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Deadline-driven approach for multi-fidelity surrogate-assisted environmental model calibration: SWAN wind wave model case study

Published: 13 July 2019 Publication History

Abstract

This paper describes the approach for calibration of environmental models with the presence of time and quality restrictions. Advantages of the suggested strategy are based on two main concepts. The first advantage was provided by reducing the overall optimisation time due to the surrogate modelling of fitness function with the iterative gradual refinement of the environmental model fidelity (spatial and temporal resolution) for improving the fitness approximation. For the demonstration of the efficiency of surrogate-assisted multi-fidelity approach, it was compared with the baseline evolutionary calibration approach. The second advantage was assured by additional increasing of optimisation quality in the presence of strict deadline due to the building the strategy of multi-fidelity fitness approximation directly during the evolutionary algorithm execution. In order to prove the efficiency of the proposed dynamic strategy, it was compared with the preliminary meta-optimisation approach. As a case study, the wind wave model SWAN is used. The conducted experiments confirm the effectiveness of the proposed anytime approach and its applicability for the complex environmental models' parameters calibration.

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cover image ACM Conferences
GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference Companion
July 2019
2161 pages
ISBN:9781450367486
DOI:10.1145/3319619
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 13 July 2019

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Author Tags

  1. SWAN model
  2. deadline-driven optimisation
  3. environmental model calibration
  4. surrogate-assisted evolutionary algorithm

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  • Research-article

Funding Sources

  • The Russian Scientific Foundation

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GECCO '19
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GECCO '19: Genetic and Evolutionary Computation Conference
July 13 - 17, 2019
Prague, Czech Republic

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Cited By

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  • (2022)Surrogate-assisted performance prediction for data-driven knowledge discovery algorithms: Application to evolutionary modeling of clinical pathwaysJournal of Computational Science10.1016/j.jocs.2022.10156259(101562)Online publication date: Mar-2022
  • (2021)Partial differential equations discovery with EPDE framework: Application for real and synthetic dataJournal of Computational Science10.1016/j.jocs.2021.10134553(101345)Online publication date: Jul-2021
  • (2020)Towards Generative Design of Computationally Efficient Mathematical Models with Evolutionary LearningEntropy10.3390/e2301002823:1(28)Online publication date: 27-Dec-2020

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