Use of computational intelligence for designing power electronics converters

This chapter presents a decoupled optimization technique of designing switching regulators by genetic algorithm (GA) and ant colony optimization (ACO). The mechanisms of the two popular computational intelligence (CI) techniques are briefly described. This chapter also emphasizes implementation issues, as GA is popularly chosen for optimization in continuous domain and ACO is more preferable to discrete domain. The optimization process selects component values in a switching regulator in order to meet both static and dynamic requirements. Although the methods inherit some characteristics of evolutionary computations, they do not optimize the circuit as a whole. Instead, the regulator is decoupled into two parts: the power conversion stage (PCS) and the feedback network (FN). The PCS is optimized for static criteria and then the FN is optimized with the required static and dynamic behaviors of the whole system. Thus, intensive computations usually found in stochastic optimization techniques can be alleviated. The optimization procedures are described systematically and the techniques are illustrated with the design of a buck converter with overcurrent protection.