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Improved dwarf mongoose optimization algorithm based on hybrid strategy for global optimization and engineering problems

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Abstract

The Dwarf Mongoose Optimization algorithm is a metaheuristic approach designed to solve single-objective optimization problems. However, DMO has certain limitations, including slow convergence rates and a tendency to get stuck in local optima, particularly when applied to multimodal and combinatorial problems. This paper introduces an enhanced version of the DMO, referred to as HDMO, which is based on a hybrid strategy. Firstly, a sine chaotic mapping function is integrated to enhance the diversity of the initial population. Secondly, the study aims to improve the algorithm’s performance through the integration of nonlinear control, adaptive parameter tuning, hybrid mutation strategies, and refined exploration–exploitation mechanisms. To evaluate the performance of the proposed HDMO, we conducted tests on the CEC2017, CEC2020, and CEC2022 benchmark problems, as well as 19 engineering design problems from the CEC2020 real-world optimization suite. The HDMO algorithm was compared with various algorithms, including (1) highly cited algorithms such as PSO, GWO, WOA and SSA; (2) recently proposed advanced algorithms, namely, BOA, GBO, HHO, SMA and STOA; and (3) high-performance algorithms like LSHADE and LSHADE_SPACMA. Experimental results demonstrate that, compared to other algorithms, HDMO exhibits superior convergence speed and accuracy. Wilcoxon rank-sum test statistics confirm the significant performance improvement of HDMO, highlight its potential in practical engineering optimization and design problems.

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Acknowledgements

This work is supported by the National Natural Science Foundation of China (Grant No. 51905257), the Hunan Provincial Natural Science Foundation of China (Grant No. 2023JJ40544), the Foundation of Hunan Education Department (Grant No. 21B0406).

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Authors and Affiliations

  1. School of Mechanical Engineering, University of South China, Hengyang City, 421001, People’s Republic of China

    Fuchun He, Chunming Fu, Youwei He & Shaoyong Huo

  2. Department of Mechanical Engineering, Hunan University of Technology, Zhuzhou City, 412007, People’s Republic of China

    Jiachang Tang

  3. College of Mechanical and Vehicle Engineering, Hunan University, Changsha, 410082, People’s Republic of China

    Xiangyun Long

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  1. Fuchun He
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Contributions

Fuchun He is responsible for algorithm development, performance testing and writing the first draft of the paper. Chunming Fu and Youwei He provide method guidance and paper review for the paper. Shaoyong Huo and Jiachang Tang contributed to the review of papers. Xiangyun Long processing data. All authors reviewed the manuscript.

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Correspondence to Chunming Fu.

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He, F., Fu, C., He, Y. et al. Improved dwarf mongoose optimization algorithm based on hybrid strategy for global optimization and engineering problems. J Supercomput 81, 483 (2025). https://doi.org/10.1007/s11227-025-06931-6

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