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...

Descripción completa

Detalles Bibliográficos
Autores: Harita, Maria, Wong, Alvaro, Suppi, Remo, Rexachs, Dolores, Luque, Emilio
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
id ES_0d0c0f6ca7dccd734d27c6ff020fad0d
oai_identifier_str oai:dnet:cora.rdr____::6f0f4cf7547b315303f0bac4ca043ba4
network_acronym_str ES
network_name_str España
repository_id_str
spelling Replication Data for: A Metaheuristic Search Algorithm Based on Sampling and ClusteringHarita, MariaWong, AlvaroSuppi, RemoRexachs, DoloresLuque, EmilioComputer and Information ScienceEngineeringBenchmarkKnapsack ProblemThis 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_methodologyCORA.Repositori de Dades de RecercaHarita Rascon, Maria de los Angeles2026info:eu-repo/semantics/datasethttps://doi.org/10.34810/DATA3157reponame:CORA.Repositori de Dades de Recercainstname:Consorci de Serveis Universitaris de Catalunya (CSUC)Inglésinfo:eu-repo/semantics/openAccessCC BY 4.0oai:dnet:cora.rdr____::6f0f4cf7547b315303f0bac4ca043ba42026-06-17T12:20:17Z
dc.title.none.fl_str_mv Replication Data for: A Metaheuristic Search Algorithm Based on Sampling and Clustering
title Replication Data for: A Metaheuristic Search Algorithm Based on Sampling and Clustering
spellingShingle Replication Data for: A Metaheuristic Search Algorithm Based on Sampling and Clustering
Harita, Maria
Computer and Information Science
Engineering
Benchmark
Knapsack Problem
title_short Replication Data for: A Metaheuristic Search Algorithm Based on Sampling and Clustering
title_full Replication Data for: A Metaheuristic Search Algorithm Based on Sampling and Clustering
title_fullStr Replication Data for: A Metaheuristic Search Algorithm Based on Sampling and Clustering
title_full_unstemmed Replication Data for: A Metaheuristic Search Algorithm Based on Sampling and Clustering
title_sort Replication Data for: A Metaheuristic Search Algorithm Based on Sampling and Clustering
dc.creator.none.fl_str_mv Harita, Maria
Wong, Alvaro
Suppi, Remo
Rexachs, Dolores
Luque, Emilio
author Harita, Maria
author_facet Harita, Maria
Wong, Alvaro
Suppi, Remo
Rexachs, Dolores
Luque, Emilio
author_role author
author2 Wong, Alvaro
Suppi, Remo
Rexachs, Dolores
Luque, Emilio
author2_role author
author
author
author
dc.contributor.none.fl_str_mv Harita Rascon, Maria de los Angeles
dc.subject.none.fl_str_mv Computer and Information Science
Engineering
Benchmark
Knapsack Problem
topic Computer and Information Science
Engineering
Benchmark
Knapsack Problem
description 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
publishDate 2026
dc.date.none.fl_str_mv 2026
dc.type.none.fl_str_mv info:eu-repo/semantics/dataset
format dataset
dc.identifier.none.fl_str_mv https://doi.org/10.34810/DATA3157
url https://doi.org/10.34810/DATA3157
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
CC BY 4.0
eu_rights_str_mv openAccess
rights_invalid_str_mv CC BY 4.0
dc.publisher.none.fl_str_mv CORA.Repositori de Dades de Recerca
publisher.none.fl_str_mv CORA.Repositori de Dades de Recerca
dc.source.none.fl_str_mv reponame:CORA.Repositori de Dades de Recerca
instname:Consorci de Serveis Universitaris de Catalunya (CSUC)
instname_str Consorci de Serveis Universitaris de Catalunya (CSUC)
reponame_str CORA.Repositori de Dades de Recerca
collection CORA.Repositori de Dades de Recerca
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869403318039609344
score 15,223283

Similares en LA Referencia