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

2026-05-04 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Expert, extended

Summary

A new instance-aware parameter configuration (IAPC) framework has been developed for Bilevel Late Acceptance Hill Climbing (b-LAHC), a metaheuristic used to solve the Electric Capacitated Vehicle Routing Problem (E-CVRP). This framework addresses the limitation of globally tuned parameter settings, which often fail to account for the diverse structural and demand characteristics of different E-CVRP instances. The approach involves an offline tuning procedure to determine instance-specific parameter labels, which are then mapped from instance features using a Ridge regression model. This model predicts optimal parameter settings for new, unseen instances prior to execution. Experimental results on the IEEE WCCI 2020 benchmark and its extensions show that this IAPC approach reduces the average objective value by 0.28% across eight held-out test instances compared to a globally tuned configuration, representing a significant cost reduction in transportation operations.

Key takeaway

For AI Scientists and Research Scientists developing or deploying metaheuristics for complex combinatorial optimization problems like E-CVRP, you should consider implementing instance-aware parameter configuration. This approach, by tailoring algorithm parameters to specific instance characteristics, can yield statistically significant performance improvements (e.g., 0.28% objective value reduction), leading to substantial operational cost savings and more robust solutions across diverse problem sets.

Key insights

Instance-aware parameter configuration significantly improves metaheuristic performance for heterogeneous combinatorial optimization problems.

Principles

• Algorithm performance is highly sensitive to parameter settings.
• Instance heterogeneity limits global parameter configurations.

Method

An offline tuning procedure generates instance-specific parameter labels, which a regression model then maps from instance features to predict configurations for unseen instances before execution.

In practice

• Use Ridge regression for parameter prediction.
• Extract graph-based and demand-related instance features.

Topics

• Electric Capacitated Vehicle Routing Problem
• Bilevel Late Acceptance Hill Climbing
• Instance-Aware Parameter Configuration
• Algorithm Parameter Tuning
• Regression Modeling

Code references

• KingQino/Instance-aware-algorithm-configuration

Best for: AI Scientist, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.