A comparative study on the ant colony optimization algorithms

The ant colony optimization (ACO) algorithm is a member of the ant colony algorithms which is part of the swarm intelligence methods. It is a probabilistic technique for finding close to optimal paths through a problem space. The ant colony optimization algorithms therefore mimic the behavior of natural ants with the use of artificial ants as agents to find a reasonable solution to optimization problems by following the model of optimization used by natural ants to get to their destination in the shortest possible time. This paper presents a review and aims to show the main variants of the ant colony optimization algorithms by comparing the results of mainly four variants on some selected combinatorial optimization problems. A review of the varieties of the ACO algorithms, application of ACO algorithms and the comparative analysis of some selected variants are presented.