A Largest Empty Hypersphere Metaheuristic for Robust Optimisation with Implementation Uncertainty

Hughes, Martin and Goerigk, Marc and Wright, Michael Bruce (2019) A Largest Empty Hypersphere Metaheuristic for Robust Optimisation with Implementation Uncertainty. Computers and Operations Research, 103. pp. 64-80. ISSN 0305-0548

[thumbnail of Largest Empty Hypersphere Metaheuristic for Robust Optimisation]
Preview
PDF (Largest Empty Hypersphere Metaheuristic for Robust Optimisation)
Largest_Empty_Hypersphere_Metaheuristic_for_Robust_Optimisationy.pdf - Accepted Version
Available under License Creative Commons Attribution.

Download (714kB)

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 of 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.

Item Type:
Journal Article
Journal or Publication Title:
Computers and Operations Research
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/2600/2611
Subjects:
?? global optimisationimplementation uncertaintymetaheuristicsrobust optimisationdecision makingglobal optimizationcomputational experimenthigh-dimensional problemsmeta heuristicssimulation optimisationstate-of-the-art methodsconstrained optimizationmodellin ??
ID Code:
128400
Deposited By:
Deposited On:
19 Oct 2018 10:24
Refereed?:
Yes
Published?:
Published
Last Modified:
08 Feb 2024 00:53