Enhanced Wolf Pack Algorithm (EWPA) and Dense-kUNet Segmentation for Arterial Calcifications in Mammograms

Breast Arterial Calcification(BAC)is a mammographic decision dissimilar to cancer and commonly observed in elderly women.Thus identifying BAC could provide an expense,and be inaccurate.Recently Deep Learning(DL)methods have been introduced for automatic BAC detection and quantification with increased accuracy.Previously,classification with deep learning had reached higher efficiency,but designing the structure of DL proved to be an extremely challenging task due to overfitting models.It also is not able to capture the patterns and irregularities presented in the images.To solve the overfitting problem,an optimal feature set has been formed by Enhanced Wolf Pack Algorithm(EWPA),and their irregularities are identified by Dense-kUNet segmentation.In this paper,Dense-kUNet for segmentation and optimal feature has been introduced for classification(severe,mild,light)that integrates DenseUNet and kU-Net.Longer bound links exist among adjacent modules,allowing relatively rough data to be sent to the following component and assisting the system in finding higher qualities.The major contribution of the work is to design the best features selected by Enhanced Wolf Pack Algorithm(EWPA),and Modified Support Vector Machine(MSVM)based learning for classification.k-Dense-UNet is introduced which combines the procedure of Dense-UNet and kU-Net for image segmentation.Longer bound associations occur among nearby sections,allowing relatively granular data to be sent to the next subsystem and benefiting the system in recognizing smaller characteristics.The proposed techniques and the performance are tested using several types of analysis techniques 826 filled digitized mammography.The proposed method achieved the highest precision,recall,F-measure,and accuracy of 84.4333%,84.5333%,84.4833%,and 86.8667%when compared to other methods on the Digital Database for Screening Mammography(DDSM).

基于Wolf的数字化变电站通信网异常流量检测系统

数字化变电站通信网异常流量检测过程中易陷入局部最优,导致检测结果不精准。为了解决这个问题,提出了基于Wolf的数字化变电站通信网异常流量检测系统。构建系统总体结构,分析通信网流量异常频域特征。通过采集异常流量模块解析目的物理地址,检查组件为系统提供信息交互引擎。使用Wolf算法将混沌序列映射到数字化变电站通信网异常流量多维相空间,设置控制收敛因子,避免检测结果陷入局部最优。计算异常流量特征值的熵,判断流量异常类型。实验结果表明,该系统一次设备异常流量检测结果与实际数据一致,二次设备异常流量检测结果与实际数据存在最大为2 Mb/s的误差,说明使用所设计系统检测结果精准。

Optimizing Grey Wolf Optimization: A Novel Agents’ Positions Updating Technique for Enhanced Efficiency and Performance

Grey Wolf Optimization (GWO) is a nature-inspired metaheuristic algorithm that has gained popularity for solving optimization problems. In GWO, the success of the algorithm heavily relies on the efficient updating of the agents’ positions relative to the leader wolves. In this paper, we provide a brief overview of the Grey Wolf Optimization technique and its significance in solving complex optimization problems. Building upon the foundation of GWO, we introduce a novel technique for updating agents’ positions, which aims to enhance the algorithm’s effectiveness and efficiency. To evaluate the performance of our proposed approach, we conduct comprehensive experiments and compare the results with the original Grey Wolf Optimization technique. Our comparative analysis demonstrates that the proposed technique achieves superior optimization outcomes. These findings underscore the potential of our approach in addressing optimization challenges effectively and efficiently, making it a valuable contribution to the field of optimization algorithms.

GNSS spoofing detection based on uncultivated wolf pack algorithm

In order to solve the problem that the global navigation satellite system（GNSS） receivers can hardly detect the GNSS spoofing when they are deceived by a spoofer,a model-based approach for the identification of the GNSS spoofing is proposed.First,a Hammerstein model is applied to model the spoofer/GNSS transmitter and the wireless channel.Then,a novel method based on the uncultivated wolf pack algorithm（UWPA） is proposed to estimate the model parameters.Taking the estimated model parameters as a feature vector,the identification of the spoofing is realized by comparing the Euclidean distance between the feature vectors.Simulations verify the effectiveness and the robustness of the proposed method.The results show that,compared with the other identification algorithms,such as least square（LS）,the iterative method and the bat-inspired algorithm（BA）,although the UWPA has a little more time-eomplexity than the LS and the BA algorithm,it has better estimation precision of the model parameters and higher identification rate of the GNSS spoofing,even for relative low signal-to-noise ratios.

An Intelligent Ellipsoid Calibration Method Based on the Grey Wolf Algorithm for Magnetic Compass

With the measurement of the Earth’s magnetic field,magnetic compass can provide high frequency heading information.However,it suffers from local magnetic interference.An intelligent ellipsoid calibration method based on the grey wolf is proposed to generate optimal parameters for magnetic compass to generate high performance heading information.With the analysis of the projection relationship among the navigation coordinate frame,the body frame and the local horizontal frame,the heading ellipsoid equation is constructed.Furthermore,an improved grey wolf algorithm is proposed to find optimization solution in a large solution space.With the improvement of the convergence factor and the evolutionary mechanism,the improved grey wolf algorithm can generate optimized solution for heading ellipsoid equation.The effectiveness of the proposed method has been verified by a series of vehicle and flight tests.The experimental results show that the proposed method can eliminate errors caused by sensor defects,hard-iron interference,and soft-iron interference effectively.The heading error generated by the magnetic compass is less than 0.2162 degree in real flight tests.

Array Antenna Pattern Synthesis Based on Selective Levy Flight Culture Wolf Pack Algorithm

Due to the shortcomings such as the premature convergence and the bad local optimal searching capability in traditional intelligence methods for pattern synthesis,a new type of wolf pack algorithm named Levy⁃Cultural Wolf Pack Algorithm(LCWPA)was designed on the basis of the Cultural Wolf Pack Algorithm(CWPA),which obeys the selective Levy flight.Because of the good overall management ability provided by the cultural algorithm in optimization process and the characteristics of excellent population diversity brought by Levy flight,the search efficiency of the new algorithm was greatly improved.When the algorithm was applied in the pattern synthesis of array antenna,the simulation results showed its high performance with multi⁃null and low side⁃lobe restrictions.In addition,the algorithm was superior to the Quantum Particle Swarm Optimization(QPSO),Particle Swarm Optimization(PSO),and Genetic Algorithm(GA)in optimization accuracy and operation speed,and is of very good generalization.

Optimal Operation of Distributed Generations Considering Demand Response in a Microgrid Using GWO Algorithm

The widespread penetration of distributed energy sources and the use of load response programs,especially in a microgrid,have caused many power system issues,such as control and operation of these networks,to be affected.The control and operation of many small-distributed generation units with different performance characteristics create another challenge for the safe and efficient operation of the microgrid.In this paper,the optimum operation of distributed generation resources and heat and power storage in a microgrid,was performed based on real-time pricing through the proposed gray wolf optimization(GWO)algorithm to reduce the energy supply cost with the microgrid.Distributed generation resources such as solar panels,diesel generators with battery storage,and boiler thermal resources with thermal storage were used in the studied microgrid.Also,a combined heat and power(CHP)unit was used to produce thermal and electrical energy simultaneously.In the simulations,in addition to the gray wolf algorithm,some optimization algorithms have also been used.Then the results of 20 runs for each algorithm confirmed the high accuracy of the proposed GWO algorithm.The results of the simulations indicated that the CHP energy resources must be managed to have a minimum cost of energy supply in the microgrid,considering the demand response program.

Optimized Controller Gains Using Grey Wolf Algorithm for Grid Tied Solar Power Generation with Improved Dynamics and Power Quality

This study proposes a control algorithm based on synchronous reference frame theory with unit templates instead of a phase locked loop for grid-connected photovoltaic(PV)solar system,comprising solar PV panels,DC-DC converter,controller for maximum power point tracking,resistance capacitance ripple filter,insulated-gate bipolar transistor based controller,interfacing inductor,linear and nonlinear loads.The dynamic performance of the grid connected solar system depends on the effect operation of the control algorithm,comprising two proportional-integral controllers.These controllers estimate the reference solar-grid currents,which in turn generate pulses for the three-leg voltage source converter.The grey wolf optimization algorithm is used to optimize the controller gains of the proportional-integral controllers,resulting in excellent performance compared to that of existing optimization algorithms.The compensation for neutral current is provided by a star-delta transformer(non-isolated),and the proposed solar PV grid system provides zero voltage regulation and eliminates harmonics,in addition to load balancing.Maximum power extraction from the solar panel is achieved using the incremental conductance algorithm for the DC-DC converter supplying solar power to the DC bus capacitor,which in turn supplies this power to the grid with improved dynamics and quality.The solar system along with the control algorithm and controller is modeled using Simulink in Matlab 2019.

过程控制工程课程混合式教学中学生学习风格模型的构建及应用实践

大学生线上线下混合式教学中存在问题产生原因之一是缺乏对网络环境下学生行为数据的分析,从而无法判断学生的学习风格。因而基于数据挖掘技术,利用网络学习平台收集过程控制工程课程学习行为数据,建立基于灰狼算法优化支持向量机构建多维度的学习风格模型,处理和预测其可能的学习风格。有助于教师及时掌握学生动态,调整线上线下混合式教学方案,实现因材施教的个性化教学模式。

基于优化功率跟随控制的E-REV能量管理策略研究

基于功率跟随控制的增程式电动汽车能量管理策略具有减缓电池寿命衰减与提高车辆NVH性能等优势,但存在阈值参数依赖性强、增程器启停频繁等问题,为此提出了一种基于优化功率跟随控制的E-REV能量管理策略。依据车速、SOC状态与驾驶员的加速踏板力度等信息特征,制定基于功率跟随控制的能量管理策略。在此基础上,针对固定规则参数的局限性,以车辆行驶总成本与SOC变化梯度为目标函数,结合灰狼优化算法对增程器启停功率阈值参数进行优化,减少发动机频繁启停现象。运用Matlab/Simulink搭建控制策略模型,并联合基于Simcenter/AMESIM搭建的整车物理模型进行仿真试验,结果表明:CHTC-LT循环工况下,优化功率跟随控制策略与功率跟随控制策略相比,SOC最大波动值降低了28%,增程器启停次数减少了28.5%,整车燃油经济性提升了6.89%。

基于VMD-IMPA-SVM的超短期风电功率预测

针对风力发电强波动性带来的预测精度不高问题,构建一种基于变模态分解(VMD)、灰狼优化算法(GWO)、海洋捕食者算法(MPA)和支持向量机(SVM)的组合预测模型。采用GWO对VMD的模态数和惩罚因子进行寻优,将原始功率序列分解为子序列进行降噪处理;运用对立学习和柯西变异等方法改进MPA的种群生成与变异方式,得到改进MPA(IMPA)并优化SVM中的核参数与惩罚参数,进而构建VMD-IMPA-SVM组合预测模型,对各子序列进行预测并叠加得到最终预测值。实际算例分析表明,所提组合预测模型具有较高的预测精度,同时具备强鲁棒性。

基于人工智能技术的分布式数据库重复记录自动检测系统设计

以人工智能技术为基础前提的分布式数据库重复记录自动检测的方式,以提高数据库查询时的准确率以及查询效率。设计系统首先对数据信息进行对应的特征提取,而后通过权衡函数对样本信息进行整合,通过自适应分解得到相应的目标函数并求解,结合灰狼算法以及Shingle完成数据查询。经过算例验证,改进设计方式准确率均超过90%,平均耗时在35 s以内,满足自动查询快速精确的要求。

变压器长圆形绕组振动仿真与机械故障诊断研究

针对目前配电变压器长圆形绕组的故障诊断研究较少的问题,本文提出一种基于灰狼优化(GWO)算法的变压器长圆形绕组机械故障诊断的方法。首先,建立长圆形绕组的辐向和轴向振动数学模型;其次,建立变压器电磁—机械耦合有限元模型,计算长圆形绕组在正常、松动以及翘曲状态下的振动分布,选取R点作为特征值提取点;最后,对变压器样机进行正常、松动以及翘曲3种状态试验,获取R点的小波包能量特征,采用GWO算法优化支持向量机(SVM)参数对变压器长圆形绕组机械故障诊断,最终优化后的诊断综合准确率达到90%。

BFRP筋与超高性能纤维混凝土黏结性能试验研究

为探究玄武岩纤维增强复合材料(BFRP)筋与超高性能纤维混凝土(UHPFRC)之间的黏结性能,通过中心拉拔试验,对BFRP-UHPFRC试件破坏模式、黏结-滑移曲线进行分析,且探讨了筋材直径、单一纤维及钢-PVA混杂纤维对黏结强度的影响。结果表明,试件破坏模式包括BFRP筋拔出破坏与拉断破坏。BFRP筋直径的增大将导致黏结强度降低。随着单一纤维掺量增加,黏结强度呈先增大后减小的趋势。而相较于单一纤维,钢-PVA混杂纤维显著提高了试件的黏结强度,当掺入1%钢纤维和0.5%PVA纤维时,黏结强度最高(25.35 MPa)。此外,通过灰狼算法优化的支持向量回归模型实现对黏结强度的预测,预测值与实际值拟合较好。

基于多目标狼群算法的机场行李导入系统仿真优化研究

针对民航机场行李导入系统运行过程中旅客行李注入等待时间长、系统能耗高等问题,综合考虑虚拟视窗控制方式、收集带式输送机运行速度、虚拟视窗长度及同时开放值机柜台数量等关键控制参数对机场行李导入系统运行效率的影响,提出一种求解该问题的仿真优化框架。通过分析机场行李导入系统实际运行工况,建立参数化仿真优化模型。以最小化旅客行李注入平均等待时间和系统能耗为优化目标,结合系统设计和运行过程中的实际约束条件,建立该问题的数学模型,并设计了一种多目标自适应并行狼群算法进行求解。该算法针对所提问题特性及经典狼群算法易陷入局部最优和收敛速度慢等不足,提出一种混合整实数单链编码方式,融合反向学习策略生成初始种群,引入自适应游走概率机制和智能行为并行机制,采用局部和全局自适应邻域搜索及启发式保优策略实现狼群算法智能行为搜索,使用Pareto非支配排序进行寻优迭代并获得最优解集。以国内某大型国际航空枢纽机场行李导入系统为例设计不同规模多种算法对比实验,验证了所提方法的有效性和优越性。

燃料电池船复合储能容量优化与配置经济性分析

复合储能系统可改善由船舶负载功率波动引起的燃料电池寿命损耗问题,但配置成本限制了其在燃料电池船上的广泛应用。为合理配置储能容量,使船舶动力系统设计具备长期的可靠性,提出了一种计及复合储能系统全寿命周期的容量优化配置方法。在构建各电源系统模型的基础上,建立包括购置成本、维护成本、置换成本和能耗成本的复合储能系统全寿命周期成本模型,并采用雨流计数法来评估储能的置换成本。最后依据“Alsterwasser”号燃料电池船的典型功率需求数据,以储能设备的容量参数、燃料电池的输出功率和电源系统的运行参数为优化变量,采用灰狼优化算法进行求解。通过不同储能类型和优化目标下的配置方案对比,验证了所提方法的经济性。

基于Vague集和响应面模型的注塑工艺多目标优化

针对注塑工艺多目标优化问题,以塑件的翘曲变形量、顶出时体积收缩率和缩痕深度作为优化目标,选取熔体温度、模具温度、注射时间、保压压力、保压时间等工艺参数为试验因素,采用中心复合试验设计结合模流分析建立试验样本,利用Vague集方法计算各优化目标相似度,通过指标相关性的指标权重确定(CRITIC)法确定各优化目标影响权重,得到综合相似度;建立综合相似度与各工艺参数之间的响应面模型,运用灰狼算法进行工艺参数寻优,得到最优工艺参数组合。结果表明,将Vague集和响应面模型相结合的优化结果显著,为实际生产过程提供了有益参考。

改进灰狼算法优化GBDT在PM_(2.5)预测中的应用

针对灰狼算法易陷入局部最优解和全局搜索能力不足的问题,通过霍尔顿序列(Halton Sequence)搜索算法初始化狼群位置,避免灰狼算法陷入局部最优解和重复运算;引入莱维飞行和随机游动策略对灰狼算法的寻优过程进行优化,以增加算法的全局搜索能力;利用粒子群算法模拟灰狼种群得出的最佳适应度以用于惩罚项改进灰狼算法中的头狼更新策略。使用改进算法优化的梯度提升树(Gradient Boosting Decision Trees,GBDT)模型对北京市大气污染物监测数据中PM_(2.5)质量浓度进行预测,采用3种评估函数对各模型以及混合模型预测效果得分进行评估。结果显示,本文改进的灰狼算法对梯度提升树的优化效果优于其他算法,均方根误差E RMS为6.65μg/m^(3),平均绝对值误差E MA为3.20μg/m^(3),拟合优度(R^(2))为99%,比传统灰狼算法优化结果的均方根误差减少了19.19μg/m^(3),平均绝对值误差降低了10.03μg/m^(3),拟合优度增加了9百分点;与霍尔顿序列和莱维飞行改进的(Levy Flight-Halton Sequence,LHGWO)相比,改进的灰狼算法预测得分的均方根误差降低了10.39μg/m^(3),平均绝对值误差减小了6.71μg/m^(3),拟合优度提高了5百分点。研究表明了预测模型优化的有效性,为未来城市改善空气质量提供了科学依据和技术支持。

基于改进灰狼算法的微网多主体主从博弈策略

为平衡包含电、热两种能源形式的微网系统内各参与者间的利益关系,通过改进灰狼算法提出了一种微网能量管理模型。首先,在充分分析微网结构及其各主体功能的基础上,为综合考虑源-网-荷的决策能力,将主从博弈方法应用于产能商、微网运营商、负荷聚合商之间的互动,建立一主多从的微网能量管理数学模型;其次,针对博弈上层模型高维、非线性的特点,在传统灰狼算法基础上,利用Tent映射对种群进行初始化、采用非线性收敛因子平衡种群搜索能力、利用莱维飞行策略降低陷入局部最优的风险。在模型求解时,博弈上层采用改进灰狼算法,下层采用二次规划方法,二者结合以探讨使各主体利益最大的策略;最后,通过算例进行验证,结果表明:本文算法更加高效,所提模型在提高参与者收益,平滑用户负荷分布方面更加优越。

基于POA-GWO-CSO 算法的新能源电力系统精准切负荷控制多目标优化方法

为解决新能源电力系统因功率缺额引发系统频率、电压偏移等一系列安全问题,文章提出了一种基于POA-GWO-CSO算法的电力系统精准切负荷控制多目标优化方法。首先,从电力系统的安全性和经济性两个方面综合考虑电力系统稳定运行和分布式电源出力特性等各项约束条件,提出一种基于负荷分类的精准切负荷控制多目标优化模型;然后,为了增强传统鹈鹕优化算法(POA)全局与局部搜索能力之间的协调关系,克服优化算法在处理复杂问题时出现收敛过早、寻优范围不够、求解精度不高等问题,引入非线性惯性权重因子、灰狼优化算法(GWO)中狼群领导者策略以及纵横交叉法(CSO),对鹈鹕新的个体的位置进行更新;最后,基于改进后的IEEE33节点进行实证分析。分析结果表明,利用改进的POA-GWO算法对紧急切负荷模型进行求解,实现了系统经济性及稳定性的协调控制。