Research on Capacitated Multi-Ship Replenishment Path Planning Problem Based on the Synergistic Hybrid Optimization Algorithm

Yang, Lin; Chen, Qinghua; Mu, Junjie; Liu, Tangying; Li, Xiaoxiao; Cai, Shuxiang

doi:10.3390/biomimetics10050285

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Open AccessArticle

Research on Capacitated Multi-Ship Replenishment Path Planning Problem Based on the Synergistic Hybrid Optimization Algorithm

by

Lin Yang

¹,

Qinghua Chen

^1,*,

Junjie Mu

¹,

Tangying Liu

²

,

Xiaoxiao Li

¹ and

Shuxiang Cai

²

¹

Navy Aviation University, Yantai 264001, China

²

School of Electromechanical and Automotive Engineering, Yantai University, Yantai 264005, China

^*

Author to whom correspondence should be addressed.

Biomimetics 2025, 10(5), 285; https://doi.org/10.3390/biomimetics10050285

Submission received: 29 March 2025 / Revised: 29 April 2025 / Accepted: 29 April 2025 / Published: 2 May 2025

(This article belongs to the Special Issue Bio-Inspired Optimization Algorithms and Designs for Engineering Applications: 3rd Edition)

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Abstract

Ship replenishment path planning is a critical problem in the field of maritime logistics. This study proposes a novel synergistic hybrid optimization algorithm (SHOA) that effectively integrates ant colony optimization (ACO), the Clarke–Wright algorithm (CW), and the genetic algorithm (GA) to solve the capacitated multi-ship replenishment path planning problem (CMSRPPP). The proposed methodology employs a three-stage optimization framework: (1) initial path generation via parallel execution of the CW and ACO; (2) population initialization for the GA by strategically combining optimal solutions from ACO and the CW with randomized solutions; (3) iterative refinement using an enhanced GA featuring an embedded evolutionary reversal operation for local intensification. To evaluate performance, the SHOA is benchmarked against ACO, the GA, the particle swarm optimization algorithm, and the simulated annealing algorithm for the capacitated vehicle routing problem. Finally, the SHOA is applied to diverse CMSRPPP instances, demonstrating high adaptability, robust planning capabilities, and promising practical potential.

Keywords: path planning problem; capacitated multi-ship replenishment; synergistic hybrid optimization algorithm; ant colony algorithm; Clarke–Wright algorithm; genetic algorithm

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MDPI and ACS Style

Yang, L.; Chen, Q.; Mu, J.; Liu, T.; Li, X.; Cai, S. Research on Capacitated Multi-Ship Replenishment Path Planning Problem Based on the Synergistic Hybrid Optimization Algorithm. Biomimetics 2025, 10, 285. https://doi.org/10.3390/biomimetics10050285

AMA Style

Yang L, Chen Q, Mu J, Liu T, Li X, Cai S. Research on Capacitated Multi-Ship Replenishment Path Planning Problem Based on the Synergistic Hybrid Optimization Algorithm. Biomimetics. 2025; 10(5):285. https://doi.org/10.3390/biomimetics10050285

Chicago/Turabian Style

Yang, Lin, Qinghua Chen, Junjie Mu, Tangying Liu, Xiaoxiao Li, and Shuxiang Cai. 2025. "Research on Capacitated Multi-Ship Replenishment Path Planning Problem Based on the Synergistic Hybrid Optimization Algorithm" Biomimetics 10, no. 5: 285. https://doi.org/10.3390/biomimetics10050285

APA Style

Yang, L., Chen, Q., Mu, J., Liu, T., Li, X., & Cai, S. (2025). Research on Capacitated Multi-Ship Replenishment Path Planning Problem Based on the Synergistic Hybrid Optimization Algorithm. Biomimetics, 10(5), 285. https://doi.org/10.3390/biomimetics10050285

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Research on Capacitated Multi-Ship Replenishment Path Planning Problem Based on the Synergistic Hybrid Optimization Algorithm

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