Effects of T-Factor on Quantum Annealing Algorithms for Integer Factoring Problem

The hardness of the integer factoring problem(IFP)plays a core role in the security of RSA-like cryptosystems that are widely used today.Besides Shor’s quantum algorithm that can solve IFP within polynomial time,quantum annealing algorithms(QAA)also manifest certain advantages in factoring integers.In experimental aspects,the reported integers that were successfully factored by using the D-wave QAA platform are much larger than those being factored by using Shor-like quantum algorithms.In this paper,we report some interesting observations about the effects of QAA for solving IFP.More specifically,we introduce a metric,called T-factor that measures the density of occupied qubits to some extent when conducting IFP tasks by using D-wave.We find that T-factor has obvious effects on annealing times for IFP:The larger of T-factor,the quicker of annealing speed.The explanation of this phenomenon is also given.

Multi-strategy hybrid whale optimization algorithms for complex constrained optimization problems

A multi-strategy hybrid whale optimization algorithm(MSHWOA)for complex constrained optimization problems is proposed to overcome the drawbacks of easily trapping into local optimum,slow convergence speed and low optimization precision.Firstly,the population is initialized by introducing the theory of good point set,which increases the randomness and diversity of the population and lays the foundation for the global optimization of the algorithm.Then,a novel linearly update equation of convergence factor is designed to coordinate the abilities of exploration and exploitation.At the same time,the global exploration and local exploitation capabilities are improved through the siege mechanism of Harris Hawks optimization algorithm.Finally,the simulation experiments are conducted on the 6 benchmark functions and Wilcoxon rank sum test to evaluate the optimization performance of the improved algorithm.The experimental results show that the proposed algorithm has more significant improvement in optimization accuracy,convergence speed and robustness than the comparison algorithm.

Improved Arithmetic Optimization Algorithm with Multi-Strategy Fusion Mechanism and Its Application in Engineering Design

This article addresses the issues of falling into local optima and insufficient exploration capability in the Arithmetic Optimization Algorithm (AOA), proposing an improved Arithmetic Optimization Algorithm with a multi-strategy mechanism (BSFAOA). This algorithm introduces three strategies within the standard AOA framework: an adaptive balance factor SMOA based on sine functions, a search strategy combining Spiral Search and Brownian Motion, and a hybrid perturbation strategy based on Whale Fall Mechanism and Polynomial Differential Learning. The BSFAOA algorithm is analyzed in depth on the well-known 23 benchmark functions, CEC2019 test functions, and four real optimization problems. The experimental results demonstrate that the BSFAOA algorithm can better balance the exploration and exploitation capabilities, significantly enhancing the stability, convergence mode, and search efficiency of the AOA algorithm.

Optimizing slope safety factor prediction via stacking using sparrow search algorithm for multi-layer machine learning regression models

The safety factor is a crucial quantitative index for evaluating slope stability.However,the traditional calculation methods suffer from unreasonable assumptions,complex soil composition,and inadequate consideration of the influencing factors,leading to large errors in their calculations.Therefore,a stacking ensemble learning model(stacking-SSAOP)based on multi-layer regression algorithm fusion and optimized by the sparrow search algorithm is proposed for predicting the slope safety factor.In this method,the density,cohesion,friction angle,slope angle,slope height,and pore pressure ratio are selected as characteristic parameters from the 210 sets of established slope sample data.Random Forest,Extra Trees,AdaBoost,Bagging,and Support Vector regression are used as the base model(inner loop)to construct the first-level regression algorithm layer,and XGBoost is used as the meta-model(outer loop)to construct the second-level regression algorithm layer and complete the construction of the stacked learning model for improving the model prediction accuracy.The sparrow search algorithm is used to optimize the hyperparameters of the above six regression models and correct the over-and underfitting problems of the single regression model to further improve the prediction accuracy.The mean square error(MSE)of the predicted and true values and the fitting of the data are compared and analyzed.The MSE of the stacking-SSAOP model was found to be smaller than that of the single regression model(MSE=0.03917).Therefore,the former has a higher prediction accuracy and better data fitting.This study innovatively applies the sparrow search algorithm to predict the slope safety factor,showcasing its advantages over traditional methods.Additionally,our proposed stacking-SSAOP model integrates multiple regression algorithms to enhance prediction accuracy.This model not only refines the prediction accuracy of the slope safety factor but also offers a fresh approach to handling the intricate soil composition and other influencing factors,making it a precise and reliable method for slope stability evaluation.This research holds importance for the modernization and digitalization of slope safety assessments.

An Improved Bald Eagle Search Algorithm with Cauchy Mutation and Adaptive Weight Factor for Engineering Optimization

The Bald Eagle Search algorithm(BES)is an emerging meta-heuristic algorithm.The algorithm simulates the hunting behavior of eagles,and obtains an optimal solution through three stages,namely selection stage,search stage and swooping stage.However,BES tends to drop-in local optimization and the maximum value of search space needs to be improved.To fill this research gap,we propose an improved bald eagle algorithm(CABES)that integrates Cauchy mutation and adaptive optimization to improve the performance of BES from local optima.Firstly,CABES introduces the Cauchy mutation strategy to adjust the step size of the selection stage,to select a better search range.Secondly,in the search stage,CABES updates the search position update formula by an adaptive weight factor to further promote the local optimization capability of BES.To verify the performance of CABES,the benchmark function of CEC2017 is used to simulate the algorithm.The findings of the tests are compared to those of the Particle Swarm Optimization algorithm(PSO),Whale Optimization Algorithm(WOA)and Archimedes Algorithm(AOA).The experimental results show that CABES can provide good exploration and development capabilities,and it has strong competitiveness in testing algorithms.Finally,CABES is applied to four constrained engineering problems and a groundwater engineeringmodel,which further verifies the effectiveness and efficiency of CABES in practical engineering problems.

An Improved Honey Badger Algorithm through Fusing Multi-Strategies

TheHoney Badger Algorithm(HBA)is a novelmeta-heuristic algorithm proposed recently inspired by the foraging behavior of honey badgers.The dynamic search behavior of honey badgers with sniffing and wandering is divided into exploration and exploitation in HBA,which has been applied in photovoltaic systems and optimization problems effectively.However,HBA tends to suffer from the local optimum and low convergence.To alleviate these challenges,an improved HBA(IHBA)through fusing multi-strategies is presented in the paper.It introduces Tent chaotic mapping and composite mutation factors to HBA,meanwhile,the random control parameter is improved,moreover,a diversified updating strategy of position is put forward to enhance the advantage between exploration and exploitation.IHBA is compared with 7 meta-heuristic algorithms in 10 benchmark functions and 5 engineering problems.The Wilcoxon Rank-sum Test,Friedman Test and Mann-WhitneyU Test are conducted after emulation.The results indicate the competitiveness and merits of the IHBA,which has better solution quality and convergence traits.The source code is currently available from:https://github.com/zhaotao789/IHBA.

基于遗传算法的汽车主动悬架变论域模糊PID控制

针对1/4车主动悬架系统,提出一种基于遗传算法(genetic algorithm,GA)的变论域模糊比例-积分-微分(proportional-integral-differential,PID)控制方法.在建立主动悬架系统模型的基础上,引入变论域思想设计模糊PID控制器.为进一步改善控制器减振效果,采用GA来优化变论域中伸缩因子描述函数的参数.结果表明:相比PID、模糊PID与未优化的变论域模糊PID等控制方法,基于GA的变论域模糊PID控制方法在降低车身垂向加速度、改善乘坐舒适性方面具有优越性;所提控制方法对簧载质量和车辆行驶速度不确定性具备较强的鲁棒性.

陆浑灌区实际蒸散发影响因素分析

实际蒸散发是水文循环的关键环节,分析灌区实际蒸散发及其影响因素对灌区水资源的高效利用和农业高质量发展具有重要意义。然而,目前蒸散发的影响因素研究在确定主要因素时往往采用解释力较差的传统统计学方法,在相关性分析时忽略了蒸散发与其影响因素在空间上的相关性。因此利用改进的随机森林模型确定实际蒸散发的主要影响因素,并通过岭回归模型和地理加权回归模型探究实际蒸散发与其影响因素的时空相关关系。结果表明:(1)在灌溉期,地表净辐射、平均气温、叶面积指数和实际水汽压是实际蒸散发的主要影响因素;在非灌溉期,地表净辐射、平均气温、风速和日照时间是实际蒸散发的主要影响因素。实际蒸散发在一定程度上代表了灌区的作物耗水量。因此,灌区作物耗水在灌溉期和非灌溉期的影响作用有一定的差异。(2)在时间上,风速与实际蒸散发为负相关关系且呈显著负相关(P<0.05),其余影响因素与实际蒸散发均为正相关关系且呈显著正相关(P<0.05);在空间上,除风速与实际蒸散发在大部分区域呈负相关关系,其余影响因素都与实际蒸散发在大部分区域呈正相关关系。因此,除风速外,其余影响因素对灌区作物耗水在大部分区域都为正向促进作用。

基于改进蚁群算法的机器人全局路径规划

针对传统蚁群算法存在初始信息素缺乏、收敛速度慢以及无法有效躲避障碍物等问题,文中提出了一种基于改进蚁群算法的全局路径规划。引入正态分布函数改进传统启发函数,提高了算法效率,缩短了算法收敛所需时间。自适应调整信息素挥发系数,限定信息素范围,避免过早收敛。对算法路径平滑处理,缩短路径长度,从而实现机器人的全局路径规划。仿真结果表明,在20×20环境下,文中算法平均迭代次数比传统蚁群算法减少了28代,收敛速度更快。平均拐点减少了33.3%,使路径更为平滑,克服了初始信息素缺乏,加快了收敛速度,减少了拐点数量,能够有效躲避环境中的障碍物,证明了该算法的可行性。

基于改进A^(*)算法的多AGV路径规划及避障研究

针对多自动导引运输车(automated guided vehicle,AGV)路径规划与避障问题,考虑AGV行驶中启、制动速度与转弯角度对行驶时间的影响,根据不同冲突类型制定避障策略,以所有AGV行驶总时间最短为目标,建立多AGV路径规划与避障模型,并设计改进A^(*)算法进行求解。对两组数量不同的多AGV进行路径规划与避障仿真,结果表明:相较于一般A^(*)算法,基于改进A^(*)算法的多AGV路径规划及避障总时间优化率分别为10.0%和10.5%,总路径长度优化率分别为7.1%和7.8%。与其他改进智能算法的进一步比较证实,改进A^(*)算法在缩短路径规划用时和路径长度方面具有明显优势。本文提出的改进A^(*)算法可高效地规划出多AGV运行的无碰撞路径,其改进思路也贴切反映了AGV的实际运行工况。

兼顾提升功率分配精度与抑制电压偏差的自适应下垂控制

直流微电网孤岛运行时,为实现下垂参数跟随直流微电网各光伏单元出口线路阻抗和本地负载分布情况自调整,提出一种基于麻雀搜索算法(sparrow search algorithm,SSA)的自适应下垂控制策略。将下垂参数、变换器输出电压参考值以函数变量的形式构成优化目标函数,利用麻雀搜索算法寻找目标函数的极小值,实时找到同时使系统运行过程中的功率偏差、母线电压偏差最小化的解。即利用麻雀搜索算法将下垂参数和电压参考值调节问题转化为函数极值寻优问题,实现了下垂系数可依据光伏出口线路阻抗、本地负载变化及光照强度变化自调整的目标。同时通过动态调节变换器输出电压参考值,减小母线电压偏差,解决了功率分配精度与母线电压偏差的固有矛盾。利用PSCAD/EMTDC建立系统仿真模型,仿真结果证明所提控制策略正确、有效。

含大规模分布式光伏接入电网的无功电压控制降损方法研究

大规模分布式光伏(distributed photovoltaic,DP)接入电网后,电网电压易出现大幅度波动,导致电网损失问题越来越突出。当前已有的电网降损方法没有考虑光伏发电自身的无功调节能力,应用效果不理想。为此,设计一种含大规模DP接入电网的无功电压控制降损方法。通过建立目标函数,设定约束条件,采用敏感性分析方法分析电压稳定性,将敏感度较高的值作为无功补偿位置,并对当前负荷进行预测,确定最佳的补偿容量,实现电网降损。试验结果表明:所提方法降损后较降损前基准电压更趋于1 pu,更为稳定,所提方法能够针对光伏控制接入分布特性,有效控制无功功率变化情况,相对于其他方法,无功功率波动较小,表现出较好的降损效果。

基于改进蚁群算法的自动落布车路径规划

针对自动落布车在使用蚁群算法(ant colony algorithm,ACA)进行路径规划过程中出现的收敛次数多、收敛速度较慢且容易陷入局部最优的问题,提出一种改进蚁群算法(improved ant colony algorithm,IACA)。首先对信息素挥发系数ρ进行自适应调整,令其做动态变化,克服算法的收敛次数过多,加快算法收敛速度,减少算法的收敛时间;其次引入细菌觅食算法中趋化操作的趋化步长因子对信息素更新公式进行改进,削减算法迭代的后期信息素浓度值,使算法在后期跳出局部最优值,提高算法全局搜索能力。利用MATLAB将改进后的算法在3种不同的栅格环境中进行仿真验证。结果表明:相比传统蚁群算法,改进后的算法收敛次数减少81.1%,最小路径长度减少6.3%,收敛时间减少20.7%。最后搭建ROS小车实验平台,利用ROS机器人系统对改进蚁群算法在模拟的织布车间环境中进行实验验证。结果表明:对比传统蚁群算法,改进蚁群算法在寻优时间上减少了8.6%。

基于无差拍预测算法的双向隔离型AC-DC矩阵变换器高动态电流控制策略

针对双向隔离型AC-DC矩阵变换器采用传统双闭环控制策略存在的控制参数多、整定困难等问题,提出了一种基于无差拍预测算法的高动态电流控制策略,通过引入无差拍预测算法对网侧电流进行控制,以减少控制器参数,消除数字控制时延。考虑到算法中电压电流检测量众多,故采用输入电压观测器以降低检测成本,减小采样误差的影响。变换器采用后级单重移相的调制方法来协调配合无差拍预测算法,其中参考输入电流指令与移相角为双射函数关系,简单易实现。仿真和实验结果表明:采用所提控制策略可以实现网侧电流正弦,功率因数接近于1;直流侧电压稳定,电流纹波率小于1%;直流电流在参考值突变时,跟踪快速且无超调、无振荡;同时,在变换器功率正向传输、反向传输以及正反向切换过程中,采用所提控制策略相比于传统的双闭环控制策略,电流动态调节时间分别缩短了69%、85%、67%。由此证明所提控制策略可以保证变换器良好的输入输出性能,并且相比于传统的双闭环控制策略在切换过程中动态性能得到显著提升。

基于改进初值带遗忘因子的递推最小二乘法的锂电池参数辨识

锂电池荷电状态(state of charge,SOC)的准确估计依赖于精确的锂电池模型参数。在采用带遗忘因子的递推最小二乘法(forgetting factor recursive least square,FFRLS)对锂电池等效电路模型进行参数辨识时,迭代初始值选取不当会造成辨识精度低、收敛速度慢的问题。为此,将电路分析法与FFRLS相结合,提出基于改进初值带遗忘因子的递推最小二乘法(improved initial value-FFRLS,IIV-FFRLS)。首先,通过离线辨识得到各荷电状态点对应的等效电路模型参数并进行多项式拟合;然后,利用初始开路电压(open circuit voltage,OCV)和OCV-SOC曲线获得初始SOC,代入参数拟合函数得到初始参数;最后,将初始参数带入递推公式得到IIV-FFRLS迭代初始值。对4种锂电池工况进行参数辨识,结果表明:与传统方法相比,IIV-FFRLS的平均相对误差、收敛时间分别减小58%、23%以上;IIV-FFRLS具有更高的辨识精度与更快的收敛速度。

MS-DFM模型的参数估计及其在股市周期识别中的应用

对已有的马尔科夫转移动态因子模型提出了一个新的参数估计方法——两步最大期望(Expectation-maximization,EM)法,通过对马尔科夫转移动态因子模型进行重新参数化,将其转换为混合动态因子模型,并将因子与状态均视为潜变量,利用EM算法实现对重新参数化后的参数以及因子得分的估计。将因子得分视为已知数据、状态视为潜变量,针对每个因子序列建立马尔科夫转移自回归模型,利用EM算法对依状态变化的截距项和自回归系数进行估计,并对状态与拐点进行识别。通过数值模拟验证该方法的有效性,并将该模型与估计方法用于我国沪深股市股票数据分析中,对股市行业周期进行度量和识别。

基于位置和能耗启发的改进蚁群算法路径规划

为了解决经典蚁群优化算法应用于移动机器人路径规划中存在综合寻优能力差、收敛速度慢和复杂环境中算法鲁棒性不强等问题,提出了一种基于位置和能耗启发的改进蚁群优化算法。综合考虑机器人行进路径长度、行进路径坡度和转弯带来的能耗问题,提出综合能耗启发因子;考虑路径起点与终点之间,直线距离最短,提出到起止点直线距离启发因子,引导蚂蚁往起止点直线附近路径靠近;提出到终点距离启发因子,引导蚂蚁往目标点方向行进。设计了综合三种启发因子的启发函数,优化状态转移计算方式。此外,通过引入动态信息素挥发因子、改进信息素增量、设计信息素限制等优化信息素更新策略。多种环境多次仿真实验结果对比分析表明,改进算法在寻优路径长度、路径高度均方差、综合性能等方面具有更加优秀的表现。

一种发射系下旋转弹的捷联惯导算法

为了提高旋转弹的姿态测量精度,提出了一种发射系下的旋转弹捷联导航算法。首先,推导了发射系导航更新方程;然后,利用传感器测量的前周期角速率对姿态更新算法进行优化,推导了基于角速率的旋转矢量更新算法及其算法误差;最后,采用陀螺仪标度因数的提升方法,实现了捷联惯导姿态测量精度有效的提高。通过对双子样、优化双子样算法在自旋运动条件下的数学仿真、基于转台的半实物仿真试验进行了验证。试验结果表明:在自旋机动条件下,相较于传统双子样算法,基于发射系的旋转弹捷联导航算法具有更高的精度,验证了算法在工程应用实现的正确性和有效性。

基于时间序列压缩分割的监测数据异常识别算法研究

为有效识别桥梁健康监测数据的异常,减少误预警、漏预警现象,保障桥梁监测数据的质量和有效性,针对大跨度斜拉桥长期监测数据的缺失、离群和漂移3类异常数据,提出基于时间序列压缩分割的监测数据异常识别算法。该算法将原始监测数据时间序列通过基于序列重要点(Series Importance Point, SIP)的时间序列线性分段(Piecewise Linear Represent, PLR)算法(PLR_SIP)得到数条时间子序列;然后采用欧氏距离进行时间子序列的相似性分析,并基于改进的局部离群因子(Local Outlier Factor, LOF)算法计算每条时间子序列的局部离群因子;最后将其与设定的阈值相比较,从而识别出监测数据的异常。为验证该算法的准确性与工程实用性,对某公路大跨度斜拉桥健康监测数据进行异常识别。结果表明:采用PLR_SIP算法对原始时间序列压缩分割得到的时间子序列能够准确地反映原序列的变化趋势和范围;改进的LOF算法突破了传统LOF算法仅能识别离群值这类无持续时间异常的局限性,能够排除噪声的干扰,实现对离群、缺失和漂移3种异常的识别。该算法无需定义训练集,直接以原始监测数据作为算法的输入,同时能够自适应调整阈值参数,具有良好的可扩展性、实时性、准确性和高效性,适用于处理实时、大量的桥梁健康监测数据。

基于因素空间理论的扫类连环多分类算法

为解决多分类问题,基于因素空间理论中因素显隐的思想,在扫类连环分类算法基础上,定义类别的合并,提出因素显隐的合并扫类连环分类方法,给出算法步骤,并用数值算例进行分析;定义类别的两两组合,提出因素显隐的两两扫类连环分类方法,给出算法步骤,并用数值算例进行分析。提出采用因素显隐的差额绝对值方法解决两个算法执行过程中出现的决策类别分不开的问题;对UCI数据集中3个实例与支持向量机作了算法对比分析,研究结果表明:提出的合并扫类连环分类方法、两两扫类连环分类方法实现了因素显隐,分类算法的精确度优于支持向量机。多分类学习的因素显隐研究结论拓展了因素空间的理论及应用研究。