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- W80598915 abstract "For many multi-objective optimization scenarios, the goal is usually to identify the set of Pareto-optimal solutions. In practice however, some problems might arise in this context. In many cases, design variables can be prone to variation, which may cause the focus to shift to finding so-called robust solutions solutions which are the least sensitive to slight changes in variables. In addition, real-world optimization problems are often subject to a wide range of uncertainties, which can be caused by stochastic models, for example. In this study, these aspects are discussed and an approach is presented which allows for both robustness and uncertainty to be taken into account. A concept is developed, building upon existing studies and is implemented in the form of an evolutionary algorithm situated in the PISA framework, which is adapted to allow the handling of robustness and uncertainty. In order to assess the developed algorithm, a testproblem was implemented which allows the effects of robustness to be visualized. The simulation showed that the optimizer successfully finds the robust solutions if the according parameters are chosen wisely." @default.
- W80598915 created "2016-06-24" @default.
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- W80598915 date "2007-01-01" @default.
- W80598915 modified "2023-09-23" @default.
- W80598915 title "Robustness and Uncertainty in Multiobjective Search" @default.
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