Improving Group Search Optimization Through Local Search Heuristics for Automatic Data Clustering

  • Luciano D. S. Pacífico UFRPE
  • Teresa B. Ludermir UFPE

Resumo


Neste trabalho, três models de Agrupamento Automático de Dados, baseados na meta-heurística de Otimização por Busca em Grupo (GSO), são introduzidos, chamados RHGSO, ADHGSO e BDHGSO. Nos modelos propostos, a busca global do GSO é melhorada através de heurísticas de busca local adaptadas ao contexto de Agrupamento Automático de Dados, onde operações de ativação, desativação e substituição de centroides de agrupamentos são executadas, objetivando a realização de perturbações que visam o aumento da velocidade de exploração do grupo do GSO. Os algoritmos propostos são comparados a outros Algoritmos Evolucionários e de Inteligência de Enxames da literatura, apresentando resultados promissores.

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Publicado
28/11/2022
PACÍFICO, Luciano D. S.; LUDERMIR, Teresa B.. Improving Group Search Optimization Through Local Search Heuristics for Automatic Data Clustering. In: ENCONTRO NACIONAL DE INTELIGÊNCIA ARTIFICIAL E COMPUTACIONAL (ENIAC), 19. , 2022, Campinas/SP. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2022 . p. 118-129. ISSN 2763-9061. DOI: https://doi.org/10.5753/eniac.2022.227578.

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