【摘要】 针对粒子群优化(particle swarm optimization,PSO)算法收敛过程中种群多样性丢失而导致早熟收敛的问题,提出一种具有双重学习能力的遗传-粒子群综合算法(genetic-particle swarm memetic algorithm,GPSMA)。该算法引入遗传操作,具有向成功和失败双重学习的能力,并融入振荡参数策略和阻尼边界条件处理方法。通过4个典型测试函数对GPSMA与其他3种优化算法的数值试验对比,表明GPSMA具有良好的全局收敛能力。在此基础上,以变速范围内控制绕组电流最小为优化目标,运用GPSMA对1台18.5 k W的定子双绕组感应发电机(dual statorwinding induction generator,DWIG)进行优化设计。结果表明,优化后的样机使控制绕组电流幅值下降了62.7%,说明GPSMA可有效应用于DWIG优化问题的求解。更多还原

【Abstract】 Aiming at the premature convergence problem of Particle Swarm Optimization(PSO)algorithm caused by the loss of population diversity in the convergence process,a Genetic-Particle Swarm Memetic Algorithm(GPSMA)with double learning ability was proposed. The genetic manipulation,the double learning abilities from success and failure,as well as approach integrating oscillation parameter strategy and damping boundary condition were introduced into the GPSMA. Comparing the numerical experiments of GPSMA with the other three optimization algorithms by four typical test functions,the results show that GPSMA has good global convergence ability. On the basis of this,setting minimum control winding current in speed-regulation range as the optimization target,and the design of a 18.5 kW dual stator-winding induction generator(DWIG)is optimized by GPSMA. The results show that the control winding current of optimized prototype is reduced by 62.7%,the GPSMA can be effectively used to solve the DWIG optimal design problem.更多还原