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A local search method for costly black-box problems and its application to CSP plant start-up optimization refinement

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Abstract

A variety of engineering applications are tackled as black-box optimization problems where a computationally expensive and possibly noisy function is optimized over a continuous domain. In this paper we present a derivative-free local method which is well-suited for such problems, and we describe its application to the optimization of the start-up phase of an innovative Concentrated Solar Power (CSP) plant. The method, referred to as rqlif, exploits a regularized quadratic model and a linear implicit filtering strategy so as to be parsimonious in terms of function evaluations. After assessing the performance of rqlif on a set of analytical test problems in comparison with three well-known local algorithms, we apply it in conjunction with a global algorithm based on RBFs interpolation to the start-up optimization of the CSP plant developed in the PreFlexMS H2020 project. For the test problems, rqlif provides good quality solutions in a limited number of function evaluations. For the application, the global–local strategy yields a substantial improvement with respect to the reference solution and significantly reduces the thermo-mechanical stress suffered by the plant components.

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Notes

  1. The Matlab implementation of rqlif is freely available at http://rqlif.deib.polimi.it.

  2. The dynamic plant developped within the PreFlexMS project is interfaced with Matlab.

  3. Due to the premature suspension and ensuing termination of the PreFlexMS project, the optimization experiments are not reproducible because the simulation code is no longer available.

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Authors and Affiliations

  1. Centro di Eccellenza DEWS, Dipartimento di Ingegneria e Scienze dell’Informazione e Matematica, Università degli Studi dell’Aquila, Via Vetoio, 67100, L’Aquila, Italy

    Andrea Manno

  2. Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Via Ponzio 34/5, 20133, Milan, Italy

    Andrea Manno, Edoardo Amaldi & Francesco Casella

  3. Dipartimento di Energia, Politecnico di Milano, Via Lambruschini 4, 20156, Milan, Italy

    Emanuele Martelli

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  1. Andrea Manno
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  2. Edoardo Amaldi
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  4. Emanuele Martelli
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Correspondence to Andrea Manno.

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Manno, A., Amaldi, E., Casella, F. et al. A local search method for costly black-box problems and its application to CSP plant start-up optimization refinement. Optim Eng 21, 1563–1598 (2020). https://doi.org/10.1007/s11081-020-09488-w

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