An improved firefly algorithm with dynamic self-adaptive adjustment.

The firefly algorithm (FA) is proposed as a heuristic algorithm, inspired by natural phenomena. The FA has attracted a lot of attention due to its effectiveness in dealing with various global optimization problems. However, it could easily fall into a local optimal value or suffer from low accuracy...

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Autores principales:	Yu Li, Yiran Zhao, Yue Shang, Jingsen Liu
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/4c1a25e7e2e848a48c274883dd4fcd30
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spelling	oai:doaj.org-article:4c1a25e7e2e848a48c274883dd4fcd302021-12-02T20:13:48ZAn improved firefly algorithm with dynamic self-adaptive adjustment.1932-620310.1371/journal.pone.0255951https://doaj.org/article/4c1a25e7e2e848a48c274883dd4fcd302021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0255951https://doaj.org/toc/1932-6203The firefly algorithm (FA) is proposed as a heuristic algorithm, inspired by natural phenomena. The FA has attracted a lot of attention due to its effectiveness in dealing with various global optimization problems. However, it could easily fall into a local optimal value or suffer from low accuracy when solving high-dimensional optimization problems. To improve the performance of the FA, this paper adds the self-adaptive logarithmic inertia weight to the updating formula of the FA, and proposes the introduction of a minimum attractiveness of a firefly, which greatly improves the convergence speed and balances the global exploration and local exploitation capabilities of FA. Additionally, a step-size decreasing factor is introduced to dynamically adjust the random step-size term. When the dimension of a search is high, the random step-size becomes very small. This strategy enables the FA to explore solution more accurately. This improved FA (LWFA) was evaluated with ten benchmark test functions under different dimensions (D = 10, 30, and 100) and with standard IEEE CEC 2010 benchmark functions. Simulation results show that the performance of improved FA is superior comparing to the standard FA and other algorithms, i.e., particle swarm optimization, the cuckoo search algorithm, the flower pollination algorithm, the sine cosine algorithm, and other modified FA. The LWFA also has high performance and optimal efficiency for a number of optimization problems.Yu LiYiran ZhaoYue ShangJingsen LiuPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 10, p e0255951 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Yu Li Yiran Zhao Yue Shang Jingsen Liu An improved firefly algorithm with dynamic self-adaptive adjustment.
description	The firefly algorithm (FA) is proposed as a heuristic algorithm, inspired by natural phenomena. The FA has attracted a lot of attention due to its effectiveness in dealing with various global optimization problems. However, it could easily fall into a local optimal value or suffer from low accuracy when solving high-dimensional optimization problems. To improve the performance of the FA, this paper adds the self-adaptive logarithmic inertia weight to the updating formula of the FA, and proposes the introduction of a minimum attractiveness of a firefly, which greatly improves the convergence speed and balances the global exploration and local exploitation capabilities of FA. Additionally, a step-size decreasing factor is introduced to dynamically adjust the random step-size term. When the dimension of a search is high, the random step-size becomes very small. This strategy enables the FA to explore solution more accurately. This improved FA (LWFA) was evaluated with ten benchmark test functions under different dimensions (D = 10, 30, and 100) and with standard IEEE CEC 2010 benchmark functions. Simulation results show that the performance of improved FA is superior comparing to the standard FA and other algorithms, i.e., particle swarm optimization, the cuckoo search algorithm, the flower pollination algorithm, the sine cosine algorithm, and other modified FA. The LWFA also has high performance and optimal efficiency for a number of optimization problems.
format	article
author	Yu Li Yiran Zhao Yue Shang Jingsen Liu
author_facet	Yu Li Yiran Zhao Yue Shang Jingsen Liu
author_sort	Yu Li
title	An improved firefly algorithm with dynamic self-adaptive adjustment.
title_short	An improved firefly algorithm with dynamic self-adaptive adjustment.
title_full	An improved firefly algorithm with dynamic self-adaptive adjustment.
title_fullStr	An improved firefly algorithm with dynamic self-adaptive adjustment.
title_full_unstemmed	An improved firefly algorithm with dynamic self-adaptive adjustment.
title_sort	improved firefly algorithm with dynamic self-adaptive adjustment.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/4c1a25e7e2e848a48c274883dd4fcd30
work_keys_str_mv	AT yuli animprovedfireflyalgorithmwithdynamicselfadaptiveadjustment AT yiranzhao animprovedfireflyalgorithmwithdynamicselfadaptiveadjustment AT yueshang animprovedfireflyalgorithmwithdynamicselfadaptiveadjustment AT jingsenliu animprovedfireflyalgorithmwithdynamicselfadaptiveadjustment AT yuli improvedfireflyalgorithmwithdynamicselfadaptiveadjustment AT yiranzhao improvedfireflyalgorithmwithdynamicselfadaptiveadjustment AT yueshang improvedfireflyalgorithmwithdynamicselfadaptiveadjustment AT jingsenliu improvedfireflyalgorithmwithdynamicselfadaptiveadjustment
_version_	1718374701328236544

An improved firefly algorithm with dynamic self-adaptive adjustment.

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