Robust optimization of multi-scenario many-objective problems with auto-tuned utility function. Issue 7 (3rd July 2021) - Kecskés, István; Odry, Péter
ABSTRACT: The main focus in optimization studies, including multi-objective and multi-scenario optimization, is placed on finding the global optimum or global Pareto-optimal solutions, representing the best possible objective values. Manual preferences and selection are required to obtain a single implementable solution from the obtainable Pareto set. For achieving such a single optimum, the multi-scenario property is applied to adjust the required preference weights automatically; as a result, robust performance for various scenarios is provided. The robust quality definition is believed to decrease the performance uncertainty owing to any possible scenario in real-world settings. These challenges have motivated the creation of a method that searches a robust solution using evolutionary algorithms and well-applied IT tools. This selection process applies two robustness aspects simultaneously: the necessary weights for the aggregation of multi-scenario and multi-objective dimensions are adjusted to obtain similar performances for scenarios as much as possible; the robustness against system parameters is evaluated by the calculation of the robustness indices. The method can be used universally where the robust optimum is required for a model having multi-scenario and multi-objective properties. The proposed method has been tested on test functions, and in conclusion it has been applied to two engineering problems.