Abstract
Many engineering design optimization problems are multi-objective, constrained, and contain uncertain parameters. For these problems, it is desirable to obtain solutions that are robustly optimum. A robust optimal solution is one which remains feasible and optimal despite perturbations in the uncertainty parameters. This article presents a scenario-generation-based method for solving multi-objective robust optimization (MORO) problems. The approach follows a sequential, single-level optimization problem workflow, with the uncertainty handled by a fixed sampling method. The proposed approach is applied to an unmanned surface vessel (USV) case study problem. The results obtained and subsequent performance analysis show that the proposed approach generally performs well.
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