Multi-objective Bayesian optimisation using an exploitative attainment front acquisition function
Gibson, FJ; Everson, RM; Fieldsend, JE
Date: 9 August 2021
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Publisher DOI
Abstract
Efficient methods for optimising expensive black-box
problems with multiple objectives can often themselves become
prohibitively expensive as the number of objectives is increased.
We propose an infill criterion based on the distance to the
summary attainment front which does not rely on the expensive
hypervolume or expected ...
Efficient methods for optimising expensive black-box
problems with multiple objectives can often themselves become
prohibitively expensive as the number of objectives is increased.
We propose an infill criterion based on the distance to the
summary attainment front which does not rely on the expensive
hypervolume or expected improvement computations, which are
the principal causes of poor dimensional scaling in current stateof-the-art approaches. By evaluating performance on the wellknown Walking Fish Group problem set, we show that our
method delivers similar performance to the current state-of-theart. We further show that methods based on surrogate mean
predictions are more often than not superior to the widely used
expected improvement, suggesting that the additional exploration
produced by accounting for the uncertainty in the surrogate’s
prediction of the optimisation landscape is often unnecessary and
does not aid convergence towards the Pareto front
Computer Science
Faculty of Environment, Science and Economy
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