基于神经网络遗传算法的GFRP拉挤双目标优化

为研究玻璃钢(GFRP)拉挤工艺参数对复合材料性能的影响,优化最佳拉挤工艺参数,建立了拉挤工艺过程数学模型,结合基于有限元/有限差分的间接解耦法进行求解,模拟得到了拉挤过程中GFRP内部的非稳态温度场和固化度变化情况.分别采用布拉格光栅光纤温度传感器和索氏萃取法检测拉挤GFRP内部的温度与固化度,实测温度和固化度均与模拟温度和固化度吻合,验证了数值模拟程序的正确性.以数值模拟结果为样本,建立反向传播神经网络,得到拉挤工艺参数(固化温度、拉挤速度)与GFRP固化度之间的非线性相关关系,再结合遗传算法解决拉挤过程中固化炉温度和拉挤速度双目标优化问题.优化得到的拉挤工艺参数可在保证复合材料固化度达标的情况下,提高拉挤速度降低固化炉温度,优化效果显著.神经网络遗传算法优化方法能有效解决此类具有复杂非线性关系的多目标优化问题.     

基于自适应非线性粒子群算法的光刻光源优化方法

光刻光源优化作为必不可少的分辨率增强技术之一,能够提高先进光刻成像质量.在先进光刻领域,光源优化的收敛效率和优化能力是至关重要的.粒子群优化算法作为一种全局优化算法,自适应控制策略可以提高粒子的全局搜索能力,非线性控制策略可以扩大粒子搜索范围.本文提出一种基于自适应非线性控制策略的粒子群优化算法,将光刻光源优化问题转换...     

多稳态电磁俘能系统的非线性动力学实验研究

为克服共振式俘能器工作频带窄和压电式俘能器输出电流低等问题,设计了多稳态电磁式振动俘能系统.建立了系统的分数阶阻尼模型,通过实验揭示了多稳态电磁俘能系统的动态分岔、势能阱逃逸、高能态轨道和混沌运动等非线性行为.结果表明:采用庞加莱截面频闪采样算法和分岔图可有效刻画系统的非线性振动特性;利用多稳态电磁式俘能系统的非线性振...     

基于混合粒子群优化算法辨识Hammerstein模型

非线性系统辨识是现代辨识领域中的一个主要问题。在非线性系统辨识中,系统常被表示为一系列块连接。针对非线性系统中的Hammerstein模型,本文提出了利用混合粒子群优化算法对非线性系统模型进行辨识。该方法的基本思想是将非线性系统的辨识问题转化为参数空间上的优化问题,然后采用粒子群优化算法(PSO)获得该优化问题的解。为了进一步增强粒子群优化算法的辨识性能,提出利用一种混合粒子群优化算法。最后,给出仿真实验,其结果验证了本文给出的辨识方法是有效的。     

基于关联规则的复杂系统稳态运行参数优化

针对系统运行过程中,工况切换可能导致系统运行参数目标值的跃变,进而引发系统运行不稳定的问题,本文提出一种基于关联规则的变工况运行参数稳态优化方法,改进了原有的关联规则挖掘策略,将历史工况作为变工况关联规则挖掘的输入条件之一,以系统运行状态稳定为约束,优化稳态运行参数目标值,并讨论了变工况下系统的准稳态运行条件.以某商场...     

一种辨识Hammerstein模型的新方法

为了提高非线性Hammerstein模型的辨识精度,提出一种利用混合优化算法对非线性模型进行辨识的新方法。该算法的基本思想是把非线性系统的参数辨识问题转化为参数空间上的函数优化问题,然后利用遗传算法和改进的粒子群优化算法相结合寻求并获得参数问题的最优解。最后通过仿真研究表明,该方法对于非线性辨识具有较好的有效性和鲁棒性,获得了良好的辨识效果,是一种可行的解决非线性辨识问题的方法。     

月球软着陆轨道优化方法比较研究

月球软着陆轨道优化问题是一非线性、终端时间自由且带有控制约束的最优控制问题.月球探测器软着陆轨道优化的难点在于怎么处理自由的终端时间,如何把非线性的控制问题转换为易于处理的优化问题.本文介绍了月球探测器的几种软着陆方式和月球探测器轨道的多种优化方法,对各种优化算法进行了一个大概的分类,并对各种优化算法的优缺点进行了分析比较.进而,总结了解决月球探测器软着陆轨道优化问题难点的几种有效方法,为未来我国的月球软着陆工程提供了参考.     

基于改进粒子群算法的非对称传动主轴多目标优化

针对某特种车辆传动系统由于结构布局限制导致的主轴两侧非等强度设计问题,综合考虑传动主轴-轴承系统内外多源激励,采用集中质量法建立系统非线性振动模型.基于所建立的模型,利用Runge-Kutta进行数值仿真求解,分析了稳态工况下系统弯扭耦合振动响应以及系统动载荷和振动能量的分布特点,得出了主轴右侧断裂的主要原因.为解决传...     

商用车全浮式驾驶室悬置系统优化设计

研究商用车全浮式驾驶室悬置隔振优化设计问题，在ADAMS中建立基于整车的驾驶室悬置系统和底盘主悬置系统匹配的优化设计模型，并进行优化设计。在此基础上，针对ADAMS不能以非线性悬架刚度阻尼参数为优化变量的问题，提出对线性参数优化结果进行非线性化处理的方法，大大地提高优化结果的适应性。     

基于李群指数映射的二阶最小化射影目标跟踪

基于空间变换模型的目标跟踪问题常归结为非线性最小二乘优化问题,射影变换模型的高度非线性使得基于向量空间的优化算法倍显局限.通过李群指数映射将正则射影变换群局部线性化,在内蕴几何优化理论的框架下,结合目标跟踪的具体特点,提出一种无需计算赫森阵的二阶最小化射影变形目标跟踪算法.与基于向量空间和基于李代数参数化的高斯-牛顿两种优化算法作对比实验,结果证实本文算法的可行性和高效性.     

Economic design of water distribution systems in buildings

This article discusses an engineering optimization problem which arises in hydraulics and is related to the use of a new criterion for sizing water distribution piping in large buildings. The optimization model aims to find the most suitable interior pipe diameters for the various pipes in the system, using commercial sizes and minimizing the overall installation cost according to some boundary conditions. The problem is formulated as a non-convex nonlinear program and a branch-and-bound algorithm is introduced for its solution. A procedure is proposed to obtain a feasible solution with standard values from the optimal solution of the non-convex program. The performance of the algorithm is analysed for a real-life problem and the cost of the computed solution is assessed, showing the appropriateness of the model and the optimization techniques.     

An uncertain optimization method for overall ballistics based on stochastic programming and a neural network surrogate model

A nonlinear stochastic programming method is proposed in this article to deal with the uncertain optimization problems of overall ballistics. First, a general overall ballistic dynamics model is achieved based on classical interior ballistics, projectile initial disturbance calculation model, exterior ballistics and firing dispersion calculation model. Secondly, the random characteristics of uncertainties are simulated using a hybrid probabilistic and interval model. Then, a nonlinear stochastic programming method is put forward by integrating a back-propagation neural network with the Monte Carlo method. Thus, the uncertain optimization problem is transformed into a deterministic multi-objective optimization problem by employing the mean value, the standard deviation, the probability and the expected loss function, and then the sorting and optimizing of design vectors are realized by the non-dominated sorting genetic algorithm-II. Finally, two numerical examples in practical engineering are presented to demonstrate the effectiveness and robustness of the proposed method.     

自航式鱼雷发射装置发射管长度优化研究

基于某型鱼雷自航发射的内弹道数学模型，选择自航管的总长度作为优化目标。讨论了约束函数的取值，确定了优化问题的数学表达，采用惩罚函数法进行寻优计算，针对约束函数具有不可微和非线性的特点。采用模式搜索算法进行了无约束优化的求解，给出了优化结果并进行了比较。     

Optimal design of unit hydrographs using probability distribution and genetic algorithms

A nonlinear optimization model is developed to transmute a unit hydrograph into a probability distribution function (PDF). The objective function is to minimize the sum of the square of the deviation between predicted and actual direct runoff hydrograph of a watershed. The predicted runoff hydrograph is estimated by using a PDF. In a unit hydrograph, the depth of rainfall excess must be unity and the ordinates must be positive. Incorporation of a PDF ensures that the depth of rainfall excess for the unit hydrograph is unity, and the ordinates are also positive. Unit hydrograph ordinates are in terms of intensity of rainfall excess on a discharge per unit catchment area basis, the unit area thus representing the unit rainfall excess. The proposed method does not have any constraint. The nonlinear optimization formulation is solved using binary-coded genetic algorithms. The number of variables to be estimated by optimization is the same as the number of probability distribution parameters; gamma and log-normal probability distributions are used. The existing nonlinear programming model for obtaining optimal unit hydrograph has also been solved using genetic algorithms, where the constrained nonlinear optimization problem is converted to an unconstrained problem using penalty parameter approach. The results obtained are compared with those obtained by the earlier LP model and are fairly similar.     

Material nonlinear topology optimization considering the von Mises criterion through an asymptotic approach: Max strain energy and max load factor formulations

This paper addresses material nonlinear topology optimization considering the von Mises criterion by means of an asymptotic analysis using a fictitious nonlinear elastic model. In this context, we consider the topology optimization problem subjected to prescribed energy, which leads to robust convergence in nonlinear problems. Two nested formulations are considered. In the first, the objective is to maximize the strain energy of the system in equilibrium, and in the second, the objective is to maximize the load factor. In both cases, a volume constraint is imposed. The sensitivity analysis is quite effective and efficient in the sense that there is no extra adjoint equation. In addition, the nonlinear structural equilibrium problem is solved using direct minimization of the structural strain energy using Newton's method with an inexact line-search strategy. Four numerical examples demonstrate the features of the proposed material nonlinear topology optimization framework for approximating standard von Mises plasticity.     

遗传算法在工程爆破参数优化中的应用

工程爆破中的参数优化问题是个复杂的非线性规划问题。以矿山爆破参数优化数学模型为例，采用遗传算法实现了爆破参数的优化。结果证实了利用遗传算法进行爆破参数优化的可行性与高效性，为求解该问题提供了一个有效的新途径。     

Normed Distances and Their Applications in Optimal Circuit Design

A new geometric method for optimal circuit design is presented. The method treats the optimal design problem through the concept of normed distances from a feasible point to the feasible region boundaries in a norm related to the probability distribution of the circuit parameters. The method treats directly the nonlinear feasible region boundaries without any region approximation. The normed distances are found through the solution of a nonlinear optimization problem. The sufficient optimality conditions for this optimization problem are established and an ordinary explicit formula for the normed distance is also derived. An iterative boundary search technique is used to solve the nonlinear optimization problem concerning the normed distances. The convergence of this technique is proved. Practical circuit examples are given to test the method.     

Sequential linear programming and particle swarm optimization for the optimization of energy districts

This article deals with the optimization of energy resource management of industrial districts, with the aim of minimizing customer energy expenses. A model of the district is employed, whose optimization gives rise to a nonlinear constrained optimization problem. Here the focus is on its numerical solution. Two different methods are considered: a sequential linear programming method and a particle swarm optimization method. Efficient implementations of both approaches are devised and the results of the tests performed on several energetic districts are reported, including a real case study.     

Optimization against instability in the large

The combination of geometrically nonlinear analysis with structural optimization against instability opens many possibilities for new formulations of the optimization problem. One of them is optimization against instability in the large. When a critical state does not exist and instability occurs at finite displacements either the lower critical loading is maximized or the generalized displacement for that load is minimized. This paper undertakes these new optimization problems providing formulations and numerical solutions for selected elements. The simple finite dimensional model is analysed first showing characteristic features of the problem, and then a real elastic element is optimized.