... control co-design dynamics uncertainty stochastic programming fuzzy programming robust optimization National Science Foundation 10.13039/100000001 CMMI-2034040 With the ever-growing complexity and integrated nature of dynamic engineering systems, the need for effective control...

... relaxing the solutions to facilitate convergence to a set of non-dominated, robust optimal solutions. Additionally, a variation of the bi-objective expected improvement criterion is proposed to encourage variety and density of the robust bi-objective non-dominated solutions. Several examples are tested...

... to be solved by optimization runs. In Ref. [ 16 ], a novel approximation-assisted robust optimization approach using constraint cuts and online approximation for multi-objective problems is presented. It is a genetic algorithm based approach and the computational efficiency can still be a serious concern given...

...Jianhua Zhou; Shuo Cheng; Mian Li Uncertainty plays a critical role in engineering design as even a small amount of uncertainty could make an optimal design solution infeasible. The goal of robust optimization is to find a solution that is both optimal and insensitive to uncertainty that may exist...

... robust optimization (McRO) approach uses a multiobjective genetic algorithm as an optimizer. McRO obtains solutions that are as best as possible in a multiobjective and multidisciplinary sense. Moreover, for McRO solutions, the variation of objective and/or constraint functions can be kept within...

... . The bilevel robust optimization strategy developed in this research provides for disciplinary autonomy in system design, while simultaneously accounting for performance uncertainties to ensure feasible robustness of the resulting system. The method is effective in locating a feasible robust optimum...

... that have discontinuous objective and constraint functions with respect to uncontrollable parameters, and can be used for objective or feasibility robust optimization, or both together. In our approach, the known parameter tolerance region maps into sensitivity regions in the objective and constraint spaces...