低碳车辆路径问题的区域增强离散多目标烟花算法

Xiaoning Shen;Jiaqi Lu;Xuan You;Liyan Song;Zhongpei Ge
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引用次数: 0

摘要

针对低碳车辆路径问题,建立了约束多目标优化模型。介绍了一种考虑多种实际因素的碳排放测量方法。在可用车辆数量有限的情况下,它最大限度地减少了总碳排放量和子旅行所消耗的最长时间。根据模型的特点,提出了一种区域增强的离散多目标烟花算法。设计了局部映射爆炸算子、用于调整子行程的混合突变算子和目标驱动扩展搜索,分别提高了算法产生的非支配解的收敛性、多样性和扩散性。通过9个不同规模的低碳VRP实例验证了新策略的有效性。此外,与四种最新算法的比较结果表明,该算法在低碳VRP上具有更好的收敛性能和分布性能。它为问题大小提供了一个很有前景的可伸缩性。
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A Region Enhanced Discrete Multi-Objective Fireworks Algorithm for Low-Carbon Vehicle Routing Problem
A constrained multi-objective optimization model for the low-carbon vehicle routing problem (VRP) is established. A carbon emission measurement method considering various practical factors is introduced. It minimizes both the total carbon emissions and the longest time consumed by the sub-tours, subject to the limited number of available vehicles. According to the characteristics of the model, a region enhanced discrete multi-objective fireworks algorithm is proposed. A partial mapping explosion operator, a hybrid mutation for adjusting the sub-tours, and an objective-driven extending search are designed, which aim to improve the convergence, diversity, and spread of the non-dominated solutions produced by the algorithm, respectively. Nine low-carbon VRP instances with different scales are used to verify the effectiveness of the new strategies. Furthermore, comparison results with four state-of-the-art algorithms indicate that the proposed algorithm has better performance of convergence and distribution on the low-carbon VRP. It provides a promising scalability to the problem size.
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