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An accurate partially attracted firefly algorithm

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Abstract

The firefly algorithm (FA) is a new and powerful algorithm for optimization. However, it has the disadvantages of high computational complexity and low convergence accuracy, especially when solving complex problems. In this paper, an accurate partially attracted firefly algorithm (PaFA) is proposed by adopting a partial attraction model and a fast attractiveness calculation strategy. The partial attraction model can preserve swarm diversity and make full use of individual information. The fast attractiveness calculation strategy ensures information sharing among the individuals and it also improves the convergence accuracy. The experimental results demonstrate the good performance of PaFA in terms of the solution accuracy compared with two state-of-the-art FA variants and two other bio-inspired algorithms.

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Acknowledgements

The authors thank the Chinese National Natural Science Foundation (No. 61379059) and the Fundamental Research Funds for the Central Universities, South-Central University for Nationalities (No. CZY18012) for financial support for this work.

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Authors and Affiliations

  1. College of Computer Science, South-Central University for Nationalities, Wuhan, 430074, Hubei, China

    Lingyun Zhou, Maode Ma & Wan Tang

  2. Computer School, Wuhan University, Wuhan, 430072, Hubei, China

    Lingyun Zhou & Lixin Ding

  3. School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore

    Maode Ma

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  1. Lingyun Zhou
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  2. Lixin Ding
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  3. Maode Ma
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  4. Wan Tang
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Correspondence to Lingyun Zhou.

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Informed consent was obtained from all authors included in the study. This manuscript does not contain any studies with human participants or animals performed by any of the authors.

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Zhou, L., Ding, L., Ma, M. et al. An accurate partially attracted firefly algorithm. Computing 101, 477–493 (2019). https://doi.org/10.1007/s00607-018-0645-2

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  • DOI: https://doi.org/10.1007/s00607-018-0645-2

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