为电动车路径问题定制参数配置，提升优化效果
Instance-Aware Parameter Configuration in Bilevel Late Acceptance Hill Climbing for the Electric Capacitated Vehicle Routing Problem
- 根据实例特征用回归模型预测最优参数
- 在8个测试实例上平均目标值降低0.28%
- 适合大规模物流调度中需个性化参数的场景
组合优化算法性能高度依赖参数设置，而全局统一调参难以适应实例差异。这一局限在电动容量车辆路径问题中尤为明显，因实例在结构、需求模式和能源约束上存在异质性。本文研究针对双层延迟接受爬山法的实例感知参数配置方法。通过离线调参获得实例特异性参数标签，并利用回归模型从实例特征映射参数，实现对未见实例的执行前参数预测。在IEEE WCCI 2020基准及其扩展数据集上的实验表明，该方法相比全局调参配置，在8个保留测试实例上平均目标值降低0.28%，对应数百万美元运输成本的显著节省。
原文摘要 · Abstract (English)
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.
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