This repository contains the source code for “Thompson sampling efficient multiobjective optimization” (TSEMO).
TS-EMO is an efficient multi-objective optimization algorithm for the optimization of expensive-to-evaluate functions using Gaussian process surrogate models. The algorithm can be applied to any number of inputs and outputs. In addition, the algorithm can identify several promising points in each iteration (batch sequential mode). This repository contains the algorithm with all the necessary files coded in Matlab with an example file to show how to use it.
To cite TS-EMO use:
E. Bradford, A. M. Schweidtmann, and A. A. Lapkin, Efficient multiobjective optimization employing Gaussian processes, spectral sampling and a genetic algorithm, Journal of Global Optimization, vol. 71, no. 2, pp. 407–438, 2018.
TS-EMO is an efficient multi-objective optimization algorithm for the optimization of expensive-to-evaluate functions using Gaussian process surrogate models. This repository contains the algorithm with all the necessary files coded in Matlab with an example file to show how to use it. [ Bradford, E., Schweidtmann, A. M., & Lapkin, A. (2018). J. Global Optim, 71(2), 407-438. ]