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Multi-strategy chimp optimization algorithm for global optimization and minimum spanning tree

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Abstract

Aiming at the shortcomings of Chimp optimization algorithm (ChOA), which is easy to fall into local optimal value and imbalance between global exploration ability and local exploitation ability. To improve ChOA from the perspective of multi-strategy mixing, MSChimp was proposed, and the algorithm was applied to global optimization and minimum spanning tree problems. The main research work of this paper is as follows: (1) In the initialization stage of ChOA, an opposition-based learning strategy was introduced to improve the population diversity; Sine Cosine Algorithm (SCA) was introduced in the exploitation process to improve the convergence speed and accuracy of the algorithm in the later stage, so as to balance the exploration and exploitation capabilities of the algorithm. (2) The improved algorithm was compared with different types of meta-heuristic algorithms in 20 benchmark functions and CEC 2019 test sets, and was used to solve the minimum spanning tree. The experimental results show that the improved ChOA has significantly improved the ability to find the optimal value, which verifies the effectiveness and feasibility of MSChimp. Compared with other algorithms, the algorithm proposed in this paper has strong competitiveness.

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Acknowledgements

This work is supported by National Science Foundation of China under Grant 62066005, and by the Project of Guangxi Natural Science Foundation under Grants No. ZL23014016.

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Authors and Affiliations

  1. College of Artificial Intelligenc, Guangxi University for Nationalities, Nanning, 530006, China

    Nating Du, Yongquan Zhou & Qifang Luo

  2. Guangxi Key Laboratories of Hybrid Computation and IC Design Analysis, Nanning, 530006, China

    Yongquan Zhou & Qifang Luo

  3. Guangxi Institute of Digital Technology, Nanning, 530000, China

    Ming Jiang

  4. College of Electronic Information and Automation, Civil Aviation University of China, Tianjin, 300300, China

    Wu Deng

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  1. Nating Du
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Contributions

ND; writing—original draft preparation and algorithm methodology; YZ; algorithm design and original draft revise, QL; experimental results and analysis, Ming Zhang; review and editing, WD; experimental results test. All authors have read and agreed to the published version of the manuscript.

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Correspondence to Yongquan Zhou.

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Du, N., Zhou, Y., Luo, Q. et al. Multi-strategy chimp optimization algorithm for global optimization and minimum spanning tree. Soft Comput 28, 2055–2082 (2024). https://doi.org/10.1007/s00500-023-09174-w

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