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An improved evolution fruit fly optimization algorithm and its application

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Abstract

Fruit fly optimization algorithm (FOA) is a kind of swarm intelligence optimization algorithm, which has been widely applied in science and engineering fields. The aim of this study is to design an improved FOA, namely evolution FOA (EFOA), which can overcome some shortcomings of basic FOA, including difficulty in local optimization, slow convergence speed, and lack of robustness. EFOA applies a few new strategies which adaptively control the search steps and swarm numbers of the fruit flies. The evolution mechanism used in EFOA can preserve dominant swarms and remove inferior swarms. Comprehensive comparison experiments are performed to compare EFOA with other swarm intelligence algorithms through 14 benchmark functions and a constrained engineering problem. Experimental results suggest that EFOA performs well both in global search ability and in robustness, and it can improve convergence speed.

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Acknowledgements

This study is supported by National Nature Science Foundation of China (Grant No. 41571016) and the National Key Research and Development Program of China (Grant No. 2018YFC0406606). The authors like to thank Prof. Yujing Lu, an English professor of Lanzhou University, for her proofreading. The authors are also thankful to anonymous referees for the valuable and constructive suggestions.

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Authors and Affiliations

  1. School of Mathematics and Statistics, Lanzhou University, No. 222, TianShui Road (South), ChengGuan District, Lanzhou City, 730000, Gansu Province, China

    Xuan Yang, Weide Li, Lili Su, Yaling Wang & Ailing Yang

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  1. Xuan Yang
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  2. Weide Li
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  3. Lili Su
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  4. Yaling Wang
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Correspondence to Weide Li.

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Yang, X., Li, W., Su, L. et al. An improved evolution fruit fly optimization algorithm and its application. Neural Comput & Applic 32, 9897–9914 (2020). https://doi.org/10.1007/s00521-019-04512-2

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