A Hybrid TCNN Optimization Approach for the Capacity Vehicle Routing Problem

Introduction The vehicle routing problem(VRP)is one ofthe well-known NP-hard optimization problems[1]occurring in many transport logistics and distribu-tion systems of considerable economic significance.The capacity vehicle routing problem(CVRP)likeVRP with the additional constraint that every vehi-cle must have a capacity,so it is also an NP-hardoptimization problems.It involves minimizing costvehicle schedules for a fleet of vehicles originatingand terminating from a central depot.The …     

Solving service selection problem based on a novel multi-objective artificial bees colony algorithm

Service computing is a new paradigm and has been widely used in many fields. The multi-objective service selection is a basic problem in service computing and it is non-deterministic polynomial (NP)-hard. This paper proposes a novel multi-objective artificial bees colony (n-MOABC) algorithm to solve service selection problem. A composite service instance is a food source in the algorithm. The fitness of a food source is related to the quality of service (QoS) attributes of a composite service instance. The search strategy of the bees are based on dominance. If a food source has not been updated in successive maximum trial (Max Trial) times, it will be abandoned. In experiment phase, a parallel approach is used based on map-reduce framework for n-MOABC algorithm. The performance of the algorithm has been tested on a variety of data sets. The computational results demonstrate the effectiveness of our approach in comparison to a novel bi-ant colony optimization (NBACO) algorithm and co-evolution algorithm.     

基于免疫优化的单目标多模态期望值规划

为了求解未知随机变量分布下单目标多模态期望值规划,通过引入检测候选解是否为局部最优解的随机函数,将该期望值规划问题转化为多目标期望值规划问题,并进一步探寻问题的转化关系,获得在一定条件下有效解是最优解的结论;根据样本平均近似化思想,将多目标规划转化为非恒定样本采样的近似化模型,并基于克隆选择和免疫记忆的机理,通过设计递归非支配分层、样本自适应采样和自适应繁殖与变异方案,引导进化种群往优质个体所在区域转移,提出了求解该近似化模型的免疫优化算法.仿真结果表明:与参与比较的多目标优化算法相比,该算法搜索多个最优解方面有明显优势,搜索效果稳定,噪声抑制能力强;求解低、高维标准测试问题获得最优解的数量分别平均提高了20%和70%.      

不确定性条件下的设备替换优化的 随机动态规划方法

本文提出了一种解决设备更新换代优化（ERO）问题的随机动态规划（SDP）模 型,用以明确地解释在车辆利用中的不确定性,并采用 Bellman 算法解决 ERO SDP 问题. 针对 SDP 状态空间的增长,提出了特殊简化算法,以解决动态规划方法中固有的“维数 灾”问题,确保所需的内存和计算时间不会随着时间范围的增加而成倍增长.并对 SDP 软 件的实现技术、功能和图形用户界面（GUI）进行了讨论,开发了基于 SDP 的 ERO 软件, 并使用美国得克萨斯交通局（TxDOT）现有车辆数据进行验证.对统计结果、软件计算时 间和求解效果进行综合分析,结果显示,使用该 ERO 软件,估计大量成本可以节省.     

一种高效的分形属性选择算法

FDR(Fractal Dimensionality Reduction)算法的主要问题在于需要多次扫描数据集,I/O开销比较大,OptFDR(Optimized FDR)通过对FD-tree进行动态调整来避免多次扫描数据集,但对算法的空间需求比较高.借鉴Z-ordring索引技术的思想,设计并实现了一种改进的分形属性选择方法ZB-FDR(Z-ordering Based FDR).该方法仅需要扫描数据集一遍建立底层网格结构,基于该底层网格结构实现分形维数的计算及后向删除维操作.在合成数据集及实际数据集上的实验结果表明ZBFDR具有较为优良的整体性能.     

基于模拟退火算法的物流配送路径优化问题分析

模拟退火算法是解决NP完全组合优化问题的有效近似算法,将该算法应用于路径优化问题中,利用该算法对类似货郎担问题的路径问题进行求解。针对城市道路行走不同的目标条件（路径最短、时问最短）进行优化,选择最佳行走路径,并用该算法优化得到的计算结果,结果表明该算法在解类似货郎担交通路径方面问题时具有较高的精确性。因而,该算法在解决城市道路交通问题方面具有一定的实用价值。     

TDOA Passive Location Based on Cuckoo Search Algorithm

This paper formulates a new framework to estimate the target position by adopting cuckoo search(CS)positioning algorithm. Addressing the nonlinear optimization problem is a crucial spot in the location system of time difference of arrival(TDOA). With the application of the Levy flight mechanism, the preferential selection mechanism and the elimination mechanism, the proposed approach prevents positioning results from falling into local optimum. These intelligent mechanisms are useful to ensure the population diversity and improve the convergence speed. Simulation results demonstrate that the cuckoo localization algorithm has higher locating precision and better performance than the conventional methods. Compared with particle swarm optimization(PSO) algorithm and Newton iteration algorithm, the proposed method can obtain the Cram′er-Rao lower bound(CRLB) and quickly achieve the global optimal solutions.     

软件无线电中调制解调的实现

软件无线电概念对于通信系统的发展起到了巨大的引导作用,调制解调技术在软件无线电系统中占据着重要的地位.实现软件无线电台的关键之一就是要解决调制解调的软件化问题.文中主要对软件无线电结构下的AM、FM调制解调算法进行研究,并在此基础上用Matlab仿真工具对AM、FM信号的调制解调算法进行仿真实验,从而证明了算法的正确性和可行性.     

基于激光与视觉融合的车辆自主定位与建图算法

定位与建图是车辆未知环境自主驾驶的基础,激光雷达依赖于场景几何特征而视觉图像 易受光线干扰,依靠单一激光点云或视觉图像的定位与建图算法存在一定局限性。本文提出一 种激光与视觉融合SLAM(Simultaneous Localization And Mapping)的车辆自主定位算法,通过融 合互补的激光与视觉各自优势提升定位算法的整体性能。为发挥多源融合优势,本文在算法前 端利用激光点云获取视觉特征的深度信息,将激光-视觉特征以松耦合的方式输入位姿估计模块 提升算法的鲁棒性。针对算法后端位姿和特征点大范围优化过程中计算量过大的问题,提出基 于关键帧和滑动窗口的平衡选取策略,以及基于特征点和位姿的分类优化策略减少计算量。实 验结果表明：本文算法的平均定位相对误差为 0.11 m 和 0.002 rad,平均资源占用率为 22.18% (CPU)和 21.50%(内存),与经典的 A-LOAM(Advanced implementation of LOAM)和 ORB-SLAM2 (Oriented FAST and Rotated BRIEF SLAM2)算法相比在精确性和鲁棒性上均有良好表现。     

基于船舶优先权的集装箱港口泊位分配问题

泊位作为港口的重要资源,它的有效管理对于港口有着重要意义。集装箱港口泊位分配问题（BAP）,实质是为到达集装箱港口的船舶安排最佳停靠位置与停靠时间。本文考虑到船舶动态到达的情况,以及实际操作中码头装卸效率对船舶在港作业时间和船舶优先权的影响,以最小化包括等待时间在内的船舶在港停留时间为目标,建立了基于船舶优先权的连续布置泊位分配模型。该模型是0-1混合整数规划模型,对于小规模问题,可采用商业优化软件求解,对于大规模问题,可设计遗传算法处理。算例分析表明,考虑了连续布置泊位分配问题的二维特性和船舶优先权,能够找到更符合实际的泊位分配方案。     

Solving the euclidean steiner minimum tree using cellular stochastic diffusion search algorithm

The Euclidean Steiner minimum tree problem is a classical NP-hard combinatorial optimization problem. Because of the intrinsic characteristic of the hard computability, this problem cannot be solved accurately by efficient algorithms up to now. Due to the extensive applications in real world, it is quite important to find some heuristics for it. The stochastic diffusion search algorithm is a newly population-based algorithm whose operating mechanism is quite different from ordinary intelligent algorithms, so this algorithm has its own advantage in solving some optimization problems. This paper has carefully studied the stochastic diffusion search algorithm and designed a cellular automata stochastic diffusion search algorithm for the Euclidean Steiner minimum tree problem which has low time complexity. Practical results show that the proposed algorithm can find approving results in short time even for the large scale size, while exact algorithms need to cost several hours.     

An Effective Hybrid Optimization Algorithm for Capacitated Vehicle Routing Problem

IntroductionVehicle routing problem(VRP)is a well-known combinatorial optimization problem and isalso a focal problem of distribution managementwithin the area of service operations managementand logistics.Capacitated vehicle routing problem(CVRP)is a very hard vehicle routing problem.The problem is to simultaneously determine theroutes for several vehicles from a central depot to aset of customers,and then return to the depotwithout exceeding the capacity constraints of eachvehicle.In prac…     

基于改进遗传算法的公交线网整体优化方法

先进公交系统(APTS)是智能交通系统(ITS)的重要组成部分,也是目前的研究热点问题.本文讨论的公交网络优化问题则是实现先进公交系统的先决与基础.然而,对于实际规模的问题,传统的技术难以经济有效的解决.为此,我们提出了新的优化模型,设计了改进的遗传算法用以求解该模型.改进的算法有两个特点:基于优先级的编码和多染色体的结构.     

SPSA算法在微观交通仿真模型VISSIM参数标定中的应用

微观交通仿真模型在交通系统管理、控制和优化中得到了广泛的应用. 然而微观交通仿真模型参数标定是一项复杂且系统的工作,特别是对于较复杂网络,其参数标定耗时长,且不容易找到最优解. 本文选取了应用较为广泛的VISSIM仿真模型作为基础平台,针对遗传算法（GA）的不足,建立了基于同步扰动随机逼近（SPSA）算法的微观仿真模型参数标定方法,并实现了程序的自动化标定;最后将该方法应用于北京市快速路仿真模型的驾驶员行为参数标定中,以速度的相对误差平方和作为收敛函数,通过对比GA算法,SPSA算法收敛速度快1.7倍,且在标定后的流量检验中相对误差的平方和小0.16,验证了SPSA算法在VISSIM参数标定上的优越性.     

Feature Selection for SVM Classifiers Based on Discretization

The rough sets and Boolean reasoning based discretization approach （RSBRA） is not suitable for feature selection for machine learning algorithms such as neural network or SVM because the information loss due to discretization is large. A modified RSBRA for feature selection was proposed and evaluated with SVM classifiers. In the presented algorithm, the level of consistency, coined from the rough sets theory, is introduced to substitute the stop criterion of circulation of the RSBRA, which maintains the fidelity of the training set after discretization. The experimental results show the modified algorithm has better predictive accuracy and less training time than the original RSBRA.     

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微观交通仿真模型在交通系统管理、控制和优化中得到了广泛的应用. 然而微观交通仿真模型参数标定是一项复杂且系统的工作,特别是对于较复杂网络,其参数标定耗时长,且不容易找到最优解. 本文选取了应用较为广泛的VISSIM仿真模型作为基础平台,针对遗传算法（GA）的不足,建立了基于同步扰动随机逼近（SPSA）算法的微观仿真模型参数标定方法,并实现了程序的自动化标定;最后将该方法应用于北京市快速路仿真模型的驾驶员行为参数标定中,以速度的相对误差平方和作为收敛函数,通过对比GA算法,SPSA算法收敛速度快1.7倍,且在标定后的流量检验中相对误差的平方和小0.16,验证了SPSA算法在VISSIM参数标定上的优越性.     

A Bandit Method Using Probabilistic Matrix Factorization in Recommendation

In recommendation system,sparse data and cold-start user have always been a challenging problem.Using a linear upper confidence bound(UCB) bandit approach as the item selection strategy based on the user historical ratings and user-item context,we model the recommendation problem as a multi-arm bandit(MAB)problem in this paper.Enabling the engine to recommend while it learns,we adopt probabilistic matrix factorization(PMF) in this strategy learning phase after observing the payoff.In particular,we propose a new approach to get the upper bound statistics out of latent feature matrix.In the experiment,we use two public datasets(Netfilx and MovieLens) to evaluate our proposed model.The model shows good results especially on cold-start users.     

A Distributed Virtual Backbone Formation for Wireless Ad Hoc and Sensor Networks

The virtual backbone is an approach for solving routing problems in wireless ad hoc and sensor networks. A connected dominating set (CDS) was proposed as a virtual backbone to improve the performance of wireless networks. The quality of a virtual backbone is measured not only by approximation factor, which is the ratio of its size to that of minimum CDS, but also time complexity and message complexity. In this paper, a distributed algorithm is presented to construct a minimum CDS for ad hoc and sensor networks. By destroying triangular loops in the virtual backbone, the proposed algorithm can effectively construct a CDS with smaller size. Moreover, our algorithm, which is fully localized, has a constant approximation ratio, linear message and time complexity, and low implementation complexity. The simulation results and theoretical analysis show that our algorithm has better efficiency and performance than conventional approaches.     

Ship appearance optimal design on RCS reduction using response surface method and genetic algorithms

Radar cross section (RCS) reduction technologies are very important in survivability of the militarynaval vessels. Ship appearance shaping as an effective countermeasure of RCS reduction redirects the scatteredenergy from one angular region of interest in space to another region of little interest. To decrease the scatteringelectromagnetic signals from ship scientifically, optimization methods should be introduced in shaping design.Based on the assumption of the characteristic section design method, mathematical formulations for optimalshaping design were established. Because of the computation-intensive analysis and singularity in shapingoptimization, the response surface method (RSM) combined genetic algorithm (GA) was proposed. The poly-nomial response surface method was adopted in model approximation. Then genetic algorithms were employedto solve the surrogate optimization problem. By comparison RCS of the conventional and the optimal design,the superiority and effectiveness of proposed design methodology were verified.Ky words: radar cross section (RCS); characteristic section design method; response surface method; genetic algorithm (GA) was proposed. The polynomial response surface method was adopted in model approximation. Then genetic algorithms were employed to solve the surrogate optimization problem. By comparison RCS of the conventional and the optimal design, the superiority and effectiveness of proposed design methodology were verified.     

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基于血液的生理特性及应急保障特性,以应急血液最晚运达时间最小、接收时的最低新鲜度最大、运输总费用最小为目标,建立考虑多品种、多式联运的应急血液调剂出救点选择,即运输路线安排问题两阶段决策优化模型,并设计了一种包含局部邻域优化的遗传,即禁忌混合算法进行求解.以“5.12”汶川大地震应急血液保障为背景构建算例,分析验证了两阶段决策方法在最晚抵达时间、新鲜度均值、运输总费用、血液积压量等指标上的优越性.