多媒体异构Mesh网络体系设计及跨层QoS路由算法研究

 针对无线Mesh网络的异构特性和多媒体业务的QoS要求,研究了一种跨域、跨层、跨节点的无线Mesh网络QoS自适应体系架构.在此基础上,利用双层规划数学模型描述之,并利用改进的蚁群算法来求解该双层规划模型,从而提出了基于双层规划模型的蚁群优化路由算法.仿真结果表明双层规划数学模型充分地考虑并优化了路径的各QoS指标,提出的蚁群优化路由算法能够很好地收敛于双层规划模型的最优解,且具有复杂度低、收敛速度快的特点.     

基于双层规划的综合运输网络优化研究

针对综合运输网络中干线运输和末端配送的分离问题,本文将两者综合考虑,统一用双层规划模型表达。上层规划优化物流总成本,下层规划考虑客户配送成本最小化。采用遗传算法求解该双层模型,实例计算结果验证了该模型的可行性和求解方法的高效性。     

基于网络设计的路段可逆车道布局优化

为了应对城市道路交通供需关系的变化情况,合理分配道路空间资源,文中对可逆车道布局进行了研究,利用双层规划方法,建立了以系统总出行时间最小为上层问题目标,以非对称影响的SUE问题为下层问题的可逆车道设置位置和规模的优化模型。该模型中,在需求层面,考虑了3种不同出行特性的车辆:社会车辆、公交车辆和紧急救援车辆;在供给层面,模型中将引入对交叉口的考虑,不仅在计算出行时间中考虑交叉口的影响,且在优化变量中将路段可变车道与交叉口车道功能一同考虑。文中给出了模型的求解方法,对于上层问题采用粒子群优化算法求解,对于下层采用结合了MSA算法的"对角化"算法求解。经计算,系统总出行时间降低了20%。证明该可逆车道的设置是合理有效的。     

EDA算法求解一类特殊的非线性双层规划问题

研究下层为线性规划上层为二次规划的非线性双层规划问题。利用单纯形最优性等价条件和基于正态分布的分布估计算法(EDA)求解该问题的全局最优解。为了提高算法效率,结合种群最优个体,给出了有效的交叉算子。设计算法时,用均匀设计产生初始种群,从而增加种群的多样性。为克服进化算法的弱局部收敛性,文中提出一种新的方法增加算法的局部收敛性。数值试验结果表明,文中提出的算法是有效且快速的。     

训练支持向量机的极大熵方法

支持向量机是基于统计学习理论的结构风险最小化原理提出来的一种新的学习算法,它把模式识别问题建模为一个简单约束的高维二次规划问题。利用Lagrangian对偶方法,把求解二次规划等价为求解低维的无约束不可微优化问题。提出极小化一个严格凸的熵函数来处理不可微问题,得到原二次规划的扰动问题的最优解。数据仿真结果表明该算法在低存储需求下有效提高了大数据量问题的训练学习速度。     

基于Q学习算术优化算法的无人机三维航迹规划

针对传统方法求解无人机三维航迹规划易导致规划代价高、精度差和容易陷入局部最优的不足,提出基于Q学习算术优化算法的无人机三维航迹规划算法。为了提升算术优化算法的寻优精度,引入Circle混沌映射提高初始种群多样性和分布均匀性,引入Q学习根据个体状态自适应调整数学优化加速函数更新,均衡算法全局搜索与局部开发,设计最优解邻域扰动优化全局搜索能力。通过建立无人机三维航迹规划模型,将航迹规划转化为多目标函数优化问题,并利用改进算法求解无人机三维航迹规划,以综合考虑航迹代价、地形代价和边界代价的目标函数评估粒子适应度,对航迹规划迭代寻优。仿真实验结果表明,所提算法规划的航迹具有更低的总代价和适应不同复杂地形环境的稳定性。     

基于生物地理学算法的稀疏平面阵列优化

天线阵列的相对旁瓣电平和阵元位置为非线性关系。对于非线性关系求最优值问题,用其他优化算法较难求解,而研究采用智能算法求解是一条值得探索的可行路径。提出了一种混合智能算法——改进生物地理学算法,即对生物地理学算法的迁移算子和变异算子进行改进,以提高种群的进化速率,进而优化对称和非对称稀疏平面阵列。仿真结果表明,与以往的算法相比,该算法提高了优化速率,降低阵列的相对旁瓣电平;阵元在阵列孔径内非对称分布比对称分布,所获得的旁瓣电平更低。     

基于云模型的含分布式光伏配电网双层优化调度方法

为提升含分布式光伏配电网双层优化调度效果和该配电网的供电质量、降低运行成本与供电损耗、保障其运行的经济性与平稳性,提出一种基于云模型的含分布式光伏配电网双层优化调度方法。以低节点电压偏移量、低线路损耗及低运行成本为目标函数,为含分布式光伏配电网构建包含上层主动配电网与下层微电网优化调度模型的双层优化调度模型,结合云模型改进的粒子群(Cloud Model Improved Particle Swarm Optimization, CMOPSO)算法求解所构建的调度模型,完成基于云模型的含分布式光伏配电网双层优化调度方法的设计。实验结果表明,采用该方法进行优化调度后,可显著降低含分布式光伏配电网的线路损耗、电压偏移量及运行总成本,提高其各节点电压,使各节点电压更接近于安全电压基准值,减少配电网的供电损耗,提高其供电电能质量,保障其整体运行的平稳性与经济性;CMOPSO算法在求解模型时收敛到全局最优解的速度较高,收敛性能优越。所设计方法的总体效果较好,具有一定应用价值。     

基于纹理特征的盲道区域分割算法

盲道分割是导盲系统中的重要一部分,核模糊C均值(KFCM)算法使用内核的方法来提高聚类的性能,然而,该算法具有初始化敏感,陷入局部极小和缺乏先验知识对核函数的最优参数等缺陷。若需进一步提高聚类性能,则需要克服上述问题。对此,文章提出了一种基于改进的KFCM算法,将生物地理学优化算法(BBO)与KFCM算法相结合。BBO算法没有聚类特性,主要是采用生物地理学中的迁移算子之间的信息共享解决方案,而KFCM算法受初值影响,对图像噪声很敏感,因此BBO算法可以对KFCM算法进行优化,从而提高聚类性能。实验结果表明,改进的KFCM算法较现有文献相比,可以对盲道区域进行更精确地分割,且聚类效果评价指数更优。     

云无线接入网络高能效功率分配和波束成形联合优化算法

针对云无线接入网络(C-RAN)的资源分配问题,该文采用max-min公平准则作为优化准则,以C-RAN用户的能量效率作为优化目标函数,在满足最大发射功率和最小传输速率约束条件下,通过最大化最差链路的能量效率来实现用户发射功率和无线远端射频单元(RRHs)波束成形向量的联合优化。上述优化问题属于非线性、分式规划问题,为了方便求解,首先将原优化问题转化为差分形式的优化问题,然后通过引入变量将差分形式的、非平滑优化问题转化为平滑优化问题。最终,提出一种双层迭代功率分配和波束成形算法。在仿真实验中,将该文算法与传统的非能效资源分配算法和能量效率最大化算法进行了比较,实验结果证明该文算法在改进C-RAN能量效率和提高资源分配公平性方面的有效性。     

A Continuous Relaxation Method for Multiuser Detection Problem

Based on the binary quadratic programming model of the code division multiple access maximum likelihood multiuser detection problem, a detection strategy by the continuous relaxation method is presented. The proposed method relaxes the binary quadratic programming as a nonlinear programming, which is a quadratic objective function with simple quadratic constraints. A feasible direction method is used to solve the nonlinear programming. Based on the KKT solution of the nonlinear programming, a near optimal solution is obtained for the multiuser detection problem. Simulation results show that the bit error rate performances of a detection strategy based on the continuous relaxation method is low. Furthermore, average CPU time of continuous relaxation method is much lower than that of the semidefinite programming relaxation method, especially for the large-scale detection problems. This approach provides good approximations to the optimal maximum likelihood performance.     

A New Rank-two Semidefinite Programming Relaxation Method for Multiuser Detection Problem

Based on the semidefinite programming relaxation model of the code division multiple access maximum likelihood multiuser detection problem, a detection strategy by the rank-two method is presented. The proposed method restricts the matrix variable in the semidefinite programming relaxation to be rank-two, and yields a quadratic objective function with simple quadratic constraints. A feasible direction method is used to solve the nonlinear programming. Coupled with randomized method, a suboptimal solution is obtained for the multiuser detection problem. Simulation results show that the bit error rate performances of a detection strategy based on the new rank-two method are almost similar to that of the detection strategy based on the semidefinite programming relaxation. Furthermore, average CPU time of the new rank-two method is at least 10 times lower than that of the semidefinite programming relaxation method, With the increasing of the users number, the average CPU time increasing rate of the new rank-two method is lower than that of the semidefinite programming relaxation method, especially for the large-scale detection problems. This approach provides good approximations to the optimal maximum likelihood performance.     

An evolutionary algorithm for solving nonlinear bilevel programming based on a new constraint-handling scheme

In this paper, a special nonlinear bilevel programming problem (nonlinear BLPP) is transformed into an equivalent single objective nonlinear programming problem. To solve the equivalent problem effectively, we first construct a specific optimization problem with two objectives. By solving the specific problem, we can decrease the leader's objective value, identify the quality of any feasible solution from infeasible solutions and the quality of two feasible solutions for the equivalent single objective optimization problem, force the infeasible solutions moving toward the feasible region, and improve the feasible solutions gradually. We then propose a new constraint-handling scheme and a specific-design crossover operator. The new constraint-handling scheme can make the individuals satisfy all linear constraints exactly and the nonlinear constraints approximately. The crossover operator can generate high quality potential offspring. Based on the constraint-handling scheme and the crossover operator, we propose a new evolutionary algorithm and prove its global convergence. A distinguishing feature of the algorithm is that it can be used to handle nonlinear BLPPs with nondifferentiable leader's objective functions. Finally, simulations on 31 benchmark problems, 12 of which have nondifferentiable leader's objective functions, are made and the results demonstrate the effectiveness of the proposed algorithm.     

Nonconvex Optimization for Power Control in Wireless CDMA Networks

In this paper, we propose an efficient power control algorithm for the downlink wireless CDMA systems. The goal of our paper is to achieve the optimum and fair resource utilization by maximizing a weighted sum utility with the power constraint. In fact, the objective function in the power optimization problem is always nonconcave, which makes the problem difficult to solve. We make progress in solving this type of optimization problem using PSO (particle swarm optimization). PSO is a new evolution algorithm based on the movement and intelligence of swarms looking for the most fertile feeding location, which can solve discontinuous, nonconvex and nonlinear problems efficiently. It’s proved that the proposed algorithm converges to the global optimal solutions in this paper. Numerical examples show that our algorithm can guarantee the fast convergence and fairness within a few iterations. It also demonstrates that our algorithm can efficiently solve the nonconvex optimization problems when we study the different utility functions in more realistic settings.     

白光扫描干涉过程的自动对焦方法研究

针对白光扫描干涉系统由于物镜景深小、干涉区间短而导致的调焦不准确、查找条纹困难、耗时长等问题,提出一种适用于白光干涉仪的自动对焦方法,该方法包括一种结合穷举法与爬山法的复合式对焦搜索算法,以及一种基于四叉树分解的粗精分步对焦评价函数。所提的搜索算法不需预先设定搜索范围,步长迭代可变,在能够快速搜索的同时具有较高的搜索精度和稳定性。所提的评价函数灵敏度较高,单峰性较好。实验结果表明,所提的自动对焦方法具有较高的对焦精度和速度,且能够在快速准确找到干涉条纹位置的同时,有效地避免由条纹以及环境噪声引起的对焦曲线多峰值问题。     

Energy‐efficient power control for uplink spectrum‐sharing heterogeneous networks

This article considers energy‐efficient power control schemes for interference management in uplink spectrum‐sharing heterogeneous networks that maximize the energy efficiency of users, protect the macro base station, and support users with QoS consideration. In the first scenario, we define the objective function as the weighted sum of the energy efficiencies and develop an efficient global optimization algorithm with global linear and local quadratic rate of convergence to solve the considered problem. To ensure fairness among individual user equipments (UEs) in terms of energy efficiency, we consider the max‐min problem, where the objective is defined as the weighted minimum of the energy efficiencies, and a fractional programming theory and the dual decomposition method are jointly used to solve the problem and investigate an iterative algorithm. As by‐products, we further discuss the global energy efficiency problem and consider near‐optimal schemes. Numerical examples are provided to demonstrate significant improvements of the proposed algorithms over existing interference management schemes.     

Parameters optimization for cooperative sensing in multi‐channel cognitive radio networks

In this paper, we propose a relatively complete and robust optimization model under the scenario where multisecondary users cooperatively sense multi‐channels. The objective of this model is to maximize the system throughput, meanwhile aims to jointly optimize the parameters including the sensing time and the weight coefficients of the sampling results. Because this model is a nonlinear optimization model, we instead adopt a heuristic sequential parameters optimization method (SPO) to solve the model. The method begins with deriving the lower bound of the objective function of the optimization model. Then, it maximizes this lower bound by optimizing the weight coefficients through solving a series of suboptimal problems using Lagrange method. Given that the weight coefficients are found, it finally transforms the problem into another monotonic programming problem and exploits a fast‐convergent polyblock algorithm to find an optimized sensing time parameter. We finally conduct extensive experiments by simulations. The results demonstrate that, in terms of the throughput gained by the system, SPO can deliver a solution that is up to 99.3% of the optimal on average, which indicates that SPO can solve the proposed optimization model effectively. In addition, we also show the performance advantage of the proposed model on improving the system throughput by comparing with other state‐of‐the‐art optimization models. Wireless Communications and Mobile Computing. Copyright © 2012 John Wiley & Sons, Ltd.     

非视距环境下基于二阶锥规划的RSS定位算法

针对无线传感器网络在非视距(NLOS)环境下利用接收信号强度(RSS)定位存在精度不足的问题,提出了一种新的基于二阶锥规划(SOCP)的鲁棒性定位算法。在假定非视距偏差上界的基础上构建了对非视距偏差量具有鲁棒性的定位方程,从而抑制了非视距偏差的干扰;接着利用凸优化技术将鲁棒性的定位问题转化为二阶锥规划问题,达到精确估计的目的,进而提高定位精度;此外,将定位问题推广到未知发射功率的情况,提出了一个迭代SOCP的算法。仿真结果表明,所提出的算法有效地解决了非视距定位中存在的问题,且定位精度要优于以往的牛顿迭代法、UT法以及SOCP法。     

Multiterminal net routing for partial crossbar-based multi-FPGA systems

Multi-FPGA (field-programmable gate arrays) systems are used as custom computing machines to solve compute-intensive problems and also in the verification and prototyping of large circuits. In this paper, we address the problem of routing multiterminal nets in a multi-FPGA system that uses partial crossbars as interconnect structures. First, we model the multiterminal routing problem as a partitioned bin-packing problem and formulate it as an integer linear programming problem where the number of variables is exponential. A fast heuristic is applied to compute an upper bound on the routing solution. Then, a column generation technique is used to solve the linear relaxation of the initial master problem in order to obtain a lower bound on the routing solution. This is followed by an iterative branch-and-price procedure that attempts to find a routing solution somewhere between the two established bounds. In this regard, the proposed algorithm guarantees an exact-routing solution by searching a branch-and-price tree. Due to the tightness of the bounds, the branch-and-price tree is small resulting in shorter execution times. Experimental results are provided for different netlists and board configurations in order to demonstrate the algorithms performance. The obtained results show that the algorithm finds an exact routing solution in a very short time.     

Neural network optimization for redundancy allocation

System reliability optimization problems such as redundancy allocation are hard to solve exactly. Neural networks offer an alternative computational model for obtaining good approximate solutions for such problems. In this paper we present a neural network for solving the redundancy allocation problem for a n-stage parallel redundant system with separable objective function and constraints. The problem is formulated as a 0–1 integer programming problem and solved using the network. The performance of the network compare favourably with that of the best fit algorithm. The number of iterations taken by the network increases very slowly with increase in number of variables. Hence the network can easily solve large problems.