Orthogonal learning particle swarm optimization for power electronic circuit optimization with free search range

Power electronic circuit (PEC) always consists of a number of components such as resistors, capacitors, and inductors which have to be optimized in order to obtain good circuit performance. In current studies, the search ranges of these components are always pre-defined carefully by expert designers, making it difficult for practical applications. In this paper, the search space is freely set to the commonly used ranges and an efficient orthogonal learning particle swarm optimization (OLPSO) is applied to optimally design the PEC with such search space. OLPSO uses an orthogonal learning (OL) strategy for PSO to discover useful information that lies in the personal historical best experience and the neighborhood´s best experience via orthogonal experimental design. Therefore, OLPSO can construct a more promising and efficient exemplar to guide particle to fly better towards the global optimal region. OLPSO is implemented to optimize the design of a buck regulator in PEC. The optimized results are compared with those obtained by using a genetic algorithm (GA) approach and those obtained by using PSO with traditional learning strategy. Results show that the OLPSO algorithm is more promising in the design and optimization of the PEC with large search space. Moreover, the simulations results demonstrate the advantages of OLPSO by showing that the circuit optimized by OLPSO exhibits better startup and large-signal disturbance performance when compared with the one optimized by GA.