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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...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2018
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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. |
format | Online Article Text |
id | pubmed-6158938 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
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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