城市轨道交通列车运行过程的多目标优化研究

为了使城市轨道交通列车运行曲线能更好地为司机提供驾驶指导,进一步保证列车高效运行,在前人研究的基础上,建立包含巡航模式的列车运行过程优化模型。在建模过程中综合考虑列车运行过程的时间、能耗、乘坐舒适度、精确停车等多个性能指标,并使用差分进化算法对建立的模型进行求解。由于传统的差分进化算法存在许多不足,采用一种具有自适应参数调整策略的改进差分进化算法。通过调整算法的变异因子和交叉因子,增加了种群的多样性、扩大了全局搜索区域,避免了局部最优解,同时在后期也加快了收敛速度。通过对所选线路的仿真优化,使各优化性能指标达到了整体相对最优,验证了模型与算法的有效性。     

The GRAAL of carpooling: GReen And sociAL optimization from crowd-sourced data

Carpooling, i.e. the sharing of vehicles to reach common destinations, is often performed to reduce costs and pollution. Recent work on carpooling takes into account, besides mobility matches, also social aspects and, more generally, non-monetary incentives. In line with this, we present GRAAL, a data-driven methodology for GReen And sociAL carpooling. GRAAL optimizes a carpooling system not only by minimizing the number of cars needed at the city level, but also by maximizing the enjoyability of people sharing a trip. We introduce a measure of enjoyability based on people’s interests, social links, and tendency to connect to people with similar or dissimilar interests. GRAAL computes the enjoyability within a set of users from crowd-sourced data, and then uses it on real world datasets to optimize a weighted linear combination of number of cars and enjoyability. To tune this weight, and to investigate the users’ interest on the social aspects of carpooling, we conducted an online survey on potential carpooling users. We present the results of applying GRAAL on real world crowd-sourced data from the cities of Rome and San Francisco. Computational results are presented from both the city and the user perspective. Using the crowd-sourced weight, GRAAL is able to significantly reduce the number of cars needed, while keeping a high level of enjoyability on the tested data-set. From the user perspective, we show how the entire per-car distribution of enjoyability is increased with respect to the baselines.     

摆盘发动机主要结构参数多目标优化数学模型的研究

通过分析鱼雷摆盘发动机主要结构参数对发动机性能的影响,提出了摆盘发动机主要结构参数多目标优化的一种数学模型,并用来进行某一型号摆盘发动机的主要结构参数设计。     

基于BP神经网络的正交异性钢桥面系多目标结构优化设计

正交异性钢桥面铺装体系结构优化就是设计出力学性能最好、重量最轻的桥面系,给出铺装结构参数的优化组合。本文建立一个钢桥面铺装体系多目标优化模型,其中目标函数是正交异性钢桥面板铺装结构众多参数的复杂函数,可以应用BP神经网络模型模拟这种函数关系。利用大量的有限元计算结果作为BP神经网络的训练样本,将训练后的BP网络用于优化求解过程中的目标函数值的仿真计算。最后给出多目标优化模型的求解步骤和算例。     

汽车机械式变速器多目标可靠性优化设计

以某轿车变速器为例,根据汽车动力性要求,在保证零件强度和刚度可靠性的条件下,按变速器齿轮系体积最小和传动齿轮重合度最大建立了变速器的多目标可靠性优化设计的数学模型,然后应用MATLAB优化工具箱进行联合优化设计计算。设计结果表明:该数学模型正确有效,采用多目标可靠性优化设计方法可以缩短汽车变速器的设计周期,减小其体积并改善传动平稳性。     

数据包络分析模型与方法在船型方案排序择优中的应用

阐述了使用具有铭结构的DEA模型──C~2WH对有限方案多准则船型方案作有效性评判择优的原理和方法。针对船型论证，对C~2WH模型作了变换处理，使模型和方法更加筒明，且便于应用。算例表明，本文方法用于船型方案排序择优，不仅能继承以往DEA模型C~2R、C~2GS~2等体现出的使船型技术经济综合评判定量化的优点，还能较好地体现船舶设计者基于经验的偏好。     

Optimising courier routes in central city areas

Determining efficient routes for couriers in central city areas is becoming more challenging due to growing levels of congestion. New technologies allow on-street loading zones to be booked to reduce waiting times and circulation.Couriers when planning deliveries need to consider both driving and walking routes to optimise their activities. A two-layer optimisation model has been developed for determining the best routes for minimising operating and environmental costs. A genetic algorithm is proposed to find (near-)optimal solutions. The output can provide guidance for couriers as well as planners who are involved in determining duration limits at loading zones.     

改进遗传算法在船舶自由浮态计算中的应用

为了更快、更准确地计算船舶自由浮态问题,对原有计算方法进行改进。根据船舶自由漂浮的平衡条件,将自由浮态计算归结为多目标约束优化问题,并引入一种改进遗传算法对该优化问题进行求解。实例计算表明,使用该方法,迭代次数明显少于基本遗传算法,船舶自由浮态计算时间大幅下降,计算结果准确可靠。研究结果对于迅速掌握船舶状态,可靠保障船舶生命力具有重要意义。     

复合电源能量匹配与多目标模糊控制策略研究

分析了复合电源纯电动汽车的电源结构和工作模式,依据纯电动汽车基本性能指标对复合电源进行了能量匹配,制定了能源安全约束,并基于此制定了多目标模糊控制策略。仿真结果表明,所提出的控制策略能够充分发挥超级电容的优势,可提升纯电动汽车续驶里程、合理分配复合电源功率及减小车速误差。     

基于层次分析法轴系校中多目标优化研究

在轴系合理校中过程中,目前仅仅是考虑单一目标函数进行最优化校中,而在实际校中过程中需要考虑多因素进行校中。本文考虑多目标因素下的合理校中,根据各目标对象所占的权重比,利用层次分析法求出满足轴系校中各项要求的轴承合理变位可行值。