Cluster-based niching differential evolution algorithm for optimizing the stable structures of metallic clusters

Abstract In this article, a cluster-based niching differential evolution algorithm, which combines the cluster pool, the niche method, and the differential evolution algorithm, has been employed to optimize the stable structures of iron clusters. The cluster pool is responsible for generation of the niche sub populations, and the differential evolutionary algorithm is used for the evolution of the population. A variety of mutation strategies have been applied in the algorithm instance. Moreover, the crossover operator of plane cut cross and the adjustment strategy make the algorithm more suitable for structural optimization of clusters. Subsequently, the performance of the algorithm has been examined by the effect of cluster pool size on the convergence speed and structural diversity. The accuracy and effectiveness of our algorithm have been verified by analyses of energy and structural evolutions. Finally, structural evolution of iron clusters with 3–80 atoms has been predicted by this algorithm.

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