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Particle Swarm Metaheuristics for Robust Optimisation with Implementation Uncertainty

Research output: Contribution to journalJournal article

Published
Article number104998
<mark>Journal publication date</mark>1/10/2020
<mark>Journal</mark>Computers and Operations Research
Volume122
Number of pages21
Publication StatusPublished
Early online date22/05/20
<mark>Original language</mark>English

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.