An algorithm based on ant colony optimization for the minimum connected dominating set problem

Ant colony optimization is a well established metaheuristic from the swarm intelligence field for solving difficult optimization problems. In this work we present an application of ant colony optimization to the minimum connected dominating set problem, which is an NP-hard combinatorial optimization problem. Given an input graph, valid solutions are connected subgraphs of the given input graph. Due to the involved connectivity constraints, out-of-the-box integer linear programming solvers do not perform well for this problem. The developed ant colony optimization algorithm uses reduced variable neighborhood search as a sub-routine. Moreover, it can be applied to the weighted and to the non-weighted problem variants. An extensive experimental evaluation presents the comparison of our algorithm with the respective state-of-the-art techniques from the literature. It is shown that the proposed algorithm outperforms the current state of the art for both problem variants. For comparison purposes we also develop a constraint programming approach based on graph variables. Even though its performance deteriorates with growing instance size, it performs surprisingly well, solving 315 out of 481 considered problem instances to optimality.Christian Blum acknowledges support from project Collectiveware, TIN2015-66863-C2-1-R (Spanish Ministry for Economy and Competitiveness, FEDER funds from the European Union ).Peer reviewe