一种基于Pareto最优概念和神经网络混合策略的多目标进化算法

基于Pareto最优的多目标进化算法得到了广泛地应用,但不适用于目标函数为非解析式的情况。基于神经网络和Pareto最优的联合策略,提出了一种解决此类问题的新方法：首先采用神经网络对历史数据进行学习,建立有效的神经网络模型来代替目标函数解析式;然后将神经网络模型嵌入多目标进化算法,进行进化计算;最后,将本文方法应用于卷烟配方比例掺配问题。实验结果表明,该方法优于传统方法,能较好地解决问题。     

基于BQPSO的电网多目标优化规划

对于多目标电网优化规划问题,建立以经济性和可靠性为目标的电网规划模型,通过二进制编码的量子粒子群算法进行优化。为了提高最优解的多样性和分布性,采用拥挤距离排序的方法对外部存储器中的最优解进行更新和维护,使得算法找到尽可能多的Pareto最优解。仿真结果显示,基于拥挤距离排序的二进制量子粒子群算法比其他智能算法寻得的最优解有更好的分布性和收敛性。     

Locally optimal cutting positions for 5-axis sculptured surface machining

The paper presents a local condition for collision-free 5-axis milling of sculptured surfaces. We consider cutter positions, which guarantee local gouging avoidance. This can replace concepts such as ‘cutting profile’ or ‘effective cutting shape’ which are of approximate nature. Based on second order approximations of the machined strip width, we also present locally optimal cutting positions for cutting directions.     

A neural network approach to multiple-objective cutting parameter optimization based on fuzzy preference information

This paper presents a neural network approach to multiple-objective cutting parameter optimization for planning turning operations. Productivity, operation cost, and cutting quality are considered as criteria for optimizing machining operations. A feedforward neural network and a dynamic training procedure are proposed for modeling manufacturers' preferences using sampled fuzzy preferential data. Optimum cutting parameters are determined based on neural network representations of manufacturers' fuzzy preference structures.     

基于神经网络的机械加工工序能耗预测

针对工序级能耗难以用数学方法精确估算的问题,提出了一个基于神经网络的机械加工工序能耗预测方法。给出了输入变量及输出变量的选取及其归一化处理方法,进行了隐含层节点数和传递函数的选取。以各切削用量组合及其对应能源消耗的历史数据作为神经网络训练的样本集,建立切削用量组合方案输入和能源消耗输出间的非线性关系,从而对新的切削用量参数组合进行能耗值的预测。以某企业导叶片的粗铣加工为例,验证了该能耗预测方法的有效性。     

A mechanism informed neural network for predicting machining deformation of annular parts

Controlling machining deformation of annular parts is crucial for ensuring the performance of high value products and equipment. For example, during manufacturing of critical parts in aircrafts and spacecrafts, accurate prediction of machining deformation is the basis for guiding the formulation of deformation control strategies. However, due to the complexity of the machining deformation of annular parts, existing methods still have limitations in accurate prediction. To this end, this paper proposes a mechanism informed neural network (MINN) to predict machining deformation of annular parts. MINN is realized by establishing the dual sub-networks structure and using enhanced loss functions with the consideration of the deformation mechanism model characteristics of annular parts. The deformation was decomposed into the axisymmetric portion and the non-axisymmetric portion according to the deformation superposition principle, and modeled separately based on the thin-shell theory and Fourier series. Experiment results showed that the proposed method could predict the machining deformation of annular parts more accurately and stably with a small amount of training data, compared with previous methods.     

Modeling of cutting geometry and forces for 5-axis sculptured surface machining

5-Axis sculptured surface machining is simulated using discrete geometric models of the tool and workpiece to determine the tool contact area, and a discrete mechanistic model to estimate the cutting forces. An extended Z-buffer model represents the workpiece, while a discrete axial slice model represents the cutting tool. Determination of the contact area for a given tool move requires a swept envelope (SWE) of the tool path. The SWE is used to find the intersections of the tool envelope with Z-buffer elements (ZDVs) representing the workpiece. A 3-axis approximation of the 5-axis tool movement is used to simplify the calculations while maintaining a desired level of accuracy. The intersection of the SWE with each ZDV yields segments which are used to find the contact area between the cutter and the workpiece for a given tool path. The contact area is subsequently used with the discrete force model to calculate the vector cutting force acting on the tool.     

A neural network based methodology for machining operations selection in Computer-Aided Process Planning for rotationally symmetrical parts

The relevant literature on machining operations selection in Computer-Aided Process Planning (CAPP) by decision trees, expert systems and neural networks has been reviewed, highlighting their contributions and shortcomings. This paper aims at contributing to the applicability of back-propagation neural network method for the selection of all possible operations for machining rotationally symmetrical components, by prestructuring the neural network with prior domain knowledge in the form of heuristic or thumb rules. It has been achieved by developing two forms of representation for the input data to the neural network. The external representation is used to enter the crisp values of the input decision variables (namely the feature type and its attributes such as diameter or width, tolerance and surface finish). The purpose of internal representation is to categorize the above crisp values into sets, which correspond to all the possible different ranges of the above input variables encountered in the antecedent ‘IF’ part of the thumb rules mentioned above. The input layer of the neural network has been designed in such a way that one neuronal node is allocated for each of the feature types and the sets of various feature attributes. In the output layer of the neural network, one neuronal node is allocated to each of the various feasible machining operation sequences found in the consequent ‘THEN’ part of the thumb rules. A systematic method for training of the neural network has been presented with the above thumb rules used to serve as guidelines for choosing the input patterns of the training examples. This method simplifies the process of training, reduces the time for preparation of training examples and hence the time to develop the overall process planning system. It can further help ensure that the entire problem domain is represented in a better manner and improve the quality of response of the neural network. The example of an industrially-relevant rotationally symmetrical workpiece has been analyzed using the proposed approach to demonstrate its potential for use in the real manufacturing environment. By this novel methodology, workpieces of complex shapes can be handled by investing a very limited amount of time, hence making it attractive and cost effective for industrial applications. Received: June 2005 / Accepted: January 2006     

基于交货期的多目标一维优化下料模型研究

根据目前对优化下料问题新特性的研究,指出优化下料问题呈现多目标和复杂约束状态,建立了以原料消耗最少和不同交货期的惩罚最小为目标函数的优化下料问题的数学模型.利用多目标智能优化算法求解模型,并结合算例验证了模型的有效性和实用性.     

Multi-objective optimization of a stacked neural network using an evolutionary hyper-heuristic

The present paper deals with the development and optimization of a stacked neural network (SNN) through an evolutionary hyper-heuristic, called NSGA-II-QNSNN. The proposed hyper-heuristic is based on the NSGA-II (Non-dominated Sorting Genetic Algorithm - II) multi-objective optimization evolutionary algorithm which incorporates the Quasi-Newton (QN) optimization algorithm. QN is used for training each neural network from the stack. The final global optimal solution provided by NSGA-II-QNSNN algorithm is a Pareto optimal front. It represents all the equally good compromises that can be made between the structural complexity of the stacked neural network and its modelling performance. The set of decision variables, which led to obtaining the set of points in the Pareto optimal front, represents the optimum values for the parameters of the stacked neural network: the number of networks in the stack, the weights for every output of the composing networks, and the number of hidden neurons in each individual neural network. Each stacked neural network determined through the optimization process was trained and tested by applying it to a real world problem: the modelling of the polyacrylamide-based multicomponent hydrogels synthesis. The neural modelling established the influence of the reaction conditions on the reaction yield and the swelling degree. The results provided by NSGA-II-QNSNN were superior, not only in terms of performance, but also in terms of structural complexity, to those obtained in our previous works, where individual or aggregated neural networks were used, but the stacks were developed manually, based on successive trials.     

一种改进的RBF神经网络参数优化方法

提出了一种改进的RBF神经网络参数优化算法。通过资源分配网络算法确定隐含层节点个数,引入剪枝策略删除对网络贡献不大的节点,用改进的粒子群算法对RBF网络的中心、宽度、权值进行优化,使RBF网络不仅可以得到合适的结构,同时也可以得到合适的控制参数。将此算法用于连续搅拌釜反应器模型的预测,结果表明,此算法优化后的RBF网络结构小,并且具有较高的泛化能力。     

Multi-objective optimization design of a complex building based on an artificial neural network and performance evaluation of algorithms

While optimization studies focusing on real-world buildings are somewhat limited, many building optimization studies to date have used simple hypothetical buildings for the following three reasons: (1) the shape and form of real buildings are complex and difficult to mathematically describe; (2) computer models built based on real buildings are computationally expensive, which makes the optimization process time-consuming and impractical and (3) although algorithm performance is crucial for achieving effective building performance optimization (BPO), there is a lack of agreement regarding the proper selection of optimization algorithms and algorithm control parameters. This study applied BPO to the design of a newly built complex building. A number of design variables, including the shape of the building’s eaves, were optimized to improve building energy efficiency and indoor thermal comfort. Instead of using a detailed simulation model, a surrogate model developed by an artificial neural network (ANN) was used to reduce the computing time. In this study, the performance of four multi-objective algorithms was evaluated by using the proposed performance evaluation criteria to select the best algorithm and parameter values for population size and number of generations. The performance evaluation results of the algorithms implied that NSGA-II (with a population size and number of generations of 40 and 45, respectively) performed the best in the case study. The final optimal solution significantly improves building performance, demonstrating the success of the BPO technique in solving complex building design problems. In addition, the findings on the performance evaluation of the algorithms provide guidance for users regarding the selection of suitable algorithms and parameter settings based on the most important performance criteria.     

基于层连优化的新型小世界神经网络

对多层前向小世界神经网络的网络参数、权值修正策略以及网络结构进行改进, 提出一种基于层连优化的小世界神经网络的改进算法. 通过对比现有各种不同形式的小世界神经网络, 验证了上述改进的必要性. 仿真结果表明, 改进模型比现有小世界神经网络收敛速度更快, 逼近精度更高, 模型稳定性更强.     

Prediction of cutting temperature in orthogonal machining of AISI 316L using artificial neural network

In this study, an approach based on artificial neural network (ANN) was proposed to predict the experimental cutting temperatures generated in orthogonal turning of AISI 316L stainless steel. Experimental and numerical analyses of the cutting forces were carried out to numerically obtain the cutting temperature. For this purpose, cutting tests were conducted using coated (TiCN + Al₂O₃ + TiN and Al₂O₃) and uncoated cemented carbide inserts. The Deform-2D programme was used for numerical modelling and the Johnson–Cook (J–C) material model was used. The numerical cutting forces for the coated and uncoated tools were compared with the experimental results. On the other hand, the cutting temperature value for each cutting tool was numerically obtained. The artificial neural network model was used to predict numerical cutting temperatures by means of the numerical cutting forces. The best results in predicting the cutting temperature were obtained using the network architecture with a hidden layer which has seven neurons and LM learning algorithm. Finally, the experimental cutting temperatures were predicted by entering the experimental cutting forces into a formula obtained from the artificial neural networks. Statistical results (R², RMSE, MEP) were quite satisfactory. This demonstrates that the established ANN model is a powerful one for predicting the experimental cutting temperatures.     

基于BP神经网络的模糊参数辨识

提出一种与TSK模糊模型相似的模糊模型—M-2模型,证明了M-2模型与一个4层前向神经网络是等价的,在此基础上提出基于BP神经网络的模糊模型参数辨别算法,即通过BP神经网络对样本数据的学习,直接从样本数据获取模型参数,建立M-2模糊模型,通过仿真实例验证了该算法的有效性。     

Rapidio网络QoS多目标优化

针对现有Rapidio网络路由管理策略的缺点,提出了一种基于改进的遗传算法的优化策略。通过改进的路由分配策略以及对传统遗传算法编码、交叉和变异等方面的改进,明显提高了Rapidio网络服务质量（QoS）诸项指标;同时相对于传统遗传算法,改进的遗传算法拥有更快的收敛速度。该算法适用于基于Rapidio网络的嵌入式应用,具有较强的工程实用价值。     

基于QPSO算法的RBF神经网络参数优化仿真研究

针对粒子群优化(PSO)算法搜索空间有限,容易陷入局部最优点的缺陷,提出一种以量子粒子群优化(QPSO)算法为基础的RBF神经网络训练算法,将RBF神经网络的参数组成一个多维向量,作为算法中的粒子进行进化,由此在可行解空间范围内搜索最优解。实例仿真表明,该学习算法相比于传统的学习算法计算简单,收敛速度快,并由于其算法模型的自身特性比基于PSO的学习算法具有更好的全局收敛性能。     

Automated surface subdivision and tool path generation for -axis CNC machining of sculptured parts

As an innovative and cost-effective method for carrying out multiple-axis CNC machining, -axis CNC machining technique adds an automatic indexing/rotary table with two additional discrete rotations to a regular 3-axis CNC machine, to improve its ability and efficiency for machining complex sculptured parts. In this work, a new tool path generation method to automatically subdivide a complex sculptured surface into a number of easy-to-machine surface patches; identify the favorable machining set-up/orientation for each patch; and generate effective 3-axis CNC tool paths for each patch is introduced. The method and its advantages are illustrated using an example of sculptured surface machining. The work contributes to automated multiple-axis CNC tool path generation for sculptured part machining and forms a foundation for further research.     

Multi-objective optimization of shield construction parameters based on random forests and NSGA-II

Metro shield construction will inevitably cause changes in the stress and strain state of the surrounding soil, resulting in stratum deformation and surface settlement (SS), which will seriously endanger the safety of nearby buildings, roads and underground pipe networks. Therefore, in the design and construction stage, optimizing the shield construction parameters (SCP) is the key to reducing the SS rate and increasing the safe driving speed (DS). However, optimization of existing SCP are challenged by the need to construct a unified multiobjective model for optimization that are efficient, convenient, and widely applicable. This paper innovatively proposes a hybrid intelligence framework that combines random forest (RF) and non-dominant classification genetic algorithm II (NSGA-II), which overcomes the shortcomings of time-consuming and high cost for the establishment and verification of traditional prediction models. First, RF is used to rank the importance of 10 influencing factors, and the nonlinear mapping relationship between the main SCP and the two objectives is constructed as the fitness function of the NSGA-II algorithm. Second, a multiobjective optimization framework for RF-NSGA-II is established, based on which the optimal Pareto front is calculated, and reasonable optimized control ranges for the SCP are obtained. Finally, a case study in the Wuhan Rail Transit Line 6 project is examined. The results show that the SS is reduced by 12.5% and the DS is increased by 2.5% with the proposed framework. Meanwhile, the prediction results are compared with the back-propagation neural network (BPNN), support vector machine (SVM), and gradient boosting decision tree (GBDT). The findings indicate that the RF-NSGA-II framework can not only meet the requirements of SS and DS calculation, but also used as a support tool for real-time optimization and control of SCP.