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Performance Analysis and Improvement of Parallel Differential Evolution
arXiv - CS - Neural and Evolutionary Computing Pub Date : 2021-01-17 , DOI: arxiv-2101.06599
Pan Zibin

Differential evolution (DE) is an effective global evolutionary optimization algorithm using to solve global optimization problems mainly in a continuous domain. In this field, researchers pay more attention to improving the capability of DE to find better global solutions, however, the computational performance of DE is also a very interesting aspect especially when the problem scale is quite large. Firstly, this paper analyzes the design of parallel computation of DE which can easily be executed in Math Kernel Library (MKL) and Compute Unified Device Architecture (CUDA). Then the essence of the exponential crossover operator is described and we point out that it cannot be used for better parallel computation. Later, we propose a new exponential crossover operator (NEC) that can be executed parallelly with MKL/CUDA. Next, the extended experiments show that the new crossover operator can speed up DE greatly. In the end, we test the new parallel DE structure, illustrating that the former is much faster.

中文翻译:

并行差分进化的性能分析与改进

差分进化(DE)是一种有效的全局进化优化算法,主要用于解决连续域中的全局优化问题。在这个领域,研究人员更加关注提高DE的能力以找到更好的全局解决方案,但是,DE的计算性能也是一个非常有趣的方面,尤其是当问题规模很大时。首先,本文分析了可在数学内核库(MKL)和计算统一设备体系结构(CUDA)中轻松执行的DE并行计算的设计。然后描述了指数交叉算子的本质,我们指出它不能用于更好的并行计算。后来,我们提出了一个新的指数交叉算子(NEC),可以与MKL / CUDA并行执行。下一个,扩展实验表明,新的交叉算子可以大大提高DE的速度。最后,我们测试了新的并行DE结构,表明前者要快得多。
更新日期:2021-01-19
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