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Efficient multiobjective optimization employing Gaussian processes, spectral sampling and a genetic algorithm

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A Correction to this article was published on 09 March 2018

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Abstract

Many engineering problems require the optimization of expensive, black-box functions involving multiple conflicting criteria, such that commonly used methods like multiobjective genetic algorithms are inadequate. To tackle this problem several algorithms have been developed using surrogates. However, these often have disadvantages such as the requirement of a priori knowledge of the output functions or exponentially scaling computational cost with respect to the number of objectives. In this paper a new algorithm is proposed, TSEMO, which uses Gaussian processes as surrogates. The Gaussian processes are sampled using spectral sampling techniques to make use of Thompson sampling in conjunction with the hypervolume quality indicator and NSGA-II to choose a new evaluation point at each iteration. The reference point required for the hypervolume calculation is estimated within TSEMO. Further, a simple extension was proposed to carry out batch-sequential design. TSEMO was compared to ParEGO, an expected hypervolume implementation, and NSGA-II on nine test problems with a budget of 150 function evaluations. Overall, TSEMO shows promising performance, while giving a simple algorithm without the requirement of a priori knowledge, reduced hypervolume calculations to approach linear scaling with respect to the number of objectives, the capacity to handle noise and lastly the ability for batch-sequential usage.

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  • 09 March 2018

    This note lists changes to the article by Bradford et al.. A number of figures have moved to the wrong positions, while the references in the text still refer to the correct figure, i.e. a number of figures are now in the wrong section, have the wrong position and have the wrong caption.

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Acknowledgements

The research leading to these results has received funding from the European Research Council under the European Union’s H2020 Programme Grant Agreement No. 636820. Artur M. Schweidtmann thanks the Ernest-Solvay-Foundation and the ERASMUS+ program for a scholarship for his exchange at the University of Cambridge.

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Authors and Affiliations

  1. Department of Engineering Cybernetics, Norwegian University of Science and Technology, Trondheim, 7491, Norway

    Eric Bradford

  2. Aachener Verfahrenstechnik - Process Systems Engineering, RWTH Aachen University, Aachen, 52062, Germany

    Artur M. Schweidtmann

  3. Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge, CB2 3RA, UK

    Eric Bradford, Artur M. Schweidtmann & Alexei Lapkin

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  1. Eric Bradford
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Correspondence to Eric Bradford.

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Bradford, E., Schweidtmann, A.M. & Lapkin, A. Efficient multiobjective optimization employing Gaussian processes, spectral sampling and a genetic algorithm. J Glob Optim 71, 407–438 (2018). https://doi.org/10.1007/s10898-018-0609-2

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