Pool-BCGA: a parallelised generation-free genetic algorithm for the ab initio global optimisation of nanoalloy clusters.
Pool-BCGA: a parallelised generation-free genetic algorithm for the ab initio global optimisation of nanoalloy clusters.
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DOI：
10.1039/c4cp04323e
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发表时间：
2015-01
期刊：
影响因子：
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通讯作者：
A. Shayeghi;D. Götz;Jack B. A. Davis;R. Schäfer;R. Johnston
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文献类型：
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作者：
A. Shayeghi;D. Götz;Jack B. A. Davis;R. Schäfer;R. Johnston
伯明翰集群遗传算法是一个包，执行基于第一性原理方法或经验潜力的全金属和双金属集群的全局优化。在这里，我们提出了一种新的并行代码实现，它采用池策略，以消除顺序步骤并显着提高性能。新方法满足进化算法的所有要求，并包含了以前实现的主要特征。利用Gupta势测试了池遗传算法的性能，并对Au10Pd10聚类进行了全局优化，证明了该方法的高效率。新的实现还用于直接在密度泛函理论水平上对Au10和Au20集群进行全局优化。
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 Au10Pd10 cluster, which demonstrates the high efficiency of the method. The new implementation is also used for the global optimisation of the Au10 and Au20 clusters directly at the density functional theory level.