A Largest Empty Hypersphere Metaheuristic for Robust Optimisation with Implementation Uncertainty
Abstract
We consider box-constrained robust optimisation problems with implementation uncertainty. In this setting, the solution that a decision maker wants to implement may become perturbed. The aim is to find a solution that optimises the worst possible performance over all possible perturbances. Previously, only few generic search methods have been developed for this setting. We introduce a new approach for a global search, based on placing a largest empty hypersphere. We do not assume any knowledge on the structure of the original objective function, making this approach also viable for simulation-optimisation settings. In computational experiments we demonstrate a strong performance of our approach in comparison with state-of-the-art methods, which makes it possible to solve even high-dimensional problems.
Cite
@article{arxiv.1809.02437,
title = {A Largest Empty Hypersphere Metaheuristic for Robust Optimisation with Implementation Uncertainty},
author = {Martin Hughes and Marc Goerigk and Michael Wright},
journal= {arXiv preprint arXiv:1809.02437},
year = {2018}
}