Applicability of highway alignment optimization models

This paper presents an intelligent optimization tool that assists planners and designers in finding preferable highway alignments, connecting specified endpoints or zones. It integrates genetic algorithms with a geographic information system (GIS) for optimizing highway alignments and processes massive amounts of relevant data associated with highway design and alternative evaluation. To show the applicability of the proposed model to a real-world problem, two actual highway projects in the state of Maryland have been analyzed using the model. An extensive analysis of sensitivity to key model parameters is also conducted to describe the model capabilities. The analysis results show that the model can effectively optimize highway alignments in an area combining complex terrain and various types of natural and cultural land-use patterns, and provide detailed information of optimized alignments as a model output. It is also found that the alignments optimized by the model are quite similar to those obtained through conventional manual methods by a state agency, but the model can greatly reduce the time required for highway planning and design as well as produce lower cost solutions. Finally, the results confirm that all dominating and alignment-sensitive costs should be simultaneously evaluated in the alignment optimization process because many trade-off opportunities exist among those costs. The proposed model can greatly contribute to the productivity of highway planners as well as to the quality of the resulting infrastructure.     

Efficient Transit Schedule Design of timing points: A comparison of Ant Colony and Genetic Algorithms

This work defines Transit Schedule Design (TSD) as an optimization problem to construct the transit schedule with the decision variables of the location of timing points and the amount of slack time associated with each timing point. Two heuristic procedures, Ant Colony and Genetic Algorithms, are developed for constructing optimal schedules for a fixed bus route. The paper presents a comparison of the fundamental features of the two algorithms. They are then calibrated based on data generated from micro-simulation of a bus route in Melbourne, Australia, to give rise to (near) optimal schedule designs. The algorithms are compared in terms of their accuracy and efficiency in providing the minimum cost solution. Although both procedures prove the ability to find the optimal solution, the Ant Colony procedure demonstrates a higher efficiency by evaluating less schedule designs to arrive at a ‘good’ solution. Potential benefits of the developed algorithms in bus route planning are also discussed.     

基于遗传算法的孔系加工路线优化

在数控机床上对大量的孔进行加工时,加工路线的优化程度将直接影响机床的使用效率.通过对在数控机床上对孔系进行加工的典型工艺路线的分析,建立了加工路线优化的数学模型,利用遗传算法,对孔系加工路线的排序进行了优化计算,并用一个算例证明了该算法的可行性.     

基于改进遗传算法的舾装件托盘多载具协同拣选方法

为提升舾装件托盘的拣选效率,建立拣选过程的数学模型,提出一种基于改进遗传算法（Improved Genetic Algorithm,IGA）的舾装件托盘多载具协同拣选方法。针对遗传算法（Genetic Algorithm,GA）流程与实际拣选过程的差异,改进GA的初始化过程和染色体交叉方式,并对变异过程进行更贴近实际生产的修改。针对GA难以得到全局最优解的问题,采用变邻域搜索（Variable Neighborhood Search,VNS）策略降低陷入局部最优解的可能性。采用实例计算验证该算法的有效性,可优化传统舾装件托盘拣选方法。     

PID模糊控制在医用理疗仪温度控制器中的应用

本文介绍了用PID模糊控制技术中来解决在温度控制器控温过程中会产生的惯性温度误差问题。系统中的PID算法采用增量算法为基本算法,并按照实际应用做了适当的修改。经过实际测试,系统运行良好,证明在此医用理疗仪温度控制器中应用此PID算法取得了令人满意的结果。     

基于遗传算法的动力总成悬置系统优化设计

发动机是汽车主要振源之一。悬置系统的设计水平直接影响到汽车的隔振性能。以能量解耦指标为目标函数对汽车动力总成悬置系统进行优化是一个有许多局部最优解的优化问题。对此,运用全局寻优能力很强的遗传算法求解。设计应用表明,对于常规梯度算法难以获得满意结果的实际问题,遗传算法往往可以给出各方向主运动均具有很高解耦度的设计方案。     

新能源船舶混合电力系统容量优化策略

在新能源船舶电力系统中,不合理的系统容量分配会引起电网的剧烈波动,增加能源消耗,提高用电成本等不利情况。为保证行驶的安全合理,提出一种考虑船舶横摇的新能源船舶混合电站系统的优化策略。该策略使用加入遗传算法交叉过程的多目标多约束粒子群算法,在考虑船舶航行时横摇的情况下,对船舶电力系统中柴油机的出力以及储能电池的容量进行优化配置,在保证船舶运行效率的同时,减少电力系统运行成本以及燃油消耗。以某远洋油轮为例,在一个航程内,优化后的船舶混合电站系统在满足用电可靠性的前提下,合理分配各电源出力,显著改善了电力系统的用电成本。     

有运送路径限制的多品种流交通网络最小费用流算法研究

传统的交通网络最小费用流分配是针对单一品种,但在实际的交通运输应用中,交通网络中往往会出现多品种流的运送情况,而且也有可能对某些品种的运送路径进行限制.首先针对交通网络中的多品种流及其流动现象进行分析,借鉴Ford-Fulkerson算法中构造伴随增流网络的思路,建立了多品种流交通网络图的顺推重构方法,在此基础上,构造了有运送路径限制的多品种流交通网络最小费用流算法.在交通运输领域,多品种流最小费用流问题普遍存在,此算法为解决实际交通网络的相关问题提供了基础.     

高速公路入口匝道的协调优化控制

为了实施高速公路路网交通流的优化控制,采用MATANET模型进行路网交通流建模,应用非线性最优控制方法,构造了路网入口匝道的协调控制模型。该模型应用全局实时交通数据,以总费用时间与密度控制的组合构建性能指标,能够提高交通运行效率,并有效处理交通拥挤。针对模型的非线性,应用遗传算法对性能指标进行优化,探究TTS-密度控制指标的优化对路网交通拥挤状况的控制效果。以南京市周边高速公路路网作为应用实例,验证了所研究模型与优化方法的可行性。     

汽车PEPS系统诊断测试的应用与研究

随着汽车技术的不断提高,汽车PEPS系统也对其诊断测试结果的速度、准确性、连续性提出了更高的要求,利用Vector公司开发的CANoe总线仿真软件,对各种车身电子模块进行模拟,建立具有改进AES算法认证加密的汽车PEPS系统的测试仿真环境。实际测试结果表明,该系统不仅可以在虚拟节点下进行模拟仿真测试,也可以在真实节点下进行实时测试,灵活有效地反映出汽车PEPS系统中各种电子模块的总线通信情况,同时保证了故障诊断测试连续性和有效性。