Liner shipping network design with emission control areas: A genetic algorithm-based approach

To curb emissions, containerized shipping lines face the traditional trade-off between cost and emissions (CO₂ and SO_x) reduction. This paper considers this element in the context of liner service design and proposes a mixed integer linear programming (MILP) model based on a multi-commodity pickup and delivery arc-flow formulation. The objective is to maximize the profit by selecting the ports to be visited, the sequence of port visit, the cargo flows between ports, as well as the number/operating speeds of vessels on each arc of the selected route. The problem also considers that Emission Control Areas (ECAs) exist in the liner network and accounts for the vessel carrying capacity. In addition to using the MILP solver of CPLEX, we develop in the paper a specific genetic algorithm (GA) based heuristic and show that it gives the possibility to reach an optimal solution when solving large size instances.     

Multi-criteria optimization of traffic signals: Mobility,safety, and environment

Two-dimensional multi-objective optimizations have been used for decades for the problems in traffic engineering although only few times so far in the optimization of signal timings. While the other engineering and science disciplines have utilized visualization of 3-dimensional Pareto fronts in the optimization studies, we have not seen many of those concepts applied to traffic signal optimization problems. To bridge the gap in the existing knowledge this study presents a methodology where 3-dimensional Pareto Fronts of signal timings, which are expressed through mobility, (surrogate) safety, and environmental factors, are optimized by use of an evolutionary algorithm. The study uses a segment of 5 signalized intersections in West Valley City, Utah, to test signal timings which provide a balance between mobility, safety and environment. In addition, a set of previous developed signal timing scenarios, including some of the Connected Vehicle technologies such as GLOSA, were conducted to evaluate the quality of the 3-dimensional Pareto front solutions. The results show success of 3-dimensinal Pareto fronts moving towards optimality. The resulting signal timing plans do not show large differences between themselves but all improve on the signal timings from the field, significantly. The commonly used optimization of standard single-objective functions shows robust solutions. The new set of Connected Vehicle technologies also shows promising benefits, especially in the area of reducing inter-vehicular friction. The resulting timing plans from two optimization sets (constrained and unconstrained) show that environmental and safe signal timings coincide but somewhat contradict mobility. Further research is needed to apply similar concepts on a variety of networks and traffic conditions before generalizing findings.     

W-SPSA in practice: Approximation of weight matrices and calibration of traffic simulation models

The development and calibration of complex traffic models demands parsimonious techniques, because such models often involve hundreds of thousands of unknown parameters. The Weighted Simultaneous Perturbation Stochastic Approximation (W-SPSA) algorithm has been proven more efficient than its predecessor SPSA (Spall, 1998), particularly in situations where the correlation structure of the variables is not homogeneous. This is crucial in traffic simulation models where effectively some variables (e.g. readings from certain sensors) are strongly correlated, both in time and space, with some other variables (e.g. certain OD flows). In situations with reasonably sized traffic networks, the difference is relevant considering computational constraints. However, W-SPSA relies on determining a proper weight matrix (W) that represents those correlations, and such a process has been so far an open problem, and only heuristic approaches to obtain it have been considered.This paper presents W-SPSA in a formally comprehensive way, where effectively SPSA becomes an instance of W-SPSA, and explores alternative approaches for determining the matrix W. We demonstrate that, relying on a few simplifications that marginally affect the final solution, we can obtain W matrices that considerably outperform SPSA. We analyse the performance of our proposed algorithm in two applications in motorway networks in Singapore and Portugal, using a dynamic traffic assignment model and a microscopic traffic simulator, respectively.     

基于神经网络和遗传算法的系泊线长度参数优化

摘 要: 针对多成分系泊线三段长度如何取值的问题,采用一种基于神经网络和遗传算法对深海多成分锚泊系统长度进行优化。应用AQWA软件计算多点系泊FPSO,其时域结果直接用于训练BP神经网络。从而利用神经网络的非线性映射功能构建替代锚泊时域计算网络,大大缩短了优化所需的时间。以FPSO最小平面运动值为目标函数,锚链破断强度作为约束条件,采用遗传算法优化系泊长度。计算结果表明,与传统设计的锚泊长度相比,优化后FPSO在各个浪向下纵荡横荡值均能减少20%以上。 关键词:系泊;神经网络;遗传算法;优化     

基于稳性实时计算技术的船舶破损智能扶正系统研究

基于船舶稳性实时计算技术研究船舶实时状态三维模型数字化算法用于快速计算船舶的大倾角稳性、破舱稳性和平衡浮态。参考遗传算法研究船舶破损智能扶正方案生成算法来实现智能生成扶正方案功能。然后运用上述两种核心算法设计船舶破损智能扶正系统软件,为船舶在突发破损事故时提供一套包含压载水调拨措施与步骤建议的智能扶正方案,提高船舶破损事故的处理能力。     

基于无线传感器网络的运动目标检测动态聚类图

有效的目标跟踪需要积极的传感器节点对运动目标群实行跟踪。与单目标跟踪相比,聚类在效能上有显著提高。本文提出准确的相干和非相干运动模式下目标的聚类,采用隐式动态时间框架来评估在创建连接组件加权图的目标关系史。该算法采用目标跟踪中定位算法的关键特征,即估计当前和预测的位置来确定移动目标的方向和距离的关系。模拟结果显示,通过动态调整历史窗口大小和预测目标之间的关系,可以显著提高聚类的准确性并减少运算时间。     

基于疲劳性能的沥青混合料组成优化

分析了沥青混合料组成结构对其疲劳性能的影响,采用BP神经网络和遗传算法相结合的方法,以疲劳性能为目标进行了沥青混合料性能优化设计,获得了4.75mm筛孔通过率、0.075mm筛孔通过率、粉胶比、空隙率与疲劳性能之间的关系,为更好地以性能为中心进行沥青混合料设计提供了参考.     

卫星罗经组成原理探讨

介绍卫星罗经的基本概念,分析卫星罗经的组成原理及设计思路,说明了卫星罗经的系统结构和主要技术及其算法,给出了卫星罗经实现方法和主要技术参数.     

一种免疫算法的改进

针对一种免疫算法在求解多模态函数优化问题的不足,利用基于相似结构的小生境技术和近优淘汰算子对免疫算法加以改进.通过仿真实验,本文分析了交叉算子、变异算子及近优淘汰算子对算法的影响.仿真结果表明改进后的算法的性能有明显的提高.     

基于遗传算法的雷达组网极化管理

为了增强雷达组网系统反应能力,生成稳定合理的极化管理方案,文章给出了一种基于遗传算法的雷达组网极化管理解决方案,并给出了应用遗传算法生成极化方案的具体实现描述,仿真结果表明该方法运算速度较快,结果精度较高,对极化方案的生成研究具有一定的参考价值。