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

IF 3.4 3区医学 Q1 ENGINEERING, MULTIDISCIPLINARY

Biomimetics Pub Date : 2025-05-02 DOI:10.3390/biomimetics10050285

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

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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.

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基于协同混合优化算法的有能力多舰补给路径规划问题研究。

船舶补给路径规划是海上物流领域的一个关键问题。本文提出了一种新型的协同混合优化算法（SHOA），该算法将蚁群优化（ACO）、Clarke-Wright算法（CW）和遗传算法（GA）有效地集成在一起，用于解决有能力多舰补给路径规划问题（CMSRPPP）。该方法采用三阶段优化框架：(1)通过并行执行连续波和蚁群算法生成初始路径；(2)将蚁群算法的最优解和CW算法的最优解与随机解相结合，对遗传算法进行种群初始化；(3)采用嵌入演化反转运算的增强遗传算法进行迭代细化。为了评估该算法的性能，将其与蚁群算法、遗传算法、粒子群优化算法和模拟退火算法进行了基准测试。最后，将SHOA应用于多种CMSRPPP实例，显示出高适应性、强大的规划能力和良好的应用潜力。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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