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Development of an adaptive infill criterion for constrained multi-objective asynchronous surrogate-based optimization

Development of an adaptive infill criterion for constrained multi-objective asynchronous surrogate-based optimization
Development of an adaptive infill criterion for constrained multi-objective asynchronous surrogate-based optimization
The use of surrogate modeling techniques to efficiently solve a single objective optimization (SOO) problem has proven its worth in the optimization community. However, industrial problems are often characterized by multiple conflicting and constrained objectives. Recently, a number of infill criteria have been formulated to solve multi-objective optimization (MOO) problems using surrogates and to determine the Pareto front. Nonetheless, to accurately resolve the front, a multitude of optimal points must be determined, making MOO problems by nature far more expensive than their SOO counterparts. As of yet, even though access to of high performance computing is widely available, little importance has been attributed to batch optimization and asynchronous infill methodologies, which can further decrease the wall-clock time required to determine the Pareto front with a given resolution. In this paper a novel infill criterion is developed for generalized asynchronous multi-objective constrained optimization, which allows multiple points to be selected for evaluation in an asynchronous manner while the balance between design space exploration and objective exploitation is adapted during the optimization process in a simulated annealing like manner and the constraints are taken into account. The method relies on a formulation of the expected improvement for multi-objective optimization, where the improvement is formulated as the Euclidean distance from the Pareto front taken to a higher power. The infill criterion is tested on a series of test cases and proves the effectiveness of the novel scheme.
0925-5001
Wauters, Jolan
4543ef85-937b-4681-a5ff-ea3127f942da
Keane, Andrew
26d7fa33-5415-4910-89d8-fb3620413def
Degroote, Joris
acfa1f6a-ccef-4242-81e6-ee8464750701
Wauters, Jolan
4543ef85-937b-4681-a5ff-ea3127f942da
Keane, Andrew
26d7fa33-5415-4910-89d8-fb3620413def
Degroote, Joris
acfa1f6a-ccef-4242-81e6-ee8464750701

Wauters, Jolan, Keane, Andrew and Degroote, Joris (2020) Development of an adaptive infill criterion for constrained multi-objective asynchronous surrogate-based optimization. Journal of Global Optimization. (doi:10.1007/s10898-020-00903-1).

Record type: Article

Abstract

The use of surrogate modeling techniques to efficiently solve a single objective optimization (SOO) problem has proven its worth in the optimization community. However, industrial problems are often characterized by multiple conflicting and constrained objectives. Recently, a number of infill criteria have been formulated to solve multi-objective optimization (MOO) problems using surrogates and to determine the Pareto front. Nonetheless, to accurately resolve the front, a multitude of optimal points must be determined, making MOO problems by nature far more expensive than their SOO counterparts. As of yet, even though access to of high performance computing is widely available, little importance has been attributed to batch optimization and asynchronous infill methodologies, which can further decrease the wall-clock time required to determine the Pareto front with a given resolution. In this paper a novel infill criterion is developed for generalized asynchronous multi-objective constrained optimization, which allows multiple points to be selected for evaluation in an asynchronous manner while the balance between design space exploration and objective exploitation is adapted during the optimization process in a simulated annealing like manner and the constraints are taken into account. The method relies on a formulation of the expected improvement for multi-objective optimization, where the improvement is formulated as the Euclidean distance from the Pareto front taken to a higher power. The infill criterion is tested on a series of test cases and proves the effectiveness of the novel scheme.

Text
Development of an Adaptive Infill Criterion for Constrained Multi-Objective Asynchronous Surrogate-Based Optimization - Accepted Manuscript
Restricted to Repository staff only until 3 April 2021.
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Accepted/In Press date: 21 March 2020
e-pub ahead of print date: 3 April 2020

Identifiers

Local EPrints ID: 439633
URI: http://eprints.soton.ac.uk/id/eprint/439633
ISSN: 0925-5001
PURE UUID: 216f1e56-f22f-4d39-95f4-5f72a43d4d40

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Date deposited: 28 Apr 2020 16:35
Last modified: 28 Jul 2020 16:37

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