Novel genetic algorithms for loading pattern optimization using state-of-the-art operators and a simple test case

Ella Israeli, Erez Gilad

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageAmerican English
Article number030901-1
JournalJournal of Nuclear Engineering and Radiation Science
Volume3
Issue number3
DOIs
StatePublished - 1 Jul 2017

All Science Journal Classification (ASJC) codes

  • Radiation
  • Nuclear Energy and Engineering

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