基于人工智能的齿轮优化CAD软件开发

本文将人工智能中BP神经网络与遗传优化算法相结合,通过VB语言与Matlab软件混合编程,有效解决局部极小和收敛速度问题,及含有连续/离散混合变量的齿轮优化问题,实现齿轮传动的自动优化设计。并将设计结果传给SolidWorks软件,实现齿轮的三维参数化造型,开发了基于人工智能的齿轮优化CAD软件,实现了齿轮传动优化及参数化造型的集成。     

多学科混合变量协同设计优化方法研究

为解决同时含有离散和连续两种变量形式的混合变量复杂产品设计优化问题,利用“分而治之”的混合参数处理思想,在协同设计优化算法的基础上,提出一种多学科混合变量协同设计优化方法．该方法先将优化问题解耦分解成相对简单的多个子系统进行优化计算,然后利用协同设计优化算法的协同机制求得全系统最优解．算例验证结果表明了所提出方法的可行性和有效性．     

Buck-Boost矩阵变换器主电路参数随电流定额的自适应优选方法

针对Buck-Boost矩阵变换器（BBMC）在不同额定输出电流下的主电路参数优化设计问题,提出了一种BBMC主电路参数随其额定电流变化的自适应优选方法.通过建立BBMC优化目标与优化对象间的数学模型,研究基于自适应狼群优化算法的BBMC主电路参数优化设计方法;在此基础上进一步研究确定BBMC主电路优化设计参数与BBMC额定输出电流间的变化规律,为实现不同电流定额下BBMC主电路的优化设计奠定基础;最后通过仿真对上述理论分析进行了验证.     

一个约束离散优化问题的粒子群算法研究

针对一个离散变量齿轮系优化设计问题搜索空间大、可行域狭小的特点,基于粒子群算法提出了新的约束与离散变量处理策略。另外,修改粒子群算法的速度更新公式以减少算法参数数目。与有关文献相比,所采用的算法应用于该优化问题时,不但发现可行解的成功率高,而且获得了更好的“最优”可行解和平均结果。与此同时,该算法不要求对该问题进行任何转化,也不依赖于人机交互。结果表明,该算法简单、易行、有效,对于类似优化设计问题的求解很有参考价值。     

基于双粒子群算法的板式换热器优化设计

在板式换热器优化设计中,针对设计变量为离散变量和方程组无法得到解析解的问题,提出了采用双粒子群算法对板式换热器进行数值求解的优化设计方法。通过实例证明,双粒子群算法在板式换热器优化设计运算中,完全可靠、有效。     

永磁型MRI系统中磁体的仿真分析与优化

针对永磁型核磁共振成像系统（MRI）中的磁体，该文阐述了一种利用计算机软件对其进行仿真建模、数据分析的优化方法，使磁体优化设计技术更具有科学性。MRI磁体的AutoCAD三维实体建模与仿真数据分析是实现该方法的主要部分，仿真数据分析中采用了有限元的分析方法，分析结果主要以图像形式给出，直观形象地表示了磁路中的磁场分布情况。使用该方法能够简单快捷地对MRI磁体进行仿真分析，得到磁体的优化方案。     

板式吸收塔优化设计研究与软件开发

以板式吸收塔系统的年总费用为目标函数，建立了优化设计数学模型，以吸收塔的液气比为决策变量，用单变量优化算法(菲波拿契法)求得最优解。用Visual Basic6．0开发出板式吸收塔优化设计软件。软件运行于Windows 9x系统，界面友好，操作方便。算列表明优化设计比常规设计节省生产成本。     

群智能算法在螺旋桨参数优化设计中的应用

螺旋桨参数优化设计一般是复杂的非线性问题,设计的难点在于如何在各种非线性约束条件下找到一组适当的参数,使得螺旋桨性能最佳。群智能算法作为一种新兴演化计算技术,能有效解决全局优化问题,是优化算法研究的新热点。首先介绍了粒子群算法和蜂群算法两种群智能算法的工作原理;然后在建立螺旋桨参数优化数学模型的基础上,将群智能算法运用到螺旋桨初步和终结设计优化问题中,并通过实例进行对比分析,结果表明群智能算法解决螺旋桨参数优化问题是实用且高效的。     

无参分组大规模变量的多目标算法研究

目前大多数多目标优化算法没有考虑到决策变量之间的交互性,只是将所有变量当作一个整体进行优化。随着决策变量的增加,多目标优化算法的性能会急剧下降。针对上述问题,提出一种无参变量分组的大规模变量的多目标优化算法(MOEA/DWPG)。该算法将协同优化与基于分解的多目标优化算法(MOEA/D)相结合,设计了一种不含参数的分组方式来提高交互变量分组的精确性,提高了算法处理含有大规模变量的多目标优化算法的性能。实验结果表明,该算法在大规模变量多目标问题上明显优于MOEA/D及其它先进算法。     

基于运动稳定性轧管机主机头模糊优化设计

为提高轧管机主机头的运动稳定性,改善钢管的轧制质量,本文在对轧管机主机头的结构及运动状态分析的基础上,推导出主机头的运动加速度解析式,并以此为基础,建立模糊优化的数学模型。文中分别以各构件长度尺寸为设计变量,进行单变量模糊优化设计,求得各构件长度的最优取值区间,使主机头的运动稳定性得到较大的提高。本文建立的数学模型对提高轧管机的稳定性及进行优化设计提供了依据,所得出的结论为轧管机的整体优化奠定了研究基础。     

Concurrent optimal design of modular robotic configuration

This paper presents a new optimization design methodology that is applicable to modular systems. This new methodology is called concurrent optimization design method (CODM). A modular robot is taken as a case study. The CODM is superior to the existing methods for modular robot configuration design in the sense that traditional type synthesis and dimensional synthesis now can be treated once. This mathematically implies that (i) variables are defined for both types and dimensions, and (ii) all the variables are defined in one optimization problem formulation. This paper illustrates that, in fact, optimization design for modular architectures necessitates a multiobjective optimization problem. A genetic algorithm is used to solve for this complex optimization model which contains both discrete and continuous variables. © 2001 John Wiley & Sons, Inc.     

Optimum cost design of reinforced concrete slabs using neural dynamics model

For structural optimization algorithms to find widespread usage among practicing engineering they must be formulated as cost optimization and applied to realistic structures subjected to the actual constraints of commonly used design codes such as the ACI code. In this article, a general formulation is presented for cost optimization of single- and multiple-span RC slabs with various end conditions (simply supported, one end continuous, both ends continuous, and cantilever) subjected to all the constraints of the ACI code. The problem is formulated as a mixed integer-discrete variable optimization problem with three design variables: thickness of slab, steel bar diameter, and bar spacing. The solution is obtained in two stages. In the first stage, the neural dynamics model of Adeli and Park is used to obtain an optimum solution assuming continuous variables. Next, the problem is formulated as a mixed integer-discrete optimization problem and solved using a perturbation technique in order to find practical values for the design variables. Practicality, robustness, and excellent convergence properties of the algorithm are demonstrated by application to four examples.     

Mathematical model for simultaneous design of module commonalization and supply chain configuration toward global product family

Manufacturing today has become global in all aspects marketing, design, production, distribution, etc. While product family design has been an essential viewpoint for meeting the demand for product variety, its interaction with the issues of supply chain, market systems, etc. makes the meaning of product family both broad and more complicated. In this paper we call such situation ‘global product family,’ and first characterizes its components and complexity. Following this, we proposes a mathematical model for the simultaneous design problem of module commonalization strategies under the given product architecture and supply chain configuration through selection of manufacturing sites for module production, assembly and final distribution as an instance of the problems. In the model, the choice of modules and various sites are represented with 0-1 design variables with the volume of production and transportation represented with non-negative continuous design variables, and the objective defined on total cost. An optimization method is configured with a genetic algorithm and a simplex method for such a mixed integer programming problem. Some numerical case studies are included to determine the validity and promise of the developed mathematical model and algorithm. Finally, we conclude with some discussion of future work.     

Interval analysis based robust truss optimization with continuous and discrete variables using mix-coded genetic algorithm

The problem of optimizing truss structures in the presence of uncertain parameters considering both continuous and discrete design variables is studied. An interval analysis based robust optimization method combined with the improved genetic algorithm is proposed for solving the problem. Uncertain parameters are assumed to be bounded in specified intervals. The natural interval extensions are employed to obtain explicitly a conservative approximation of the upper and lower bounds of the structural response, and hereby the bounds of the objective function and the constraint function. This way the uncertainty design may be performed in a very efficient manner in comparison with the probabilistic analysis based method. A mix-coded genetic algorithm (GA), where the discrete variables are coded with binary numbers while the continuous variables are coded with real numbers, is developed to deal with simultaneously the continuous and discrete design variables of the optimization model. An improved differences control strategy is proposed to avoid the GA getting stuck in local optima. Several numerical examples concerning the optimization of plane and space truss structures with continuous, discrete or mixed design variables are presented to validate the method developed in the present paper. Monte Carlo simulation shows that the interval analysis based optimization method gives much more robust designs in comparison with the deterministic optimization method.     

Topology optimization of planar cross-sections

Topology optimization provides a rigorous method for the conceptual design of structural components. In this note, a practical approach for solving topology optimization problems of planar cross-sections is discussed. A problem formulation involving the use of continuous design variables is presented, and a standard nonlinear programming algorithm is used to solve the optimization problem. Results of the technique for two examples are presented and compared to similar results in the literature.     

用鱼群算法求解通风系统风机定位优化问题

为了解决矿井通风系统风机定位优化问题,建立了该问题的大规模非线性最优规划模型。在优化模型中,在兼顾变量约束条件的空间限制和求解精度的情况下,在正交交叉算子中将求解空间离散化,离散方法是将每个连续因素离散化为一个有限值,量化每个变量连续空间区域为有限个水平。由于该问题维数太高,传统优化技术无法有效获取其最优解,采用改进的鱼群算法对该问题进行了求解。在算法中,为了消除优化模型的约束条件,大幅度压缩变量数,在算子中将变量分组;使用了基于邻域竞争进化的演化算法,有效地融合了全局搜索和局部搜索的本质属性,实现了算法效率与效果的平衡;使用了自适应学习和变异算子、正交交叉算子、邻域竞争算子等多种算子改进基本人工鱼群算法的各种行为。应用结果表明,该算法计算速度和稳定性大幅度提高,可在简单计算环境下稳定地获取该模型的最优解。     

Stacking sequence optimization and blending design of laminated composite structures

The stacking sequence optimization problem for multi-region composite structures is studied in this work by considering both blending and design constraints. Starting from an initial stacking sequence design, unnecessary plies can be removed from this initial design and layer thicknesses of necessary plies are optimally determined. The existence of each ply is represented with discrete 0/1 variables and ply thicknesses are treated as continuous variables. A first-level approximate problem is constructed with branched multipoint approximate functions to replace the primal problem. To solve this approximate problem, genetic algorithm is firstly used to optimize discrete variables, and meanwhile, a blending design scheme is proposed to generate a blended structure. Starting from the thinnest region, this scheme shares all layers of current thinnest region with its adjacent regions. For non-shared layers in the adjacent regions, local mutation is implemented to add or delete plies to make them efficient designs. The whole process is repeated until the blending rule is satisfied. After that, a second-level approximate problem is built to optimize the continuous variables of ply thicknesses for retained layers. Those procedures are repeated until the optimal solution is obtained. Numerical applications, including a two-patch panel and a corrugated central cylinder in a satellite, are conducted to demonstrate the efficacy of the optimization strategy.

     

Interval-based global optimization in engineering using model reformulation and constraint propagation

This paper deals with the preliminary design problem when the product is modeled as an analytic model. The analytic models based method aims to use mathematical equations to address both multi-physic and economic characteristics of a product. The proposed approach is to convert the preliminary design problem into a global constrained optimization problem. The objective is to develop powerful optimization methods enough to handle complex analytical models. We propose to adapt an approach to solve this problem based on interval analysis, constraint propagation and model reformulation. In order to understand the optimization algorithm used for engineering design problems, some basic definitions and properties of interval analysis are introduced. Then, the basic optimization algorithms for both unconstrained and constrained problems are introduced and illustrated. The next section introduces the reformulation technique as main accelerating device. An application of the reformulation device and its global optimization algorithm on the optimal design of electrical actuators is presented.     

Optimal design of large-scale space steel frames using cascade enhanced colliding body optimization

In structural size optimization usually a relatively small number of design variables is used. However, for large-scale space steel frames a large number of design variables should be utilized. This problem produces difficulty for the optimizer. In addition, the problems are highly non-linear and the structural analysis takes a lot of computational time. The idea of cascade optimization method which allows a single optimization problem to be tackled in a number of successive autonomous optimization stages, can be employed to overcome the difficulty. In each stage of cascade procedure, a design variable configuration is defined for the problem in a manner that at early stages, the optimizer deals with small number of design variables and at subsequent stages gradually faces with the main problem consisting of a large number of design variables. In order to investigate the efficiency of this method, in all stages of cascade procedure the utilized optimization algorithm is the enhanced colliding bodies optimization which is a powerful metaheuritic. Three large-scale space steel frames with 1860, 3590 and 3328 members are investigated for testing the algorithm. Numerical results show that the utilized method is an efficient tool for optimal design of large-scale space steel frames.