Artigo

Análise paramétrica em algoritmo de inteligência de enxame utilizando funções Benchmark

Optimization problems are present in applications in the scientific, financial, industrial and management areas, and in recent years several methodologies have emerged that aim to obtain their solution. One of these techniques is known as Swarm Intelligence (SI), based on the behavior of relatively...

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Autor principal: LIMA, Weverson Celio Silva de
Grau: Artigo
Publicado em: 2023
Assuntos:
Otimização
Algoritmos
CNPQ::CIENCIAS EXATAS E DA TERRA
Acesso em linha: https://bdm.ufpa.br:8443/jspui/handle/prefix/5770
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spelling oai:https:--bdm.ufpa.br:8443:prefix-57702025-01-16T14:49:10Z Análise paramétrica em algoritmo de inteligência de enxame utilizando funções Benchmark LIMA, Weverson Celio Silva de FERREIRA JUNIOR, José Jailton Henrique http://lattes.cnpq.br/9031636126268760 VIDAL, Juan Ferreira http://lattes.cnpq.br/9977260139812745 Otimização Algoritmos CNPQ::CIENCIAS EXATAS E DA TERRA Optimization problems are present in applications in the scientific, financial, industrial and management areas, and in recent years several methodologies have emerged that aim to obtain their solution. One of these techniques is known as Swarm Intelligence (SI), based on the behavior of relatively simple beings, but who manage to solve complex problems when they are placed in a collective. In the literature, several SI algorithms were found, but there was a lack of in-depth studies on the behavior of each of its parameters. Therefore, this work presents a parametric analysis of two widely used SI algorithms, namely Particle swarm optimization (PSO) and Firefly Algorithm (FA). For this, a benchmarking study was carried out using benchmark functions in three search intervals of different sizes, in order to evaluate metrics such as accuracy, precision and average processing time. For this purpose, respectively, 315 and 207 scenarios were developed for PSO and FA. Furthermore, to compare the SI in relation to the traditional heuristic, 171 scenarios were developed for the Random Walk (RW) algorithm. With results, sets of parameters were obtained with accuracy and precision around 100% in the best scenarios of the SI algorithms, demonstrating the importance of a good parameterization for an optimal performance of the method. 2023-06-07T13:42:34Z 2023-06-07T13:42:34Z 2022-10-28 Trabalho de Curso - Graduação - Artigo LIMA, Weverson Celio Silva de. Análise paramétrica em algoritmo de inteligência de enxame utilizando funções Benchmark. 2022. Trabalho de Curso (Bacharelado em Engenharia de Computação) – Faculdade de Engenharia da Computação, Campus Universitário de Castanhal, Universidade Federal do Pará, Castanhal, 2022. Disponível em: https://bdm.ufpa.br:8443/jspui/handle/prefix/5770. Acesso em:. https://bdm.ufpa.br:8443/jspui/handle/prefix/5770 Acesso Aberto Disponível via internet no e-mail: bibufpacastanhal@gmail.com
institution Biblioteca Digital de Monografias - UFPA
collection MonografiaUFPA
topic Otimização
Algoritmos
CNPQ::CIENCIAS EXATAS E DA TERRA
spellingShingle Otimização
Algoritmos
CNPQ::CIENCIAS EXATAS E DA TERRA
LIMA, Weverson Celio Silva de
Análise paramétrica em algoritmo de inteligência de enxame utilizando funções Benchmark
topic_facet Otimização
Algoritmos
CNPQ::CIENCIAS EXATAS E DA TERRA
description Optimization problems are present in applications in the scientific, financial, industrial and management areas, and in recent years several methodologies have emerged that aim to obtain their solution. One of these techniques is known as Swarm Intelligence (SI), based on the behavior of relatively simple beings, but who manage to solve complex problems when they are placed in a collective. In the literature, several SI algorithms were found, but there was a lack of in-depth studies on the behavior of each of its parameters. Therefore, this work presents a parametric analysis of two widely used SI algorithms, namely Particle swarm optimization (PSO) and Firefly Algorithm (FA). For this, a benchmarking study was carried out using benchmark functions in three search intervals of different sizes, in order to evaluate metrics such as accuracy, precision and average processing time. For this purpose, respectively, 315 and 207 scenarios were developed for PSO and FA. Furthermore, to compare the SI in relation to the traditional heuristic, 171 scenarios were developed for the Random Walk (RW) algorithm. With results, sets of parameters were obtained with accuracy and precision around 100% in the best scenarios of the SI algorithms, demonstrating the importance of a good parameterization for an optimal performance of the method.
author_additional FERREIRA JUNIOR, José Jailton Henrique
author_additionalStr FERREIRA JUNIOR, José Jailton Henrique
format Artigo
author LIMA, Weverson Celio Silva de
title Análise paramétrica em algoritmo de inteligência de enxame utilizando funções Benchmark
title_short Análise paramétrica em algoritmo de inteligência de enxame utilizando funções Benchmark
title_full Análise paramétrica em algoritmo de inteligência de enxame utilizando funções Benchmark
title_fullStr Análise paramétrica em algoritmo de inteligência de enxame utilizando funções Benchmark
title_full_unstemmed Análise paramétrica em algoritmo de inteligência de enxame utilizando funções Benchmark
title_sort análise paramétrica em algoritmo de inteligência de enxame utilizando funções benchmark
publishDate 2023
url https://bdm.ufpa.br:8443/jspui/handle/prefix/5770
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score 11.753896

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