Differential evolution for power electronic circuit optimization

Power electronic circuit (PEC) design and optimization is a significant problem in both scientific and engineering communities. Due to the complex search space of the PEC optimization problem, lots of works have tried to use evolutionary computation (EC) algorithms to solve it, and have gained great progress. However, some existing EC based algorithms for PEC are still complex in algorithm design, or the solutions are still needed to be improved when considering the solution accuracy. Therefore, design a simpler yet powerful algorithm to solve the PEC problem efficiently is in great need. This paper makes the first attempt to proposing a novel differential evolution (DE), which is a kind of new, simple, yet efficient EC algorithm for the PEC design and optimization. The advantage of this paper is that the DE algorithm is the first time directly applied to PEC design and optimization, making the approach very simple for use. The results are compared with those obtained by using genetic algorithm (GA), particle swarm optimization (PSO), and brain storm optimization (BSO). Results show that the DE algorithm outperforms GA, PSO, and BSO in our PEC design and optimization study.