Replication Data for: A Metaheuristic Search Algorithm Based on Sampling and Clustering

This repository contains the experimental data associated with the MCSA (Montecarlo-Clustering Search Algorithm), a stochastic metaheuristic designed for solving optimization problems. The dataset supports the results presented in the associated publication and includes raw outputs, benchmark evaluations, and problem-specific instances such as the Knapsack Problem (KP) and Multi-Objective Knapsack Problem (MOKP). Detailed tabular and file-specific descriptions are provided within each experiment folder (EX01_instance–EX04_Synthetic). For a deep dive into the algorithmic steps, binary encoding, and problem decomposition, please refer to the README_methodology