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Chaotic Teaching-Learning-Based Optimization with Lévy Flight for Global Numerical Optimization

Recently, teaching-learning-based optimization (TLBO), as one of the emerging nature-inspired heuristic algorithms, has attracted increasing attention. In order to enhance its convergence rate and prevent it from getting stuck in local optima, a novel metaheuristic has been developed in this paper,...

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Detalles Bibliográficos
Autores principales: He, Xiangzhu, Huang, Jida, Rao, Yunqing, Gao, Liang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi Publishing Corporation 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4753053/
https://www.ncbi.nlm.nih.gov/pubmed/26941785
http://dx.doi.org/10.1155/2016/8341275
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author He, Xiangzhu
Huang, Jida
Rao, Yunqing
Gao, Liang
author_facet He, Xiangzhu
Huang, Jida
Rao, Yunqing
Gao, Liang
author_sort He, Xiangzhu
collection PubMed
description Recently, teaching-learning-based optimization (TLBO), as one of the emerging nature-inspired heuristic algorithms, has attracted increasing attention. In order to enhance its convergence rate and prevent it from getting stuck in local optima, a novel metaheuristic has been developed in this paper, where particular characteristics of the chaos mechanism and Lévy flight are introduced to the basic framework of TLBO. The new algorithm is tested on several large-scale nonlinear benchmark functions with different characteristics and compared with other methods. Experimental results show that the proposed algorithm outperforms other algorithms and achieves a satisfactory improvement over TLBO.
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spelling pubmed-47530532016-03-03 Chaotic Teaching-Learning-Based Optimization with Lévy Flight for Global Numerical Optimization He, Xiangzhu Huang, Jida Rao, Yunqing Gao, Liang Comput Intell Neurosci Research Article Recently, teaching-learning-based optimization (TLBO), as one of the emerging nature-inspired heuristic algorithms, has attracted increasing attention. In order to enhance its convergence rate and prevent it from getting stuck in local optima, a novel metaheuristic has been developed in this paper, where particular characteristics of the chaos mechanism and Lévy flight are introduced to the basic framework of TLBO. The new algorithm is tested on several large-scale nonlinear benchmark functions with different characteristics and compared with other methods. Experimental results show that the proposed algorithm outperforms other algorithms and achieves a satisfactory improvement over TLBO. Hindawi Publishing Corporation 2016 2016-01-31 /pmc/articles/PMC4753053/ /pubmed/26941785 http://dx.doi.org/10.1155/2016/8341275 Text en Copyright © 2016 Xiangzhu He et al. https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
He, Xiangzhu
Huang, Jida
Rao, Yunqing
Gao, Liang
Chaotic Teaching-Learning-Based Optimization with Lévy Flight for Global Numerical Optimization
title Chaotic Teaching-Learning-Based Optimization with Lévy Flight for Global Numerical Optimization
title_full Chaotic Teaching-Learning-Based Optimization with Lévy Flight for Global Numerical Optimization
title_fullStr Chaotic Teaching-Learning-Based Optimization with Lévy Flight for Global Numerical Optimization
title_full_unstemmed Chaotic Teaching-Learning-Based Optimization with Lévy Flight for Global Numerical Optimization
title_short Chaotic Teaching-Learning-Based Optimization with Lévy Flight for Global Numerical Optimization
title_sort chaotic teaching-learning-based optimization with lévy flight for global numerical optimization
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4753053/
https://www.ncbi.nlm.nih.gov/pubmed/26941785
http://dx.doi.org/10.1155/2016/8341275
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