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结合聚类分解的增强蚁群算法求解复杂绿色车辆路径问题
胡蓉; 李洋; 钱斌; 金怀平; 向凤红
Source Publication自动化学报
ISSN0254-4156
2022
Volume48Issue:12Pages:3006-3023
Abstract针对带时间窗的低能耗多车场多车型车辆路径问题(Low-energy-consumption multi-depots heterogeneousfleet vehicle routing problem with time windows, LMHFVPR_TW),提出一种结合聚类分解策略的增强蚁群算法(Enhanced ant colony optimization based on clustering decomposition, EACO_CD)进行求解.首先,由于该问题具有强约束、大规模和NP-Hard等复杂性,为有效控制问题的求解规模并合理引导算法在优质解区域搜索,根据问题特点设计两种基于K-means的聚类策略,将LMHFVPR_TW合理分解为一系列带时间窗的低能耗单车场单车型车辆路径子问题(Low-energy-consumption vehicle routing problem with time windows, LVRP_TW);其次,本文提出一种增强蚁群算法(Enhanced ant colony optimization, EACO)求解分解后的各子问题(LVRP_TW),进而获得原问题的解. EACO不仅引入信息素挥发系数控制因子进一步动态调节信息素挥发系数,从而有效控制信息素的挥发以提高算法的全局搜索能力,而且设计基于4种变邻域操作的两阶段变邻域局部搜索(Two-stage variable neighborhood search, TVNS)来增强算法的局部搜索能力.最后,在不同规模问题上的仿真和对比实验验证了所提EACO_CD的有效性.
Keyword低能耗车辆路径问题 多车场多车型 时间窗 聚类分解 增强蚁群算法
DOI10.16383/j.aas.c190872
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/56235
Collection学术期刊_自动化学报
Recommended Citation
GB/T 7714
胡蓉,李洋,钱斌,等. 结合聚类分解的增强蚁群算法求解复杂绿色车辆路径问题[J]. 自动化学报,2022,48(12):3006-3023.
APA 胡蓉,李洋,钱斌,金怀平,&向凤红.(2022).结合聚类分解的增强蚁群算法求解复杂绿色车辆路径问题.自动化学报,48(12),3006-3023.
MLA 胡蓉,et al."结合聚类分解的增强蚁群算法求解复杂绿色车辆路径问题".自动化学报 48.12(2022):3006-3023.
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