A Self-Adaptive Variant of CMSA: Application to the Minimum Positive Influence Dominating Set Problem - Mehmet Anıl Akbay; Albert López Serrano; Christian Blum (International Journal of Computational Intelligence Systems, 2022)
Abstract Construct, merge, solve and adapt (CMSA) is a recently developed, generic algorithm for combinatorial optimisation. Even though the usefulness of the algorithm has been demonstrated by applications to a range of combinatorial optimisation problems, in some applications, it was observed that the algorithm can be sensitive to parameter settings. In this work, we propose a self-adaptive variant of CMSA, called Adapt-CMSA, with the aim of reducing the parameter sensitivity of the original version of CMSA. The advantages of this new CMSA variant are demonstrated in the context of the application to the so-called minimum positive influence dominating set problem. It is shown that, in contrast to CMSA, Adapt-CMSA does not require a computation time intensive parameter tuning process for subsets of the considered set of problem instances. In fact, after tuning Adapt-CMSA only once for the whole set of benchmark instances, the algorithm already obtains state-of-the-art results. Nevertheless, note that the main objective of this paper is not the tackled problem but the improvement of CMSA.