Novel genetic algorithms (GAs) 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 (FP)) selection operator and novel crossover and mutation operators by considering the chromosomes as permutations (which is a specific feature of the loading pattern (LP) problem). The algorithm is applied to a representative model of a modern pressurized water reactor (PWR) core and implemented using a single objective fitness function (FF), i.e., keff. The results obtained for some reference cases using this setup are excellent. They are obtained using a tournament selection operator with a linear ranking (LR) selection probability method and a new geometric crossover operator that allows for geometrical, rather than random, swaps of gene segments between the chromosomes and control over the sizes of the swapped segments. Finally, the effect of boundary conditions (BCs) on the symmetry of the obtained best solutions is studied and the validity of the “symmetric loading patterns” assumption is tested.
Novel Genetic Algorithms for Loading Pattern Optimization Using State-of-the-Art Operators and a Simple Test Case
Manuscript received October 30, 2016; final manuscript received December 26, 2016; published online May 25, 2017. Assoc. Editor: Ilan Yaar.
- Views Icon Views
- Share Icon Share
- Search Site
Israeli, E., and Gilad, E. (May 25, 2017). "Novel Genetic Algorithms for Loading Pattern Optimization Using State-of-the-Art Operators and a Simple Test Case." ASME. ASME J of Nuclear Rad Sci. July 2017; 3(3): 030901. https://doi.org/10.1115/1.4035883
Download citation file: