Multi-objective Optimization Method Based On Metamodel And Its Applications In Vehicle Body Design

Most engineering optimization problems involve multiple objectives, which can not be expressed explicitly but acquired by complex computational model, and thus it increases the difficulty of solving multi-objective optimization problems. Intelligent optimization method is able to search for multiple optimal solutions in one single simulation run, and it is suitable for dealing with engineering problems, but the low efficiency limits its application to complex problems. Common multi-objective optimization methods based on metamodel can well deal with the low efficiency and become a research focus, but the solution accuracy is usually low. Therefore, this paper studies the multi-objective optimization methods based on metamodel, aims to improve the efficiency and accuracy, and makes the method well-employed in the design of vehicle body. The main contents are given as follows:A new multi-objective optimization algorithm is proposed based on adaptive radial basis function. This method effectively assesses metamodel by using inherit Latin hypercube design, radial basis function and intergeneration projection genetic algorithm. Then through the combination of sampling points and testing points, the method gradually improves the metamodel accuracy. An extented greed algorithm is adopted to filter testing points from the last iterative into the final sample space to acquire adaptive radial basis function in the entire design region, and then adaptive radial basis function combines NSGA-Ⅱ to perform multi-objective optimization. The test functions have verified that adaptive radial basis function possesses the abilities of effectively assessing and gradually improving the accuracy. At last, the proposed method is applied to the thin-walled sections for structural crashworthiness. With the application of the method, it is beneficial to quickly find multi-group design schemes, which can well balance energy absorption and collision force.A micro multi-objective genetic algorithm based on intelligent sampling technology is put forward. The algorithm adopts the extented radial basis function to build a global metamodel, and then employs the efficient micro multi-objective genetic algorithm for approximate optimization. In the following, intelligent sampling is achieved accroding to the optimization result with its feedback to the design space, and then continuously updates the metamodel, forming a closed loop process of experiment design and approximate optimization. In that case, the approximate optimization information has been fully utilized, and due to the method focusing only on the accuracy of metamodel in the concerned region rather than the global region, the optimization efficiency has been improved. Test functions have verified the accuracy and efficiency. Finally, the method has been used in the dynamic characteristic optimization of a heavy commercial vehicle cab and obtains many optimal design schemes.Optimization algorithm based on trust region model management is proposed to solve the multi-objective optimization problem in complex engineering. The method transforms the complex optimization problems in the entire design space into a series of approximation problems in trust region, in which the optimazation result determines the reliability of center and radius of the next region. With constantly zooming, translating the trust region, the method ensures the non-dominated solutions in consistent with the true problem. Numerical examples show that this method reduces the dependance on metamodel accuracy and prove the method has certain advantages and potential in dealing with complex multi-objective optimization problems. Finally, the method has been applied in a door structure optimization, and well balances the static and dynamic performance by matching the thickness of key components.Based on trust region and intelligent sampling technology, an efficient multi-objective method is developed. With the help of sample inheriting strategy, the method can inherit samples falling in the next trust region to reduce the number of experimental design samples, and thus the efficiency is increased. Based on intelligent sampling strategy, the method can select part of the solutions from external solutions into the next trust region, so improves the accuracy of the metamodel in concerned space to accelerate convergence. The method has successfully solved different types of testing problems, and compared with the common method, it not only obtains better optimial solutions, but also improves the optimization efficiency. Finally, the method has been successfully used in the lightweight design of car body based on crashworthiness and modal characteristics, and demonstrates its ability to solve multi-objective optimization problems in practical engineering.