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.
CITATION STYLE
Muniglia, M., Verel, S., Le Pallec, J. C., & Do, J. M. (2018). A fitness landscape view on the tuning of an asynchronous master-worker EA for nuclear reactor design. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10764 LNCS, pp. 30–46). Springer Verlag. https://doi.org/10.1007/978-3-319-78133-4_3
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