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research-article

Novel genetic algorithms for in-core fuel management

[+] Author and Article Information
Ella Israeli

The Unit of Nuclear Engineering Ben-Gurion University of the Negev Beer-Sheva 84105, Israel
ellaisra@post.bgu.ac.il

Erez Gilad

The Unit of Nuclear Engineering Ben-Gurion University of the Negev Beer-Sheva 84105, Israel
gilade@bgu.ac.il

1Corresponding author.

ASME doi:10.1115/1.4035883 History: Received October 30, 2016; Revised December 26, 2016

Abstract

Novel genetic algorithms are developed by using state of-the-art selection and crossover operators, e.g., rank selection or tournament selection instead of the traditional roulette (fitness proportionate) selection operator and novel crossover and mutation operators by considering the chromosomes as permutations (which is a specific feature of the loading pattern problem). The algorithm is implemented and applied to a representative model of a modern PWR core and for a single objective fitness function, i.e., k_eff. The results obtained for some reference cases using this setup are excellent and are obtained by utilizing a tournament selection operator with a linear ranking selection probability method, and a new geometric crossover operator that allows for geometrical swaps, rather than random, of genes segments between the chromosomes and control the sizes of the swapped segments. Finally, the effect of boundary conditions on the symmetry of the obtained best solutions is studied and the validity of the "symmetric loading patterns" assumption is tested.

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