Modified Black Widow Optimization-Based Enhanced Threshold Energy Detection Technique for Spectrum Sensing in Cognitive Radio Networks

This study develops an Enhanced Threshold Based Energy Detection approach(ETBED)for spectrum sensing in a cognitive radio network.The threshold identification method is implemented in the received signal at the secondary user based on the square law.The proposed method is implemented with the signal transmission of multiple outputs-orthogonal frequency division multiplexing.Additionally,the proposed method is considered the dynamic detection threshold adjustments and energy identification spectrum sensing technique in cognitive radio systems.In the dynamic threshold,the signal ratio-based threshold is fixed.The threshold is computed by considering the Modified Black Widow Optimization Algorithm(MBWO).So,the proposed methodology is a combination of dynamic threshold detection and MBWO.The general threshold-based detection technique has different limitations such as the inability optimal signal threshold for determining the presence of the primary user signal.These limitations undermine the sensing accuracy of the energy identification technique.Hence,the ETBED technique is developed to enhance the energy efficiency of cognitive radio networks.The projected approach is executed and analyzed with performance and comparison analysis.The proposed method is contrasted with the conventional techniques of theWhale Optimization Algorithm(WOA)and GreyWolf Optimization(GWO).It indicated superior results,achieving a high average throughput of 2.2 Mbps and an energy efficiency of 3.8,outperforming conventional techniques.

AMicroseismic Signal Denoising Algorithm Combining VMD and Wavelet Threshold Denoising Optimized by BWOA

The denoising of microseismic signals is a prerequisite for subsequent analysis and research.In this research,a new microseismic signal denoising algorithm called the Black Widow Optimization Algorithm(BWOA)optimized VariationalMode Decomposition(VMD)jointWavelet Threshold Denoising(WTD)algorithm(BVW)is proposed.The BVW algorithm integrates VMD and WTD,both of which are optimized by BWOA.Specifically,this algorithm utilizes VMD to decompose the microseismic signal to be denoised into several Band-Limited IntrinsicMode Functions(BLIMFs).Subsequently,these BLIMFs whose correlation coefficients with the microseismic signal to be denoised are higher than a threshold are selected as the effective mode functions,and the effective mode functions are denoised using WTD to filter out the residual low-and intermediate-frequency noise.Finally,the denoised microseismic signal is obtained through reconstruction.The ideal values of VMD parameters and WTD parameters are acquired by searching with BWOA to achieve the best VMD decomposition performance and solve the problem of relying on experience and requiring a large workload in the application of the WTD algorithm.The outcomes of simulated experiments indicate that this algorithm is capable of achieving good denoising performance under noise of different intensities,and the denoising performance is significantly better than the commonly used VMD and Empirical Mode Decomposition(EMD)algorithms.The BVW algorithm is more efficient in filtering noise,the waveform after denoising is smoother,the amplitude of the waveform is the closest to the original signal,and the signal-to-noise ratio(SNR)and the root mean square error after denoising are more satisfying.The case based on Fankou Lead-Zinc Mine shows that for microseismic signals with different intensities of noise monitored on-site,compared with VMD and EMD,the BVW algorithm ismore efficient in filtering noise,and the SNR after denoising is higher.

Black Widow Optimization for Multi Area Economic Emission Dispatch

The optimizationfield has grown tremendously,and new optimization techniques are developed based on statistics and evolutionary procedures.There-fore,it is necessary to identify a suitable optimization technique for a particular application.In this work,Black Widow Optimization(BWO)algorithm is intro-duced to minimize the cost functions in order to optimize the Multi-Area Economic Dispatch(MAED).The BWO is implemented for two different-scale test systems,comprising 16 and 40 units with three and four areas.The performance of BWO is compared with the available optimization techniques in the literature to demonstrate the strategy’s efficacy.Results show that the optimized cost for four areas with 16 units is found to be 7336.76$/h,whereas it is 121,589$/h for four areas with 40 units using BWO.It is also noted that optimization algo-rithms other than BWO require higher cost value.The best-optimized solution for emission is achieved at 9.2784e+06 tones/h,and it is observed that there is a considerable difference between the worst and the best values.Also,the suggested technique is implemented for large-scale test systems successfully with high precision,and rapid convergence occurs in MAED.

融合GBWO与ENN的人体尺寸预测模型

为了提高人体尺寸预测的效率和准确性,该文提出了GBWO-ENN(Grey Black Wolf Optimization-Elman Neural Network)的方法。针对传统灰狼算法易于陷入局部最优和无法平衡全局与局部搜索的平衡性问题,提出了GBWO算法。该算法融合黑寡妇优化算法中蜘蛛的运动方式对灰狼优化算法中α狼位置更新进行了优化,通过非线性递减的方法降低了收敛系数,并且提出了按位置等级更新种群的策略。随后采用GBWO算法对Elman神经网络的权值和阈值进行优化,并将GBWO-ENN模型应用于三维人体尺寸预测。实验结果表明,GBWO-ENN模型结构简单,能够准确预测人体尺寸,具有较好的预测能力。

基于VMD能量权重法与BWO-SVM的铣刀磨损状态监测

针对铣刀磨损状态监测中信号噪声大、监测效率低等问题,提出了一种基于能量权重法的变分模态分解(VMD)与黑寡妇(BWO)-支持向量机(SVM)的铣刀磨损状态监测方法。首先,运用VMD将铣削时产生的振动信号分解成若干固有模态函数(IMF)分量,并通过能量加权合成峭度指标自适应提取出了包含磨损状态特征的IMF分量,并进行了信号重构,对重构信号进行了特征提取;然后,利用BWO算法优化SVM的参数,构建了BWO-SVM铣刀磨损状态监测模型;最后,为了验证上述方法的有效性,以某公司真实加工现场的PHM Society 2010铣刀全寿命周期的振动数据进行了实验,并且又通过实际的工程案例对此进行了验证。研究结果表明:通过所提方法自适应提取有效分量并进行信号重构后,降噪效果明显,并通过与遗传算法(GA)和粒子群算法(PSO)优化的SVM相比,经过BWO优化的SVM的训练时间缩短至25.142 s,同时监测精度达到97.246%;采用该方法对铣刀磨损状态进行监测,能够获得更快的识别速度与更高的准确性,提高了铣刀磨损状态监测的效率。

基于BWOA-GAM-BiGRU的火电厂SCR系统出口NOx排放预测

提出了一种基于数据驱动结合全局注意力机制与双向门控循环单元深度神经网络的选择性催化还原系统出口NOx排放预测模型,并使用黑寡妇优化算法对网络超参数进行自动寻优。对某300MW电厂选择性催化还系统的历史运行数据进行深度挖掘,确定模型输入变量并校准各输入变量与NOx排放量之间的时延并重构样本序列。实验结果显示,所提出模型的预测结果比典型预测模型具有更高预测精度和更好的泛化能力。

基于BWO-SVM算法的广陈皮陈化年份高光谱鉴别模型

针对市场中存在广陈皮年份造假、以次充好等问题,提出一种基于黑寡妇优化算法(BWO)和支持向量机模型(SVM)的广陈皮陈化年份高光谱鉴别方法。以四类陈化年份(5~20年)的广陈皮作为实验对象,采集样本的高光谱图像数据(385~1014 nm波长),通过镜头校准和反射率校准后提取样本感兴趣区域的平均光谱数据。首先,采用多项式平滑算法(SG),结合多元散射校正算法(MSC)和去趋势算法(detrend)对数据进行降噪处理;然后,分别采用连续投影算法(SPA)、竞争性自适应重加权与逐步回归混合算法(CARS_SR)筛选出特征波段;最后,提出以均方根误差(RMSE)为适应度函数,分别使用偏最小二乘判别分析模型(PLS)、粒子群算法优化SVM模型(PSO-SVM)和蝗虫算法优化SVM模型(GOA-SVM)对广陈皮的陈化年份进行鉴别,并通过采用BWO算法优化SVM模型(BWO-SVM)来得到鉴别模型的最优参数。结果发现:SG_detrend算法对广陈皮高光谱数据具有较好的降噪能力,CARS_SR算法具有较好的特征信息提取能力;与PLS、PSO-SVM和GOA-SVM相比,BWO-SVM算法可以得到更好的鉴别模型控制参数;SG_detrend-CARS_SR-BWO-SVM模型对广陈皮陈化年份的鉴别准确率达到97.59%,RMSE为0.0602R^(2)为0.9529。该工作为实现广陈皮陈化年份的快速无损鉴别提供了新方法,也为便携式鉴别仪器或在线生产设备研发提供了理论依据。

Optimization of synchronized frequency and voltage control for a distributed generation system using the Black Widow Optimization algorithm

A distributed generation network could be a hybrid power system that includes wind-diesel power generation based on induction generators(IGs)and synchronous generators(SGs).The main advantage of these systems is the possibility of using renewable energy in their structures.The most important challenge is to design the voltage-control loop with the frequency-control loop to obtain optimal responses for voltage and frequency deviations.In this work,the voltage-control loop is designed by an automatic voltage regulator.A linear model of the hybrid system has also been developed with coordinated voltage and frequency control.Dynamic frequency response and voltage deviations are compared for different load disturbances and different reactive loads.The gains of the SG and the static volt-ampere reactive compensator(SVC)controllers in the IG terminal are calculated using the Black Widow Optimization(BWO)algorithm to insure low frequency and voltage deviations.The BWO optimization algorithm is one of the newest and most powerful optimization methods to have been introduced so far.The results showed that the BWO algorithm has a good speed in solving the proposed objective function.A 22%improvement in time adjustment was observed in the use of an optimal SVC.Also,an 18%improvement was observed in the transitory values.

基于特征融合和B-SVM的鸟鸣声识别算法

为了实现在野外通过低成本嵌入式系统识别鸟类,提出了基于特征融合和B-SVM的鸟鸣声识别方法。对鸟鸣声信号提取梅尔频率倒谱系数、翻转梅尔频率倒谱系数、短时能量和短时过零率组成特征参数,通过线性判别算法对特征参数进行特征融合。利用黑寡妇算法通过测试集对支持向量机模型的核参数和损失值进行优化得到B-SVM模型。利用Xeno-canto鸟鸣声数据集对本文算法进行了测试,结果表明该方法的识别准确率为93.23%。算法维度参数的大小和融合特征维度的高低是影响算法识别效果的重要因素。在相同条件下,文中所提的基于特征融合和B-SVM模型的鸟鸣声识别算法相较于其他特征参数和模型,识别的准确率更高,为野外鸟类识别提供了参考。

基于改进LSTM-AdaBoost的铣刀磨损量预测

针对铣刀磨损量预测时精度低的问题,提出一种基于黑寡妇算法(BWO)优化的长短期记忆神经网络(LSTM)与AdaBoost集成学习算法相结合的铣刀磨损量预测方法。在铣刀磨损振动信号中提取时域、频域以及时频域多域特征。通过BWO算法优化LSTM的核心参数,并将优化后的LSTM网络与AdaBoost算法进行结合,构建铣刀磨损量预测模型。最后用PHM Society 2010铣刀全寿命周期的振动数据进行实验。研究结果表明:所提方法能够有效地预测出铣刀磨损量变化值,优化后模型的平均绝对误差百分比为3.436%、均方根误差为6.471、决定系数R^(2)为0.935。该方法能够获得准确率更高的铣刀磨损量预测值,预测效率更高。

基于改进黑寡妇算法的海上风机塔筒生产线布局优化

为减少海上风机塔筒生产车间的物资搬运距离并提高搬运效率,降低生产车间的物流成本,分析塔筒生产线的特点和存在的问题,包括生产工位配置不合理、物流繁琐等,提出一种基于改进黑寡妇算法的生产线布局优化方法,旨在通过最佳的生产工位配置和物流规划,实现生产线的高效运转。将新方法与遗传算法、和声算法以及原始黑寡妇优化算法等进行对比试验,结果表明,所提出的布局优化方法能够显著提高海上风机塔筒生产线的生产效率并降低物流成本,对促进海上风机生产效能的提升具有一定的指导意义。

基于改进蛇优化算法的WSN覆盖优化研究

为解决无线传感器网络中节点随机部署易出现分散不均、低覆盖率等问题,提出一种改进蛇优化算法的WSN覆盖优化算法。首先,使用Circle映射种群初始化增强种群的多样性,并在蛇的探索阶段使用自适应螺旋搜索算法增大搜索范围。其次,通过引入黑寡妇算法中的信息素解决蛇的开发阶段易陷入局部最优解,并使用差分进化策略提高寻优的能力。最后,将改进蛇优化算法应用到传感器节点的部署中,利用传感器的覆盖模型确定最大的覆盖率。实验表明,改进后的算法能有效提升节点覆盖率,扩大WSN覆盖范围,从而减少节点能耗并延长网络寿命。

角逐和信息素引导的多目标黑寡妇优化算法

黑寡妇优化算法(BWOA)是一种群体智能优化算法,具有收敛速度快、收敛精度高等优点。但BWOA所采用的更新策略过于简单,容易陷入局部最优解;其次在多维空间中搜索能力欠缺,种群结构单一,算法的收敛性和多样性有待改善。为提高BWOA的综合性能,并使其能够应用于多目标优化问题,提出一种角逐机制和改进信息素机制引导的多目标黑寡妇优化算法(MBWOA)。MBWOA采用动态分配种群的方法,在迭代过程中将种群一分为二,分别使用不同的角逐机制,增强迭代过程中种群的多样性,提升算法的收敛性;同时,使用改进的信息素机制对经过角逐的子代个体进行更新,引导个体向种群间隙方向优化,改善种群的分布,增强算法的收敛能力。MBWOA与四个对比算法在IGD、HV、Spread三个指标上分别进行对比实验,结果表明MBWOA具有更好的收敛精度、收敛速度和多样性。最后,通过在三个指标上对MBWOA所用机制的对比实验,证实了所用机制的有效性。

基于黑寡妇算法的特征选择方法研究

特征选择通过去除无关和冗余特征提高学习算法性能,本质是组合优化问题。黑寡妇算法是模拟黑寡妇蜘蛛生命周期的元启发式算法,在收敛速度、适应度值优化等方面具有诸多优势。针对黑寡妇算法不能进行特征选择的问题,设计五种优化策略:二进制策略、“或门”策略、种群限制策略、快速生殖策略以及适应度优先策略,提出黑寡妇特征选择算法(black widow optimization feature selection algorithm,BWOFS)和生殖调控黑寡妇特征选择算法(procreation controlled black widow optimization feature selection algorithm,PCBWOFS),从特征空间中搜索有效特征子集。在多个分类、回归公共数据集上验证新方法,实验结果表明,相较其他对比方法(全集、AMB、SFS、SFFS、FSFOA),BWOFS和PCBWOFS能找到预测精度更高的特征子集,可提供有竞争力、有前景的结果,而且与BWOFS相比,PCBWOFS计算量更小,性能更好。

基于卡方跃迁策略的黑蜘蛛优化算法及应用

针对黑蜘蛛优化算法(Black Widow Spider Optimization Algorithm,BWOA)存在全局勘探和局部开发难以协调、收敛速度慢、易陷入局部最优等问题,提出了一种基于卡方跃迁策略的黑蜘蛛优化算法(Chi-Square Transition Mechanism for Black Widow Spider Optimization Algorithm,CTBWOA).首先,根据佳点集产生初始种群解,为全局搜索奠定基础;其次,提出卡方跃迁策略,帮助黑蜘蛛跳出局部最优,避免算法早熟收敛;再次通过自选取运动策略,实现黑蜘蛛在不同迭代时期选取合适的运动策略,以平衡算法的勘探和开发能力;最后,采取三蛛竞争及回溯机制,增加种群多样性,提高算法收敛速度.将CTBWOA算法与其他五种群智能算法在12个标准测试函数上进行仿真实验,结果表明对BWOA算法的改进能有效提高算法的收敛速度和求解精度,并将CTBWOA算法应用到了支持向量机(Support Vector Machine,SVM)的参数优化中,在UCI的7个标准数据集及威斯康星州乳腺癌数据集上的仿真实验证明,经CTBWOA优化后,SVM的参数设定更加准确,SVM的分类准确率有了明显提高,这进一步表明了改进算法的有效性.

二阶应变梯度波场效应的高阶有限差分模拟优化

二阶应变梯度波动方程依托的公理体系是广义连续介质力学理论,其通过引入位移的二阶空间导数项,丰富了经典连续介质力学理论的内容,并同时通过附加的介质特征尺度参数以反映介质内的微结构特性,弥合微观模型和经典连续介质力学之间的差距。王之洋等人从非局部理论出发,推导单参数二阶应变梯度理论的本构方程,进而结合几何方程和运动微分方程,给出二阶应变梯度非对称弹性波动方程的数学表达式,二阶应变梯度理论可以反映以介质平均颗粒直径l为邻域的内部更小尺度的地震波传播尺度效应。考虑到较小的空间尺度效应较弱,差分算子逼近微分算子时产生的数值频散会抑制尺度效应。因此,为了更好地描述和分析尺度效应,需要对有限差分算子进行优化。本文提出了一种改进的BWOA算法来得到优化的有限差分系数,并基于二阶应变梯度理论进行了弹性波数值模拟。数值模拟分析表明,数值频散得到了很好的压制,从地震记录中可以明显地观察和提取较小的空间尺度效应。

基于多策略黑寡妇算法的断路器全寿命周期成本优化

合理的电力设备运维方案对降低全寿命周期成本、提高设备可靠性具有重要意义.为制定合理的预防性检修方案降低断路器全寿命周期成本,本文提出一种基于多策略黑寡妇算法(Multi-strategey Black Window Optimization Algorithm,MBWO)的断路器全寿命周期成本优化方法.首先,考虑不同检修方式对中断成本的影响,建立全寿命周期成本模型.其次,以断路器可靠性、预防性检修程度为约束条件构建年均成本最小目标函数.最后利用多策略黑寡妇算法对目标函数进行求解并将结果同其他算法进行比较.通过实例验证,MBWO具备较好的收敛速度,能进一步降低断路器全寿命周期成本,提高设备可靠性,可以为断路器预防性检修方案的制定提供一定的参考.

数据分解技术与若干智能算法优化的高斯过程回归总氮预测

总氮(TN)是反映水体污染程度和衡量湖库营养化状态的重要指标之一。为提高TN预测精度,基于经验小波变换(EWT)、小波包变换(WPT)分解技术,分别提出鱼鹰优化算法(OOA)、雾凇优化算法(ROA)、秃鹰搜索(BES)算法、黑寡妇优化算法(BWOA)优化的高斯过程回归(GPR)预测模型。首先分别利用EWT、WPT将TN时间序列分解为若干更具规律的子序列分量;然后简要介绍OOA、ROA、BES、BWOA算法原理,利用OOA、ROA、BES、BWOA优化GPR超参数;最后利用优化获得的最佳超参数建立EWT-OOA-GPR、EWT-ROA-GPR、EWT-BES-GPR、EWT-BWOA-GPR、WPT-OOA-GPR、WPT-ROA-GPR、WPT-BES-GPR、WPT-BWOA-GPR模型(简称EWT-OOA-GPR等8种模型)对TN各分量进行预测,重构后得到最终预测结果,并构建基于小波变换(WT)的WT-OOA-GPR、WT-ROA-GPR、WT-BES-GPR、WT-BWOA-GPR模型,基于支持向量机(SVM)的EWT-OOA-SVM等8种模型,未经优化的EWT-GPR、WPT-GPR模型和未经分解的OOA-GPR、ROA-GPR、BES-GPR、BWOA-GPR模型作对比分析,通过全国重要饮用水水源地暮底河水库2008—2022年月监测TN浓度时序数据对各模型进行验证。结果表明:①EWT-OOA-GPR等8种模型对TN预测的平均绝对百分比误差在0.161%~0.219%,决定系数均为0.9999,优于其他对比模型,具有更高的预测精度和更好的泛化能力;②EWT兼顾WT、EMD优势,WPT能同时分解低频、高频信号,二者均可将TN时序数据分解为更具规律的模态分量,显著提高模型预测精度,分解效果均优于WT方法;③OOA、ROA、BES、BWOA能有效优化GPR超参数,提高GPR预测性能。