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 evalua...
| Autores: | , , , , |
|---|---|
| Tipo de recurso: | conjunto de datos |
| Fecha de publicación: | 2026 |
| País: | España |
| Institución: | Consorci de Serveis Universitaris de Catalunya (CSUC) |
| Repositorio: | CORA.Repositori de Dades de Recerca |
| OAI Identifier: | oai:dnet:cora.rdr____::6f0f4cf7547b315303f0bac4ca043ba4 |
| Acceso en línea: | https://doi.org/10.34810/DATA3157 |
| Access Level: | acceso abierto |
| Palabra clave: | Computer and Information Science Engineering Benchmark Knapsack Problem |
| Sumario: | 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 |
|---|