Abstract
Clustering Search (*CS) has been proposed as a generic way of combining search metaheuristics with clustering to detect promising search areas before applying local search procedures. The clustering process may keep representative solutions associated to different search subspaces (search areas). In this work, a new approach is proposed, based on Artificial Bee Colony (ABC), observing the inherent characteristics of detecting promissing food sources employed by that metaheuristic. The proposed hybrid algorithm, performing a Hooke & Jeeves based local, is compared against other versions of ABC: a pure ABC and another hybrid ABC, exploring an elitist criteria.
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Costa, T.S., de Oliveira, A.C.M. (2013). Artificial Bee Clustering Search. In: Rojas, I., Joya, G., Cabestany, J. (eds) Advances in Computational Intelligence. IWANN 2013. Lecture Notes in Computer Science, vol 7903. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38682-4_3
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DOI: https://doi.org/10.1007/978-3-642-38682-4_3
Publisher Name: Springer, Berlin, Heidelberg
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