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A New Hybrid Approach Using gwo and MFO Algorithms to Detect Network Attack

Computer Modeling in Engineering & Sciences 2023年第8期136卷 1277-1314页

作者： Hasan Dalmaz Erdal Erdal Halil Muratünver Department of Computer Engineering Faculty of Engineering and ArchitectureKırıkkale UniversityKırıkkale71450Turkey

This paper addresses the urgent need to detect network security attacks,which have increased significantly in recent years,with high accuracy and avoid the adverse effects of these *** intrusion detection system should respond seamlessly to attack patterns and *** use of metaheuristic algorithms in attack detection can produce near-optimal solutions with low computational *** achieve better performance of these algorithms and further improve the results,hybridization of algorithms can be used,which leads to more successful ***,many studies are conducted on this *** this study,a new hybrid approach using Gray Wolf Optimizer(gwo)and Moth-Flame Optimization(MFO)algorithms was developed and applied to widely used data sets such as NSL-KDD,UNSW-NB15,and CIC IDS 2017,as well as various benchmark *** ease of hybridization of the gwo algorithm,its simplicity,its ability to perform global optimal search,and the success of the MFO algorithm in obtaining the best solution suggested that an effective solution would be obtained by combining these two *** these reasons,the developed hybrid algorithm aims to achieve better results by using the good aspects of both the gwo algorithm and the MFO *** reviewing the results,it was found that a high level of success was achieved in the benchmark *** achieved better results in 12 of the 13 benchmark functions *** addition,the success rates obtained according to the evaluation criteria in the different data sets are also *** the 97.4%,98.3%,and 99.2% classification accuracy results obtained in the NSL-KDD,UNSW-NB15,and CIC IDS 2017 data sets with the studies in the literature,they seem to be quite successful.

关键词： Network attack detection hybrid gwo MFO

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Early Warning of Commercial Housing Market Based on Bagging-gwo-SVM

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Computer Systems Science & Engineering 2023年第5期45卷 2207-2222页

作者： Yonghui Duan Keqing Zhao Yibin Guo Xiang Wang Department of Civil Engineering Henan University of TechnologyZhengzhou450001China Department of Civil Engineering Zhengzhou University of AeronauticsZhengzhou450015China

A number of risks exist in commercial housing,and it is critical for the government,the real estate industry,and consumers to establish an objective early warning indicator system for commercial housing risks and to conduct research regarding its measurement and early *** this paper,we examine the commodity housing market and construct a risk index for the commodity housing market at three levels:market level,the real estate industry and the national *** the Bootstrap aggregating-grey wolf optimizer-support vector machine(Bagging-gwo-SVM)model after synthesizing the risk index by applying the CRITIC objective weighting method,the commercial housing market can be monitored for risks and early *** on the empirical study,the following conclusions have been drawn:(1)The commodity housing market risk index accurately reflect the actual risk situation in Tianjin;(2)Based on comparisons with other models,the Bagging-gwo-SVM model provides higher accuracy in early warning.A final set of suggestions is presented based on the empirical study.

关键词： Bagging SVM gwo risk metrics early warning

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Automatic Detection and Classification of Insects Using Hybrid FF-gwo-CNN Algorithm

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Intelligent Automation & Soft Computing 2023年第5期36卷 1881-1898页

作者： B.Divya M.Santhi Department of ECE Saranathan College of EngineeringTrichyIndia

Pest detection in agricultural cropﬁelds is the most challenging task,so an effective pest detection technique is required to detect insects *** processing techniques are widely preferred in agricultural science because they offer multiple advantages like maximal crop protection,improved crop man-agement and *** the other hand,developing the automatic pest mon-itoring system dramatically reduces the workforce and *** image processing approaches are limited due to the disadvantages like poor efﬁciency and less ***,a successful image processing technique based on FF-gwo-CNN classiﬁcation algorithm is introduced for effective pest monitor-ing and *** four-step image processing technique begins with image pre-processing,removing the insect image’s noise and sunlight illumination by utilizing an adaptive medianﬁ*** insects’size and shape are identiﬁed using the Expectation Maximization Algorithm(EMA)based clustering technique,which involves not only clustering the data but also uncovering the correlations by visualizing the global shape of an *** up robust feature(SURF)method is employed to select the best possible image ***,the image with best features is classiﬁed by introducing a hybrid FF-gwo-CNN algorithm,which combines the beneﬁts of Fireﬂy(FF),Grey Wolf Optimization(gwo)and Convolutional Neural Network(CNN)classiﬁcation algorithm for enhancing the classiﬁcation *** entire work is executed in MATLAB simulation *** test result reveals that the suggested technique has deliv-ered optimal performance with high accuracy of 97.5%,precision of 94%,recall of 92%and F-score value of 92%.

关键词： Adaptive medianﬁlter EMA SURF FF algorithm gwo CNN

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QLgwoNM:Quantum Leaping gwo for Feature Selection in Big Data Analytics

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Journal of Harbin Institute of Technology(New Series) 2023年第4期30卷 85-98页

作者： Rachna Kulhare S.Veenadhari Department of Computer Science and Engineering Rabinadranath Tagore UniversityBhopal(M.P.)464993India

In learning and classification problems,feature selection(FS)is critical in finding features that are both meaningful and ***,big data is an integral aspect of all industry *** firms in any industry,such as power,finance,commerce,electronics,communications,and so on,create massive amounts of heterogeneous data that needed to be handled effectively and evaluated *** it comes to big data,feature selection approaches are taken as game-changer since they can assist in minimizing the complexity of genetic data,making it simpler to study and translating it into meaningful *** enhance classification performance,feature selection is done to remove unnecessary and redundant characteristics from the *** this paper,we presented a novel Grey Wolf Approach based on Quantum leaping neighbor memeplexes which is termed QLgwoNM for feature selection and reduction to achieve better classification *** paper implemented other optimization algorithms such as particle swarm optimization(PSO),slime mould algorithm(SMA),salp swarm algorithm(SSA),artificial butterfly algorithm(ABA),whale optimization(WO),crow search optimization algorithm(CSA),and Jaya *** the implementation of these algorithms,QLgwoNM outperformed other *** QLgwoNM model performed well with an accuracy of 100%for Brain Tumor,CNS,Lung dataset and 97.1%for Ionosphere dataset,and 99%for *** from these,some state-of-art comparisons were also evaluated and QLgwoNM gave better results as compared with other existing algorithms.

关键词： big data feature extraction machine learning gwo classification

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基于SDM-Prony和改进gwo算法的多机PSS参数最优设计

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电力系统保护与控制 2019年第10期47卷 88-95页

作者：孙宁杰王德林魏久林康积涛周鑫吴水军和鹏西南交通大学电气工程学院四川成都610031 云南电网有限责任公司电力科学研究院云南昆明650217 云南电力试验研究院(集团)有限公司云南昆明650217

异步联网后的云南电网动态稳定性问题突出,考虑到云南电网水轮机组众多的特点,为了在抑制低频振荡的同时抑制"无功反调"现象,选用PSS2B稳定器进行参数优化整定。针对灰狼算法后期收敛速度慢的缺点,引入动态权重策略,平衡全局搜索和局部... 详细信息

异步联网后的云南电网动态稳定性问题突出,考虑到云南电网水轮机组众多的特点,为了在抑制低频振荡的同时抑制"无功反调"现象,选用PSS2B稳定器进行参数优化整定。针对灰狼算法后期收敛速度慢的缺点,引入动态权重策略,平衡全局搜索和局部搜索能力,提高寻优精度。首先利用SDM-Prony对振荡信号进行机电模式辨识,然后通过改进gwo算法迭代寻优最佳PSS参数,最后搭建了云南电网东南地区部分电网。通过两种方式下的时域仿真校验,验证了优化PSS2B参数能够有效地提高系统对振荡的阻尼,具有一定的鲁棒性和适用性。

关键词：水轮机组 PSS2B 参数优化 SDM-Prony gwo

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高压断路器声纹及振动熵特征联合的gwo-KFCM故障诊断

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高压电器 2023年第3期59卷 53-60页

作者：董耀陈云樊万昌刘会兰常珂国网青海省电力公司海西供电公司青海格尔木817000 华北电力大学河北保定071003

针对复杂环境下高压断路器故障诊断算法的准确率和泛化性问题,提出一种声纹及振动熵特征联合的gwo-KFCM故障诊断算方法。首先,对声音信号进行广义S变换,提取反应声纹的盒维数、方向度和对比度纹理特征;对振动信号进行变分模态分解(VMD)... 详细信息

针对复杂环境下高压断路器故障诊断算法的准确率和泛化性问题,提出一种声纹及振动熵特征联合的gwo-KFCM故障诊断算方法。首先,对声音信号进行广义S变换,提取反应声纹的盒维数、方向度和对比度纹理特征;对振动信号进行变分模态分解(VMD),计算信号的排列熵。最后,构造联合特征向量送入模糊核C—均值聚类(KFCM)学习训练,利用灰狼优化(gwo)算法优化KFCM初始聚类中心,对训练样本进行预分类后输入SVM,辨识操动机构运行状态。结果表明,声纹及振动熵特征联合的gwo-KFCM故障诊断方法充分利用声振信号互补优势,对实验样本总体诊断准确率达到了100%,并且有较好的泛化能力。

关键词：断路器声纹振动熵 gwo KFCM 广义S变换 VMD

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Trajectory optimization of SUAV communication relay based on gwo algorithm in the environments with sea breeze

Trajectory optimization of SUAV communication relay based on...

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第三十九届中国控制会议

作者： Yuebin Lun Honglun Wang Jianfa Wu Yanxiang Wang School of Automation Science and Electrical Engineering Beihang University Science and Technology on Aircraft Control Laboratory Beihang University Shenyuan Honors College Beihang University

The study is based on the combination of communication relay and energy absorption of solar-powered unmanned aerial vehicle（SUAV）,which is a multiple extremum *** SUAV kinematics model is changed from two dimensions to three dimensions with adding the interference of sea breeze to reduce or offset the impact of wind on mission optimization and improve the energy *** the performance of the on-line optimization of SUAVs,the particle swarm optimizer（PSO） algorithm is replaced by a faster grey wolf optimizer（gwo） *** order to accelerate the convergence speed of the algorithm and avoid falling into local optimality,a penalty function is added in this paper to judge whether there is a SUAV as the last branch of the communication *** results prove the necessity of the model with consideration of the wind and the effectiveness of the methods.

关键词： SUAV sea breeze communication relay energy absorption optimal trajectories distributed model predictive control(DMPC) gwo

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基于KPCA-gwo-SVM的埋地管道土壤腐蚀速率预测

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油气储运 2021年第8期40卷 938-944页

作者：梁昌晶谢波刘延庆刘志娟郭自强任春燕陈琼陶刘钇池中国石油天然气股份有限公司华北油田分公司河北省062552 河北雄安华油清洁能源有限公司

为提高埋地管道土壤腐蚀速率的预测精度,对土壤腐蚀的影响因素进行了梳理和分析。通过核主成分分析法(Kernel Principle Component Analysis,KPCA)对影响因素进行了数据降维,随后对支持向量机(Support Vector Machine,SVM)关键参数进行... 详细信息

为提高埋地管道土壤腐蚀速率的预测精度,对土壤腐蚀的影响因素进行了梳理和分析。通过核主成分分析法(Kernel Principle Component Analysis,KPCA)对影响因素进行了数据降维,随后对支持向量机(Support Vector Machine,SVM)关键参数进行了寻优,并将gwo-SVM、GASVM、PSO-SVM及FOA-SVM共4种模型进行了对比。结果表明:KPCA模型可有效降低预测模型的维度,其中土壤电阻率、氧化还原电位、含盐量、Cl^(-)质量分数及含水量5种因素对腐蚀影响较大;gwo-SVM的平均绝对误差和均方根误差最小,分别为1.90%、0.098909,且训练时间在4种模型中用时最少,仅为2.55 s。可见,KPCA-gwo-SVM模型更适合对埋地管道土壤腐蚀速率进行预测,研究结果可为管道完整性管理提供理论依据和实际参考。

关键词：埋地管道土壤腐蚀 KPCA gwo SVM 腐蚀预测

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基于gwo-DBN的反导装备体系效能评估方法研究

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现代防御技术 2024年

作者：赵海燕周峰杨文静刘迪杨添元空军工程大学防空反导学院国防科技大学信息通信学院陆军边海防学院职业教育中心

针对现有效能预测方法难以反映反导装备体系实际效能的问题，提出一种基于“数据驱动+深度学习”的反导装备体系效能评估方法。在大量实验数据抽取、处理、分析的基础上，构建灰狼优化算法-深度置信网络（gwo-DBN）模型对数据进行训练... 详细信息

针对现有效能预测方法难以反映反导装备体系实际效能的问题，提出一种基于“数据驱动+深度学习”的反导装备体系效能评估方法。在大量实验数据抽取、处理、分析的基础上，构建灰狼优化算法-深度置信网络（gwo-DBN）模型对数据进行训练学习，以此获得反导装备体系效能的非线性拟合，并以某次反导体系效能评估为例进行了仿真实验，结果表明该评估方法可行、可靠，能够为反导装备体系论证和改进提供较高的参考价值和借鉴意义。

关键词：反导装备体系效能评估数据驱动深度学习 gwo DBN

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基于gwo-CMFH和改进ResNet轴承故障诊断

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机床与液压 2023年第22期51卷 215-222页

作者：欧巧凤彭泗田李新民熊邦书南昌航空大学图像处理与模式识别省重点实验室江西南昌330063 中国直升机设计研究所直升机旋翼动力重点实验室江西景德镇333001

针对不同程度的小分类轴承故障,现有故障诊断方法准确率不高的问题,提出基于gwo-CMFH和改进ResNet的滚动轴承故障诊断方法。对于同一类型不同程度故障,提出基于gwo自适应优化结构元素参数的CMFH滤波方法,增强振动信号的脉冲故障特征并... 详细信息

针对不同程度的小分类轴承故障,现有故障诊断方法准确率不高的问题,提出基于gwo-CMFH和改进ResNet的滚动轴承故障诊断方法。对于同一类型不同程度故障,提出基于gwo自适应优化结构元素参数的CMFH滤波方法,增强振动信号的脉冲故障特征并抑制背景噪声;采用连续小波变换将滤波后的信号转换成二维时频图谱;最后,提出基于混合注意力机制改进的残差网络模型,提高轴承故障诊断精度。在西储大学、东南大学及所选轴承数据集上进行验证实验,不同故障程度的小分类诊断准确率分别达到99.73%、98.12%和99.07%,表明所提方法具有很好的抗噪性、鲁棒性,可提高滚动轴承不同故障程度的诊断效果。

关键词：滚动轴承故障诊断 gwo CMFH 混合注意力机制残差网络(ResNet)

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