Self-adaptive Cooperation Scheme in a Parallel ACO Algorithm for Binary Combinatorial Problems

Abstract

[Abstract] The ant colony optimization (ACO) is widely used for combinatorial optimization problems, although it can suffer from fast convergence to local minima. In order to provide a versatile implementation of ACO, we present a parallel multicolony strategy with an improved cooperation scheme for binary combinatorial problems. Our proposal is based on a self-adaptive method, which assigns appropriate run-time cooperation levels to each problem based on its size and available computational resources. We evaluate this proposal with problems with different levels of cooperation and number of processes. All these configurations combined show its flexibility as a versatile solver for this type of problems