An improved Wolf pack algorithm for optimization problems: Design and evaluation.

Wolf Pack Algorithm (WPA) is a swarm intelligence algorithm that simulates the food searching process of wolves. It is widely used in various engineering optimization problems due to its global convergence and computational robustness. However, the algorithm has some weaknesses such as low convergen...

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Autores principales:	Xuan Chen, Feng Cheng, Cong Liu, Long Cheng, Yin Mao
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/8f9c8d73e7474f8cbc94cfe9da906533
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id	oai:doaj.org-article:8f9c8d73e7474f8cbc94cfe9da906533
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spelling	oai:doaj.org-article:8f9c8d73e7474f8cbc94cfe9da9065332021-12-02T20:14:54ZAn improved Wolf pack algorithm for optimization problems: Design and evaluation.1932-620310.1371/journal.pone.0254239https://doaj.org/article/8f9c8d73e7474f8cbc94cfe9da9065332021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0254239https://doaj.org/toc/1932-6203Wolf Pack Algorithm (WPA) is a swarm intelligence algorithm that simulates the food searching process of wolves. It is widely used in various engineering optimization problems due to its global convergence and computational robustness. However, the algorithm has some weaknesses such as low convergence speed and easily falling into local optimum. To tackle the problems, we introduce an improved approach called OGL-WPA in this work, based on the employments of Opposition-based learning and Genetic algorithm with Levy's flight. Specifically, in OGL-WPA, the population of wolves is initialized by opposition-based learning to maintain the diversity of the initial population during global search. Meanwhile, the leader wolf is selected by genetic algorithm to avoid falling into local optimum and the round-up behavior is optimized by Levy's flight to coordinate the global exploration and local development capabilities. We present the detailed design of our algorithm and compare it with some other nature-inspired metaheuristic algorithms using various classical test functions. The experimental results show that the proposed algorithm has better global and local search capability, especially in the presence of multi-peak and high-dimensional functions.Xuan ChenFeng ChengCong LiuLong ChengYin MaoPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 8, p e0254239 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Xuan Chen Feng Cheng Cong Liu Long Cheng Yin Mao An improved Wolf pack algorithm for optimization problems: Design and evaluation.
description	Wolf Pack Algorithm (WPA) is a swarm intelligence algorithm that simulates the food searching process of wolves. It is widely used in various engineering optimization problems due to its global convergence and computational robustness. However, the algorithm has some weaknesses such as low convergence speed and easily falling into local optimum. To tackle the problems, we introduce an improved approach called OGL-WPA in this work, based on the employments of Opposition-based learning and Genetic algorithm with Levy's flight. Specifically, in OGL-WPA, the population of wolves is initialized by opposition-based learning to maintain the diversity of the initial population during global search. Meanwhile, the leader wolf is selected by genetic algorithm to avoid falling into local optimum and the round-up behavior is optimized by Levy's flight to coordinate the global exploration and local development capabilities. We present the detailed design of our algorithm and compare it with some other nature-inspired metaheuristic algorithms using various classical test functions. The experimental results show that the proposed algorithm has better global and local search capability, especially in the presence of multi-peak and high-dimensional functions.
format	article
author	Xuan Chen Feng Cheng Cong Liu Long Cheng Yin Mao
author_facet	Xuan Chen Feng Cheng Cong Liu Long Cheng Yin Mao
author_sort	Xuan Chen
title	An improved Wolf pack algorithm for optimization problems: Design and evaluation.
title_short	An improved Wolf pack algorithm for optimization problems: Design and evaluation.
title_full	An improved Wolf pack algorithm for optimization problems: Design and evaluation.
title_fullStr	An improved Wolf pack algorithm for optimization problems: Design and evaluation.
title_full_unstemmed	An improved Wolf pack algorithm for optimization problems: Design and evaluation.
title_sort	improved wolf pack algorithm for optimization problems: design and evaluation.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/8f9c8d73e7474f8cbc94cfe9da906533
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An improved Wolf pack algorithm for optimization problems: Design and evaluation.

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