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Modification of Fish Swarm Algorithm Based on Lévy Flight and Firefly Behavior

Artificial fish swarm algorithm easily converges to local optimum, especially in solving the global optimization problem of multidimensional and multiextreme value functions. To overcome this drawback, a novel fish swarm algorithm (LFFSA) based on Lévy flight and firefly behavior is proposed. LFFSA...

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Detalles Bibliográficos
Autores principales: Peng, Zhenrui, Dong, Kangli, Yin, Hong, Bai, Yu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6158938/
https://www.ncbi.nlm.nih.gov/pubmed/30344601
http://dx.doi.org/10.1155/2018/9827372
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author Peng, Zhenrui
Dong, Kangli
Yin, Hong
Bai, Yu
author_facet Peng, Zhenrui
Dong, Kangli
Yin, Hong
Bai, Yu
author_sort Peng, Zhenrui
collection PubMed
description Artificial fish swarm algorithm easily converges to local optimum, especially in solving the global optimization problem of multidimensional and multiextreme value functions. To overcome this drawback, a novel fish swarm algorithm (LFFSA) based on Lévy flight and firefly behavior is proposed. LFFSA incorporates the moving strategy of firefly algorithm into two behavior patterns of fish swarm, i.e., chasing behavior and preying behavior. Furthermore, Lévy flight is introduced into the searching strategy. To limit the search band, nonlinear view and step size based on dynamic parameter are considered. Finally, the proposed algorithm LFFSA is validated with several benchmark problems. Numerical results demonstrate that LFFSA has a better performance in convergence speed and optimization accuracy than the other test algorithms.
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spelling pubmed-61589382018-10-21 Modification of Fish Swarm Algorithm Based on Lévy Flight and Firefly Behavior Peng, Zhenrui Dong, Kangli Yin, Hong Bai, Yu Comput Intell Neurosci Research Article Artificial fish swarm algorithm easily converges to local optimum, especially in solving the global optimization problem of multidimensional and multiextreme value functions. To overcome this drawback, a novel fish swarm algorithm (LFFSA) based on Lévy flight and firefly behavior is proposed. LFFSA incorporates the moving strategy of firefly algorithm into two behavior patterns of fish swarm, i.e., chasing behavior and preying behavior. Furthermore, Lévy flight is introduced into the searching strategy. To limit the search band, nonlinear view and step size based on dynamic parameter are considered. Finally, the proposed algorithm LFFSA is validated with several benchmark problems. Numerical results demonstrate that LFFSA has a better performance in convergence speed and optimization accuracy than the other test algorithms. Hindawi 2018-09-13 /pmc/articles/PMC6158938/ /pubmed/30344601 http://dx.doi.org/10.1155/2018/9827372 Text en Copyright © 2018 Zhenrui Peng et al. http://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
Peng, Zhenrui
Dong, Kangli
Yin, Hong
Bai, Yu
Modification of Fish Swarm Algorithm Based on Lévy Flight and Firefly Behavior
title Modification of Fish Swarm Algorithm Based on Lévy Flight and Firefly Behavior
title_full Modification of Fish Swarm Algorithm Based on Lévy Flight and Firefly Behavior
title_fullStr Modification of Fish Swarm Algorithm Based on Lévy Flight and Firefly Behavior
title_full_unstemmed Modification of Fish Swarm Algorithm Based on Lévy Flight and Firefly Behavior
title_short Modification of Fish Swarm Algorithm Based on Lévy Flight and Firefly Behavior
title_sort modification of fish swarm algorithm based on lévy flight and firefly behavior
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6158938/
https://www.ncbi.nlm.nih.gov/pubmed/30344601
http://dx.doi.org/10.1155/2018/9827372
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