Evaluation of a heuristic search algorithm based on sampling and clustering

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 strate...

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Autores principales: Harita, Maria, Wong, Alvaro, Rexachs del Rosario, Dolores, Luque Fadón, Emilio
Formato: Objeto de conferencia
Lenguaje:Inglés
Publicado: 2021
Materias:
Ciencias Informáticas
Optimization
Heuristic methods
Clustering
Benchmark
Acceso en línea:http://sedici.unlp.edu.ar/handle/10915/125155
Aporte de:
SEDICI (UNLP) de Universidad Nacional de La Plata
id I19-R120-10915-125155
record_format dspace
institution Universidad Nacional de La Plata
institution_str I-19
repository_str R-120
collection SEDICI (UNLP)
language Inglés
topic Ciencias Informáticas
Optimization
Heuristic methods
Clustering
Benchmark
spellingShingle Ciencias Informáticas
Optimization
Heuristic methods
Clustering
Benchmark
Harita, Maria
Wong, Alvaro
Rexachs del Rosario, Dolores
Luque Fadón, Emilio
Evaluation of a heuristic search algorithm based on sampling and clustering
topic_facet Ciencias Informáticas
Optimization
Heuristic methods
Clustering
Benchmark
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%.
format Objeto de conferencia
Objeto de conferencia
author Harita, Maria
Wong, Alvaro
Rexachs del Rosario, Dolores
Luque Fadón, Emilio
author_facet Harita, Maria
Wong, Alvaro
Rexachs del Rosario, Dolores
Luque Fadón, Emilio
author_sort Harita, Maria
title Evaluation of a heuristic search algorithm based on sampling and clustering
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
publishDate 2021
url http://sedici.unlp.edu.ar/handle/10915/125155
work_keys_str_mv AT haritamaria evaluationofaheuristicsearchalgorithmbasedonsamplingandclustering
AT wongalvaro evaluationofaheuristicsearchalgorithmbasedonsamplingandclustering
AT rexachsdelrosariodolores evaluationofaheuristicsearchalgorithmbasedonsamplingandclustering
AT luquefadonemilio evaluationofaheuristicsearchalgorithmbasedonsamplingandclustering
bdutipo_str Repositorios
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