  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.
