An Improved DV-Hop Localization Algorithm Based on Hop Distances Correction

DV-Hop localization algorithm has greater localization error which estimates distance from an unknown node to the different anchor nodes by using estimated average size of a hop to achieve the location of the unknown node.So an improved DV-Hop localization algorithm based on correctional average size of a hop,HDCDV-Hop algorithm,is proposed.The improved algorithm corrects the estimated distance between the unknown node and different anchor nodes based on fractional hop count information and relatively accurate coordinates of the anchor nodes information,and it uses the improved Differential Evolution algorithm to get the estimate location of unknown nodes so as to further reduce the localization error.Simulation results show that our proposed algorithm have lower localization error and higher localization accuracy compared with the original DV-Hop algorithm and other classical improved algorithms.

An Improved Genetic Algorithm for UWB Localization

The UWB localization problem can be mapped as an optimization problem, which can be solved by genetic algorithm. In the localization process, the traditional fitness function does not include the ranging information between tags, resulting in insufficient ranging information and limited improvement of the localization accuracy. In view of this, an improved genetic localization algorithm is proposed. First, a new fitness function is constructed, which not only includes the ranging information between the tag and the base station, but also the ranging information between the tags to ensure that the ranging information is fully utilized in the localization process. Then, the search method based on Brownian motion is adopted to ensure that the improved algorithm can speed up the convergence speed of the localization result. The simulation results show that, compared with the traditional genetic localization algorithm, the improved genetic localization algorithm can reduce the influence of the ranging error on the localization error and improve the localization performance.

Symmetric Workpiece Localization Algorithms: Convergence and Improvements

Symmetric workpiece localization algorithms combine alternating optimization and linearization. The iterative variables are partitioned into two groups. Then simple optimization approaches can be employed for each subset of variables, where optimization of configuration variables is simplified as a linear least-squares problem (LSP). Convergence of current symmetric localization algorithms is discussed firstly. It is shown that simply taking the solution of the LSP as start of the next iteration may result in divergence or incorrect convergence. Therefore in our enhanced algorithms, line search is performed along the solution of the LSP in order to find a better point reducing the value of objective function. We choose this point as start of the next iteration. Better convergence is verified by numerical simulation. Besides, imposing boundary constraints on the LSP proves to be another efficient way.

Improved Dual Algorithm for Constrained Optimization Problems

One class of effective methods for the optimization problem with inequality constraints are to transform the problem to a unconstrained optimization problem by constructing a smooth potential function. In this paper, we modifies a dual algorithm for constrained optimization problems and establishes a corresponding improved dual algorithm; It is proved that the improved dual algorithm has the local Q-superlinear convergence; Finally, we performed numerical experimentation using the improved dual algorithm for many constrained optimization problems, the numerical results are reported to show that it is valid in practical computation.

Self Recovery of Localization Loss for Indoor Mobile Robot

In order to solve the problem of localization loss that an autonomous mobile robot may encounter in indoor environment,an improved Monte Carlo localization algorithm is proposed in this paper.The algorithm can identify the state of the robot by real time monitoring of the mean weight changes of the particles and introduce more high weight particles through the divergent sampling function when the robot is in the state of localization loss.The observation model will make the particle set slowly approach to the real position of the robot and the new particles are then sampled to reach the position.The loss self recovery experiments of different algorithms under different experimental scenarios are presented in this paper.

Dynamic Reconstruction of Total-cross-tied Photovoltaic Array Based on Arrays Using an Improved Dung Beetle Algorithm

A dynamic reconfiguration method for photovoltaic(PV)arrays based on an improved dung beetle algorithm(IDBO)to address the issue of PV array mismatch loss caused by partial shading conditions(PSCs)is proposed.To establish the output power-current(P-I)segmentation function for the total-cross-tied(TCT)PV array and the constraint function for the electrical switches,the IDBO algorithm was used to optimize both the P-I segmentation function and the electrical switch constraint function.IDBO is compared with algorithm-free reconfiguration and five other heuristic algorithms using two evaluation criteria:mismatch loss and power enhancement percentage,across six shading scenarios for 6x6 PV arrays.The irradiation distribution of PV arrays reconfigured by IDBO is also presented.The results show that IDBO effectively increases the output power of PV arrays and reduces mismatch loss.The output PV curves tend to exhibit a single peak,and the reconstruction results are superior to those obtained with the other methods.

SOME IMPROVED PROJECTED QUASI-NEWTON ALGORITHMS AND THEIR CONVERGENCE Ⅱ.LOCAL CONVERGENCE RATE AND NUMERICAL TESTS

For the improved two-sided projected quasi-Newton algorithms, which were presented in PartI, we prove in this paper that they are locally one-step or two-step superlinearly convergent. Numerical tests are reported thereafter. Results by solving a set of typical problems selectedfrom literature have demonstrated the extreme importance of these modifications in making Nocedal& Overton's original methon practical. Furthermore, these results show that the improved algoritnmsare very competitive in comparison with some highly praised sequential quadratic programmingmethods.

New Hybrid Genetic Algorithm for Vertex Cover Problems

This paper presents a new hybrid genetic algorithm for the vertex cover problems in which scan-repair and local improvement techniques are used for local optimization. With the hybrid approach, genetic algorithms are used to perform global exploration in a population, while neighborhood search methods are used to perform local exploitation around the chromosomes. The experimental results indicate that hybrid genetic algorithms can obtain solutions of excellent quality to the problem instances with different sizes. The pure genetic algorithms are outperformed by the neighborhood search heuristics procedures combined with genetic algorithms.

The Objective Function Value Optimization of Cloud Computing Resources Security Allocation of Artificial Firefly Algorithm

Based on the current cloud computing resources security distribution model’s problem that the optimization effect is not high and the convergence is not good, this paper puts forward a cloud computing resources security distribution model based on improved artificial firefly algorithm. First of all, according to characteristics of the artificial fireflies swarm algorithm and the complex method, it incorporates the ideas of complex method into the artificial firefly algorithm, uses the complex method to guide the search of artificial fireflies in population, and then introduces local search operator in the firefly mobile mechanism, in order to improve the searching efficiency and convergence precision of algorithm. Simulation results show that, the cloud computing resources security distribution model based on improved artificial firefly algorithm proposed in this paper has good convergence effect and optimum efficiency.

基于改进鹦鹉算法的光伏多峰值MPPT控制

针对光伏阵列在光照不均条件下呈现多峰突变,导致传统的最大功率点追踪失衡问题,提出了一种基于改进鹦鹉算法的MPPT控制。首先考量Halton序列进行种群初始化,使多样性变化显著;其次选取切线搜索算法中的切线飞行机制减少动态跳跃,克服早熟和局部极点问题;后期通过鹦鹉翻筋斗觅食策略进行二次更新,减小自适应范围,加速收敛。对比传统鹦鹉算法、金豺算法以及灰狼优化算法,测试结果显示,改进的鹦鹉算法的MPPT控制跟踪效率分别为98.23%、97.26%、96.91%和96.81%,跟踪时间分别为0.077 s、0.112 s、0.127 s、0.156 s,最后通过实验验证跟踪精度和速率均显著高于其余3种算法。

基于改进RRT的清扫机器人全覆盖路径规划

针对钢筋轧制车间中大型非结构化环境下清扫机器人在全覆盖路径规划时所面临的算法运行成本高、避障性能差和区域覆盖率低等问题,提出了一种利用单元分解法分区覆盖和融合跳点搜索法的RRT区域转移的全覆盖路径规划算法。利用MCD(movement cell decomposition)算法实现自由区域覆盖,为了解决区域间路径规划时的避障问题,引入融合跳点搜索策略的RRT算法,通过增加节点扩展的导向性,使其更偏向目标区域进行搜索,并利用贪婪算法裁剪冗余点修正路径以及三次B样条曲线法平滑处理。通过仿真与实验验证了算法在不同环境下的可行性和有效性,相比于其他方法所规划的路径更短且大大降低了路径重复率,提高了机器人避障效率的同时实现了全区域路径覆盖。

基于IKNN和LOF的变压器回复电压数据清洗方法研究

基于回复电压极化谱提取特征参量是目前广泛应用的变压器油纸绝缘状态评估方法,但极化谱易受工况干扰、人工失误等因素影响而出现特征数据异常的情况,严重降低评估准确性。针对上述问题,该文提出了一种基于局部离群因子(LOF)和改进K最近邻(IKNN)的回复电压数据清洗方法。首先,选取回复电压极化谱的回复电压极大值Urmax、初始斜率Sr与主时间常数tcdom作为老化特征参量,并基于LOF算法对非标准极化谱中的异常特征量数据进行识别与筛除。其次,利用模糊C均值(FCM)聚类算法减小噪声点对KNN算法的干扰,并通过加权欧氏距离标度突出各特征量间的关联性,进而构建出基于IKNN的数据填补模型架构以实现特征缺失数据的填补。最后,代入多组实测数据验证所提数据清洗方法的实效性。结果表明,数据清洗后的状态评估准确率相较于原有数据上升了50%左右,有效提高了变压器回复电压数据质量,为准确感知变压器运行状况奠定坚实的基础。

基于RRT算法的移动机器人安全光滑路径生成

在多障碍物复杂工厂环境中,针对快速探索随机树算法(RRT)生成的路径存在冗余点、贴近障碍物且存在锯齿状转折的问题,改进得到了安全-光滑RRT(Safe-SmoothRRT)路径规划算法。首先,引入目标偏置策略;其次,该算法利用融合目标点引力思想的新节点扩展方式以及改进的近邻点度量策略以减少树的盲目扩展,提高生长的目标性;随后,引入节点安全约束,将安全节点加入树中;改进路径简化方法,剔除冗余点的同时兼顾了安全性;最后通过B样条局部平滑来改善路径的平滑性。在MATLAB仿真实验中分别与标准RRT算法、自适应目标偏向性RRT算法和改进RRT算法相比,在平均路径长度方面最大下降了7.1%,在平均有效节点数方面最大下降了64.1%,且所得路径始终与障碍物保持一定的安全距离,结果表明改进算法有效提升了路径的光滑性和安全性。

主被动协同定位空能资源优化配置方法

无人机的快速发展为当今社会带来巨大便利,但其潜在的滥用现象对公共安全构成严重威胁,因此面向无人机的监测与定位技术近年来得到广泛研究。针对远距低飞无人机难以准确定位的应用问题,提出以无源为主、有源为辅的主被动协同定位框架,在基于到达时间差实现无源被动定位的基础上,引入支持往返到达时间测量的有源主动探测设备,择机对无人机进行主动式定位,补偿无源定位缺失的目标高程信息,从而提升无人机的三维定位精度。为充分挖掘主被动的协同定位潜力,文中深入探究无源被动定位节点预先部署的情况下,有源主动定位节点的空域和能域资源的配置方式,推导了主被动协同定位框架下的定位精度衡量指标,构建了空能资源联合优化问题,提出了基于非线性收敛因子和记忆指导的改进灰狼优化的空能资源优化算法。仿真结果表明,针对无人机定位时,主被动协同定位效果优于无源被动定位,典型场景下高程定位精度显著提升约96.33%。此外,所提的空能资源优化算法在求解空能资源联合优化问题时,性能优于标准(传统)灰狼算法、改进灰狼算法等。

基于改进粒子群算法的冗余机械臂逆运动学求解

为构造最小位姿误差及最优适应值的目标函数,对标准粒子群算法进行优化改进,以解决冗余机械臂逆运动学求解问题,从4个方面对标准粒子群算法进行改进,包括种群初始化、惯性权重参数调整、差分变异及局部深度搜索等,形成的改进粒子群算法(IPSO2)具备粒子群进化及差分变异进化两种性质。使用IPSO2、IPSO1、PSOTD、PSOd及传统PSO进行冗余机械臂逆运动学求解,通过Matlab仿真实验可知,IPSO2算法相较于其他算法,其最优、最差及平均适应值、标准差、最优解成功率更加出色,模拟曲线收敛精度更佳,收敛速度更快。为了验证IPSO2算法的实用性及鲁棒性,将其用于PUMA型机器人的逆运动学求解,试验结果表明,最优适应值精度可维持在10-15数量级,说明IPSO2算法在冗余机械臂逆运动学求解中具有实用性,可为相关研究提供参考。

基于子区域切分与SSA-XGBoost的室内定位方法

在利用位置指纹进行实时室内定位时,由于多径效应、信号闭塞或无线AP本身不稳定,而影响最终的定位效果。对此,提出了一种基于子区域切分结合麻雀算法(Sparrow Search Algorithm,SSA)优化XGBoost的室内定位方法。离线训练阶段,利用改进的FCM(Fuzzy C-means)算法和区域相关系数指标将待定位区域划分为多个子域,通过AP优化为每个子域选择最优AP集合。针对XGBoost算法性能易受到初始参数问题影响,利用麻雀算法对XGBoost初始参数寻优得到相对较优的参数,并分别为各个子区域构建SSA-XGBoost定位模型。在线定位阶段,目标点通过匹配子区域的聚类中心得到所属子区域,最终利用该子区域的定位模型预测目标点的位置。与其他定位算法相比,所提算法平均误差分别减少14.7%、22.4%、37.1%,证明所提方法在实际环境中较其他算法具有更好的定位效果。

基于精度因子与距离残差的加权最小二乘算法在DTMB辅助北斗定位中的应用

北斗三号系统已完成建设,该系统可以在空旷的室外提供较为准确的定位信息。但是在城市峡谷区域,北斗信号会受到遮挡。当能够提供有效定位信息的北斗卫星数目逐渐减少时,利用北斗卫星进行定位得到的定位结果偏差会逐渐增大。本文针对以上问题,在采用地面数字多媒体广播信号辅助北斗定位的基础上,利用基于精度因子与距离残差的加权最小二乘算法进行定位,相比于利用最小二乘算法进行定位,定位精度提高了40%~50%。本文提出的算法对于解决定位基站数目不足时增加其他不同类型基站来进行辅助定位的问题具有借鉴作用。

基于改进GCC算法的交流配电电缆局部放电在线定位方法研究

由于受到电磁噪声等因素的影响,现有的电缆局部放电定位方法不能准确定位放电故障。为处理该问题,在双端定位法的基础上,提出一种面向工程实际应用的电缆局部放电信号在线定位方法。基于改进的广义相关算法估计信号时延,并考虑局放信号的波速不定性,采用修剪均值数据滤波算法提高多定位样本下的局部定位精度。最后在仿真及试验中,通过与传统时延估计法进行对比,对所提方法的准确性进行验证。试验结果表明,相比传统的时延估计定位方法,所提方法在环境干扰严重、检测设备采样频率受限的条件下,能够有效提高时延估计精度,减小定位误差,试验定位相对误差为0.47%,满足工程实际要求。

基于改进遗传算法的换纱筒机器人路径规划

为了解决针织换筒机器人顺序换筒耗时长、功耗高问题,首先综合考虑换筒机器人移动距离和翻转等待时间,建立具有时空约束的换筒路径数学模型;其次基于自适应遗传算法,提出一种改进的近邻度初始种群算子和按位变异的局部搜索算子,得到改进的遗传算法。结果表明,改进遗传算法规划的换筒机器人路径所消耗的生产时间相对于人工排序缩减55.5%,改进算法收敛速度相对于传统遗传算法提升48.3%,改进算法最优解质量也优于其他几种传统启发式算法,提升了系统效率,证明改进遗传算法应用在换筒机器人路径规划优化问题中的可行性和有效性。

基于改进二进制蛇优化算法的配电网故障定位

分布式电源(distributed generation,DG)大规模接入给配电系统带来更多不确定性、随机性,系统运行方式更复杂,传统故障定位方法难以适应新型电力系统构建。提出了一种基于改进二进制蛇优化算法(improved binary snake optimization,IBSO)的新型故障区段定位方法。利用SPM混沌映射生成高质量的随机数序列,以提高算法种群中个体的随机性,并引入了遗传算法的动态变异策略,根据不同的搜索状态和进化阶段来调整变异率和变异方式,提高算法的灵活性和准确性。通过仿真证明,该方法适用于在含有分布式电源的配电网中定位单一和多重故障区段,相比蛇优化算法、传统二进制粒子群算法以及遗传算法在收敛性、快速性和准确性方面更优。