基于多目标优化NSGA2改进算法的结构动力学模型确认

The traditional structural dynamics model validation methods usually use single-objective optimization.Due to poor accuracy and stability,it is difficult to meet the actual engineering needs.This paper uses neural network as agent model,and establishes multi-objective optimization model with Mahalanobis distance and robustness as optimization targets,which is solved by NSGA2.Since NSGA2 has some design defects,such as ineffectiveness in identifying pseudo non-dominant individuals,low efficiency,poor convergence and distribution,this paper proposes an improved NSGA2 algorithm based on dominant strength (INSGA2-DS).INSGA2-DS introduces dominant strength to non-dominated sorting method,and adopts a new crowding distance formula and the adaptive elitist retention strategy to improve the convergence efficiency and Pareto solution quality.The simulation results of GARTEUR airplane show that INSGA2-DS has better convergence and distribution when solving complex engineering problems.The structural dynamics model validation method considering robustness can provide a variety of Pareto solution sets which satisfy different target requirements,and improve the accuracy and stability of model validation.