New Clustering Search approaches applied to continuous domain optimization

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, new approaches are proposed, based on Artificial Bee Colony (ABC) and Differential Evolution (DE), observing the inherent characteristics of detecting promising food sources employed by that metaheuristic. The proposed hybrid algorithms, performing a Hooke & Jeeves based local, are compared against another hybrid versions of ABC and DE, exploring an elitist criteria.