基于混合遗传算法的连采机减速器试验模态参数识别  

Experiment Modal Parameter Identification for Continuous Miner Speed Reducer Based on Mixture Genetic Algorithm

作　　者：程珩[1,2] 于亮亮[1,2] 黄超勇[1,2] 

机构地区：[1]太原理工大学新型传感器与智能控制教育部重点实验室,太原030024 [2]太原理工大学机械电子工程研究所,太原030024

出　　处：《机械工程学报》2013年第3期74-79,共6页Journal of Mechanical Engineering

基　　金：国家自然科学基金(51035007);山西省自然科学基金(2011011026-3)资助项目

摘　　要：针对经典遗传算法在复杂空间上局部搜索能力较弱,易过早陷入未成熟收敛,并在接近最优解时,由于优化压力较小导致搜索效率低等问题,在传统遗传算法群体进化中引入拉马克学习机制,构造基于拉马克学习机制的局部搜索算子,建立起混合遗传算法模型。两种算法相互融合,使学习的优势得到发挥,提高局部深度搜索能力并加快了全局收敛速度。将其应用于连采机减速器试验模态参数识别中,结果证明了混合遗传算法的有效性和准确性。The local search ability of the classical genetic algorithm in the complex search space is weak, and easy to drop into premature convergence. When close to the optimal solution the search is inefficient, due to less pressure of optimization. In view of problems above, Larnarckian learning mechanism is introduced into the population evolution of the traditional genetic algorithm, the local search operator based on the Lamarckian learning mechanism is designed, and hybrid genetic algorithm model is constructed, which can make the advantage of learning fully, enhance the local depth search capability and accelerate the rate of global convergence. The application in experiment modal parameter identification for continuous miner speed reducer proves the effectiveness and accuracy of the hybrid genetic algorithm.

关 键 词：减速器 遗传算法 试验模态 参数识别 拉马克学习 POWELL搜索法 

分 类 号：TH132[机械工程—机械制造及自动化]