A Largest Empty Hypersphere Metaheuristic for Robust Optimisation with\n Implementation Uncertainty

2018/09/07 by Martin Hughes, Hughes, Martin, Marc Goerigk +3 · 1 citation
Computer Science · Decision Sciences · Engineering · #Advanced Multi-Objective Optimization Algorithms #FOS: Mathematics #Optimization and Control (math.OC) #Risk and Portfolio Optimization #Simulation Techniques and Applications #Vehicle Routing Optimization Methods

paper · pdf · doi:10.48550/arxiv.1809.02437

openalex publication_date 2018/09/07 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

We consider box-constrained robust optimisation problems with implementation\nuncertainty. In this setting, the solution that a decision maker wants to\nimplement may become perturbed. The aim is to find a solution that optimises\nthe worst possible performance over all possible perturbances.\n Previously, only few generic search methods have been developed for this\nsetting. We introduce a new approach for a global search, based on placing a\nlargest empty hypersphere. We do not assume any knowledge on the structure of\nthe original objective function, making this approach also viable for\nsimulation-optimisation settings. In computational experiments we demonstrate a\nstrong performance of our approach in comparison with state-of-the-art methods,\nwhich makes it possible to solve even high-dimensional problems.\n

Citations

• Robust Convex Optimization
• The Price of Robustness

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