摘要
针对粒子群优化算法(PSO)无法处理反求问题中的病态问题,基于粒子群优化算法,通过遗传算法对粒子群优化算法进行改进,提出一种改进的粒子群优化算法(GAPSO),通过载荷识别对该方法进行验证,并应用于静态载荷识别和动态载荷识别算例中。研究结果表明:改进后的粒子群优化算法既能使粒子群优化算法处理病态问题,又提高了反求问题的求解精度。
Considering that particle swarm optimization algorithm(PSO)cannot deal with ill-posed problem,an improved particle swarm optimization algorithm(GAPSO)was proposed by genetic algorithm based on particle swarm optimization.This method was verified by common inversion problems such as load identification.Finally,the improved optimization algorithm was applied in static load identification and dynamic load identification.The results show that the improved particle swarm optimization algorithm can not only solve ill-posed problems,but also improve the accuracy of inverse problem.
作者
谢兵
谢博群
张猛
曲先强
XIE Bing;XIE Boqun;ZHANG Meng;QU Xianqiang(Key Laboratory of Information Service of Hunan Province for Rural Area of Southwestern Hunan,Shaoyang 422000,China;College of Computer Science and Electronic Engineering,Hunan University,Changsha 410082,China;College of Shipbuilding Engineering,Harbin Engineering University,Harbin 150001,China)
出处
《中南大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2019年第2期343-349,共7页
Journal of Central South University:Science and Technology
基金
西部交通建设科技项目(2014364554050)
国家自然科学基金资助项目(61672356)~~
关键词
载荷识别
反问题
粒子群优化算法(PSO)
遗传算法
load identification
inverse problem
particle swarm optimization algorithm(PSO)
genetic algorithm
