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We consider box-constrained robust optimisation problems with implementationuncertainty. In this setting, the solution that a decision maker wants toimplement may become perturbed. The aim is to find a solution that optimisesthe worst possible performance over all possible perturbances.  Previously, only few generic search methods have been developed for thissetting. We introduce a new approach for a global search, based on placing alargest empty hypersphere. We do not assume any knowledge on the structure ofthe original objective function, making this approach also viable forsimulation-optimisation settings. In computational experiments we demonstrate astrong performance of our approach in comparison with state-of-the-art methods,which makes it possible to solve even high-dimensional problems.

A Largest Empty Hypersphere Metaheuristic for Robust Optimisation with Implementation Uncertainty