Differential Evolution (DE) is a highly successful population based global optimisation algorithm, commonly used for solving numerical optimisation problems. However, as the complexity of the objective function increases, the wall-clock run-time of the algorithm suffers as many fitness function evaluations must take place to effectively explore the search space. Due to the inherently parallel nature of the DE algorithm, graphics processing units (GPU) have been used to effectively accelerate both the fitness evaluation and DE algorithm. This work reviews the main architectural choices made in the literature for GPU based DE algorithms and introduces a new GPU based numerical optimisation benchmark to evaluate and compare GPU based DE algorithms.

翻译：差分进化（DE）是一种极为成功的基于种群的全局优化算法，常用于求解数值优化问题。然而，随着目标函数复杂度的增加，算法所需的实际运行时间会显著增加，因为需要执行大量适应度函数评估以有效探索搜索空间。由于DE算法固有的并行特性，图形处理器（GPU）已被用于有效加速适应度评估和DE算法本身。本文综述了文献中基于GPU的DE算法的主要架构设计选择，并引入了一种新的基于GPU的数值优化基准测试，用于评估和比较基于GPU的DE算法。