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针对传统遗传算法在求解带时间窗的车辆路径问题时容易陷入局部最优解和收敛速度慢等问题，提出一种融合组织型P系统与自适应遗传算法的车辆路径优化方法．该算法借鉴组织型P系统的结构特点，设计多个进化膜与指导膜协同进化结构，显著提升算法的局部和全局收敛能力．在此基础上，提出自适应交叉变异算子、基于破坏-修复算子的自适应局部搜索策略及精英保留策略以改进遗传算法，有效增强了算法的全局搜索能力．最后，在Solomon数据集上进行实验．实验结果表明，所提算法在大多数算例中优于9种最先进的优化算法，验证了其在解决带时间窗的车辆路径问题中的有效性和应用潜力．
Abstract：To address the limitations of traditional genetic algorithm,including susceptibility to local optima and slow convergence in solving vehicle routing problem with time windows,this paper proposes a novel vehicle routing optimization approach by integrating tissue-like P systems and adaptive genetic algorithm.By utilizing the structural characteristics of the tissue-like P system,the algorithm designs a multiple evolutionary membranes and guide membrane co-evolution structure,which significantly improves the local and global convergence capabilities.On this basis,the adaptive crossover-mutation operator,adaptive local search strategy based on the destruction-repair operator,and the elite preservation strategy are used to improve the global search capability of genetic algorithm.Finally,experiments were conducted on the Solomon dataset,and experimental results indicate that the proposed algorithm outperforms the nine optimization algorithms in most cases,and validate the effectiveness and application potential in solving vehicle routing problem with time windows.
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基本信息:
中图分类号:U492.22;TP18
引用信息:
[1]王婷婷,许家昌.融合组织型P系统与自适应遗传算法的车辆路径优化[J].宁夏师范大学学报,2026,47(01):69-84.
基金信息:
南方林业与生态应用技术国家工程实验室开放基金项目（2023NFLY08）; 安徽理工大学医学专项培育项目（YZ2023H2B008）
2026-01-15
2026-01-15