First Competitive Ant Colony Scheme for the CARP

TL;DR

This paper introduces a competitive Ant Colony Optimization scheme for large-scale Capacitated Arc Routing Problems, demonstrating its effectiveness and competitiveness against existing metaheuristics like genetic algorithms and tabu search.

Contribution

The paper presents a novel Ant Colony scheme tailored for large-scale CARP instances, coupled with local search, and benchmarks its performance against established methods.

Findings

Achieves high-quality solutions comparable to CARPET.

Competitively matches genetic algorithms in solution quality.

More time-consuming than genetic algorithms on large instances.

Abstract

This paper addresses the Capacitated Arc Routing Problem (CARP) using an Ant Colony Optimization scheme. Ant Colony schemes can compute solutions for medium scale instances of VRP. The proposed Ant Colony is dedicated to large-scale instances of CARP with more than 140 nodes and 190 arcs to service. The Ant Colony scheme is coupled with a local search procedure and provides high quality solutions. The benchmarks we carried out prove possible to obtain solutions as profitable as CARPET ones can be obtained using such scheme when a sufficient number of iterations is devoted to the ants. It competes with the Genetic Algorithm of Lacomme et al. regarding solution quality but it is more time consuming on large scale instances. The method has been intensively benchmarked on the well-known instances of Eglese, DeArmon and the last ones of Belenguer and Benavent. This research report is a step forward CARP resolution by Ant Colony proving ant schemes can compete with Taboo search methods and Genetic Algorithms