A hybrid genetic algorithm–neural network strategy for simulation optimization

Simulation optimization aims at determining the best values of input parameters, while the analytical objective function and constraints are not explicitly known in terms of design variables and their values only can be estimated by complicated analysis or time-consuming simulation. In this paper, a hybrid genetic algorithm–neural network strategy (GA–NN) is proposed for such kind of optimization problems. The good approximation performance of neural network (NN) and the effective and robust evolutionary searching ability of genetic algorithm (GA) are applied in hybrid sense, where NNs are employed in predicting the objective value, and GA is adopted in searching optimal designs based on the predicted fitness values. Numerical simulation results and comparisons based on a well-known pressure vessel design problem demonstrate the feasibility and effectiveness of the framework, and much better results are achieved than some existed literature results.     

Improved Estimation and Uncertainty Quantification Using Monte Carlo-Based Optimization Algorithms

In this article, we present a novel method to obtain both improved estimates and reliable stopping rules for stochastic optimization algorithms such as the Monte Carlo EM (MCEM) algorithm. By characterizing a stationary point, θ*, of the algorithm as the solution to a fixed point equation, we provide a parameter estimation procedure by solving for the fixed point of the update mapping. We investigate various ways to model the update mapping, including the use of a local linear (regression) smoother. This simple approach allows increased stability in estimating the value of θ* as well as providing a natural quantification of the estimation uncertainty. These uncertainty measures can then also be used to construct convergence criteria that reflect the inherent randomness in the algorithm. We establish convergence properties of our modified estimator. In contrast to existing literature, our convergence results do not require the Monte Carlo sample size to go to infinity. Simulation studies are provided to illustrate the improved stability and reliability of our estimator.     

基于灰关联分析的GA-BP神经网络在汽车油耗估算中的应用

从设计参数特征入手分析影响汽车油耗的因素,利用灰关联分析方法,解析了各设计参数对汽车油耗的影响程度,选择其中灰关联度较大的设计参数作为输入数据,综合工况油耗作为输出数据,构建6-5-1层结构的BP神经网络预测模型,并利用遗传算法获得优化后的BP神经网络的权值和阈值,然后训练BP神经网络得到最优值,最后以国内市场340款汽车作为研究样本,进行有效性验证.研究结果表明,模型利用灰关联分析获得影响汽车油耗的主要因素,简化了网络结构;与优化前的BP神经网络相比,具有更高的预测精度和可靠性.     

单幅同轴全息图两步迭代收缩重建

利用压缩传感理论中的两步迭代收缩重建算法,开展单幅同轴全息图重建实验研究,实现单幅同轴全息图共轭重建像的消除并克服数字全息技术在轴向聚焦平面识别能力的不足。以数字图像和标准分辨率板为记录物体,比较分析了基于两步迭代收缩算法和菲涅尔近似衍射重建算法的重建质量;以两根裸光纤为实验样本,分析了两步迭代收缩重建算法对记录物体轴向不同焦平面的识别能力。实验结果表明两步迭代收缩重建算法可得到清晰度高于68.73%的重建信息,同时对直径为125 m的两根光纤在9 mm的轴向间距条件下,显示出了比全息菲涅尔近似算法更好的聚焦平面识别能力。这一轴向聚焦识别能力有助于数字全息技术应用于功能材料梯度参数或功能涂层光学器件涂层厚度检测。     

一类广义非线性隐拟变分包含

引入和研究了一类新的涉及集值极大单调映象的广义非线性隐拟变分包含.对此类变分包含在没有紧性假设下证明了解的存在定理.为寻求此类变分包含的近似解,建议和分析了一个新的迭代算法.给出了由新算法产生的迭代序列的收敛性.作为特殊情形,也讨论了在此领域内的某些已知结果.     

Simplified analysis for full-Newton step infeasible interior-point algorithm for semidefinite programming

We present an analysis of the full-Newton step infeasible interior-point algorithm for semidefinite optimization, which is an extension of the algorithm introduced by Roos [C. Roos, A full-Newton step 𝒪(n) infeasible interior-point algorithm for linear optimization, SIAM J. Optim. 16 (2006), pp. 1110–1136] for the linear optimization case. We use the proximity measure σ(V)?=?‖I???V ²‖ to overcome the difficulty of obtaining an upper bound of updated proximity after one full-Newton step, where I is an identity matrix and V is a symmetric positive definite matrix. It turns out that the complexity analysis of the algorithm is simplified and the iteration bound obtained is improved slightly.     

一种改进的无约束非光滑优化问题的信赖域算法

本文提出了一种新的求解无约束非光滑优化问题的信赖域算法,并证明了该算法的迭代点列的任何聚点都是的问题的稳定点。     

A rational Arnoldi approach for ill-conditioned linear systems

For the solution of full-rank ill-posed linear systems a new approach based on the Arnoldi algorithm is presented. Working with regularized systems, the method theoretically reconstructs the true solution by means of the computation of a suitable function of matrix. In this sense, the method can be referred to as an iterative refinement process. Numerical experiments arising from integral equations and interpolation theory are presented. Finally, the method is extended to work in connection with the standard Tikhonov regularization with the right-hand side contaminated by noise.