Ant Colony Optimization for Configuration

An inherent difficulty in enumerative search algorithms for optimisation is the combinatorial explosion that occurs when increasing the size of the input. Among incomplete algorithms that address this issue, ant colony optimization(ACO) uses a combination of random and heuristic methods plus reinforcement learning, which proved efficient on a wide range of CSPs problems. This paper presents results in applying an ACO-based algorithm to configuration, which to the best of our knowledge was never investigated before. We describe how the nature of unbounded configuration problems impacts the ACO approach due to the presence of set-variables with open domains. We propose an ACO framework able to deal with those issues through an original pheromone model and algorithm. We also present the use of particle swarm optimization (PSO) to converge towards good parameter sets. Finally, we provide early experimental results, both for random problem instances andthe "racks" optimisation problem.