Implementation of Genetic Algorithms and Parallel Simulated Annealing in OCEON-P

Abstract

OCEON-P is a computer program whose purpose is to minimize the levelized fuel cycle cost over a multi-cycle planning horizon. It integrates a core simulator, fuel cycle cost calculator and mathematical optimization engine. The accuracy of the predicted fuel cycle cost, whose minimization guides the optimization of the decision variables, is directly related to the fidelity of the reactor core simulator used by the program. Unfortunately, high fidelity core simulators also require longer run times. To improve these run times, this project sought to parallelize the optimization process so that multiple processors may share the computational burden. In addition, an effort was made to reduce the number of fuel cycles that must be examined to complete the optimization, which also reduces the computer run times. Parallelization of the process was introduced by the replacement of the current serial simulated annealing method with parallel simulated and genetic algorithms. It was hoped that genetic algorithms would also reduce the number of fuel cycles that must be examined during the optimization search. However, it was found that although genetic algorithms could find the same caliber of best solutions as simulated annealing, simulated annealing could produce a better family of acceptable solutions. Furthermore, parallel simulated annealing was able to reproduce the same quality and robustness of serial simulated annealing while decreasing run times significantly through use of multiple processors.