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• Cuckoo Search (CS) algorithm was applied to Loading Pattern Optimization problems. • The optimization problems are based on data of the benchmarks IAEA and BIBLIS. • Data from the 7th cycle of Angra 1 NPP in Brazil were also used. • CS was compared to Artificial Bee Colony and Population Based Incremental Learning. • CS was the most robust algorithm for the set of instances tested. The Loading Pattern Optimization (LPO) is related to important goals in a Nuclear Power Plant (NPP) operation such as the extension of the cycle according to safety margins. The LPO is a combinatorial problem of relevance and interest for Nuclear Engineering. Optimization metaheuristics have been efficient in solving the LPO. The recent metaheuristic Cuckoo Search (CS) is based on the brood parasitism of some cuckoo species, combined with the behavior of the Lévy flight of some birds. In the present work the results of the application of CS to the LPO using IAEA-3D and BIBLIS-2D benchmarks are presented, as well as the application of CS in the optimization of 7th cycle of Angra 1 NPP, in Brazil. The results are compared to the metaheuristics Artificial Bee Colony and Population-Based Incremental Learning. Statistical analyses show that CS is the most robust algorithm for the set of instances selected for tests. [ABSTRACT FROM AUTHOR]