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A hybrid northern goshawk optimization algorithm based on cluster collaboration

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Abstract

To address the problems that the northern goshawk optimization algorithm (NGO) has a slow convergence speed and is highly susceptible to fall into local optimal solutions, this paper proposes a hybrid northern goshawk optimization algorithm based on cluster collaboration (HHNGO), which effectively improves the convergence speed and alleviates the problem of falling into the local optimum. Firstly, piecewise chaotic map** is used to initialize the population, which makes the initial population more evenly distributed in the search space and improves the quality of the initial solution. Secondly, the prey recognition position update formula in the harris hawk optimization algorithm is introduced to improve the exploration phase. Meanwhile, a nonlinear factor can be added to accelerate the process which reaches the minimum difference between the prey best position and the average position of the eagle group. Thus the iteration number is reduced during the search process, and the convergence speed of the algorithm is improved. Finally, the Cauchy variation strategy is used to perturb the optimal solution of the algorithm. Then, its probability jum** out of the local optimal solution is increased, and the global search capability is enhanced. The experimental comparison is carried out to analyze the 12 standard functions, CEC-2019 and CEC-2021 test functions in HHNGO and PSO, GWO, POA, HHO, NGO, INGO, DFPSO, MGLMRFO, GMPBSA algorithms, and HHNGO is applied in PID parameter rectification. The results prove the feasibility and superiority of the proposed method.

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No datasets were generated or analysed during the current study.

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Funding

This work was supported by the Henan Province Key R&D Project, China under Grant No. 241111222400, and the Joint Fund Key Project of Science and Technology R&D Plan of Henan Province, China under Grant No. 225200810029.

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Author notes

  1. Qingzhen Li, Qiaohua Wang, Huanlong Zhang and **aohui Song have contributed equally to this work.

Authors and Affiliations

  1. Zhengzhou University of Light Industry, Henan Key Laboratory of Intelligent Manufacturing of Mechanical Equipment, No. 5, Dongfeng Road, **shui District, Zhengzhou, 450002, Henan, China

    Changjun Wu, Qingzhen Li & Qiaohua Wang

  2. Zhengzhou University of Light Industry, College of Electrical and Information Engineering, No. 5, Dongfeng Road, **shui District, Zhengzhou, 450002, Henan, China

    Huanlong Zhang

  3. Henan Academy of Sciences, No. 228 Chongshili, Zhengdong New District, Zhengzhou, 450058, Henan, China

    **aohui Song

Authors
  1. Changjun Wu
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Contributions

Changjun Wu: Conceptualizatio; Methodology; Analysis; Resources; Writing areview and editing; Investigation; Supervision. Qingzhen Li: Data curation: Investigation: Software; Validation; riting aoriginal draftand editing. Qiaohua Wang: Conceptualizatio; Methodology; Analysis; Resources; Investigation; Supervision. Huanlong Zhang: Conceptualizatio; Methodology; Validation Analysis; Review andediting. **aohui Song: Data curation; Investigation; Software; Resources; Data curation.

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Correspondence to **aohui Song.

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Wu, C., Li, Q., Wang, Q. et al. A hybrid northern goshawk optimization algorithm based on cluster collaboration. Cluster Comput (2024). https://doi.org/10.1007/s10586-024-04571-8

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