Pseudocoevolutionary genetic algorithms for power electronic circuits optimization

This correspondence presents pseudocoevolutionary genetic algorithms (GAs) for power electronic circuit (PEC) optimization. Circuit parameters are optimized through two parallel coadapted GA-based optimization processes for the power conversion stage (PCS) and feedback network (FN), respectively. Each process has tunable and untunable parametric vectors. The best candidate of the tunable vector in one process is migrated into the other process as an untunable vector through a migration controller, in which the migration strategy is adaptively controlled by a first-order projection of the maximum and minimum bounds of the fitness value in each generation. Implementation of this method is suitable for systems with parallel computation capacity, resulting in considerable improvement of the training speed. Optimization of a buck regulator for meeting requirements under large-signal changes and at steady state is illustrated. Simulation predictions are verified with experimental results.