Abstract—Research into adjusting the probabilities of crossover and mutation � � in genetic algorithms (GAs) is one of the most significant and promising areas in evolutionary computation. � � and � � greatly determine whether the algorithm will find a near-optimum solution or whether it will find a solution efficiently. Instead of using fixed values of � � and ��, this paper presents the use of fuzzy logic to adaptively adjust the values of � � and � � in GA. By applying the u-means algorithm, distribution of the population in the search space is clustered in each generation. A fuzzy system is used to adjust the values of � � and ��. It is based on considering the relative size of the cluster containing the best chromosome and the one containing the worst chromosome. The proposed method has been applied to optimize a buck regulator that requires satisfying several static and dynamic operational requirements. The optimized circuit component values, the regulator’s performance, and the convergence rate in the training are favorably compared with the GA using fixed values of � � and ��. The effectiveness of the fuzzy-controlled crossover and mutation probabilities is also demonstrated by optimizing eight multidimensional mathematical functions. Index Terms—Evolutionary computation, fuzzy logics, genetic algorithms (GA), power electronics. I.

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. Index Terms—Circuit optimization, circuit simulation, computer-aided design, genetic algorithms, power electronics. I.

Abstract- Research on adjusting the probabilities of crossover pI and mutation p. in genetic algorithms (GA’s) is one of the most significant and promising areas of investigation in evolutionary computation, since pr and p. greatly determine whether the algorithm will find a near-optimum solution or whether it will find a solution emciently. Instead ef having fixed pr and p-, this paper presents the use of iurzy logic to adaptively tunep, andp. for optimization of power electronic circuits throughout the process. By applying the K-means algorithm, distribution of the population in the search space is clustered in each training generation. Inferences ofp. sndp. are performed by a furzy-based system that fuuifies the relative sizes of the clusters containing the best and worst chromosomes. The proposed adaptation method is applied to optimize a buck regulator that requires satisfying some static and dynamic requirements. The optimized circuit component valuer, the regulator’s performance, and the convergence rate in the training are favorably compared with the GA’i using rued pr andp.. 1.

Abstract—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. Index Terms—Evolutionary computation, genetic algorithms (GAs), power electronics. I.

Abstract. The development of power electronics results in a growing need for automatic design and optimization for power electronic circuits (PECs). This paper presents a particle swarm optimization (PSO) approach for the PECs design. The optimization problem is divided into two processes using a decoupled technique and PSO is employed to optimize the values of the circuit components in the power conversion stage (PCS) and the feedback network (FN), respectively. A simple mutation operator is also incorporated into PSO to enhance the population diversity. The algorithm is applied to the optimization of a buck regulator for meeting requirements under large-signal changes and at steady state. Compared with genetic algorithm (GA), PSO can yield more optimized values of circuit components with lower computational effort.