组合优化中的算法性能对参数设置高度敏感,，而单个全局调整的配置通常无法利用实例的异构性。这种限制在电动电容车辆路径问题, 中尤其明显，其中实例在结构, 需求模式, 和能源限制方面有所不同。本文研究了 Bilevel Late Acceptance Hill Climbing, 的实例感知参数配置，这是电动电容车辆路径问题的最先进的元启发式算法。离线调整过程用于获取特定于实例的参数标签,，然后通过回归模型从实例特征映射这些标签，以便在执行之前启用对未见过的实例的参数预测。 IEEE WCCI 2020 基准及其扩展的实验结果表明，相对于全局调整的配置，所提出的方法在八个保留的测试实例中实现了平均目标值降低 $0.28\%$。这相当于数百万美元的运输运营成本的大幅降低。
Algorithm performance in combinatorial optimization is highly sensitive to parameter settings, while a single globally tuned configuration often fails to exploit the heterogeneity of instances. This limitation is particularly evident in the Electric Capacitated Vehicle Routing Problem, where instances differ in structure, demand patterns, and energy constraints. This paper investigates instance-aware parameter configuration for Bilevel Late Acceptance Hill Climbing, a state-of-the-art metaheuristic for the Electric Capacitated Vehicle Routing Problem. An offline tuning procedure is used to obtain instance-specific parameter labels, which are then mapped from instance features via a regression model to enable parameter prediction for unseen instances prior to execution. Experimental results on the IEEE WCCI 2020 benchmark and its extensions show that the proposed approach achieves an average objective value reduction of $0.28\%$ across eight held-out test instances relative to a globally tuned configuration. This corresponds to a significant cost reduction in multimillion-dollar transportation operations.
科目: 人工智能 (cs.AI); 优化与控制 (math.OC)
Subjects: Artificial Intelligence (cs.AI); Optimization and Control (math.OC)