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Particle Swarm Metaheuristics for Robust Optimisation with Implementation Uncertainty

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Particle Swarm Metaheuristics for Robust Optimisation with Implementation Uncertainty. / Hughes, Martin; Goerigk, Marc; Dokka Venkata Satyanaraya, Trivikram.

In: Computers and Operations Research, Vol. 122, 104998, 01.10.2020.

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@article{87757727be0b435d99bc4b222f9510d3,
title = "Particle Swarm Metaheuristics for Robust Optimisation with Implementation Uncertainty",
abstract = "We consider global non-convex optimisation problems under uncertainty. In this setting, it is not possible to implement a desired solution exactly. Instead, any other solution within some distance to the intended solution may be implemented. The aim is to find a robust solution, i.e., one where the worst possible solution nearby still performs as well as possible. Problems of this type exhibit another maximisation layer to find the worst case solution within the minimisation level of finding a robust solution, which makes them harder to solve than classic global optimisation problems. So far, only few methods have been provided that can be applied to black-box problems with implementation uncertainty. We improve upon existing techniques by introducing a novel particle swarm based framework which adapts elements of previous methods, combining them with new features in order to generate a more effective approach. In computational experiments, we find that our new method outperforms state of the art comparator heuristics in almost 80% of cases.",
keywords = "Robust optimisation, Implementation uncertainty, Metaheuristics, Global optimisation, Particle swarm optimisation",
author = "Martin Hughes and Marc Goerigk and {Dokka Venkata Satyanaraya}, Trivikram",
year = "2020",
month = oct,
day = "1",
doi = "10.1016/j.cor.2020.104998",
language = "English",
volume = "122",
journal = "Computers and Operations Research",
issn = "0305-0548",
publisher = "Elsevier Ltd",

}

RIS

TY - JOUR

T1 - Particle Swarm Metaheuristics for Robust Optimisation with Implementation Uncertainty

AU - Hughes, Martin

AU - Goerigk, Marc

AU - Dokka Venkata Satyanaraya, Trivikram

PY - 2020/10/1

Y1 - 2020/10/1

N2 - We consider global non-convex optimisation problems under uncertainty. In this setting, it is not possible to implement a desired solution exactly. Instead, any other solution within some distance to the intended solution may be implemented. The aim is to find a robust solution, i.e., one where the worst possible solution nearby still performs as well as possible. Problems of this type exhibit another maximisation layer to find the worst case solution within the minimisation level of finding a robust solution, which makes them harder to solve than classic global optimisation problems. So far, only few methods have been provided that can be applied to black-box problems with implementation uncertainty. We improve upon existing techniques by introducing a novel particle swarm based framework which adapts elements of previous methods, combining them with new features in order to generate a more effective approach. In computational experiments, we find that our new method outperforms state of the art comparator heuristics in almost 80% of cases.

AB - We consider global non-convex optimisation problems under uncertainty. In this setting, it is not possible to implement a desired solution exactly. Instead, any other solution within some distance to the intended solution may be implemented. The aim is to find a robust solution, i.e., one where the worst possible solution nearby still performs as well as possible. Problems of this type exhibit another maximisation layer to find the worst case solution within the minimisation level of finding a robust solution, which makes them harder to solve than classic global optimisation problems. So far, only few methods have been provided that can be applied to black-box problems with implementation uncertainty. We improve upon existing techniques by introducing a novel particle swarm based framework which adapts elements of previous methods, combining them with new features in order to generate a more effective approach. In computational experiments, we find that our new method outperforms state of the art comparator heuristics in almost 80% of cases.

KW - Robust optimisation

KW - Implementation uncertainty

KW - Metaheuristics

KW - Global optimisation

KW - Particle swarm optimisation

U2 - 10.1016/j.cor.2020.104998

DO - 10.1016/j.cor.2020.104998

M3 - Journal article

VL - 122

JO - Computers and Operations Research

JF - Computers and Operations Research

SN - 0305-0548

M1 - 104998

ER -