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Multi-objective optimization algorithm for expensive-to-evaluate function

version 1.0.0.0 (897 KB) by Artur Schweidtmann
Thompson sampling efficient multiobjective optimization (TSEMO) algorithm

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Updated 19 Jun 2020

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This repository contains the source code for “Thompson sampling efficient multiobjective optimization” (TSEMO) algorithm [1].
The algorithm is designed for global multi-objective optimization of expensive-to-evaluate black-box functions. For example, the algorithm has been applied to the simultaneous optimization of the life-cycle assessment (LCA) and cost of a chemical process simulation [2]. However, the algorithm can be applied to other black-box function such as CFD simulations as well. It is based on the Bayesian optimization approach that builds Gaussian process surrogate models to accelerate optimization. Further, the algorithm can identify several promising points in each iteration (batch sequential mode). This allows to evaluate several simulations in parallel.
[1] Bradford, E., Schweidtmann, A.M. & Lapkin, A. J Glob Optim (2018). https://doi.org/10.1007/s10898-018-0609-2
[2] D. Helmdach, P. Yaseneva, P. K. Heer, A. M. Schweidtmann, A. A. Lapkin, ChemSusChem 2017, 10, 3632. https://doi.org/10.1002/cssc.201700927

Cite As

Artur Schweidtmann (2021). Multi-objective optimization algorithm for expensive-to-evaluate function (https://github.com/Eric-Bradford/TS-EMO), GitHub. Retrieved .

MATLAB Release Compatibility
Created with R2018a
Compatible with any release
Platform Compatibility
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To view or report issues in this GitHub add-on, visit the GitHub Repository.
To view or report issues in this GitHub add-on, visit the GitHub Repository.