A Machine Learning Firefly Algorithm Applied to the Matrix Covering Problem

At the business level, there are a large number of combinatorial problems. A subset of these is NP-hard type. The study of algorithms that address this type of problem is of great interest. On the other hand, there are a large number of metaheuristic algorithms that naturally work in continuous spaces. Adapting the latter to solve combinatorial problems is of great interest at an industrial level. In this article, we explore a general binarization mechanism of continuous metaheuristics based on cauterization techniques. The experiments are designed to demonstrate the utility of the clustering technique in binarization. Besides, we verify the effectiveness of our algorithm through reference instances. The results indicate that the binary firefly optimization algorithm (MLFA) obtains adequate results when evaluated with a combinatorial problem such as the SCP.