CT-ACO - hybridizing ant colony optimization with cyclic transfer search for the vehicle routing problem

Abstract

Ant colony optimization (ACO) is a meta-heuristic approach to tackle hard combinatorial optimization problems. The basic component of ACO is a solution construction mechanism, which simulates the decision-making processes of ant colonies as they forage for food and find the most efficient routes from their nests to food sources. Due to its constructive nature, we hybridize the solution construction mechanism of ACO with cyclic transfers (CT), which is a new class of neighborhood search algorithm. A CT-ACO algorithm, a hybrid search approach, is proposed to solve the vehicle routing problem. The method has both the advantages of ant colony optimization, the ability to find the higher performance solutions, and that of cyclic transfer algorithm, the ability to conduct fine-tuning in the quality of solutions and to find better solutions. The experimental results have shown that the method is very efficient and competitive to solve the vehicle routing problem compared with the best existing methods in terms of solution quality. Moreover, CT-ACO algorithm improves the best solutions known for some benchmark instances of the literature