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Neural Computing and Applications
Erschienen in: Neural Computing and Applications 5/2019

23.03.2018 | S.I. : Emerging Intelligent Algorithms for Edge-of-Things Computing

A new and efficient firefly algorithm for numerical optimization problems

verfasst von: Xiuqin Pan, Limiao Xue, Ruixiang Li

Erschienen in: Neural Computing and Applications | Ausgabe 5/2019

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Abstract

Firefly algorithm (FA) is an excellent global optimizer based on swarm intelligence. Some recent studies show that FA was used to optimize various engineering problems. However, there are some drawbacks for FA, such as slow convergence rate and low precision solutions. To tackles these issues, a new and efficient FA (namely NEFA) is proposed. In NEFA, three modified strategies are employed. First, a new attraction model is used to determine the number of attracted fireflies. Second, a new search operator is designed for some better fireflies. Third, the step factor is dynamically updated during the iterations. Experiment verification is carried out on ten famous benchmark functions. Experimental results demonstrate that our new approach NEFA is superior to three other different versions of FA.
Vorheriger Artikel A self-adaptive genetic algorithm with improved mutation mode based on measurement of population diversity
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Metadaten
Titel
A new and efficient firefly algorithm for numerical optimization problems
verfasst von
Xiuqin Pan
Limiao Xue
Ruixiang Li
Publikationsdatum
23.03.2018
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 5/2019
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-018-3449-6

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