO-CS-OELM) is proposed in this paper. The chaotic sequence can improve the global search ability of the HHO algorithm due to enhancing the diversity of the population, and the opposite elite learning can enhance the local search ability of the HHO algorithm by maintaining the optimal individual. Meanwhile, it also overcomes the shortcoming that the exploration cannot be carried out at the late iteration in the HHO algorithm and balances the exploration and exploitation capabilities of the HHO algorithm. The performance of the HHO-CS-OELM algorithm is verified by comparison with 14 optimization algorithms on 23 benchmark functions and an engineering problem. Experimental results show that the HHO-CS-OELM algorithm performs better than the state-of-the-art swarm intelligence optimization algorithms.
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spelling pubmed-99462682023-02-23 An improved harris hawks optimization algorithm based on chaotic sequence and opposite elite learning mechanism Yang, Ting Fang, Jie Jia, Chaochuan Liu, Zhengyu Liu, Yu PLoS One Research Article The Harris hawks optimization (HHO) algorithm is a new swarm-based natural heuristic algorithm that has previously shown excellent performance. However, HHO still has some shortcomings, which are premature convergence and falling into local optima due to an imbalance of the exploration and exploitation capabilities. To overcome these shortcomings, a new HHO variant algorithm based on a chaotic sequence and an opposite elite learning mechanism (HHO-CS-OELM) is proposed in this paper. The chaotic sequence can improve the global search ability of the HHO algorithm due to enhancing the diversity of the population, and the opposite elite learning can enhance the local search ability of the HHO algorithm by maintaining the optimal individual. Meanwhile, it also overcomes the shortcoming that the exploration cannot be carried out at the late iteration in the HHO algorithm and balances the exploration and exploitation capabilities of the HHO algorithm. The performance of the HHO-CS-OELM algorithm is verified by comparison with 14 optimization algorithms on 23 benchmark functions and an engineering problem. Experimental results show that the HHO-CS-OELM algorithm performs better than the state-of-the-art swarm intelligence optimization algorithms. Public Library of Science 2023-02-22 /pmc/articles/PMC9946268/ /pubmed/36812174 http://dx.doi.org/10.1371/journal.pone.0281636 Text en © 2023 Yang et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Yang, Ting
Fang, Jie
Jia, Chaochuan
Liu, Zhengyu
Liu, Yu
An improved harris hawks optimization algorithm based on chaotic sequence and opposite elite learning mechanism
title An improved harris hawks optimization algorithm based on chaotic sequence and opposite elite learning mechanism
title_full An improved harris hawks optimization algorithm based on chaotic sequence and opposite elite learning mechanism
title_fullStr An improved harris hawks optimization algorithm based on chaotic sequence and opposite elite learning mechanism
title_full_unstemmed An improved harris hawks optimization algorithm based on chaotic sequence and opposite elite learning mechanism
title_short An improved harris hawks optimization algorithm based on chaotic sequence and opposite elite learning mechanism
title_sort improved harris hawks optimization algorithm based on chaotic sequence and opposite elite learning mechanism
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9946268/
https://www.ncbi.nlm.nih.gov/pubmed/36812174
http://dx.doi.org/10.1371/journal.pone.0281636
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