FUEL-MANAGEMENT OPTIMIZATION USING GENETIC ALGORITHMS AND EXPERT KNOWLEDGE

The CIGARO fuel management optimization code based on genetic algorith ms is described and tested. The test problem optimized the core lifeti me for a pressurized water reactor with a penalty function constraint on the peak normalized power. A bit-string genotype encoded the loadin g patterns, and genotype bias was reduced with additional bits. Expert knowledge about fuel management was incorporated into the genetic alg orithm. Regional crossover exchanged physically adjacent fuel assembli es and improved the optimization slightly. Biasing the initial populat ion toward a known priority table significantly improved the optimizat ion.