The Research Of Dynamic Response Optimization Algorithms Based On Simulation Model

Supported by the National "863" High-Tech Development Project of China under the grant No.2006AA04Z121 and National Natural Science Fund under the grant No.50775084, the simulation model based dynamic response optimization methods have been studied, which focus on dynamic response optimization problems and parallel processing, time spectral element method based on the dynamic response optimization, MARS based dynamic response optimization techniques and simulation based global dynamic response optimization of fuzzy clustering, and so on.Firstly, a parallel processing and scheduling strategy of SQP algorithm in simulation optimization are studied. We propose the abstract scheduling model in parallel optimization problem:a discrete variable optimization model with equality constraints. The thorough theoretical feasibility of parallel algorithm is deeply discussed. The parallel task dispatch is implemented by the centralized dynamic load balancing technology and the work pond. The efficiency of SQP optimization of parallel technique is proved by an example of the dynamic response optimization of control parameters of a simple F14 aircraft model.Secondly, the optimization design of systems for dynamic response based on temporal spectral element is studied. This paper makes further exploration of how to discrete-time dynamic response, to convert the movement differential equations into algebraic equations, to obtain exact solutions of transient response, and processes the constraints related to the time using GLL points method and key-point method. The optimization example of spring shock absorber of linear 1-DOF, the spring shock absorber of linear 2-DOF and automotive suspension system of 5-DOF are given, the introduction of artificial design variable, a detailed analysis of the advantages and disadvantages for two methods to deal with constraints of, but also shows the correctness of this method. These can provide a reference to be further optimized dynamic response, such as the dynamic optimization design of the rectangular variable cross-section beam bearing vibration at the fixed end, the dynamic response optimization design of the elastic beam bearing uniform transient load of vertical plane at the different boundary conditions and so on.Thirdly, MARS based dynamic response optimization is studies, which combines the global response surface (GRS) based multivariate adaptive regression splines (MARS) with Move-Limit strategy (MLS) and combines augmented Lagrangian method, trust region method and data driven by using characteristics of MARS. MARS is an adaptive regression process, which fits in with the multidimensional problems. And it adopts a modified recursive partitioning strategy to simplify high-dimensional problems into smaller yet highly accurate models. MLS for moving and resizing the search sub-regions is employed in the space of design variables. The quality of the approximation functions and the convergence history of the optimization process are reflected in MLS. The disadvantages of the conventional response surface method (RSM) have been avoided, specifically, highly nonlinear high-dimensional problems. The method is applied to a high-dimensional test function and an engineering problem and ANSYS based tapered beam shape optimization problem, and compared with quadratic response surface (QRS) models in terms of computational efficiency and accuracy. And calculating examples of the linear 2-DOF spring shock absorber suspension design and 5-DOF vehicle dynamic response optimization problems are given to illustrate its feasibility and convergence.A data driven based optimization approach combines augmented Lagrangian method, MARS with effective data processing. In the approach, an expensive simulation run is required if and only if a nearby data point does not exist in the cumulatively growing database. Over time the database matures and is enriched as more and more optimizations have been performed. Combining the local response surface of MARS and augmented Lagrangian method improve sequential approximation optimization and reduce simulation times by effective data processing, yet maintain a low computational cost. The approach is applied to a ten dimensional engineering problem, the linear 2-DOF spring shock absorber suspension design and 5-DOF vehicle dynamic response optimization problems to demonstrate its feasibility and convergence and the data provided in literature are compared.Finally, fuzzy clustering applied in simulation based dynamic response of global optimization, which gets better result. The approximate model substitutes for the computation-intensive analysis and simulation, which result in fast calculation. The sampling locations determined by fuzzy clustering and the subregion size determined by the iterative results of the latest two events. Simulation and Evaluation of the objective function and constrained functions were implemented after the sampling subregion determined. Then, Kriging response surface was structured and fuzzy clustering was implemented, which were not repeated until the convergence. In the fuzzy clustering based global optimization, three clustering center are used in each iteration, which showed a better effect. Three clustering center may include the optimum points or its nearby points. The geometric center is obtained from the cluster centers, and Sort Descending is implemented which minimum value is the subregion center. The proposed algorithm is tested using 10 benchmark global optimization problems and linear single degree of freedom vibration absorber, and the GA, SA, PSO and MPS algorithms are compared to prove its accuracy, robustness. The number of simulation of Expensive black box functions significantly is significantly reduced.