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A multi-role steered artificial bee colony algorithm with variable dimensionality perturbation for multimodal optimization problems

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Abstract

To address the issues of slow and premature convergence of artificial bee colony (ABC) for handling complex multimodal optimization problems, this paper proposes a multi-role steered variable dimensionality perturbation ABC called MrABC-vdp. MrABC-vdp assigns the nectar sources four distinct roles based on their quality at each iteration. To strike the global exploration and local exploitation equilibrium of the solution space, the bee colony's search behavior is tightly controlled and corrected based on the synergistic relationship established between various preference search equations under multi-role nectar source guidance and the employed bee, onlooker bee, and scout bee phases. To achieve the dynamic trade-off of the global exploration speed and local convergence accuracy of the bee colony across the iteration, a variable dimensionality perturbation is imposed on the candidate nectar sources by a simulated binary crossover operator with time-varying crossover probability. Experimental results on the CEC 2017 multimodal benchmarks show that the proposed MrABC-vdp has a significant performance advantage and strong robustness versus nine advanced homogenous and heterogenous swarm-inspired intelligent algorithms.

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The data are available from the corresponding author on reasonable request.

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China (Grant Number 62106237), the Joint Funds of the National Natural Science Foundation of China (Grant Number U21A20524), the Shanxi Province Science Foundation for Youths (Grant Number 202203021222057), the Special Fund for Science and Technology Innovation Teams of Shanxi Province (Grant Number 202204051002026), and the Natural Science Research Project of Shanxi Province (Grant Number 202103021224218).

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Authors and Affiliations

  1. School of Electrical and Control Engineering, North University of China, Taiyuan, 030051, China

    Yaxin Kang & Jianchao Zeng

  2. School of Computer Science and Technology, North University of China, Taiyuan, 030051, China

    Haibo Yu, Gangzhu Qiao & Jianchao Zeng

  3. School of Environment and Safety Engineering, North University of China, Taiyuan, 030051, China

    Li Kang

  4. School of Environment and Resources, Taiyuan University of Science and Technology, Taiyuan, 030024, China

    Dongpeng Guo

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  1. Yaxin Kang
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  2. Haibo Yu
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  3. Li Kang
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  4. Gangzhu Qiao
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  5. Dongpeng Guo
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  6. Jianchao Zeng
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Contributions

Yaxin Kang: Software, Writing—Original Draft. Haibo Yu: Conceptualization, Methodology, Writing—Review & Editing. Li Kang: Formal analysis, Investigation. Gangzhu Qiao: Writing—Review& Editing. Dongpeng Guo: Investigation. Jianchao Zeng: Supervision.

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Correspondence to Haibo Yu.

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Kang, Y., Yu, H., Kang, L. et al. A multi-role steered artificial bee colony algorithm with variable dimensionality perturbation for multimodal optimization problems. Memetic Comp. 16, 159–178 (2024). https://doi.org/10.1007/s12293-024-00411-9

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