Self-adaptive CMSA

While the standard CMSA algorithm has proven its utility across a range of different combinatorial optimization problems, certain applications have highlighted its susceptibility to variations in parameter settings. In order to deal with this issue, an innovative self-adaptive variant of the CMSA algorithm, termed Adapt_Cmsa, was developed and will be presented in this chapter. The primary objective is to mitigate the parameter sensitivity that might occur in the standard CMSA variant. The merits of this novel CMSA variant are substantiated through its application to the Minimum Positive Influence Dominating Set (MPIDS) problem and to the Far From Most String (FFMS) problem. Notably, Adapt_Cmsa distinguishes itself from standard CMSA by not presenting the need for a computationally intensive parameter tuning process across subsets within the considered set of problem instances.