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SLDChOA: a comprehensive and competitive multi-strategy-enhanced chimp algorithm for global optimization and engineering design

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Abstract

The Chimp Optimization Algorithm (ChOA) is a cutting-edge swarm intelligence algorithm that models the social status ties and hunting behavior of chimps to solve complex optimization problems. Although ChOA is known for its simplicity and efficiency, it may encounter challenges such as convergence speed and local optima. This study presents a comprehensive and competitive multi-strategy-enhanced chimp optimization algorithm (SLDChOA), which comprehensively enhances the optimization performance of the algorithm through four strategies. Firstly, a low-difference Sobol sequence strategy is used to initialize the chimp population to increase the diversity of the initial population. Secondly, different location update strategies are adopted according to different iteration stages. The early iteration stage employs the Lévy flight-based location update strategy to help chimps explore the space more abundantly and improve the global search ability of the algorithm. In contrast, the proposed probability-based elitist operation strategy is used in the late iteration stage to help the chimps obtain higher-quality optimal solutions and improve the convergence accuracy and speed of the algorithm. Finally, the dimension learning-based hunting search strategy is introduced to facilitate information sharing among chimps and enable the algorithm to jump out of the local optimum effectively. To demonstrate its comprehensive performance, SLDChOA is compared with 17 state-of-the-art algorithms on 23 traditional benchmark functions, CEC 2014 and CEC 2019 test sets (totaling 63 test functions). Moreover, its efficacy and excellence are further demonstrated in 4 well-known engineering optimization issues and 2 feature selection problems of multimodal Parkinson’s speech datasets. A series of simulations demonstrate that SLDChOA has good comprehensive merit-seeking ability and is extremely competitive.

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Funding

This work was in part supported by the Key Research and Development Project of Hubei Province (No. 2020BAB114 & 2023BAB094), the Key Project of Science and Technology Research Program of Hubei Educational Committee (No. D20211402), the Project of Xiangyang Industrial Institute of Hubei University of Technology (No. XYYJ2022C04), and the Open Foundation of Hubei Key Laboratory for High-efficiency Utilization of Solar Energy and Operation Control of Energy Storage System (No. HBSEES201903 & HBSEES202106).

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Author notes

  1. Shanshan Wang, Mai Hu and Liang Zeng have contributed equally to this work.

Authors and Affiliations

  1. School of Electrical and Electronic Engineering, Hubei University of Technology, Wuhan, 430068, Hubei Province, China

    Quan Yuan, Shanshan Wang, Mai Hu & Liang Zeng

  2. Hubei Key Laboratory for High-efficiency Utilization of Solar Energy and Operation Control of Energy Storage System, Hubei University of Technology, Wuhan, 430068, Hubei Province, China

    Shanshan Wang & Liang Zeng

  3. Xiangyang Industrial Institute of Hubei University of Technology, Xiangyang, 441100, Hubei Province, China

    Shanshan Wang & Liang Zeng

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  1. Quan Yuan
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QY, SW, MH and LZ conceived the experiments, QY conducted the experiments. All authors reviewed the manuscript.

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Correspondence to Liang Zeng.

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Completed at Hubei University of Technology on June 4, 2023.

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Yuan, Q., Wang, S., Hu, M. et al. SLDChOA: a comprehensive and competitive multi-strategy-enhanced chimp algorithm for global optimization and engineering design. J Supercomput 80, 3589–3643 (2024). https://doi.org/10.1007/s11227-023-05617-1

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