Deadline-driven approach for multi-fidelity surrogate-assisted environmental model calibration

Nikitin N.O., Vychuzhanin P., Hvatov A., Deeva I., Kalyuzhnaya A.V., Kovalchuk S.V.
Тип документаProceedings Article
Дата публикации2019-07-13
Название журналаGECCO 2019 Companion - Proceedings of the 2019 Genetic and Evolutionary Computation Conference Companion
Издатель
Краткое описание
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.
Пристатейные ссылки: 32
Цитируется в публикациях: 2
Multifidelity Surrogate Based on Single Linear Regression
Zhang Y., Kim N.H., Park C., Haftka R.T.
Q1 AIAA Journal 2018 цитирований: 34
Multi-fidelity surrogate model approach to optimization
van Rijn S., Schmitt S., Olhofer M., van Leeuwen M., Bäck T.
2018 цитирований: 4
A conceptual approach to complex model management with generalized modelling patterns and evolutionary identification
Kovalchuk S.V., Metsker O.G., Funkner A.A., Kisliakovskii I.O., Nikitin N.O., Kalyuzhnaya A.V., Vaganov D.A., Bochenina K.O.
Q1 Complexity 2018 цитирований: 9
Open Access
Open access
A survey on handling computationally expensive multiobjective optimization problems with evolutionary algorithms
Chugh T., Sindhya K., Hakanen J., Miettinen K.
Q2 Soft Computing 2017 цитирований: 92
Automatic Model Calibration: A New Way to Improve Numerical Weather Forecasting
Gan Y., Ye A., Miao C., Miao S., Liang X., Fan S.
Q1 Bulletin of the American Meteorological Society 2017 цитирований: 36
The Art and Science of Climate Model Tuning
Hourdin F., Mauritsen T., Gettelman A., Golaz J., Balaji V., Duan Q., Folini D., Ji D., Klocke D., Qian Y., Rauser F., Rio C., Tomassini L., Watanabe M., Williamson D.
Q1 Bulletin of the American Meteorological Society 2017 цитирований: 238
Surrogate-Assisted Multicriteria Optimization: Complexities, Prospective Solutions, and Business Case
Allmendinger R., Emmerich M.T., Hakanen J., Jin Y., Rigoni E.
Q2 Journal of Multi-Criteria Decision Analysis 2017 цитирований: 45
Multiobjective adaptive surrogate modeling‐based optimization for parameter estimation of large, complex geophysical models
Gong W., Duan Q., Li J., Wang C., Di Z., Ye A., Miao C., Dai Y.
Q1 Water Resources Research 2016 цитирований: 40
A multi-fidelity surrogate-model-assisted evolutionary algorithm for computationally expensive optimization problems
Liu B., Koziel S., Zhang Q.
Q1 Journal of Computational Science 2016 цитирований: 74
Half a Billion Simulations: Evolutionary Algorithms and Distributed Computing for Calibrating the Simpoplocal Geographical Model
Schmitt C., Rey-Coyrehourcq S., Reuillon R., Pumain D.
Q1 Environment and Planning B: Planning and Design 2015 цитирований: 19
Automatically improving the anytime behaviour of optimisation algorithms
López-Ibáñez M., Stützle T.
Q1 European Journal of Operational Research 2014 цитирований: 44
Scenario Based Risk Management for Arctic Shipping and Operations
Ehlers S., Kujala P., Veitch B., Khan F., Vanhatalo J.
2014 цитирований: 3
Deadline-Driven Resource Management within Urgent Computing Cyberinfrastructure
Kovalchuk S.V., Smirnov P.A., Maryin S.V., Tchurov T.N., Karbovskiy V.A.
Q2 Procedia Computer Science 2013 цитирований: 17
Deadline-driven provisioning of resources for scientific applications in hybrid clouds with Aneka
Vecchiola C., Calheiros R.N., Karunamoorthy D., Buyya R.
Q1 Future Generation Computer Systems 2012 цитирований: 107
Parameter tuning for configuring and analyzing evolutionary algorithms
Eiben A.E., Smit S.K.
Q1 Swarm and Evolutionary Computation 2011 цитирований: 363
Метрики
Поделиться
Цитировать
ГОСТ |
Цитировать
1. Nikitin N. O. и др. Deadline-driven approach for multi-fidelity surrogate-assisted environmental model calibration // Proceedings of the Genetic and Evolutionary Computation Conference Companion. 2019.
RIS |
Цитировать

TY - CPAPER

DO - 10.1145/3319619.3326876

UR - http://dx.doi.org/10.1145/3319619.3326876

TI - Deadline-driven approach for multi-fidelity surrogate-assisted environmental model calibration

T2 - Proceedings of the Genetic and Evolutionary Computation Conference Companion

AU - Nikitin, Nikolay O.

AU - Vychuzhanin, Pavel

AU - Hvatov, Alexander

AU - Deeva, Irina

AU - Kalyuzhnaya, Anna V.

AU - Kovalchuk, Sergey V.

PY - 2019

DA - 2019/07/13

PB - ACM

ER -

BibTex |
Цитировать

@inproceedings{Nikitin_2019,

doi = {10.1145/3319619.3326876},

url = {https://doi.org/10.1145%2F3319619.3326876},

year = 2019,

month = {jul},

publisher = {{ACM}},

author = {Nikolay O. Nikitin and Pavel Vychuzhanin and Alexander Hvatov and Irina Deeva and Anna V. Kalyuzhnaya and Sergey V. Kovalchuk},

title = {Deadline-driven approach for multi-fidelity surrogate-assisted environmental model calibration},

booktitle = {Proceedings of the Genetic and Evolutionary Computation Conference Companion}

}

MLA
Цитировать
Nikitin, Nikolay O., et al. “Deadline-Driven Approach for Multi-Fidelity Surrogate-Assisted Environmental Model Calibration.” Proceedings of the Genetic and Evolutionary Computation Conference Companion, July 2019. Crossref, https://doi.org/10.1145/3319619.3326876.