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.
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Oliveira, A.C.M., Lorena, L.A.N. (2006). Pattern Sequencing Problems by Clustering Search. In: Sichman, J.S., Coelho, H., Rezende, S.O. (eds) Advances in Artificial Intelligence - IBERAMIA-SBIA 2006. IBERAMIA SBIA 2006 2006. Lecture Notes in Computer Science(), vol 4140. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11874850_26
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DOI: https://doi.org/10.1007/11874850_26
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