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Technical Papers

Solving Sensor Assignment Problem of Nuclear Power Plant Systems by Tuning Genetic Algorithm with Bayesian Optimization

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Pages 1832-1846 | Received 10 Feb 2022, Accepted 08 Jun 2022, Published online: 29 Aug 2022

References

  • Electric Power Annual 2020,” U.S. Energy Information Administration (2022).
  • R. VILIM, “Automating O&M Monitoring Using Physics-based Qualitative and Quantitative Reasoning,” presented at the Pacific Basin Nuclear Conf. and Technology Exhibition, San Francisco, California (2018).
  • T. NGUYEN and R. VILIM, “Development of Process Constraints for the Sensor Calibration Problem: Process-Constrained Data Analytics for Sensor Assignment and Calibration,” ANL/NSE-19/4, Argonne National Laboratory (Mar. 29, 2019).
  • T. NGUYEN, T. DOWNAR, and R. VILIM, “A Probabilistic Model-based Diagnostic Framework for Nuclear Engineering Systems,” Ann. Nucl. Energy, 149, 107767 (2020); https://doi.org/10.1016/j.anucene.2020.107767.
  • T. NGUYEN et al., “A Digital Twin Approach to System-Level Fault Detection and Diagnosis for Improved Equipment Health Monitoring,” Ann. Nucl. Energy, 170, 109002 (2022); https://doi.org/10.1016/j.anucene.2022.109002.
  • T. NGUYEN et al., “Process-Constrained Data Analytics for Sensor Assignment and Calibration,” ANL/NSE–21/73, Argonne National Laboratory (2021).
  • T. ZAMAN, S. K. PAUL, and A. AZEEM, “Sustainable Operator Assignment in an Assembly Line Using Genetic Algorithm,” Int. J. Prod. Res., 50, 5077 (2012); https://doi.org/10.1080/00207543.2011.636764.
  • P. PONGCHAROEN et al., “Determining Optimum Genetic Algorithm Parameters for Scheduling the Manufacturing and Assembly of Complex Products,” Int. J. Prod. Econ., 78, 331 (2002); https://doi.org/10.1016/S0925-5273(02)00104-4.
  • C. D. YANG et al., “Applications of Genetic-Taguchi Algorithm in Flight Control Designs,” J. Aerosp. Eng., 18, 4, 232 (2005); https://doi.org/10.1061/(ASCE)0893-1321(2005)18:4(232).
  • I. KUCUKKOC, A. KARAOGLAN, and R. YAMAN, “Using Response Surface Design to Determine the Optimal Parameters of Genetic Algorithm and a Case Study,” Int. J. Prod. Res., 51, 5039 (2013); https://doi.org/10.1080/00207543.2013.784411.
  • G. BOX and K. WILSON, “On the Experimental Attainment of Optimum Conditions,” J. R. Stat. Soc., Series B, 13, 1, 1 (1951).
  • J. MOCKUS, V. TIESIS, and A. ZILINSKAS, “The Application of Bayesian Methods for Seeking the Extremum,” in Towards Global Optimiation, Vol. 2, p. 117–129, Elsevier (1978).
  • B. SHAHRIARI et al., “Taking the Human Out of the Loop: A Review of Bayesian Optimization,” Proc. IEEE, 104, 1, 148 (2016); https://doi.org/10.1109/JPROC.2015.2494218.
  • J. SNOEK, H. LAROCHELLE, and R. ADAMS, “Practical Bayesian Optimization of Machine Learning Algorithms,” Adv. Neural Inf. Process Syst., 25, 2960 (2012).
  • I. ROMAN et al., “Bayesian Optimization for Parameter Tuning in Evolutionary Algorithms,” Proc. 2016 IEEE Congress on Evolutionary Computation (CEC), July 24–29, 2016, pp. 4839–4845 (2016).
  • M. RADAIDEH et al., “NEORL,” Github Repository (2021); https://github.com/mradaideh/neorl.
  • Y. LIU et al., “A Parallel Capability Using Genetic Algorithm for Sensor Assignment Optimization with Process-Constrained Data-Analytic Diagnosis,” Proc. 12th Nuclear Plant Instrumentation, Control and Human-Machine Interface Technologies (NPIC&HMIT), Virtual Mtg., June 14–17, 2021.
  • R. VILIM et al., “Description of Sensor Assignment Optimization Method as Deployed on a Multi-Node Cluster,” ANL/NSE-20-13, Argonne National Laboratory (Mar. 31, 2020).
  • T. N. NGUYEN, “Model-Based Diagnostic Frameworks for Fault Detection and System Monitoring in Nuclear Engineering Systems,” PhD Dissertation, University of Michigan, Nuclear Engineering and Radiological Sciences, Ann Arbor (2020).
  • A. MAHAJAN et al., “Minotaur: A Mixed-Integer Nonlinear Optimization Toolkit,” ANL/MCS-P8010-0817, Argonne National Laboratory (Mar. 22, 2020).
  • M. KOCHENDERFER and T. WHEELER, Algorithms for Optimization, MIT Press, Cambridge, Massachusetts (2019).
  • S. SIVANANDAM and S. DEEPA, Introduction to Genetic Algorithms, Springer, New York (2008).
  • A. HASSANAT et al., “Choosing Mutation and Crossover Ratios for Genetic Algorithms—A Review with a New Dynamic Approach,” Information, 10, 390 (2019); https://doi.org/10.3390/info10120390.
  • M. GUTOWSKI, “Biology, Physics, Small Worlds and Genetic Algorithms,” Chap. 6 in Leading Edge Computer Science Research, pp. 165–218 (2006).
  • F. DE RAINVILLE et al., “DEAP: A Python Framework for Evolutionary Algorithms,” Proc. 14th Annual Conf. Companion on Genetic and Evolutionary Computation, July 7–11, 2012.
  • “Multiprocessing”; https://docs.python.org/3/library/multiprocessing.html (accessed July 11, 2022).
  • “SCOOP”; https://scoop.readthedocs.io/en/0.7/usage.html (accessed July 11, 2022).
  • “OMEGA”; https://www.omega.com/en-us/ (accessed July 11, 2022).
  • C. RASMUSSEN and C. WILLIAMS, Gaussian Processes for Machine Learning, MIT Press (2006).
  • H. J. KUSHNER, “A New Method of Locating the Maximum Point of an Arbitrary Multipeak Curve in the Presence of Noise,” J. Basic Eng., 86, 1, 97 (1964); https://doi.org/10.1115/1.3653121.
  • N. SRINIVAS et al., “Gaussian Process Optimization in the Bandit Setting: No Regret and Experimental Design,” Proc. 27th Int. Conf. Machine Learning (ICML-10), Haifa, Israel, p. 1015 (2010).
  • R. BYRD et al., “A Limited Memory Algorithm for Bound Constrained Optimization,” SIAM J. Sci. Comput., 16, 5, 1190 (1995); https://doi.org/10.1137/0916069.
  • F. PEDREGOSA et al., “Scikit-learn: Machine Learning in Python,” J. Mach. Learn. Res., 12, 2825 (2011).
  • G. BOX and D. BEHNKEN, “Some New Three Level Designs for the Study of Quantitative Variables,” Technometrics, 2, 455 (1960); https://doi.org/10.1080/00401706.1960.10489912.
  • S. KATOCH, S. CHAUHAN, and V. KUMAR, “A Review on Genetic Algorithm: Past, Present, and Future,” Multimedia Tools and Appl., 80, 8091 (2021); https://doi.org/10.1007/s11042-020-10139-6.
  • T. EL-MIHOUB et al., “Hybrid Genetic Algorithms: A Review,” Eng. Lett., 13, 2, 124 (2006).
  • J. BEVINS and R. N. SLAYBAUGH, “Gnowee: A Hybrid Metaheuristic Optimization Algorithm for Constrained, Black Box, Combinatorial Mixed-Integer Design,” Nucl. Technol., 205, 542 (2018); https://doi.org/10.1080/00295450.2018.1496692.

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