MVMOO: Mixed variable multi-objective optimisation

Abstract

In many real-world problems there is often the requirement to optimise multiple conflicting objectives in an efficient manner. In such problems there can be the requirement to optimise a mixture of continuous and discrete variables. Herein, we propose a new multi-objective algorithm capable of optimising both continuous and discrete bounded variables in an efficient manner. The algorithm utilises Gaussian processes as surrogates in combination with a novel distance metric based upon Gower similarity. The MVMOO algorithm was compared to an existing mixed variable implementation of NSGA-II and random sampling for three test problems. MVMOO shows competitive performance on all proposed problems with efficient data acquisition and approximation of the Pareto fronts for the selected test problems.

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Item Type:	Article
Authors/Creators:	Manson, JA https://orcid.org/0000-0001-7392-3197 Chamberlain, TW https://orcid.org/0000-0001-8100-6452 Bourne, RA https://orcid.org/0000-0001-7107-6297
Copyright, Publisher and Additional Information:	© The Author(s) 2021. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
Keywords:	Global optimisation; Hypervolume; Multi-objective; Mixed variable; Bayesian optimisation
Dates:	Published: August 2021 Published (online): 9 July 2021 Accepted: 25 May 2021
Institution:	The University of Leeds
Academic Units:	The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Chemical & Process Engineering (Leeds) The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Chemistry (Leeds) > Inorganic Chemistry (Leeds)
Funding Information:	Funder Grant number EPSRC (Engineering and Physical Sciences Research Council) EP/R032807/1 Royal Academy of Engineering RCSRF1920\9\38
Depositing User:	Symplectic Publications
Date Deposited:	03 Jun 2021 15:17
Last Modified:	25 Jun 2023 22:40
Status:	Published
Publisher:	Springer
Identification Number:	10.1007/s10898-021-01052-9
Open Archives Initiative ID (OAI ID):	oai:eprints.whiterose.ac.uk:174737

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MVMOO: Mixed variable multi-objective optimisation

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