The paper deals with testing different selection strategies of Evolution Strategy on different discrete event simulation models. The models reflect real production systems in industrial companies. We specified different objective functions of models considering the simulated system. All possible solutions and their objective function values were mapped to find the global optimum in the search space. We tested different settings of selection strategies and other Evolution Strategy parameters using a simulation optimizer we developed for simulation optimization. We evaluated these settings by the success of finding the optimum by the optimization algorithms. We also used another evaluation criterion - the difference between the objective function value of the best solution found in the series (replication of optimization experiments with a concrete optimization method setting) and the optimum objective function value.
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