Pattern sequencing problems by clustering search

Authors:
Alexandre C. M. Oliveira;Luiz A. N. Lorena
Affiliations:
Departamento de Informática, Universidade Federal do Maranhão – UFMA, São Luís, MA, Brazil;Laboratório Associado de Computação e Matemática Aplicada, Instituto Nacional de Pesquisas Espaciais – INPE, São José dos Campos, SP, Brazil
Venue:
IBERAMIA-SBIA'06 Proceedings of the 2nd international joint conference, and Proceedings of the 10th Ibero-American Conference on AI 18th Brazilian conference on Advances in Artificial Intelligence
Year:
2006

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Abstract

Modern search methods for optimization consider hybrid search metaheuristics those employing general optimizers working together with a problem-specific local search procedure. The hybridism comes from the balancing of global and local search procedures. A challenge in such algorithms is to discover efficient strategies to cover all the search space, applying local search only in actually promising search areas. This paper proposes the Clustering Search (*CS): a generic way of combining search metaheuristics with clustering to detect promising search areas before applying local search procedures. The clustering process aims to gather similar information about the problem at hand into groups, maintaining a representative solution associated to this information. Two applications to combinatorial optimization are examined, showing the flexibility and competitiveness of the method.