Parallel Differential Evolution Algorithm Accelerated by Graphics Processing Unit for Harmonic Minimization in Power Converters

Intelligent optimization algorithms, such as the genetic algorithm (GA) and particle swarm optimization (PSO), have been widely used for harmonic minimization in power converters. However, these algorithms usually evaluate the fitness function for hundreds of or even more populations, which leads to a huge computing burden and memory consumption. Hence, in practical applications, it is difficult to real-time implement these algorithms on the traditional central processing units (CPUs). On the other hand, although the population size is huge, each of them performs the same calculations independently, which is very suitable for parallelization on the graphical processing units (GPUs). In this paper, a parallel version of differential evolution (DE) on GPUs is proposed. Compared to the traditional CPU-based DE algorithm, the GPU-based parallel DE algorithm executes hundreds of times faster in solving the harmonic minimization problem. Some computational results of 21 switching angles for three-level inverters are given. Also, some guidelines for the parameter selection, such as the population size, the grid allocation, etc., on algorithm parallelization are discussed and summarized.