Over the past few years, cloud computing model (e.g., Spark) has aroused huge attention. Differential evolution (DE) has been applied to cloud computing models by a number of researchers for its merits in solving large-scale global optimization problems (LSGO), and remarkable results have been achieved. Moreover, we noticed that a combination of better topology and migration strategy is critical to solve the mentioned problems when DE algorithm acts as an internal optimizer for Spark cloud computing model. However, rare studies have been conducted to combine the combination to enhance the performance of DE algorithm for solving large-scale global optimization problems. Thus, inspired by the mentioned insights, we propose a novel grouping topology model that uses DE variants as internal optimizers to solve LSGO problems, called SgtDE. In SgtDE, population is split into subgroups evenly, and various topology structures are introduced to migrate individuals between and within subgroups. In this paper, five types of DE are adopted as the internal optimizers. By comparing the 20 benchmark functions presented on CEC2010, the results demonstrate that the SgtDE, especially a combination of better topology and migration strategy, exhibits significant performance in applying various DE variants. Thus, the SgtDE can act as the next generation optimizer of the cloud computing platform.

在过去的几年中，云计算模型（例如Spark）引起了极大的关注。许多研究人员将差分进化（DE）应用于云计算模型，因为它具有解决大规模全局优化问题（LSGO）的优点，并且已经取得了显著成果。此外，我们注意到，当DE算法充当Spark云计算模型的内部优化器时，更好的拓扑和迁移策略的组合对于解决上述问题至关重要。然而，进行了很少的研究来结合该组合以增强DE算法的性能以解决大规模全局优化问题。因此，受上述见解的启发，我们提出了一种新颖的分组拓扑模型，该模型使用DE变量作为内部优化器来解决LSGO问题，称为SgtDE。在SgtDE中，种群被均匀地划分为子组，并且引入了各种拓扑结构以在子组之间和内部迁移个体。本文采用五种类型的DE作为内部优化器。通过比较CEC2010上提供的20个基准功能，结果表明SgtDE，尤其是更好的拓扑和迁移策略的组合，在应用各种DE变体时表现出显着的性能。因此，SgtDE可以充当云计算平台的下一代优化器。特别是更好的拓扑和迁移策略的组合，在应用各种DE变体时显示出显着的性能。因此，SgtDE可以充当云计算平台的下一代优化器。特别是更好的拓扑和迁移策略的组合，在应用各种DE变体时显示出显着的性能。因此，SgtDE可以充当云计算平台的下一代优化器。