An adaptive golden jackal optimization algorithm for mobile robotic path planning

Siwen Chen; Xiujuan Zheng

doi:10.1117/12.3052803

16 January 2025 An adaptive golden jackal optimization algorithm for mobile robotic path planning

Siwen Chen, Xiujuan Zheng

Author Affiliations +

Proceedings Volume 13447, International Conference on Mechatronics and Intelligent Control (ICMIC 2024); 134472K (2025) https://doi.org/10.1117/12.3052803
Event: International Conference on Mechatronics and Intelligent Control (ICMIC 2024), 2024, Wuhan, China

Abstract

Given the shortcomings of the traditional Golden Jackal Optimization (GJO) algorithm, including limited accuracy and slow convergence speed in solving mobile robot path planning problems, an improved adaptive Golden Jackal Optimization (IAGJO) method is proposed. Firstly, a nonlinear adaptive energy strategy is presented to adjust the energy decay pattern, balancing global and local search. Secondly, an enhanced position update mechanism based on Cauchy and Gaussian mutation increases population diversity and guide the search based on optimal individuals, thereby facilitating efficient exploration of unknown regions and avoiding local optima. Finally, the IAGJO algorithm is applied to mobile robot path planning (MRPP), demonstrating that the IAGJO achieves shorter path lengths and higher search efficiency, exhibiting significant advantages over existing methods.

(2025) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Siwen Chen and Xiujuan Zheng "An adaptive golden jackal optimization algorithm for mobile robotic path planning", Proc. SPIE 13447, International Conference on Mechatronics and Intelligent Control (ICMIC 2024), 134472K (16 January 2025); https://doi.org/10.1117/12.3052803

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