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A new and efficient firefly algorithm for numerical optimization problems

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

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Acknowledgements

This work is supported by the project of the First-Class University and the First-Class Discipline (No. 10301-017004011501), and the National Natural Science Foundation of China.

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Authors and Affiliations

  1. School of Information Engineering, Minzu University of China, Beijing, 100081, China

    Xiuqin Pan, Limiao Xue & Ruixiang Li

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  1. Xiuqin Pan
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  2. Limiao Xue
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Correspondence to Xiuqin Pan.

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We declare that we do not have any commercial or associative interest that represents a conflict of interest in connection with the work submitted.

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Pan, X., Xue, L. & Li, R. A new and efficient firefly algorithm for numerical optimization problems. Neural Comput & Applic 31, 1445–1453 (2019). https://doi.org/10.1007/s00521-018-3449-6

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