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Moth-flame optimization algorithm based on diversity and mutation strategy

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Abstract

In this work, an improved moth-flame optimization algorithm is proposed to alleviate the problems of premature convergence and convergence to local minima. From the perspective of diversity, an inertia weight of diversity feedback control is introduced in the moth-flame optimization to balance the algorithm’s exploitation and global search abilities. Furthermore, a small probability mutation after the position update stage is added to improve the optimization performance. The performance of the proposed algorithm is extensively evaluated on a suite of CEC’2014 series benchmark functions and four constrained engineering optimization problems. The results of the proposed algorithm are compared with the ones of other improved algorithms presented in literatures. It is observed that the proposed method has a superior performance to improve the convergence ability of the algorithm. In addition, the proposed algorithm assists in escaping the local minima.

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Acknowledgments

This work was supported by the National Natural Science Foundation of China (Grant No.11705002), and the Scientific Research Foundation of Education Department of Anhui Province,China(Grant No.KJ2019A0091, Grant No.KJ2019ZD09), the Humanities and Social Science Fund of Ministry of Education of China (Grant NO.19YJAZ H098). The authors would like to thank all the anonymous referees for their valuable comments and suggestions to further improve the quality of this work.

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Authors and Affiliations

  1. Department of Management Science and Engineering, Anhui University of Technology, Anhui, Ma’anshan, 243002, People’s Republic of China

    Lei Ma & Neng-gang Xie

  2. Anhui Province Key Laboratory of Multidisciplinary Management and Control of Complex Systems, Anhui, Ma’anshan, 243002, People’s Republic of China

    Lei Ma, Neng-gang Xie & Miao Shi

  3. Department of Mechanical Engineering, Anhui University of Technology, Anhui, Ma’anshan, 243002, People’s Republic of China

    Chao Wang, Miao Shi, Ye Ye & Lu Wang

  4. Department of Engineering Mechanics, Hohai University, Nanjing, 211100, People’s Republic of China

    Chao Wang

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  1. Lei Ma
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Correspondence to Chao Wang or Lu Wang.

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Ma, L., Wang, C., Xie, Ng. et al. Moth-flame optimization algorithm based on diversity and mutation strategy. Appl Intell 51, 5836–5872 (2021). https://doi.org/10.1007/s10489-020-02081-9

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