An easy procedure to determine Magic Formula parameters: a comparative study between the starting value optimization technique and the IMMa optimization algorithm

In 2004, a new searching algorithm for Magic Formula tyre model parameters was presented. Now, a summary of the results, for pure and combined slip, that this algorithm is able to achieve is presented. The Magic Formula tyre model needs a set of parameters to describe the tyre properties. The determination of these parameters is dealt with in this article. A new method, called IMMa Optimization Algorithm (IOA), based on genetic techniques, is used to determine these parameters. Here, we show the computational cost that has been used to obtain the optimum parameters of every characteristic of the Magic Formula tyre model, called Delft Tyre 96. The main advantages of the method are its simplicity of implementation and its fast convergence to optimal solution, with no need of deep knowledge of the searching space. Hence, to start the search, it is not necessary to know a set of starting values of the Magic Formula parameters (null sensitivity to starting values). The search can be started with a randomly generated set of parameters between [0, 1]. Nowadays, MF-Tool, an application developed by TNO, uses an optimization technique to fit Magic Formula parameters from Matlab toolbox [van Oosten, J.J.M. and Bakker, E., 1993, {Determination of magic tyre model parameters}. Vehicle System Dynamics, 21, 19–29; van Oosten, J.J.M., Savi, C., Augustin, M., Bouhet, O., Sommer, J. and Colinot, J.P., 1999, {Time, tire, measurements, forces and moments, a new standard for steady state cornering tyre testing}. EAEC Conference, Barcelona, 30 June–2 July.]. We refer to that algorithm as the starting value optimization technique. The comparison between the optimization technique employed by TNO and the proposed IOA method is discussed in this article. In order to give a relative idea of adjustment accuracy, the sum-squared error and the mean-squared error, from the curves of the tyre model with the parameters optimized by both applications compared with test data are evaluated.     

Ant Colony Routing algorithm for freeway networks

Dynamic traffic routing refers to the process of (re)directing vehicles at junctions in a traffic network according to the evolving traffic conditions. The traffic management center can determine desired routes for drivers in order to optimize the performance of the traffic network by dynamic traffic routing. However, a traffic network may have thousands of links and nodes, resulting in a large-scale and computationally complex non-linear, non-convex optimization problem. To solve this problem, Ant Colony Optimization (ACO) is chosen as the optimization method in this paper because of its powerful optimization heuristic for combinatorial optimization problems. ACO is implemented online to determine the control signal – i.e., the splitting rates at each node. However, using standard ACO for traffic routing is characterized by four main disadvantages: 1. traffic flows for different origins and destinations cannot be distinguished; 2. all ants may converge to one route, causing congestion; 3. constraints cannot be taken into account; and 4. neither can dynamic link costs. These problems are addressed by adopting a novel ACO algorithm with stench pheromone and with colored ants, called Ant Colony Routing (ACR). Using the stench pheromone, the ACR algorithm can distribute the vehicles over the traffic network with less or no traffic congestion, as well as reduce the number of vehicles near some sensitive zones, such as hospitals and schools. With colored ants, the traffic flows for multiple origins and destinations can be represented. The proposed approach is also implemented in a simulation-based case study in the Walcheren area, the Netherlands, illustrating the effectiveness of the approach.     

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。     

铁路罐式集装箱空箱调配优化模型及遗传模拟退火算法

针对罐式集装箱运输的特殊性,考虑重箱流和空箱流调配的综合优化,以罐箱运输费用最小为目标,建立铁路罐式集装箱空箱调配优化多商品网络流模型,并构造了1种嵌入模拟退火操作的遗传算法对之进行求解。为了使模型与算法可得到更符合实际、操作性更强的结果,给出了3种空罐箱调配的策略,作为隐含条件加入到算法求解过程中。利用自适应遗传模拟退火算法对随机生成的实际规模问题算例进行求解,并与用通用代数建模系统软件GAMS的计算结果进行对比。结果表明,前者得出的结果与最优解差距不大,而且运算速度更快,更能满足解决实际问题的需要,为铁路罐箱调配优化提供了良好的决策支持模型和算法。     

浅析钢筋混凝土Y形桥墩的优化设计

介绍并分析立交桥中桥墩构造尺寸的拟定,及其优化设计的过程与思路。     

浅谈运营商互联网骨干网扩容优化方案

随着人们对互联网络依赖程度的增加，互联网骨干网的扩容与优化逐渐成为各运营商需要解决的课题。本文从多种角度分析并提出了承载网扩容和优化的解决方案。     

汽车全塑前端模块结构优化设计

随着塑料材料性能的日益提升,汽车零部件塑料化是汽车轻量化的主要手段之一。文章针对某车型的全塑前端模块结构,根据前端模块的性能指标,利用仿真分析完成前端模块的优化设计,在前端模块性能基本不降低的前提下,整个结构优化实现重量降低10%左右,最终达到降低零件重量和成本的要求。     

宽带网络优化

稳定的网络是运营商服务用户的基础,也是获得用户认可的前提。在用户不断增加的前提下,根据公司现有资源情况,利用合理的投资计划,完善管内互联网接入层、汇聚层的调整优化,实现网络带宽的冗余和提高网络的抗干扰能力。     

铁路站场最优进路研究

本文介绍了计算机联锁进路搜索的现状以及基于图论理论建立的铁路站场控制的图模型，阐述了通过最短路原理实现的铁路站场最优进路控制的方法。     

船舶结构混合变量优化的遗传算法

本文针对船舶结构优化设计中普遍存在的混合离散变量现象，对遗传算法进行了若干探讨和改进，以直接获得实用的最优解。通过两个船舶结构优化的算例，验证了本文方法的有效性。