In recent years, research on large scale global optimization (LSGO) provided metaheuristics able to effectively tackle real-valued objective functions depending on thousand of variables. Nevertheless, finding a suitable solution of LSGO problems othen requires a significantly high number of fitness evaluations. Therefore, when the objective function is computationally expensive, metaheuristicsbased solutions of LSGO problems can easily become infeasible or at least unafiractive. In this paper, we address such an issue with a joint approach based on problem decomposition, fitness meta-modeling and parallel computing. We present a preliminary numerical investigation of the proposed methodology, which provided significant gains in terms of both exact evaluations of the objective functions and parallel speedup.

Large scale optimization of computationally expensive functions: An approach based on parallel cooperative coevolution and fitness metamodeling

DELLA CIOPPA, Antonio;
2017

Abstract

In recent years, research on large scale global optimization (LSGO) provided metaheuristics able to effectively tackle real-valued objective functions depending on thousand of variables. Nevertheless, finding a suitable solution of LSGO problems othen requires a significantly high number of fitness evaluations. Therefore, when the objective function is computationally expensive, metaheuristicsbased solutions of LSGO problems can easily become infeasible or at least unafiractive. In this paper, we address such an issue with a joint approach based on problem decomposition, fitness meta-modeling and parallel computing. We present a preliminary numerical investigation of the proposed methodology, which provided significant gains in terms of both exact evaluations of the objective functions and parallel speedup.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11386/4691677
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