| 引用本文: | 郭宁,申秋义,钱斌,那靖,胡蓉,毛剑琳.双重信息引导的蚁群算法求解绿色多舱车辆路径问题[J].控制理论与应用,2024,41(6):1067~1078.[点击复制] |
| Guo Ning,Shen Qiu-yi,Qian Bin,Na Jing,Hu Rong,Mao Jian-lin.Dual-information guided ant colony optimization algorithm for green multi-compartment vehicle routing problem[J].Control Theory and Technology,2024,41(6):1067~1078.[点击复制] |
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| 双重信息引导的蚁群算法求解绿色多舱车辆路径问题 |
| Dual-information guided ant colony optimization algorithm for green multi-compartment vehicle routing problem |
| 摘要点击 619 全文点击 136 投稿时间：2022-10-24 修订日期：2024-04-25 |
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| DOI编号 DOI: 10.7641/CTA.2023.20930 |
| 2024,41(6):1067-1078 |
| 中文关键词 多舱车辆路径问题 绿色 蚁群优化算法 双重信息引导 信息素浓度平衡机制 |
| 英文关键词 multi-compartment vehicle routing problem green ant colony optimization algorithm dual-information guided pheromone concentration balance mechanism |
| 基金项目 国家自然科学基金项目(61963022, 62173169, 61922037), 云南省基础研究重点项目(202201AS070030), 云南省教育厅科学研究基金项目(2022J0062)资助. |
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| 中文摘要 |
| 针对当前实际运输中广泛存在的绿色多舱车辆路径问题(GMCVRP), 文章提出一种双重信息引导的蚁群优化算法(DIACO)进行求解. 首先, 在DIACO的全局搜索阶段, 重新构建传统蚁群优化算法(TACO)中的信息素浓度矩阵(PCM), 使其同时包含客户块信息和客户序列信息, 即建立具有双重信息的PCM(DIPCM), 从而更全面学习和累积优质解的信息; 采用3种启发式方法生成较高质量个体, 用于初始化DIPCM, 可快速引导算法朝向解空间中优质区域进行搜索. 其次, 在DIACO的局部搜索阶段, 设计结合自适应策略的多种变邻域操作, 用于对解空间的优质区域执行深入搜索. 再次, 提出信息素浓度平衡机制, 以防止搜索陷入停滞. 最后, 使用不同规模的算例进行仿真测试和算法对比, 结果验证了DIACO是求解GMCVRP的有效算法. |
| 英文摘要 |
| For dealing with the green multi-compartment vehicle routing problem (GMCVRP) widely existing in actual transportation, a dual-information guided ant colony optimization algorithm (DIACO) is proposed to solve it. First, in the
global search stage of DIACO, the pheromone concentration matrix (PCM) in the traditional ant colony optimization algorithm (TACO) is reconstructed. The reconstructed PCM contains both customer block information and customer sequence information. That is, the dual-information PCM (DIPCM) is established so as to more comprehensively learn and accumulate high-quality solution information. Three types of heuristic methods are adopted to generate higher quality individuals for initializing DIPCM, which can guide the algorithm to search for high-quality regions in the solution space quickly. Second, in the local search stage of DIACO, multiple variable neighborhood operations combined with adaptive strategy are designed to perform in-depth searches on high-quality regions of the solution space. Third, the pheromone concentration balance mechanism is proposed to prevent the search from stagnating. Last, simulation tests and algorithm comparisons are carried out with different scale examples. The results show that DIACO is an effective algorithm for solving the GMCVRP. |
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