混合遗传粒子群优化算法的研究  

Research on Hybrid Genetic Algorithm and Particle Swarm Optimization Algorithm

作　　者：贾建芳[1] 杨瑞峰[1] 王莉[1] 

机构地区：[1]中北大学信息与通信工程学院,山西太原030051

出　　处：《自动化仪表》2013年第9期1-3,共3页Process Automation Instrumentation

基　　金：教育部高等学校博士学科点专项科研基金资助项目(编号:20121420110003)

摘　　要：为了准确辨识电动舵机的参数,提出了一种混合遗传粒子群优化算法。该算法的具体步骤是对所有粒子进行交叉操作,并对粒子交叉前后的适应度函数进行比较。如果适应度优于前者,则进行粒子替换。仿真结果表明,该算法能够减少寻优迭代次数,缩短优化计算时间,具有良好的实用性。In order to precisely recognize the parameters of electric steering gear, the hybrid GA-PSO algorithm that is composed of genetic algorithm （ GA ） and particle swarm optimization （PSO） algorithm is proposed. Firstly, all the particles are operated crossover. Then, the fitness functions before and after crossover are compared, if the fitness function becomes better, then replacement of the particles is conducted. The result of simulation illustrates that this algorithm reduces the optimizing iterations and shortens optimizing calculation and possesses excellent practicability.

关 键 词：电动舵机 粒子群优化 遗传算法 参数估计 适应度函数 收敛性 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置]