An enhanced integer coded genetic algorithm to optimize PWRs

The aim of this work is to develop a new hybrid mutation integer for integer coded genetic algorithm, ICGA, to design the loading pattern, LP, in pressurized water reactors. Because of the huge number of possible combinations for the fuel assemblies, FAs, loading in a core and finding the optimum solution is a truly complex problem. In common genetic algorithms the mutation and crossover techniques are used to optimize an objective function. In this study flattening of power inside a reactor core is chosen as an objective function. To obtain optimal FA arrangement an Enhanced Integer Coded Genetic Algorithm, EICGA, is developed in order to obtain an optimal FA arrangement. This code is applicable to all types of PWR cores having different geometries and designs with many number of FA types. The results show a marked improvement in comparison to published data.