ACO algorithms with multi-core implementation

Ant colony optimization is a successful swarm intelligence method for solving various combinatorial optimization problems. It uses a population-based meta-heuristic that is based on the foraging behavior of real ant colonies, and these ants use pheromones to communicate indirectly with others. While the scale of problem increases, ACO necessitates much more time and resource to solve the optimization problem. Two main solutions to this bottleneck can be used: distributed implementations and parallel implementations. The rapid development of computer architecture enables the easily reachable parallel implementation platforms by multi-core processors. In this paper, it is aimed to present the performance increase of two main ACO algorithms on multi-core processors with parallel programming. Parallelization is done on a single ant colony by using Java thread programming approach with minimal communication and coordination between threads. The paper also draws future works that can be done on this topic.