摘要：AbstractOptimization via Simulation (OvS)is an useful optimization tool to find a solution to an optimization problem that is difficult to model analytically.OvSconsists in evaluating potential solutions through simulation executions; however, its high computational cost is a factor that can make its implementation infeasible. This issue also occurs in multi-objective problems, which tend to be expensive to solve. In this work, we present a new hybrid multi-objectiveOvSalgorithm, which uses Kriging-type metamodels to estimate the simulations results and a multi-objective evolutionary algorithm to manage the optimization process. Our proposal succeeds in reducing the computational cost significantly without affecting the quality of the results obtained. The evolutionary part of the hybrid algorithm is based on the popularNSGA-II. The hybrid method is compared to the canonicalNSGA-IIand other hybrid approaches, showing a good performance not only in the quality of the solutions but also as computational cost saving.
关键词：KeywordsOptimization via simulationMetamodelMulti-Objective optimizationKrigingNSGA-II
