检索结果-内蒙古大学图书馆

Artificial immune algorithm-sparrow search algorithm and its application in network intrusion detection

JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2022年第4期43卷 5001-5011页

作者： Liu, Jianhua Wang, Zhiheng Xian Univ Posts & Telecommun Sch Comp Sci Xian 710000 Shaanxi Peoples R China

In order to solve the problem that the population diversity of sparrow search algorithm (SSA) decreases and easily falls into the local optimal solution when it approaches the global optimal, an artificial immune algorithm-sparrow search algorithm (AIA-SSA) is proposed in this paper by combining artificial immune algorithm and sparrow search algorithm. This paper uses 10 benchmark functions for experimental simulation of AIA-SSA algorithm, and compares it with five widely used intelligent algorithms and SSA. Experimental results show that AIA-SSA overcomes the deficiency of SSA and improves the search accuracy, convergence speed and stability of the algorithm. Meanwhile, this paper applies AIA-SSA to network intrusion detection and constructs a network intrusion detection model based on support vector machine (SVM). After testing, the accuracy of AIA-SSA-SVM prediction for various network attacks has been greatly improved. It not only shows that AIA-SSA-SVM has a broad application prospect in the field of network security, but also verifies the feasibility and advanced nature of AIA-SSA in solving practical engineering problems.

关键词： Intrusion detection SSA intelligence algorithm adaptive search differential evolution algorithm

来源：评论

学校读者我要写书评

暂无评论

Stress prediction of bridges using ANSYS soft and general regression neural network

引用

STRUCTURES 2022年 40卷 812-823页

作者： Wu, Ying Zhang, Lihua Liu, Hongbing Lu, Pengzhen Jiaxing Nanhu Univ Jiaxing 314001 Zhejiang Peoples R China Zhejiang Univ Technol Coll Civil Engn Hangzhou 320023 Zhejiang Peoples R China

Bridge managers are often eager to identify the carrying capacities and service performance characteristics of bridge structures. In view of the massive number of bridges managed and maintained in China, providing a quick evaluation of bridge performance bridges has become an urgent challenge for bridge engineering. Aiming at many adverse problems of bridge load tests, such as their high costs, long time consumption, low efficiency, impact on traffic, and damage to bridge structures, a new method for bridge static behavior prediction is proposed herein, based on dynamic load test data. The method adopts a generalized regression neural network algorithm with fast convergence and a nonlinear approximation, determines the sensitive parameters based on a full-bridge sensitivity analysis, forms training samples using a uniform design sampling method, and establishes intelligent agent model. The initial finite element analysis model of the bridge structure is updated using the proposed proxy model and dynamic load test data, and the static behavior of the existing bridge structure is accurately predicted based on the updated finite element model. To verify the correctness of the proposed method, a practical bridge is considered as the research object, and the static response of the bridge structure is predicted. The predicted results are consistent with the static load test results for the bridge, verifying the correctness of the proposed method. The results show that the maximum error between the theoretical calculation result and measured value is only 4.34% and the minimum error is only 3.52%. The strain prediction results are consistent with the measured results, and the modified theoretical analysis model provides high accuracy for strain prediction results under different working conditions. The application in practical engineering shows that the prediction results under different load conditions are adaptable and highly accurate.

关键词： Bridge engineering Dynamic load test data intelligence algorithm Regression analysis Static behavior prediction

来源：评论

学校读者我要写书评

暂无评论

Parameter Identification of a Soil Constitutive Model Based on a Hybrid Genetic Differential Evolution algorithm

引用

BUILDINGS 2024年第11期14卷 3665-3665页

作者： Long, Lin Li, Yunyu Yang, Peiling Tang, Bo Hunan Inst Technol Coll Civil & Construct Engn Hengyang 421002 Peoples R China Wuhan Univ Technol Sch Transportat & Logist Engn Wuhan 430063 Peoples R China 1 High Sch Shaodong Hunan Hunan 422800 Shaodong Peoples R China Zhongda Intelligent Technol Co Ltd Changsha 410005 Peoples R China

Aiming to address the problem of selecting the parameters of a soil constitutive model in the calculation of foundation pit stability, this paper proposes a hybrid genetic differential evolution algorithm (GADE) which performs by "jumping out of local optima" with "fast convergence" based on the hybrid optimization algorithm strategy and compares the advantages and disadvantages of genetic algorithms (GAs) and differential evolution algorithms (DEs). Three typical test functions were used to evaluate the search efficiency and convergence speed of GAs, DEs, and GADE, respectively. It was found that GADE has the fastest convergence speed and can search for the global optimal solution to the problem, which highlights its excellent optimization performance. At the same time, taking the Shimao Binjiang deep foundation pit as an example, GADE was used to invert the soil modulus parameters of a CX1 measuring point and construct a finite-element model for calculation. The results showed that the simulated calculation curve and the measured displacement curve were in good agreement and the curve fitting reached 95.05%, indicating the applicability and feasibility of applying GADE to identify soil parameters.

关键词： differential evolution genetic algorithm intelligence algorithm constitutive parameter soil creep

来源：评论

学校读者我要写书评

暂无评论

Grey Wolf Optimization algorithm with random local optimal regulation and first-element dominance

引用

EGYPTIAN INFORMATICS JOURNAL 2024年 27卷 1-14页

作者： Xuan, Yanzhuang Xuan, Shibin Guangxi Minzu Univ Sch Artificial Intelligence Guangxi Key Lab Hybrid Computat & IC Design Anal Daxue east Rd 188 Naning 530006 Guangxi Peoples R China

Due to the classical Grey Wolf algorithm GWO does not consider the characteristics of the local information of individual in population, a novel local random optimization strategy is proposed to make up for the defect of GWO. In this method, several points in the neighborhood of the current location of each individual are selected at random in the axial direction as candidates, and the best points are selected to participate in the renewal decision of the individual. Furthermore, in our experiments, a special first -element dominance characteristic is found and can greatly improve the combination effect of global and local information. In order to ensure that all constraints are not violated in the process of constraint optimization in industrial design, the random mixed population initialization method is proposed to generate population individuals that meet the constraint requirements and contain boundary values randomly. In addition, a treatment method of shrinking in a specific direction is proposed for dealing with individuals who cross the boundary. Experimental results on several test function sets show that compared with recent improved algorithms for GWO, the proposed algorithm has obvious advantages in fitness value, convergence speed and stability.

关键词： intelligence algorithm Grey Wolf algorithm First-element dominance Random local optimization Random mixed population

来源：评论

学校读者我要写书评

暂无评论

Establishment of critical non-depositing velocity prediction model for sediment in drip irrigation laterals based on PSO-SVM

引用

JOURNAL OF CLEANER PRODUCTION 2024年 457卷

作者： Wang, Chang Bai, Dan Li, Yibo Zhang, Qian Ma, Xin Tian, Donglin Shan, Mingming Xian Univ Technol State Key Lab Ecohydraul Northwest Arid Reg Xian 710048 Peoples R China Water Resources Res Inst Inner Mongolia Hohhot 010051 Peoples R China

Accurately determining the critical non-depositing velocity for sediment (CNDVS) in drip irrigation laterals is essential to address issues of sediment deposition and clogging in drip irrigation pipes caused by the use of muddy water. This paper focuses on three main factors: the percentage of intermediate-sized sediment particles (P), pipe diameter (D), and sediment concentration (S), all of which significantly influence the CNDVS in drip irrigation laterals. To predict the CNDVS, a comprehensive gradient test was developed and the Particle Swarm Optimization-Support Vector Machine (PSO-SVM) algorithm was applied to establish a response surface for predicting the CNDVS. Additionally, the PSO algorithm optimized the penalty parameter (C), loss parameter (epsilon), and kernel parameter (delta) in the SVM, resulting in a prediction model for the CNDVS. The proposed model was trained and tested using experimental data, and its prediction performance and accuracy were compared with the regression fitting model. The results indicate that within a specific range, the CNDVS increases with higher values of the three factors. The PSO-SVM model, with optimal parameters combination values of C = 486.37, epsilon = 0.04, and delta = 68.49, provided the most prediction accurate. Compared with the regression fitting model, the PSO-SVM model has the smaller values of Mean Absolute Error (MAE) = 0.0049, Root Mean Square Error (RMSE) = 0.0057, and Percent Bias (PBIAS) = 0.0004, and the larger value of Nash-Sutcliffe Efficiency (NSE) = 0.9874, Willmott index (WI) = 0.9401, and Coefficient of Determination (R2) = 0.9875. These results demonstrate that the PSO-SVM model established in this study exhibits high prediction performance and accuracy. The findings of this research can serve as a foundation for developing optimal operating and flushing flow velocity within the laterals in muddy water drip irrigation systems.

关键词： Utilization of muddy water Sediment deposition intelligence algorithm Intermediate -sized sediment particles Pipe diameter Sediment concentration

来源：评论

学校读者我要写书评

暂无评论

Research on multi-objective optimization method in the design of high-power nanocrystalline alloy high-frequency transformer

引用

JOURNAL OF MAGNETISM AND MAGNETIC MATERIALS 2024年 606卷

作者： Zhang, Pengning Li, Pengyang Zhu, Hailong Li, Bofan Wang, Ning Li, Wei Li, Lei China Univ Min & Technol Beijing Sch Mech & Elect Engn Beijing 100083 Peoples R China State Grid Corp China North Elect Power Dispatch & Control Sub Ctr Beijing 100053 Peoples R China

The optimization design of high-frequency transformer (HFT) is a multi-objective optimization problem owing to the mutual constraints between design parameters such as power density, efficiency and temperature rise, and traditional design methods often face difficulties in balancing and selecting multiple design parameters. Therefore, this paper derives the structural parameters of HFT, calculates the magnetic core losses and winding losses considering temperature effects, and establishes a 7-node thermal network model. Selecting power density and efficiency as optimization objectives, the HFT is optimized based on multi-objective particle swarm optimization algorithm and non-dominated genetic algorithm, and compared with traditional free parameter scanning method to analyze the advantages of intelligent optimization algorithm. Further, the influence of temperature rise calculation methods on the optimization design results of HFT is compared and analyzed. Finally, a nanocrystalline HFT with 17 MW/m3 power density and 99 % efficiency and an optimization scheme of HFT with 26 MW/m3 power density and 99.2 % efficiency are designed based on the proposed optimization design processes. The effectiveness of the calculation model and optimization method is verified through experimental measurement, and the essential principle of improving the HFT performance through optimization methods is explored through finite element simulation, providing theoretical reference and data support for the optimization design of nanocrystalline HFT.

关键词： High-frequency transformer Multi-objective optimization Optimization method intelligence algorithm Temperature rise calculation

来源：评论

学校读者我要写书评

暂无评论

Slope Stability Analysis Based on Leader Dolphins Herd algorithm and Simplified Bishop Method

引用

IEEE ACCESS 2021年 9卷 28251-28259页

作者： Ma, Li Zhao, Jianqiang Zhang, Jianguo Xiao, Shuangshuang Xian Univ Sci & Technol Sch Energy Engn Xian 710054 Peoples R China China Univ Min & Technol State Key Lab Geomech & Deep Underground Engn Xuzhou 221116 Jiangsu Peoples R China Xuzhou Univ Technol Sch Math & Stat Xuzhou 221018 Jiangsu Peoples R China

According to the principles of bionics and predatory behavior of dolphins, Leader Dolphin Herd algorithm (LDHA) was designed, whose performance is also tested on the basis of heuristic algorithm, showing that LDHA is obviously superior to the other algorithms in calculation accuracy and efficiency. The minimum safety factor of sliding surface was taken as the objective function, the recurrence formula of safety factor based on simplified Bishop Method was regarded as constraint condition, a non-linear multi-objective optimization mathematical model used for analyzing slope stability was established. the process of searching slope sliding surface and solving safety factor were designed on account of LDHA. By taking homogeneous slope and layered slope as calculation examples compared to other intelligent algorithms, the results show that the calculation result of LDHA is close to that of Genetic algorithm and Simulated Annealing algorithm and improved Genetic algorithm. Moreover, the LDHA has less iterations and faster convergence rate, and the result is closer to the optimal solution, which indicates the feasibility and practicability of LDHA in solving the minimum stability safety factor.

关键词： Dolphins Optimization Safety Heuristic algorithms Surface treatment Simulated annealing Genetic algorithms Leader Dolphin Herd algorithm slope stability analysis model Simplified Bishop Method intelligence algorithm

来源：评论

学校读者我要写书评

暂无评论

Modelling method of data-driven model combined with a priori knowledge and its application in average particle size estimation of composite colloidal sols

引用

CANADIAN JOURNAL OF CHEMICAL ENGINEERING 2021年第2期99卷 558-570页

作者： Zhou, Yang Li, Shaojun East China Univ Sci & Technol Key Lab Adv Control & Optimizat Chem Proc Minist Educ Shanghai 200237 Peoples R China

A universally applicable hybrid modelling method is proposed for nonlinear industrial processes that combine the a priori process knowledge with a data-driven model. This method constructs a unified framework for the modelling process by integrating a data-driven modelling technique, sampling detection technique, constraint optimization problem, and an evolutionary algorithm. In the modelling process, a swarm intelligence algorithm is used to optimize the model parameters under the circumstances of satisfying the constraints of a priori knowledge. By adding the constraints of process a priori knowledge, more information can be obtained about the actual process and the over-fitting problem can be avoided to some extent, especially when modelling a system with a small quantity of samples. In order to show the effectiveness of the method proposed in this paper, two general data-driven models, the polynomial regression model and radial basis function network model, are used as case studies. Moreover, a function simulation experiment is designed to test effectiveness, and applied to estimate average particle size of ZrO2-TiO2 composite colloidal sols.

关键词： constraint handling data-driven modelling hybrid model intelligence algorithm prior knowledge

来源：评论

学校读者我要写书评

暂无评论

A Survey of intelligence Optimization algorithm with Thermodynamics

A Survey of Intelligence Optimization Algorithm with Thermod...

引用

International Conference on Advances in Materials Science and Information Technologies in Industry (AMSITI)

作者： Xu, Xing Hu, Na Ying, Weiqin Wuhan Univ State Key Lab Software Engn Wuhan 430072 Peoples R China

ISBN: (纸本)9783038350125

There are many similarities between the intelligence algorithms and the Statistical Physics and Thermodynamics in many aspects, such as the research object, tasks and methods. Firstly, this paper presents the hybrid intelligence algorithms improved by Thermodynamics. Then the theory analysis of intelligence algorithms by Thermodynamics is presented. Finally, a new research direction, that is the novel intelligence algorithm based on statistical physics and thermodynamics, is proposed for the future.

关键词： intelligence algorithm Thermodynamics Statistical Physics Optimization

来源：评论

学校读者我要写书评

暂无评论

Impact of the Pandemic on Online Grocery Delivery:Taking Instacart Company as an Example

Impact of the Pandemic on Online Grocery Delivery:Taking Ins...

引用

作者： Jiaxuan Tang Syracuse University

The Pandemic has had an unprecedented impact on online grocery shopping;the worldwide lockdown has made online grocery shopping a rigid demand for citizens. During the global pandemic, many online grocery shopping companies grew up rapidly to the enormous demand. Especially Instacart, the most developed company in online grocery shopping,gained huge success in late 2019. This article analyzes Instacart's strengths, such as the three algorithms, its weaknesses, and how it can grow sustainably after Covid-19.

关键词： online grocery shopping Instacart Covid-19 intelligence algorithm

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：