HC-ACO: The Hyper-Cube Framework for Ant Colony Optimization

Ant Colony Optimization (ACO) [2] is a recently proposed metaheuristic approach for solving hard combinatorial optimization problems. The inspiring source of ACO is the foraging behavior of real ants. In most ACO implementations the hyperspace for the pheromone values used by the ants to build solutions is only implicitly limited. In this paper we propose a new way of implementing ACO algorithms, which explicitly defines the hyperspace for the pheromone values as the convex hull of the set of 0-1 coded feasible solutions of the combinatorial optimization problem under consideration. We call this new implementation the hyper-cube framework for ACO algorithms. The organization of this extended abstract is as follows. In section 2 we briefly present the original Ant System [3] for static combinatorial optimization problems. In section 3 we propose the hyper-cube framework for ACO algorithms and we present pheromone updating rules for Ant System (AS) and MAX -MIN Ant System (MMAS). In section 4 we discuss some of the advantages involved with the hyper-cube framework for ACO algorithms, while Section 5 outlines future work.