Merging the Ranking and Selection into ITO Algorithm for Simulation Optimization

Due to simulation models are stochastic systems, how to account for the noise in simulation model is a rigorous issue in the field of simulation optimization. We proposed a framework, which merging the Ranking & Selection into ITO algorithm for solving simulation optimization problems in this paper. When the probability of correct choice and the parameters of indifference zone are given, Ranking & Selection + ITO algorithm can allocate the number of evaluations that each alternative needed automatically, and it can evaluate individuals by a smaller budget.

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