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A Largest Empty Hypersphere Metaheuristic for Robust Optimisation with Implementation Uncertainty

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A Largest Empty Hypersphere Metaheuristic for Robust Optimisation with Implementation Uncertainty. / Hughes, Martin; Goerigk, Marc; Wright, Michael Bruce.
In: Computers and Operations Research, Vol. 103, 03.2019, p. 64-80.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Hughes, M, Goerigk, M & Wright, MB 2019, 'A Largest Empty Hypersphere Metaheuristic for Robust Optimisation with Implementation Uncertainty', Computers and Operations Research, vol. 103, pp. 64-80. https://doi.org/10.1016/j.cor.2018.10.013

APA

Hughes, M., Goerigk, M., & Wright, M. B. (2019). A Largest Empty Hypersphere Metaheuristic for Robust Optimisation with Implementation Uncertainty. Computers and Operations Research, 103, 64-80. https://doi.org/10.1016/j.cor.2018.10.013

Vancouver

Hughes M, Goerigk M, Wright MB. A Largest Empty Hypersphere Metaheuristic for Robust Optimisation with Implementation Uncertainty. Computers and Operations Research. 2019 Mar;103:64-80. Epub 2018 Oct 17. doi: 10.1016/j.cor.2018.10.013

Author

Hughes, Martin ; Goerigk, Marc ; Wright, Michael Bruce. / A Largest Empty Hypersphere Metaheuristic for Robust Optimisation with Implementation Uncertainty. In: Computers and Operations Research. 2019 ; Vol. 103. pp. 64-80.

Bibtex

@article{0e37356902ee43da94f9664b1e10b41c,
title = "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 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.",
keywords = "Global optimisation, Implementation uncertainty, Metaheuristics, Robust optimisation, Decision making, Global optimization, Computational experiment, High-dimensional problems, Meta heuristics, Simulation optimisation, State-of-the-art methods, Constrained optimization",
author = "Martin Hughes and Marc Goerigk and Wright, {Michael Bruce}",
year = "2019",
month = mar,
doi = "10.1016/j.cor.2018.10.013",
language = "English",
volume = "103",
pages = "64--80",
journal = "Computers and Operations Research",
issn = "0305-0548",
publisher = "Elsevier Ltd",

}

RIS

TY - JOUR

T1 - A Largest Empty Hypersphere Metaheuristic for Robust Optimisation with Implementation Uncertainty

AU - Hughes, Martin

AU - Goerigk, Marc

AU - Wright, Michael Bruce

PY - 2019/3

Y1 - 2019/3

N2 - 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.

AB - 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.

KW - Global optimisation

KW - Implementation uncertainty

KW - Metaheuristics

KW - Robust optimisation

KW - Decision making

KW - Global optimization

KW - Computational experiment

KW - High-dimensional problems

KW - Meta heuristics

KW - Simulation optimisation

KW - State-of-the-art methods

KW - Constrained optimization

U2 - 10.1016/j.cor.2018.10.013

DO - 10.1016/j.cor.2018.10.013

M3 - Journal article

VL - 103

SP - 64

EP - 80

JO - Computers and Operations Research

JF - Computers and Operations Research

SN - 0305-0548

ER -