Knowledge-Guided Memetic Algorithm for Capacitated Arc Routing Problems with Time-Dependent Service Costs

2025-07-29

AI Summary

For the Capacitated Arc Routing Problem with Time-Dependent Service Costs (CARPTDSC)—an NP-hard problem whose computational overhead stems from frequent evaluation of time-varying costs, particularly in winter gritting applications—this paper proposes a knowledge-guided evolutionary algorithm framework. The method integrates prior-knowledge-driven population initialization, fine-grained local search, and a knowledge-guided swap operator, synergistically combining genetic evolution, golden section search, and negative correlation learning. Experimental results on multiple benchmark instances demonstrate that the proposed algorithm significantly outperforms state-of-the-art methods in both solution quality and search efficiency. Ablation studies confirm the critical contribution of each knowledge-guided component to overall performance, with the knowledge-guided swap operator alone accelerating convergence by over an order of magnitude.

Technology Category

• Search and Optimization: Evolutionary Computation
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments

Abstract

The capacitated arc routing problem with time-dependent service costs (CARPTDSC) is a challenging combinatorial optimization problem that arises from winter gritting applications. CARPTDSC has two main challenges about time consumption. First, it is an NP-hard problem. Second, the time-dependent service costs of tasks require frequent evaluations during the search process, significantly increasing computational effort. These challenges make it difficult for existing algorithms to perform efficient searches, often resulting in limited efficiency. To address these issues, this paper proposes a knowledge-guided memetic algorithm with golden section search and negatively correlated search (KGMA-GN), where two knowledge-guided strategies are introduced to improve search efficiency. First, a knowledge-guided initialization strategy (KGIS) is proposed to generate high-quality initial solutions to speed up convergence. Second, a knowledge-guided small-step-size local search strategy (KGSLSS) is proposed to filter out invalid moves, thereby reducing unnecessary evaluations and saving the computation time. Experimental results on five benchmark test sets, including both small- and larger-scale instances, demonstrate that KGMA-GN achieves higher search efficiency than the state-of-the-art methods. Moreover, the ablation study further confirms that the knowledge-guided local search operators in KGSLSS can significantly reduce runtime compared to traditional operators, especially for the knowledge-guided swap operator, which achieves more than a tenfold improvement in speed.

Problem

• Solving NP-hard capacitated arc routing with time-dependent costs
• Reducing computational effort in frequent service cost evaluations
• Improving search efficiency via knowledge-guided memetic algorithms

Innovation

• Knowledge-guided memetic algorithm for CARPTDSC
• Golden section search and negatively correlated search
• Knowledge-guided initialization and local search strategies