Two new multi-objective differential evolution (DE) algorithms are used to optimize heterogeneous low-enriched uranium + mixed oxide fuel assemblies for use in a pressurized water reactor. The objectives were to maximize plutonium content and minimize the power peaking factor. A performance comparison to a genetic algorithm is used to evaluate the applicability of DE algorithms to nuclear fuel assembly design optimization problems. Results show that DE performs highly competitively against a more established algorithm and can arguably better represent the trade-off between both objectives through greater variety in the number of different pin arrangements explored and a higher reliability in finding the ‘true’ Pareto-front.

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