Optimizing Power Electronic Circuit Design with Uniform Search Range: An Orthogonal Learning Particle Swarm Optimization Approach with Predictive Solution Strategy

Power electronic circuit (PEC) design is a complicated optimization problem that calls for evolutionary computation (EC) algorithms. The existing EC algorithms for optimizing PEC have difficulty in practical applications because they demand users to carefully define very narrow search ranges for different circuit components. Aiming at this problem, this paper models PEC with uniform search range (USR), where the components’ search ranges are set uniformly according to the commonly used ranges in industrial applications. In order to solve the complex PEC problem with USR, an efficient orthogonal learning particle swarm optimization with predictive solution (OLPSO/PS) is proposed. Firstly, OLPSO/PS uses an orthogonal learning strategy to construct a more promising and efficient exemplar to guide particles to fly towards better searching areas dynamically. Secondly OLPSO/PS utilizes the predictive solution (PS) strategy to help save computational burden. OLPSO/PS is compared not only with the well-studied genetic algorithm and PSO for optimizing PEC in the literature, but also with OLPSO without PS and some other well-performed EC algorithms. Results show that OLPSO/PS is more promising in the optimization of PEC with USR, outperforming other algorithms in terms of higher fitness quality, faster optimization speed, stronger reliability, and better simulation results on both voltage and current. Moreover, the effectiveness of OLPSO/PS is further validated on the experiments in a practical circuit. Optimizing Power Electronic Circuit Design with Uniform Search Range: An Orthogonal Learning Particle Swarm Optimization Approach with Predictive Solution Strategy