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Abstract

Abstract The hybridization of population-based meta-heuristics and Local Search strategies is an effective algorithmic proposal for solving complex continuous optimization problems. Such hybridization becomes much more effective when the local search heuristics are applied in the most promising areas of the solution space. This paper presents a hybrid method based on Clustering Search (CS) to solve continuous optimization problems. The CS divides the search space in clusters, which are composed of solutions generated by a population meta-heuristic, called Variable Mesh Optimization. Each cluster is explored further with local search procedures. Computational results considering a benchmark of multimodal continuous functions are presented.

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... These methods lacked the use of efficient hybrid metaheuristic methods (except the hybrid GRASP-ILS method of Haddadene et al., 2016). In fact, Salas et al. (2015) and Masmoudi et al. (2016) suggest that the hybridization of population-based metaheuristics with advanced local search mechanism (i.e., single solution-based metaheuristics) is effective to solve such complex variants of the VRP. We, therefore, propose three hybrid populationbased metaheuristics based on ABC algorithm. ...
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... Also, new clusters may be introduced or existing ones removed. Costa Salas et al. (2015) utilized the CS for continuous optimization by combining variable mesh optimization (VMO) ( Puris et al., 2012) and a couple of LS procedures. Since CS is a generic framework, Nagano et al. (2014) successfully applied it to a combinatorial optimization problem i.e. a flow shop scheduling problem. ...
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