个性化定制下的产品配置优化模型

为充分反映个性化定制产品特征,基于用户性能需求与功能需求关系,以及待配置产品的构件与功能关系分析,提出了优化目标函数;针对可配置产品的构件、价格及用户需求的特点,建立了约束函数;进而建立了个性化定制产品的配置优化数学模型。并采用改进遗传算法进行优化求解,从而避免了组合爆炸的产生,提高了求解效率。配置实例表明该模型及求解方法是有效、可行的。

多热源环状管网优化方法的研究

针对多热源环状管网的结构参数不能满足多工况运行可及性及与管网经济性相割裂的问题,采用理论分析和计算机模拟的方法,建立了管网参数优化的数学模型,并结合优化模型的特点,建立了对数学模型进行求解的遗传算法。利用建立的数学模型及求解算法对多热源环状管网的结构参数进行优化,既满足了多热源环状管网在不同运行工况下的可及性,同时保证了管网的经济性。

基于极差分析法与GA-ELM的电器连接器壳体注射成型工艺优化

以某电器连接壳体为例,借助Moldflow软件对正交试验方案组合进行模拟,对正交试验模拟结果进行极差分析,得到各工艺参数对塑件翘曲变形量的影响程度为:保压时间>模具温度>注射时间>熔体温度>保压压力。极差分析得到的最优工艺参数组合对应的翘曲变形量与正交试验方案中最小翘曲变形量相比降低了6.7%。关键点采用遗传算法优化后的预测模型(GA-ELM)对塑件翘曲变形量进行预测。由于传统极限学习算法(ELM)的权值和阈值随机产生,网络系统预测稳定性及精度较差,故通过GA全局寻优能力寻找最佳的权值和阈值,得到GA-ELM。选择正交试验前80%样本作为训练集训练ELM与GA-ELM模型,通过样本后20%作为测试集验证ELM与GA-ELM模型预测精度。对比分析可看到:使用GA-ELM预测模型比直接使用ELM预测模型预测结果有更高预测精度及稳定性。此GA-ELM模型可用来预测该塑件翘曲变形量。对同类模具设计优化提供一定的思路及理论参考。

新型圆筒型错相式横向磁通永磁直线发电机结构及其漏磁分析与优化

直线电机目前已经大量应用于工业、军事、民用及其他各种直线运动的场合,在波浪能发电装置中,直线发电机也将得到越来越广泛的应用。提出一种新型圆筒形错相式横向磁通永磁直线发电机的拓扑结构,该结构具有可由硅钢片叠压、设计更自由、整体性更强的优点。同时,其初级铁心单元加工方便,能够充分利用次级永磁体。阐述该电机的工作原理,推导电机的空载主磁通和空载感应电势。通过三维有限元分析计算电机电磁特性,发现了发电机漏磁较大的问题。在此基础上,引入基于元模型的遗传算法对发电机齿形、叠压系数、永磁体间距进行优化设计,实现对发电机关键参数和尺寸的合理选取。最后,给出样机电磁方案,并试制一台150W的样机。通过样机实验,验证了新型发电机拓扑结构的正确性。

铁路混凝土搅拌站优化选址决策研究

我国铁路建设发展迅速,对混凝土的需求量巨大,根据铁建设[2005]160号文的要求,针对建设工程规模和对混凝土的需求量,布设混凝土拌和站集中拌和混凝土进行施工,因此混凝土搅拌站位置的优选也变得十分重要。在综合考虑铁路混凝土搅拌站的建设成本、作为临时工程的拆除及生态恢复成本、运输距离等因素的基础上,建立了铁路混凝土搅拌站优化选址模型并应用遗传算法对其求解。通过具体工程实例,对此模型和其算法进行求解,确定了最优方案,验证了此模型在铁路混凝土搅拌站优化选址方面的可行性及其算法有效性。为铁路混凝土搅拌站位置优选提供了重要的理论支持和实践思路。

Optimal design of structural parameters for shield cutterhead based on fuzzy mathematics and multi-objective genetic algorithm

In order to improve the strength and stiffness of shield cutterhead, the method of fuzzy mathematics theory in combination with the finite element analysis is adopted. An optimal design model of structural parameters for shield cutterhead is formulated,based on the complex engineering technical requirements. In the model, as the objective function of the model is a composite function of the strength and stiffness, the response surface method is applied to formulate the approximate function of objective function in order to reduce the solution scale of optimal problem. A multi-objective genetic algorithm is used to solve the cutterhead structure design problem and the change rule of the stress-strain with various structural parameters as well as their optimal values were researched under specific geological conditions. The results show that compared with original cutterhead structure scheme, the obtained optimal scheme of the cutterhead structure can greatly improve the strength and stiffness of the cutterhead, which can be seen from the reduction of its maximum equivalent stress by 21.2%, that of its maximum deformation by 0.75%, and that of its mass by 1.04%.

Soft measurement model of ring's dimensions for vertical hot ring rolling process using neural networks optimized by genetic algorithm

Vertical hot ring rolling(VHRR) process has the characteristics of nonlinearity,time-variation and being susceptible to disturbance.Furthermore,the ring's growth is quite fast within a short time,and the rolled ring's position is asymmetrical.All of these cause that the ring's dimensions cannot be measured directly.Through analyzing the relationships among the dimensions of ring blanks,the positions of rolls and the ring's inner and outer diameter,the soft measurement model of ring's dimensions is established based on the radial basis function neural network(RBFNN).A mass of data samples are obtained from VHRR finite element(FE) simulations to train and test the soft measurement NN model,and the model's structure parameters are deduced and optimized by genetic algorithm(GA).Finally,the soft measurement system of ring's dimensions is established and validated by the VHRR experiments.The ring's dimensions were measured artificially and calculated by the soft measurement NN model.The results show that the calculation values of GA-RBFNN model are close to the artificial measurement data.In addition,the calculation accuracy of GA-RBFNN model is higher than that of RBFNN model.The research results suggest that the soft measurement NN model has high precision and flexibility.The research can provide practical methods and theoretical guidance for the accurate measurement of VHRR process.

基于级联失效的相互作用网络的动态分析

相互作用网络是一种除了相互作用网络内部中节点间存在边外,网络之间还存在相互依赖的边。这种情况下,如果其中一个节点失效,由于该点存在依赖边指向另外一个网络,会导致依赖边所指向的节点失效,其称为级联失效,级联失效的存在会导致整个相互依赖系统的崩溃。文章针对级联失效所引起的整个相互依赖系统崩溃问题选取合适的优化模型,求出最大相互作用联通网络和最少的节点数量,最后根据实验过程对不同模型求解的优劣进行分析。

An integer multi-objective optimization model and an enhanced non-dominated sorting genetic algorithm for contraflow scheduling problem

To determine the onset and duration of contraflow evacuation, a multi-objective optimization(MOO) model is proposed to explicitly consider both the total system evacuation time and the operation cost. A solution algorithm that enhances the popular evolutionary algorithm NSGA-II is proposed to solve the model. The algorithm incorporates preliminary results as prior information and includes a meta-model as an alternative to evaluation by simulation. Numerical analysis of a case study suggests that the proposed formulation and solution algorithm are valid, and the enhanced NSGA-II outperforms the original algorithm in both convergence to the true Pareto-optimal set and solution diversity.

Multi-objective modeling and optimization for scheduling of cracking furnace systems

Cracking furnace is the core device for ethylene production. In practice, multiple ethylene furnaces are usually run in parallel. The scheduling of the entire cracking furnace system has great significance when multiple feeds are simultaneously processed in multiple cracking furnaces with the changing of operating cost and yield of product. In this paper, given the requirements of both profit and energy saving in actual production process, a multi-objective optimization model contains two objectives, maximizing the average benefits and minimizing the average coking amount was proposed. The model can be abstracted as a multi-objective mixed integer non- linear programming problem. Considering the mixed integer decision variables of this multi-objective problem, an improved hybrid encoding non-dominated sorting genetic algorithm with mixed discrete variables （MDNSGA-II） is used to solve the Pareto optimal front of this model, the algorithm adopted crossover and muta- tion strategy with multi-operators, which overcomes the deficiency that normal genetic algorithm cannot handle the optimization problem with mixed variables. Finally, using an ethylene plant with multiple cracking furnaces as an example to illustrate the effectiveness of the scheduling results by comparing the optimization results of multi-objective and single objective model.

Optimum design of carbon/carbon ablative property based on genetic algorithm

An optimum design model has been proposed for carbon/carbon ablative property based on genetic algorithm,in which the optimum parameters are the number of woven satins,K of fiber bundles,layers per unit height,the average distance of puncture fibers in Z direction and Ply Stacking angle,and the constraint conditions are the density and diameter of carbon fibers and the density of carbon matrix.The results demonstrate that after optimization,the overall height of the ablative carbon/carbon surface is reduced by 56.5%,the standard deviation is reduced by 34.9% and the surface roughness is reduced by 12.6%,which suggests the remarkable improvement of ablative homogeneity.The present investigation can provide practical methodology for the optimum design of carbon/carbon ablative property and the development of new carbon/carbon composites.

OPTIMIZATION OF A REDUCED CHEMICAL KINETIC MODEL FOR HCCI ENGINE SIMULATIONS BY MICRO-GENETIC ALGORITHM

A reduced chemical kinetic model （44 species and 72 reactions） for the homogeneous charge compression ignition （HCCI） combustion of n-heptane was optimized to improve its autoignition predictions under different engine operating conditions. The seven kinetic parameters of the optimized model were determined by using the combination of a micro-genetic algorithm optimization methodology and the SENKIN program of CHEMKIN chemical kinetics software package. The optimization was performed within the range of equivalence ratios 0.2-1.2, initial temperature 310- 375 K and initial pressure 0, 1-0.3 MPa, The engine simulations show that the optimized model agrees better with the detailed chemical kinetic model （544 species and 2 446 reactions） than the original model does.

基于热传导模型对高温作业服装设计的研究

针对高温作业专用服装设计的问题,首先,利用MATLAB软件对一些专用服装的参数值进行处理分析,再依据多层平面壁稳定热传导的原理构建热传导模型;接着,构建出多目标最优化模型,利用LINGO软件求出防热服各层厚度的粗略范围,结合遗传算法精确求解得出结论;最后,结合前面的讨论,对热传导模型进行总结和推广.

Optimization model research on efficacy in treatment of chronic urticaria by Chinese and Western Medicine based on a genetic algorithm

OBJECTIVE： To investigate the potential rules and knowledge of Traditional Chinese Medicine （TCM） and Western Medicine （WM） treatment on chronic urticaria （CU） based on data-mining methods. METHODS： Sixty patients with chronic urticaria, treated with TCM and WM, were selected. Gray correlation analyses were adopted to determine therapeutic efficacy. Association algorithms were utilized to ascertain the correlation between the disease course and treatment results. A genetic algorithm was applied to discover the optimization model in theTCM and WM treatment on CU. RESULTS： The total symptom scores after 4 weeks and 8 weeks of treatment in the TCM spleen-strengthening group correlated highly with the pretreatment total symptom score. The duration of treatment showed the greatest impact on the total symptom score. A quartic equation was established （y= - 1.6403x 10 - 6x4＋0.00025576x3＋0.0012819 x2 - 1.024x＋79.5879, and x=106.9518, y=83.0036） using the genetic algorithm. CONCLUSION： TCM treatment had a better effect in the later stage, whereas WM was better in the early stage. The duration of disease course had an impact on the effects of treatment. If the average total symptom score before treatment was 〈 83.0036, TCM or WM treatment could achieve better efficacy.

Water Resource Allocation under Consideration of the National NIY Plan in Harbin, China

Water resource allocation （WRA） is a useful but complicated topic in water resource management. With the targets set out in the Plan of Newly Increasing Yield （NIY） of 10×1011 Jin （1 kg=2 Jin） from 2009 to 2020, the immediate question for the Songhua River Region （SHRR） is whether water is sufficient to support the required yield increase. Very few studies have considered to what degree this plan influences the solution of WRA and how to adapt. This paper used a multi-objective programming model for WRA across the Harbin region located in the SHRR in 2020 and 2030 （p=75%）. The Harbin region can be classified into four types of sub-regions according to WRA： Type I is Harbin city zone. With rapid urbanization, Harbin city zone has the highest risk of agricultural water shortage. Considering the severe situation, there is little space for Harbin city zone to reach the NIY goal. Type II is sub-regions including Wuchang, Shangzhi and Binxian. There are some agricultural water shortage risks in this type region. Because the water shortage is relatively small, it is possible to increase agricultural production through strengthening agricultural water-saving countermeasures and constructing water conservation facilities. Type III is sub-regions including Acheng, Hulan, Mulan and Fangzheng. In this type region, there may be a water shortage if the rate of urbanization accelerates. According to local conditions, it is needed to enhance water-saving countermeasures to increase agricultural production to a certain degree. Type IV is sub-regions including Shuangcheng, Bayan, Yilan, Yanshou and Tonghe. There are good water conditions for the extensive development of agriculture. Nevertheless, in order to ensure an increase in agricultural production, it is necessary to enhance the way in which water is utilized and consider soil resources. These results will help decision makers make a scientific NIY plan for the Harbin region for sustainable utilization of regional water resources and an increase in agricultural production.