Instance-Aware Parameter Configuration in Bilevel Late Acceptance Hill Climbing for the Electric Capacitated Vehicle Routing Problem

TL;DR

This paper introduces an instance-aware parameter tuning method for a metaheuristic solving the Electric Capacitated Vehicle Routing Problem, leading to improved solution quality over global tuning.

Contribution

It develops a regression-based approach to predict instance-specific parameters, enhancing metaheuristic performance on diverse problem instances.

Findings

Achieves an average 0.28% reduction in objective value on benchmark instances.

Demonstrates significant cost savings in real-world transportation scenarios.

Validates effectiveness across multiple instance types and extensions.

Abstract

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.