Evaluating the impacts of urban corridor traffic signal optimization on vehicle emissions and fuel consumption

This study investigates the impacts of traffic signal timing optimization on vehicular fuel consumption and emissions at an urban corridor. The traffic signal optimization approach proposed integrates a TRANSIMS microscopic traffic simulator, the VT-Micro model (a microscopic emission and fuel consumption estimation model), and a genetic algorithm (GA)-based optimizer. An urban corridor consisting of four signalized intersections in Charlottesville, VA, USA, is used for a case study. The result of the case study is then compared with the best traffic signal timing plan generated by Synchro using the TRANSIMS microscopic traffic simulator. The proposed approach achieves much better performance than that of the best Synchro solution in terms of air quality, energy and mobility measures: 20% less network-wide fuel consumption, 8–20% less vehicle emissions, and nearly 27% less vehicle-hours-traveled (VHT).     

基于遗传算法收敛特性的停机准则

关于遗传算法及改进遗传算法收敛问题的研究表明,理论上分析各类不同结构遗传算法的收敛性是可行的.对已被证明收敛的遗传算法,在计算过程中最突出的问题便是如何判断当前计算结果已经到达最优解从而停止迭代.文中从遗传算法收敛特点及不同种群中最优个体适应值的一致性、种群的多样性出发,提出判断算法自动停止迭代的依据.     

Establishment and Optimization of State Feature System of Diesel Engine Fault Diagnosis

For too many state features are used in the diesel engine state evaluation and fault diagnosis,it is not easy to obtain the rational eigenvalues.In the paper,the cylinder subassembly of diesel engine is used to search for the method of establishing state feature system and optimal approach.The signal of diesel engine has been collected when the piston ring and airtight ring are working at different states,then with the Bootstrap method and Genetic Algorithm(GA),an optimum parameter combination is received.Example shows this method is simple and efficient for establishing diesel engine state feature system,Thus,this method is valuable for the virtual state evaluation of similar complex system.     

Design optimisation of switch rails in railway turnouts

Inspired by a manufacturing process of switch rails for railway turnouts, a method for the optimisation of switch rail profile geometry is presented. The switch rail profile geometry is parameterised with four design variables to define a B-spline curve for the milling tool profile, and two design variables to prescribe the deviation from the nominal vertical path of the milling tool. The optimisation problem is formulated as a multi-objective minimisation problem with objective functions based on the contact pressure and the energy dissipation in the wheel–rail contact. The front of Pareto optimal solutions is determined by applying a genetic type optimisation algorithm. The switch rail profile designs are evaluated by simulations of dynamic train–turnout interaction. It is concluded that the obtained set of Pareto optimal solutions corresponds to a rather small variation in design variables where increased profile height and increased profile shoulder protuberance are preferred for both objectives. The improvement in the objectives comes at the cost of an earlier wheel transition to the switch rail and thus increased vertical loading at a thinner rail cross-section. The performance of the optimised geometry is evaluated using a set of 120 measured wheel profiles, and it is shown that the optimised geometry reduces damage also for this large load collective. It is concluded that accurate limits on switch rail loading need to be established to determine the feasible design space for switch rail geometry optimisation.     

基于遗传算法实现数字图像边缘检测

介绍了遗传算法的基本原理和算法步骤，并通过规定适应度计算方法和设定初始化子群、选择、交叉和变异等过程的重要参数，实现了该算法在图像边缘检测的应用，实例证明该方法可以取得很好的效果。     

一种基于遗传优化算法的潜艇高空磁场换算方法

研究潜艇的高空磁特征分布是评估潜艇磁防护能力的重要前提。为了综合分析潜艇的高空磁特征分布,基于潜艇上方近场测量数据,采用磁体模拟法建立了潜艇磁场模型。针对磁体的个数及位置常依赖于人为经验的弊端,分别建立了以磁体位置和个数为未知量的优化模型,并采用全局优化能力强、稳定性好的遗传算法对优化模型进行了求解。仿真实验测试了该方法的有效性,并分析了实际测量误差对模型稳定性的影响。结果表明,该方法建模稳定性好、精度高,是一种有效的高空磁场分析方法,有一定的工程实用价值。     

基于遗传算法的反舰导弹火力分配优化

通过建立基于遗传算法的火力分配模型,研究多平台反舰导弹武器系统打击目标的最优方案,并依据实例仿真得到优化结果,说明该方法具有较强的实用性和参考价值。     

基于混合学习算法的水下机器人神经网络辨识

借鉴Elman和Jordan神经网络的特点,构造了一种新的动态神经网络.该网络能对隐含层的历史进行状态记忆,实时调整过去的信号对现在值的影响,并且增加了输出层节点的反馈以增强神经网络的信号处理能力.将基于遗传算法(GA)和误差反传算法(BP)的混合学习算法用于神经网络权值的修改,既可提高收敛速度又能避免陷于局部极小值.最后,将改进的神经网络应用于水下机器人动力学模型辨识,仿真结果表明,基于混合学习算法的神经网络提高了学习的收敛速度和辨识精度.     

基于GA的大型船舶制造企业风险管理研究

介绍了运用遗传算法（GA）解决实际问题通常包括的步骤。探讨了散货船船体吊装顺序优化,建立了在给定周期下实现成本最小的目标函数,提出了一种改进GA来求解全局最优或者次优的装配顺序,采用了整数编码、单点交叉方法和新的变异方式。采用了最小偏差法,构造了FPSO船型优化的统一目标函数,采用了实数编码,以容量最大、造价最小为目标进行了多目标优化。