Implementation of a decoupled optimization technique for design of switching regulators using genetic algorithms

Abstract

This paper presents an implementation of a decoupled optimization technique for design of switching regulators using genetic algorithms (GAs). The optimization process entails the selection of component values in a switching regulator, in order to meet the static and dynamic requirements. Although the proposed method inherits characteristics of evolutionary computations that involve randomness, recombination, and survival of the fittest, it does not perform a whole-circuit optimization. Thus, intensive computations that are usually found in stochastic optimization techniques can be avoided. Similar to many design approaches for power electronics circuits, a regulator is decoupled into two components, namely the power conversion stage (PCS) and the feedback network (FN). The PCS is optimized with the required static characteristics, whilst the FN is optimized with the required static and dynamic behaviors of the whole system. Systematic optimization procedures will be described and the technique is illustrated with the design of a buck regulator with overcurrent protection. The predicted results are compared with the published results available in the literature and are verified with experimental measurements.

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

“…However, these approaches are very sensitive to the initial solution. Moreover, they might be inefficient enough to search globally and are subjective to be trapped into local optima when the problems are complex [16]. Therefore, the obtained values for the circuit components may be sub-optimal, leading to low satisfaction when used in practical applications.…”

“…As the OLPSO algorithm generally performs better on complex functions, this paper goes further to test its performance on solving the PEC problem. Simulation results on a buck regulator design are compared with those obtained by GA [16] and traditional PSO [23] approaches.…”

“…For example, the search range for some resistors is from 470Ω to 47kΩ and the search range for some capacitors is from 0.33μF to 33μF [16]. However, such search ranges are difficult to be defined for different components in different PECs when used in practical applications.…”

“…Moreover, previous studies using GA and ACO approaches always optimize the circuit components within careful predefined search ranges which are determined by expert designers [16]- [19]. For example, the search range for some resistors is from 470Ω to 47kΩ and the search range for some capacitors is from 0.33μF to 33μF [16].…”

“…These optimization algorithms have played an important role in many science and engineering problems. Specifically, genetic algorithm (GA) [16] and ant colony optimization (ACO) [17] have been successfully applied to optimize the component values of PEC. However, there still exist disadvantages in the GA and ACO approaches that they have to consume a lot of computation before obtaining the good component values because of the slow convergence speed [18] [19].…”