Global cutting-path optimization considering the minimum heat effect with microgenetic algorithms

This article considers microgenetic algorithms (GAs), which explore in a small population with a few genetic operators, for cutting-path optimization problems. The major difference between GAs and simple genetic algorithms (SGAs) is how to make a reproductive plan for an improved searching technique because of population choice. It is shown that GAs implementation reaches the near-optimal region much earlier than the SGAs approach, and the GAs give a better solution than simulated annealing (SA). The main objective was to determine what temperature distribution can be obtained from the solution of a travelling distributed heat source. The solution of the travelling heat source on nested raw plate provides information about the vertices of each nested part of the raw plate. From the fact that the initial temperature at a piercing point strongly depends on the heat flow which stems from the previous cutting contour, the temperature of all piercing points must be lower than the critical temperature after each cutting of the components of a part. The critical temperature is identified as the mechanical melting temperature of steels. A heuristic back-tracking method is introduced to find the near-optimum cutting path considering the minimum heat effect on deformation. The heuristic back-tracking method is incorporated with the GAs.     

高速列车横向平稳性主动控制规律研究

提高列车的运行平稳性是机车车辆动力学的研究热点。本文以列车的横向运行平稳性为研究对象,采用面向对象的建模方法,建立了由3节车辆组成的车组横向动力学模型,并将作动器放置在车端构成车端主动悬挂系统。采用轨道不平顺高速谱作为输入,考虑轮轴间时延,运用遗传算法对控制器进行优化设计。研究表明,采用车端主动悬挂系统,运用本文算法,车组的横向运行平稳性得到了提高;当在端部车辆的端部二系悬挂中再并行设置作动器,并采用改进的天棚阻尼器算法后,端部车体的振动得到了有效抑制,整个列车横向运行平稳性进一步得到了改善。     

基于GA的汽车故障特征选择

特征选择是模式识别中的一个关键问题。在很多实际问题中,很难找到那些最重要的特征,或受条件限制不能对它们进行测量,从而使特征选择成为模式识别系统最困难的任务之一。遗传算法作为一种智能化的优化方法,具有适合特征选择的优势。对遗传算法做了简约介绍,通过相应的程序并结合汽车故障实例进行了分析。     

Application of genetic algorithms for joint optimization of signal setting parameters and dynamic traffic assignment for the real network data

This paper presents the joint optimization of signal setting parameters and dynamic user equilibrium (DUE) traffic assignment for the congested urban road network. The simulation-based approach is employed to obtain the DUE condition for the case of multiple-origin multiple-destination traffic flows. The dynamic traffic assignment simulation program (DTASP), developed in C language is used to assign the traffic dynamically on the road network, whereas method of successive averages (MSA) is modified and used to arrive at the DUE condition. The artificial intelligence technique of genetic algorithms (GAs) is applied to obtain the optimal signal setting parameters and path flow distribution factor for DUE condition. The methodology developed in such a way that joint optimization of signal setting parameters with DUE is obtained. The proposed method is applied to the real network data of Fort Area of Mumbai city comprising of 17 nodes and 56 unidirectional links with 72 Origin–Destination pairs, where all the 17 nodes are signalized intersections. The traffic flow condition for the optimized signal setting parameters is considerably improved compared to the existing signal settings. The results prove that the GA is an effective technique to solve the joint optimization problem for the real network data.     

基于神经网络与遗传算法的潜艇舱壁结构优化

建立了某潜艇端部耐压平面舱壁结构优化设计的数学模型,以舱壁上加强桁(筋)结构的剖面尺寸为设计变量,结构强度、稳定性、工艺性要求为约束条件,以加强桁(筋)结构总体积为目标函数,用人工神经网络方法代替结构的有限元分析,结合遗传算法完成了舱壁的结构优化设计。在为网络生成学习样本时,自行设计了正交表,解决了因为设计变量多,无法选取合适正交表而随机取样的问题。数值仿真结果表明,优化设计方案质量较原始初步设计方案减少了18.3%。     

基于遗传算法的重载与非重载去向开行方案比较研究

本文道德简要介绍重载运输在我国开行的现状，在此基础上建立一个重载列车去向模型。引进一个先进的算法－遗传算法求解模型，根据求得最终优化方案，比较普通直达列车和重载列车开行方案的不同，计算结果表明重载列车的开行应该重新调整现有编组计划，而不是仅仅加大原有开行方向上的编成辆数以实现重载。     

A design methodology for mechatronic vehicles: application of multidisciplinary optimization,multibody dynamics and genetic algorithms

A design methodology for mechatronic vehicles is presented. With multidisciplinary optimization (MDO) methods, strongly coupled mechanical, control and other subsystems are integrated as a synergistic vehicle system. With genetic algorithms (GAs) at the system level, the mechanical, control and other relevant parameters can be optimized simultaneously. To demonstrate the feasibility and efficacy of the proposed design methodology for mechatronic vehicles, it is used to resolve the conflicting requirements for ride comfort, suspension working spaces and unsprung mass dynamic loads in the optimization of half-vehicle models with active suspensions. Both deterministic and random road excitations, both rigid and flexible vehicle bodies and both perfect measurement of full state variables and estimated limited state variables are considered. Numerical results show that the optimized vehicle systems based on the methodology have better overall performance than those using the linear quadratic Gaussian (LQG) controller. It is shown that the methodology is suitable for complex design optimization problems where: (1) there is interaction between different disciplines or subsystems; (2) there are multiple design criteria; (3) there are multiple local optima; (4) there is no need for sensitivity analysis for the optimizer at the system level; and (5) there are multiple design variables.     

基于语义的遗传算法在创新设计中的应用

通过分析目前国内在产品创新设计领域研究的现状与各种算法的优缺点,提出了一种利用基于语义的遗传算法在产品形态创新设计的一种新型的算法,并且阐述了该算法的应用方式与步骤.通过一个桌面设计的实例验证了算法的可行性与实用性.结果显示,该算法能生成具有一定创新性的产品形态,并验证利用该算法进行创新设计是非常容易的.     

基于运输效益的城市公交发车频率

公交车发车频率的确定是公交运营调度的主要内容。受车站附近其他公交车的影响，乘客在各站点的可接受公交车容量和等车时间是不相等的；平均可接受公交车容量是乘客心理容量的平均值，如果车内乘客数量超过该容量，则采用容量向量来解释乘客上车过程。文中以运营利润最大化为目标函数，在车辆资源有限的情况下建立分析模型，应用遗传算法求解最大利润下的最优发车频率，并运用实例验证了所提出的方法。     

轮乘制下的铁路乘务排班系统

针对铁路乘务制度由包乘制改为轮乘制,建立基于回溯算法的乘务排班系统,完成乘务计划快速自动编排,降低乘务成本。