Evaluation of a heuristic search algorithm based on sampling and clustering

Autores
Harita, Maria; Wong, Alvaro; Rexachs del Rosario, Dolores; Luque Fadón, Emilio
Año de publicación
2021
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
Systems have evolved in such a way that today’s parallel systems are capable of offering high capacity and better performance. The design of approaches seeking for the best set of parameters in the context of a high-performance execution is fundamental. Although complex, heuristic methods are strategies that deal with high-dimensional optimization problems. We are proposing to enhance the evaluation method of a baseline heuristic that uses sampling and clustering techniques to optimize a complex, large and dynamic system. To carry out our proposal we selected the benchmark test functions and perform a density-based analysis along with k-means to cluster into feasible regions, discarding the non-relevant areas. With this, we aim to avoid getting trapped in local minima. Ultimately, the recursive execution of our methodology will guarantee to obtain the best value, thus, getting closer to method validation without forgetting the future lines, e.g. its distributed parallel implementation. Preliminary results turned out to be satisfactory, having obtained a solution quality above 99%.
Facultad de Informática
Materia
Ciencias Informáticas
Optimization
Heuristic methods
Clustering
Benchmark
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/4.0/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/125155
Acceder
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repository_id_str 1329
network_name_str SEDICI (UNLP)
spelling Evaluation of a heuristic search algorithm based on sampling and clusteringHarita, MariaWong, AlvaroRexachs del Rosario, DoloresLuque Fadón, EmilioCiencias InformáticasOptimizationHeuristic methodsClusteringBenchmarkSystems have evolved in such a way that today’s parallel systems are capable of offering high capacity and better performance. The design of approaches seeking for the best set of parameters in the context of a high-performance execution is fundamental. Although complex, heuristic methods are strategies that deal with high-dimensional optimization problems. We are proposing to enhance the evaluation method of a baseline heuristic that uses sampling and clustering techniques to optimize a complex, large and dynamic system. To carry out our proposal we selected the benchmark test functions and perform a density-based analysis along with k-means to cluster into feasible regions, discarding the non-relevant areas. With this, we aim to avoid getting trapped in local minima. Ultimately, the recursive execution of our methodology will guarantee to obtain the best value, thus, getting closer to method validation without forgetting the future lines, e.g. its distributed parallel implementation. Preliminary results turned out to be satisfactory, having obtained a solution quality above 99%.Facultad de Informática2021info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdf55-59http://sedici.unlp.edu.ar/handle/10915/125155enginfo:eu-repo/semantics/altIdentifier/isbn/978-950-34-2016-4info:eu-repo/semantics/reference/hdl/10915/121564info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/4.0/Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-03T11:02:13Zoai:sedici.unlp.edu.ar:10915/125155Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-03 11:02:13.918SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Evaluation of a heuristic search algorithm based on sampling and clustering
title Evaluation of a heuristic search algorithm based on sampling and clustering
spellingShingle Evaluation of a heuristic search algorithm based on sampling and clustering
Harita, Maria
Ciencias Informáticas
Optimization
Heuristic methods
Clustering
Benchmark
title_short Evaluation of a heuristic search algorithm based on sampling and clustering
title_full Evaluation of a heuristic search algorithm based on sampling and clustering
title_fullStr Evaluation of a heuristic search algorithm based on sampling and clustering
title_full_unstemmed Evaluation of a heuristic search algorithm based on sampling and clustering
title_sort Evaluation of a heuristic search algorithm based on sampling and clustering
dc.creator.none.fl_str_mv Harita, Maria
Wong, Alvaro
Rexachs del Rosario, Dolores
Luque Fadón, Emilio
author Harita, Maria
author_facet Harita, Maria
Wong, Alvaro
Rexachs del Rosario, Dolores
Luque Fadón, Emilio
author_role author
author2 Wong, Alvaro
Rexachs del Rosario, Dolores
Luque Fadón, Emilio
author2_role author
author
author
dc.subject.none.fl_str_mv Ciencias Informáticas
Optimization
Heuristic methods
Clustering
Benchmark
topic Ciencias Informáticas
Optimization
Heuristic methods
Clustering
Benchmark
dc.description.none.fl_txt_mv Systems have evolved in such a way that today’s parallel systems are capable of offering high capacity and better performance. The design of approaches seeking for the best set of parameters in the context of a high-performance execution is fundamental. Although complex, heuristic methods are strategies that deal with high-dimensional optimization problems. We are proposing to enhance the evaluation method of a baseline heuristic that uses sampling and clustering techniques to optimize a complex, large and dynamic system. To carry out our proposal we selected the benchmark test functions and perform a density-based analysis along with k-means to cluster into feasible regions, discarding the non-relevant areas. With this, we aim to avoid getting trapped in local minima. Ultimately, the recursive execution of our methodology will guarantee to obtain the best value, thus, getting closer to method validation without forgetting the future lines, e.g. its distributed parallel implementation. Preliminary results turned out to be satisfactory, having obtained a solution quality above 99%.
Facultad de Informática
description Systems have evolved in such a way that today’s parallel systems are capable of offering high capacity and better performance. The design of approaches seeking for the best set of parameters in the context of a high-performance execution is fundamental. Although complex, heuristic methods are strategies that deal with high-dimensional optimization problems. We are proposing to enhance the evaluation method of a baseline heuristic that uses sampling and clustering techniques to optimize a complex, large and dynamic system. To carry out our proposal we selected the benchmark test functions and perform a density-based analysis along with k-means to cluster into feasible regions, discarding the non-relevant areas. With this, we aim to avoid getting trapped in local minima. Ultimately, the recursive execution of our methodology will guarantee to obtain the best value, thus, getting closer to method validation without forgetting the future lines, e.g. its distributed parallel implementation. Preliminary results turned out to be satisfactory, having obtained a solution quality above 99%.
publishDate 2021
dc.date.none.fl_str_mv 2021
dc.type.none.fl_str_mv info:eu-repo/semantics/conferenceObject
info:eu-repo/semantics/publishedVersion
Objeto de conferencia
http://purl.org/coar/resource_type/c_5794
info:ar-repo/semantics/documentoDeConferencia
format conferenceObject
status_str publishedVersion
dc.identifier.none.fl_str_mv http://sedici.unlp.edu.ar/handle/10915/125155
url http://sedici.unlp.edu.ar/handle/10915/125155
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/isbn/978-950-34-2016-4
info:eu-repo/semantics/reference/hdl/10915/121564
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
dc.format.none.fl_str_mv application/pdf
55-59
dc.source.none.fl_str_mv reponame:SEDICI (UNLP)
instname:Universidad Nacional de La Plata
instacron:UNLP
reponame_str SEDICI (UNLP)
collection SEDICI (UNLP)
instname_str Universidad Nacional de La Plata
instacron_str UNLP
institution UNLP
repository.name.fl_str_mv SEDICI (UNLP) - Universidad Nacional de La Plata
repository.mail.fl_str_mv alira@sedici.unlp.edu.ar
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