Abstract
In the context of the introduction of intermittent renewable energies, we propose to optimize the main variables of the control rods of a nuclear power plant to improve its capability to load-follow. The design problem is a black-box combinatorial optimization problem with expensive evaluation based on a multi-physics simulator. Therefore, we use a parallel asynchronous master-worker Evolutionary Algorithm scaling up to thousand computing units. One main issue is the tuning of the algorithm parameters. A fitness landscape analysis is conducted on this expensive real-world problem to show that it would be possible to tune the mutation parameters according to the low-cost estimation of the fitness landscape features.
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References
Alba, E., Tomassini, M.: Parallelism and evolutionary algorithms. IEEE Trans. Evol. Comput. 6(5), 443–462 (2002)
Arnaud, G., Do, J.-M., Lautard, J.-J., Baudron, A.-M., Douce, S.: Selection combinatory algorithm for loading pattern design of light water reactor with two levels of heterogeneity. In: Proceedings of ICAPP (2011)
Daolio, F., Liefooghe, A., Verel, S., Aguirre, H., Tanaka, K.: Problem features vs. algorithm performance on rugged multi-objective combinatorial fitness landscapes. Evolutionary Computation (2016)
de Moura Meneses, A.A., Gambardella, L.M., Schirru, R.: A new approach for heuristics-guided search in the in-core fuel management optimization. Prog. Nucl. Energy 52, 339–351 (2010)
Dubreuil, M., Gagne, C., Parizeau, M.: Analysis of a master-slave architecture for distributed evolutionary computations. IEEE Trans. Syst. Man Cybern. Part B 36, 229–235 (2006)
Dumont, O.: Ademe energie 2030: production d’énergies renouvelables. Technical report, Ademe (2012)
Eiben, A.E., Michalewicz, Z., Schoenauer, M., Smith, J.E.: Parameter control in evolutionary algorithms. In: Lobo, F.G., Lima, C.F., Michalewicz, Z. (eds.) Parameter Setting in Evolutionary Algorithms. SCI, vol. 54, pp. 19–46. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-69432-8_2
Grard, H.: Physique, fonctionnement et sûreté des REP. EDP Sciences (2014)
Hordijk, W.: A measure of landscapes. Evol. Comput. 4(4), 335–360 (1996)
Kim, J.H., Park, S.H., Na, M.G.: Design of a model predictive load-following controller by discrete optimization of control rod speed for PWRs. Ann. Nucl. Energy 71, 343–351 (2014)
Liefooghe, A., Derbel, B., Verel, S., Aguirre, H., Tanaka, K.: Towards landscape-aware automatic algorithm configuration: preliminary experiments on neutral and rugged landscapes. In: Hu, B., López-Ibáñez, M. (eds.) EvoCOP 2017. LNCS, vol. 10197, pp. 215–232. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-55453-2_15
Lokhov, A.: Technical and economic aspect of load following with nuclear power plants. In: Nuclear Energy Agency. OECD, June 2011
López-Ibáñez, M., Dubois-Lacoste, J., Stützle, T., Birattari, M.: The Rpackageirace package, iterated race for automatic algorithm configuration. Technical Report TR/IRIDIA/2011-004, IRIDIA, Université Libre de Bruxelles (2011)
Muniglia, M., Do, J.-M., Le Pallec, J.-C., Grard, H., Verel, S.V., David, S.: A multi-physics PWR model for the load following. In: ICAPP (2016)
Na, M.G., Hwang, I.J., Lee, Y.J.: Design of a fuzzy model predictive power controller for pressurized water reactors. IEEE Trans. Nucl. Sci. 53(3), 1504–1514 (2006)
Pereira, C.M., Lapa, C.M.: Coarse-grained parallel genetic algorithm applied to a nuclear reactor core design optimization problem. Ann. Nucl. Energy 30, 555–565 (2003)
Rice, J.R.: The algorithm selection problem. Adv. Comput. 15, 65–118 (1976)
Sacco, W.F., De Oliveira, C.R., Pereira, C.M.: Two stochastic optimization algorithms applied to nuclear reactor core design. Prog. Nucl. Energy 48, 525–539 (2006)
Sacco, W.F., Lapa, C.M., Pereira, C.M., Filho, H.A.: A metropolis algorithm applied to a nuclear power plant auxiliary feedwater system surveillance tests policy optimization. Prog. Nucl. Energy 50, 15–21 (2008)
Schneider, D., Dolci, F., Gabriel, F., Palau, J.-M.: Apollo3\(^{\textregistered }\): CEA/DEN deterministic multi-purpose code for reactor physics analysis. In: PHYSOR (2016)
Stadler, P.F.: Fitness landscapes. In: Lässig, M., Valleriani, A. (eds.) Biological Evolution and Statistical Physics. LNP, vol. 585, pp. 183–204. Springer, Heidelberg (2002). https://doi.org/10.1007/3-540-45692-9_10
Vanneschi, L., Tomassini, M., Collard, P., Vérel, S., Pirola, Y., Mauri, G.: A comprehensive view of fitness landscapes with neutrality and fitness clouds. In: Ebner, M., O’Neill, M., Ekárt, A., Vanneschi, L., Esparcia-Alcázar, A.I. (eds.) EuroGP 2007. LNCS, vol. 4445, pp. 241–250. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-71605-1_22
Weinberger, E.D.: Local properties of Kauffman’s NK model, a tuneably rugged energy landscape. Phys. Rev. A 44, 6399–6413 (1991)
Wessing, S., Rudolph, G., Menges, D.A.: Comparing asynchronous and synchronous parallelization of the SMS-EMOA. In: Handl, J., Hart, E., Lewis, P.R., López-Ibáñez, M., Ochoa, G., Paechter, B. (eds.) PPSN 2016. LNCS, vol. 9921, pp. 558–567. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-45823-6_52
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Muniglia, M., Verel, S., Le Pallec, JC., Do, JM. (2018). A Fitness Landscape View on the Tuning of an Asynchronous Master-Worker EA for Nuclear Reactor Design. In: Lutton, E., Legrand, P., Parrend, P., Monmarché, N., Schoenauer, M. (eds) Artificial Evolution. EA 2017. Lecture Notes in Computer Science(), vol 10764. Springer, Cham. https://doi.org/10.1007/978-3-319-78133-4_3
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