Ant colony optimization techniques for the vehicle routing problem

This research applies the meta-heuristic method of ant colony optimization (ACO) to an established set of vehicle routing problems (VRP). The procedure simulates the decision-making processes of ant colonies as they forage for food and is similar to other adaptive learning and artificial intelligence techniques such as Tabu Search, Simulated Annealing and Genetic Algorithms. Modifications are made to the ACO algorithm used to solve the traditional traveling salesman problem in order to allow the search of the multiple routes of the VRP. Experimentation shows that the algorithm is successful in finding solutions within 1% of known optimal solutions and the use of multiple ant colonies is found to provide a comparatively competitive solution technique especially for larger problems. Additionally, the size of the candidate lists used within the algorithm is a significant factor in finding improved solutions, and the computational times for the algorithm compare favorably with other solution methods.