An improved hybrid Aquila Optimizer and Harris Hawks Optimization for global optimization

This paper introduces an improved hybrid Aquila Optimizer (AO) and Harris Hawks Optimization (HHO) algorithm, namely IHAOHHO, to enhance the searching performance for global optimization problems. In the IHAOHHO, valuable exploration and exploitation capabilities of AO and HHO are retained firstly,...

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Autores principales:	Shuang Wang, Heming Jia, Qingxin Liu, Rong Zheng
Formato:	article
Lenguaje:	EN
Publicado:	AIMS Press 2021
Materias:	aquila optimizer harris hawks optimization hybrid algorithm representative-based hunting opposition-based learning Biotechnology TP248.13-248.65 Mathematics QA1-939
Acceso en línea:	https://doaj.org/article/0c60a2e4d48d4f689dc84cb5aa2bbe88
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spelling	oai:doaj.org-article:0c60a2e4d48d4f689dc84cb5aa2bbe882021-11-23T01:41:25ZAn improved hybrid Aquila Optimizer and Harris Hawks Optimization for global optimization10.3934/mbe.20213521551-0018https://doaj.org/article/0c60a2e4d48d4f689dc84cb5aa2bbe882021-08-01T00:00:00Zhttps://www.aimspress.com/article/doi/10.3934/mbe.2021352?viewType=HTMLhttps://doaj.org/toc/1551-0018This paper introduces an improved hybrid Aquila Optimizer (AO) and Harris Hawks Optimization (HHO) algorithm, namely IHAOHHO, to enhance the searching performance for global optimization problems. In the IHAOHHO, valuable exploration and exploitation capabilities of AO and HHO are retained firstly, and then representative-based hunting (RH) and opposition-based learning (OBL) strategies are added in the exploration and exploitation phases to effectively improve the diversity of search space and local optima avoidance capability of the algorithm, respectively. To verify the optimization performance and the practicability, the proposed algorithm is comprehensively analyzed on standard and CEC2017 benchmark functions and three engineering design problems. The experimental results show that the proposed IHAOHHO has more superior global search performance and faster convergence speed compared to the basic AO and HHO and selected state-of-the-art meta-heuristic algorithms.Shuang WangHeming Jia Qingxin LiuRong ZhengAIMS Pressarticleaquila optimizerharris hawks optimizationhybrid algorithmrepresentative-based huntingopposition-based learningBiotechnologyTP248.13-248.65MathematicsQA1-939ENMathematical Biosciences and Engineering, Vol 18, Iss 6, Pp 7076-7109 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	aquila optimizer harris hawks optimization hybrid algorithm representative-based hunting opposition-based learning Biotechnology TP248.13-248.65 Mathematics QA1-939
spellingShingle	aquila optimizer harris hawks optimization hybrid algorithm representative-based hunting opposition-based learning Biotechnology TP248.13-248.65 Mathematics QA1-939 Shuang Wang Heming Jia Qingxin Liu Rong Zheng An improved hybrid Aquila Optimizer and Harris Hawks Optimization for global optimization
description	This paper introduces an improved hybrid Aquila Optimizer (AO) and Harris Hawks Optimization (HHO) algorithm, namely IHAOHHO, to enhance the searching performance for global optimization problems. In the IHAOHHO, valuable exploration and exploitation capabilities of AO and HHO are retained firstly, and then representative-based hunting (RH) and opposition-based learning (OBL) strategies are added in the exploration and exploitation phases to effectively improve the diversity of search space and local optima avoidance capability of the algorithm, respectively. To verify the optimization performance and the practicability, the proposed algorithm is comprehensively analyzed on standard and CEC2017 benchmark functions and three engineering design problems. The experimental results show that the proposed IHAOHHO has more superior global search performance and faster convergence speed compared to the basic AO and HHO and selected state-of-the-art meta-heuristic algorithms.
format	article
author	Shuang Wang Heming Jia Qingxin Liu Rong Zheng
author_facet	Shuang Wang Heming Jia Qingxin Liu Rong Zheng
author_sort	Shuang Wang
title	An improved hybrid Aquila Optimizer and Harris Hawks Optimization for global optimization
title_short	An improved hybrid Aquila Optimizer and Harris Hawks Optimization for global optimization
title_full	An improved hybrid Aquila Optimizer and Harris Hawks Optimization for global optimization
title_fullStr	An improved hybrid Aquila Optimizer and Harris Hawks Optimization for global optimization
title_full_unstemmed	An improved hybrid Aquila Optimizer and Harris Hawks Optimization for global optimization
title_sort	improved hybrid aquila optimizer and harris hawks optimization for global optimization
publisher	AIMS Press
publishDate	2021
url	https://doaj.org/article/0c60a2e4d48d4f689dc84cb5aa2bbe88
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_version_	1718417360527818752

An improved hybrid Aquila Optimizer and Harris Hawks Optimization for global optimization

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