STRUCTURAL OPTIMIZATION USING A NEW LOCAL APPROXIMATION METHOD

A new method for solving structural optimization problems using a local function approximation algorithm is proposed. This new algorithm, called the Generalized Convex Approximation (GCA), uses the design sensitivity information from the current and previous design points to generate a sequence of convex, separable subproblems. The paper contains the derivation of the parameters associated with the approximation and the formulation of the approximated problem. Numerical results from standard test problems solved using this method are presented. It is observed that this algorithm generates local approximations which lead to faster convergence for structural optimization problems.     

NEW DEVELOPMENTS IN STRUCTURAL OPTIMIZATION USING ADAPTIVE MESH REFINEMENT AND MULTIPOINT APPROXIMATIONS

Combining Shape Optimization (SO) with Adaptive Mesh Refinement (AMR) potentially offers a higher accuracy and higher computational efficiency, especially if the applied target error for AMR is reduced in the course of the optimization process. The disadvantage of that approach is that the rate of convergence of the corresponding optimization processes can be significantly lower as compared to processes which apply a fixed target error for AMR. In the present paper the so-called Multipoint Approximation Method (MAM) is used as a basis for SO in conjunction with AMR. Several techniques for improvement of the rates of convergence are presented and investigated. Firstly, alternative algorithms for determining the approximation functions using a weighted least squares method are investigated. The focus is on weights which depend on the discretization errors. Secondly, different strategies for moving and resizing the search sub-regions in the space of design variables are presented. The proposed methods are illustrated by means of several optimization problems in which the effect of AMR with changing discretization errors is modelled by artificially introduced numerical noise.     

A global optimization algorithm for simulation-based problems via the extended DIRECT scheme

This article presents a global optimization algorithm via the extension of the DIviding RECTangles (DIRECT) scheme to handle problems with computationally expensive simulations efficiently. The new optimization strategy improves the regular partition scheme of DIRECT to a flexible irregular partition scheme in order to utilize information from irregular points. The metamodelling technique is introduced to work with the flexible partition scheme to speed up the convergence, which is meaningful for simulation-based problems. Comparative results on eight representative benchmark problems and an engineering application with some existing global optimization algorithms indicate that the proposed global optimization strategy is promising for simulation-based problems in terms of efficiency and accuracy.     

An adaptive metamodel-based global optimization algorithm for black-box type problems

In this article, an adaptive metamodel-based global optimization (AMGO) algorithm is presented to solve unconstrained black-box problems. In the AMGO algorithm, a type of hybrid model composed of kriging and augmented radial basis function (RBF) is used as the surrogate model. The weight factors of hybrid model are adaptively selected in the optimization process. To balance the local and global search, a sub-optimization problem is constructed during each iteration to determine the new iterative points. As numerical experiments, six standard two-dimensional test functions are selected to show the distributions of iterative points. The AMGO algorithm is also tested on seven well-known benchmark optimization problems and contrasted with three representative metamodel-based optimization methods: efficient global optimization (EGO), GutmannRBF and hybrid and adaptive metamodel (HAM). The test results demonstrate the efficiency and robustness of the proposed method. The AMGO algorithm is finally applied to the structural design of the import and export chamber of a cycloid gear pump, achieving satisfactory results.     

混合搜索法在显微镜自动对焦中的应用

针对基于显微镜的自动对焦系统,本文提出了一种爬山搜索法和函数逼近法相结合的混合搜索算法。该算法中的爬山搜索法采用粗精结合的两段式算法。在粗略对焦时,大步距选用速度较快的灰度方差函数;当精细对焦时,小步距采用灵敏度较高的Laplacian函数;通过比较三幅图片来缩小对焦区间并且在该区间内采用函数逼近法来拟合出最佳对焦位置。该方法不仅大大减少了自动对焦所需要的图片数量,而且可以大幅度提高搜索精度。经实验验证,提出的新的搜索算法可以使搜索精度优于1 μm。     

Enhancing particle swarm optimization algorithm using two new strategies for optimizing design of truss structures

This work develops an augmented particle swarm optimization (AugPSO) algorithm using two new strategies,: boundary-shifting and particle-position-resetting. The purpose of the algorithm is to optimize the design of truss structures. Inspired by a heuristic, the boundary-shifting approach forces particles to move to the boundary between feasible and infeasible regions in order to increase the convergence rate in searching. The purpose of the particle-position-resetting approach, motivated by mutation scheme in genetic algorithms (GAs), is to increase the diversity of particles and to prevent the solution of particles from falling into local minima. The performance of the AugPSO algorithm was tested on four benchmark truss design problems involving 10, 25, 72 and 120 bars. The convergence rates and final solutions achieved were compared among the simple PSO, the PSO with passive congregation (PSOPC) and the AugPSO algorithms. The numerical results indicate that the new AugPSO algorithm outperforms the simple PSO and PSOPC algorithms. The AugPSO achieved a new and superior optimal solution to the 120-bar truss design problem. Numerical analyses showed that the AugPSO algorithm is more robust than the PSO and PSOPC algorithms.     

SHAPE OPTIMIZATION WITH ADAPTIVE MESH REFINEMENT: TARGET ERROR SELECTION STRATEGIES

The multi-point approximation method in conjunction with Adaptive Mesh Refinement (AMR) for shape optimization of thin-walled structures is studied. Application of AMR is done in such a manner that in the beginning of an optimization process large discretization errors are accepted, while finite element discretizations become more accurate as the optimization process progresses. In this paper several strategies for selecting the target discretization errors are investigated. Special attention is paid to both the overall computational effort and the convergence of the optimization processes.     

有限元新型自然坐标方法研究进展

网格畸变敏感问题一直是当前有限元法难以解决的问题,而新型自然坐标方法的诞生可以在一定程度上对解决这个难题有所帮助。该文介绍了有限元新型自然坐标方法研究的新近进展。包括第一类四边形面积坐标及其应用(单元构造,解析刚度矩阵的建立,以及在几何非线性问题中的应用等);第二类四边形面积坐标及其应用;六面体体积坐标及其应用。数值算例表明:无论网格如何扭曲畸变,这些基于新型自然坐标方法的有限元模型仍然保持高精度,对网格畸变不敏感。这显示了新型自然坐标方法是构造高性能单元模型的有效工具。     

Metamodel-based inverse method for parameter identification: elastic–plastic damage model

This article proposed a metamodel-based inverse method for material parameter identification and applies it to elastic–plastic damage model parameter identification. An elastic–plastic damage model is presented and implemented in numerical simulation. The metamodel-based inverse method is proposed in order to overcome the disadvantage in computational cost of the inverse method. In the metamodel-based inverse method, a Kriging metamodel is constructed based on the experimental design in order to model the relationship between material parameters and the objective function values in the inverse problem, and then the optimization procedure is executed by the use of a metamodel. The applications of the presented material model and proposed parameter identification method in the standard A 2017-T4 tensile test prove that the presented elastic–plastic damage model is adequate to describe the material's mechanical behaviour and that the proposed metamodel-based inverse method not only enhances the efficiency of parameter identification but also gives reliable results.     

Multi-objective optimization based on meta-modeling by using support vector regression

Practical engineering design problems have a black-box objective function whose forms are not explicitly known in terms of design variables. In those problems, it is very important to make the number of function evaluations as few as possible in finding an optimal solution. So, in this paper, we propose a multi-objective optimization method based on meta-modeling predicting a form of each objective function by using support vector regression. In addition, we discuss a way how to select additional experimental data for sequentially revising a form of objective function. Finally, we illustrate the effectiveness of the proposed method through some numerical examples.     

An efficient decision-making framework for hybrid metamodelling

In engineering design optimization, the usage of hybrid metamodels (HMs) can take full advantage of the individual metamodels, and improve robustness of the predictions by reducing the impact of a poor metamodel. When there are plenty of candidates, it is difficult to make decisions on which metamodels to choose before building an HM. The decisions should simultaneously take into account of the number, accuracy and diversity of the selected metamodels. To address this problem, this research developed an efficient decision-making framework based on partial least squares for metamodel screening. A new significance index is firstly derived from the view of fitting error in a regression model. Then, a desirable metamodel combination which consist of only the significant ones is subsequently configured for further constructing the final HM. The effectiveness of the proposed framework is demonstrated through several benchmark problems.