New Clustering Search approaches applied to continuous domain optimization

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, 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.

A survey on the studies employing machine learning (ML) for enhancing artificial bee colony (ABC) optimization algorithm

“…Clustering Search (*CS) combines search algorithms with clustering to discover promising search areas before applying local search operations. Costa and De Oliveira (2013) and ) proposed new approaches based on ABC and *CS, based on the essential properties of promising food sources employed by ABC.…”