基于遗传规划的连接板优化排料算法的研究

遗传规划(GP)是遗传算法的一个重要分支,是提供找到解决问题的计算机程序的统一途径。矩形排料优化问题是NP完全问题,每块给定尺寸的板材上所排的矩形件数量随环境的动态改变而改变,类似于遗传规划中的分层特性,对应于不同深度的算法树个体。针对连接板下料中的排料优化问题,将连接板优化排料问题转化为矩形优化排料问题,提出一种全新的基于遗传规划的连接板优化排料算法。介绍了连接板的最小包容矩形的求取方法、遗传规划用于矩形件排料的算法及应用遗传规划进行矩形件排料的基本步骤,并用具体的排料实例进行验证。最后得到结论并指出未来可能的发展方向。     

基于遗传进化算法的机器人全局路径规划

研究了机器人在已知环境下用遗传进化算法进行最优路径搜索的方法。在此方法中，把二维编码简化为一维编码，提高了搜索的速度；把障碍物作为排斥子，提出了简单、确定的适应度函数；最后提出了对路径的转折节点处进行平滑处理的方法。仿真实验证明了该方法能够得到机器人行进的最优化路径。     

基于遗传模拟退火算法的机器人全局路径规划

研究了机器人在已知环境下用遗传模拟退火算法进行最优路径搜索的方法，此算法兼备了遗传算法和模拟退火算法的优点，还对路径的转折节点处进行了光滑性的改进，并且通过仿真实验证明了此方法能够快速得到最优路径。     

基于MATLAB的PID控制器的设计算法及其实现

本文介绍了Ziegler-Nichols规则的PID控制器设计及其改进算法，并给出了基于MATLAB的实现方法和仿真。仿真结果表明此算法设计的PID控制器有良好的性能指标。     

逆波兰表达式在VB中的算法设计与实现

在计算机中执行算术表达式的计算是通过栈来实现的.编译系统不考虑表达式的优先级别,只是对表达式从左到右进行扫描,找到运算符和操作数,完成运算.本文以VB为开发平台,利用数组实现顺序栈工作原理,将中缀表达式转化为逆波兰表达式,便于计算.     

基于遗传模拟退火算法的产品装配序列规划方法

为有效地获取方便可行的装配序列,在分析了装配序列的几何可行推理约束之后,建立了包含稳定性、聚合性及装配方向改变次数因素的优化评价模型。采用遗传模拟退火算法进行产品装配序列规划,通过分析算法的相关参数的变化趋势和大致范围,利用正交试验（初步定位）和对比试验（精确定位）相结合的方法,确定算法的近优运行参数,最后以8E150ZLC柴油机油泵为例,与遗传算法运行结果对比,验证了该方法是一种高效的、具有工程实际意义的复杂产品装配序列规划方法。     

基于遗传优化的车辆稳定性模糊控制器设计

为了保证车辆在极限危险工况下的行驶稳定性,提出了利用后轮主动转向与差动制动联合产生校正横摆力矩的稳定性控制策略,并进行了相应的车辆稳定性模糊控制器设计;为了改进所设计模糊控制器的控制品质,克服在控制器设计中隶属函数划分与控制参数确定的主观性,利用遗传算法对所设计模糊控制器的隶属函数分布及比例、量化因子进行了优化研究.     

基于遗传模拟退火算法的弧焊机器人系统协调路径规划

深入地研究了弧焊机器人系统的协调路径规划。从全局的角度用5元组序列描述了焊接路径。设计了评价 焊接路径目标函数:焊接位置函数、焊缝成形质量函数、关节位置函数和运动平稳性函数。以线性加权法为求解 多目标规划的基本思想,把遗传模拟退火算法用于弧焊机器人与变位机协调路径规划,取得了很好的效果。协调 路径规划精确地保证焊缝的最佳焊接位置与最佳的焊枪姿态,并能找到柔顺的焊接路径,提高了机器人焊接的质 量和效率。     

基于改进遗传模拟退火算法的刀具准时化配送路径规划

为了解决准时生产下的刀具准时化配送路径规划问题,在对数控车间刀具配送流程进行分析的基础上,建立以工序平均满意度和配送车辆数为优化目标的带模糊预约时间窗的刀具配送路径模型。采用改进遗传模拟退火算法对该模型进行求解,在标准遗传算法的基础上,使用模拟退火算法改进遗传算法的变异算子,通过最佳保存策略和排序选择法结合保护最优个体,并辅以自适应交叉概率。最后,通过具体实例证明了该方法解决刀具准时化配送路径优化问题的有效性与可行性。     

遗传PID在挤出机中的应用设计研究

注射压力是影响挤出成型过程的关键因素之一,其控制水平的高低将直接影响产品的质量.因此对注射压力的控制不仅有数值上的要求,而且还要稳定、可控.针对目前PID控制器往往整定参数不良、性能欠佳的弊端,应用遗传算法的并行性特征搜索其全局最优解整定PID参数.仿真结果说明应用该算法优化得到的参数使得系统具有无超调量且调节时间短的优点.     

Tolerance design optimization of machine elements using genetic algorithm

An important problem that faces design engineers is how to assign tolerance limits. In practical applications, tolerances are most often assigned as an informal compromise between functionality, quality and manufacturing cost. Frequently, the compromise is obtained iteratively by trial and error. A more scientific approach is often desirable for better performance. In this paper, a genetic algorithm (GA) is used for the design of tolerances of machine elements to obtain the global optimal solution. The objective is to design the optimum tolerances of the individual components to achieve the required assembly tolerance, zero percentage rejection of the components and minimum cost of manufacturing. The proposed procedure using GA is described in this paper for two tolerance design optimization problems: gear train and overrunning clutch assemblies. Results are compared with conventional techniques and the performances are analyzed.     

基于遗传算法的轿车轮毂轴承数值优化

为了提高设计效率和质量,优化系统性能指标,在给定的装配空间条件下满足几何和性能约束,以获得最大的疲劳寿命为目的,运用遗传算法对双列角接触球轴承结构的轿车轮毂轴承进行数值优化设计。并基于一个实际例子编制了优化程序,给出了运算结果。结果表明,遗传算法能够有效地解决离散变量全局寻优问题,全部约束得到满足并且优化设计方案在系统疲劳寿命方面显著优于当前设计方案。     

Multi-objective optimization in single-row layout design using a genetic algorithm

This paper presents the development of a genetic algorithm for determining a common linear machine sequence for multi-products with different operation sequences and facilities with a limited number of duplicate machine types available for a job. This work aims to minimize the total flow distance traveled by products, reduce the number of machines arranged in the final linear sequence, and decrease the total investment cost of the machines used in the final sequence. We assume that product flow runs only in the forward direction, either via in-sequence or bypass movement. We demonstrate the effectiveness of the proposed algorithm by solving a typical layout design problem taken from literature, and several randomly generated problems. Results indicate that the proposed algorithm serves as a practical decision support tool for resolving layout problems in manufacturing facilities.     

Welding parameters optimization for economic design using neural approximation and genetic algorithm

Welding is a basic manufacturing process for making components or assemblies. Recent welding economics research has focused on developing the reliable machinery database to ensure optimum production. It is an important issue, especially for the expensive equipment and the high quality preference in welding. An integrated approach is proposed to address the welding economic design problem. The integrated approach applies general regression neural network to approximate the relationship between welding parameters (welding current, electrode force, welding time, and sheet thickness) and the failure load. Analytical formula can be generated from the trained general regression neural network, and the mathematical model for the economic welding design can be constructed. An optimization method based on genetic algorithms is then applied to resolve the mathematical model and to select the optimum welding parameters. These parameters are used to obtain the preferred welding quality at the least possible cost.     

Multiobjective optimization design of porthole extrusion die using Pareto-based genetic algorithm

The extrusion die plays a crucial role in aluminum alloy profile production, which influences product quality and service life of extrusion die directly. In this paper, a profile with irregular shape was taken as an analysis example, and multiobjective optimization for porthole extrusion die based on modern intelligence algorithm was carried out. Aiming at achieving the uniform velocity distribution in the cross-section of the profile as well as decreasing the maximum stress on the extrusion die and the deflection of the mandrel, the angle between port bridges, the position of die orifice, and the height of welding chamber were considered as the design variables. Then Kriging model was established on the basis of Latin hypercube samplings, and above design variables were optimized using Pareto-based genetic algorithm. Finally, an optimal die structure is gained. Compared with the initial scheme, the velocity distribution in the extrudate was more even, and the stress on the die and the deflection of the mandrel were decreased obviously in the optimal scheme. The optimal design method for porthole die has strong commonality, thus, it could give useful guidelines for practical production of the same kind of aluminum profile.     

运用遗传算法的产品造型设计方案优化方法

从产品语义出发,提出一种基于遗传算法的产品造型设计方案优化方法,构造基于语义的产品造型进化设计模型。通过构造产品造型基因编码和基因串及适应度函数,对产品造型设计方案进行优化并形成新的方案,应用板式家具的造型设计验证了方法的适宜性。     

Injection molding optimization with weld line design constraint using distributed multi-population genetic algorithm

Weld lines not only detract from an injection-molded part??s surface quality, but also significantly reduce its mechanical strength. It is not always easy to completely eliminate weld lines by simply adjusting the relevant injection mold design or the molding conditions. One solution is to prevent the weld lines from forming in regions that are structurally or aesthetically sensitive. The influence of weld lines on the quality of injection-molded parts cannot be overlooked and weld lines should be regarded as an important design constraint, especially for parts with aesthetic concerns. Since precisely predicting the number of weld lines and their positions and lengths is difficult without executing simulation routines, especially when part geometry is considered a design variable, this study adopts an enhanced genetic algorithm, referred to as distributed multi-population genetic algorithm (DMPGA), combining an optimization algorithm and commercial MoldFlow software with a dominance-based constraint-handling technique and a master?Cslave distributed architecture. MoldFlow obtains relevant data regarding warpage and weld lines and evaluates the corresponding designs. The dominance-based constraint-handling technique handles the weld line design constraint without needing additional penalty factors. Finally, the master?Cslave distributed architecture reduces the formidable computational time required for injection molding optimization. To illustrate the high viability of DMPGA, this study provides an outer frame of a digital photo frame as an optimization example. The results of this study show that DMPGA cannot only effectively decrease maximum part warpage without violating the weld line constraint, but also conquer hurdles attributed to constraint handling and computational demand.     

Development of energy-saving optimization for the oval-edging oval pass design using genetic algorithm

In the multi-pass rod rolling systems, where the final products are round bars with different sizes, oval-edging oval passes are widely employed as intermediate passes. This study attempts to present a generalized strategy to support the optimal design of oval-edging oval passes. A design system based on this strategy is developed to minimize power consumption, to reduce trials and errors in industry, as well as to improve the design efficiency and flexibility. A series of equations are established for geometrical modeling and graphic plotting of oval-edging oval passes, which can realize the parametric transformation for roll pass design and optimization. A novel formula based on statistical analysis and data fitting is proposed for the calculation of power consumption. To verify this formula, results obtained are compared with those calculated from using the Smirnov’s formula. The comparison confirmed the accuracy and efficiency of this formula. Finally, the proposed approach is applied for the optimal design of oval-edging oval passes, which are widely employed for the intermediate passes in the rod rolling systems. Through the comparison between simulation results and experimental data from literature, it is concluded that the proposed method can reduce power consumption of these passes and is able to improve the efficiency and flexibility of intermediate pass design without compromising its accuracy.     

改进的遗传算法及其在多目标优化设计中的应用

遗传算法是一种模拟自然界进化过程的启发式优化计算算法,具有高效及可收敛到全局最优点的特点。文中针对遗传算法局部搜索能力弱的特点,将其与梯度下降法结合,提高其局部搜索能力,并进行了测试。然后结合一多目标工程问题进行了优化计算,结果表明此算法可有效解决工程问题的优化。     

免疫遗传算法在多目标优化设计中的应用

文中介绍的免疫遗传算法是在免疫算法的抗体多样性维持机制中引入遗传算法,使其性能比标准免疫算法更进了一步。并通过测试函数,证明了免疫遗传算法既保留了免疫算法的优点,又提高了免疫算法中抗体的多样性和收敛速度。最后结合压铸机合模机构的优化设计,结果表明此算法可有效解决工程问题的优化。