Pool-BCGA: a parallelised generation-free genetic algorithm for the ab initio global optimisation of nanoalloy clusters

The Birmingham cluster genetic algorithm is a package that performs global optimisations for homo- and bimetallic clusters based on either first principles methods or empirical potentials. Here, we present a new parallel implementation of the code which employs a pool strategy in order to eliminate sequential steps and significantly improve performance. The new approach meets all requirements of an evolutionary algorithm and contains the main features of the previous implementation. The performance of the pool genetic algorithm is tested using the Gupta potential for the global optimisation of the Au 10 Pd 10 cluster, which demonstrates the high efficiency of the method. The new implementation is also used for the global optimisation of the Au 10 and Au 20 clusters directly at the density functional theory level. A new parallelised generation free implementation of the Birmingham cluster genetic algorithm is presented for the efficient global optimisation of nanoalloy clusters.