Q-Learning Ant Colony Optimization supported by Deep Learning for Target Set Selection

The use of machine learning techniques within metaheuristics is a rapidly growing field of research. In this paper, we show how a deep learning framework can be beneficially used to improve an ant colony optimization algorithm. In particular, problem information obtained via deep learning is combined in our algorithm by means of Q-learning with the usual pheromone and greedy information. Our algorithm is applied to the Target Set Selection (TSS) problem, which is an NP-hard combinatorial optimization problem with applications, for example, in social networks. The specific problem variant considered in this paper asks for finding a smallest subset of the nodes of a given graph such that their influence can be spread to all other nodes of the graph via a diffusion process. The experimental results show, first, that the pure ant colony optimization approach can already compete with the state of the art. Second, the obtained results indicate that the hybrid algorithm variant outperforms the pure ant colony optimization approach especially in the context of large problem instances.