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Research on Nonlinear Inversion of Pre-stack AVO based on intelligent optimization algorithm

AGRO FOOD INDUSTRY HI-TECH 2017年第3期28卷 1848-1851页

作者： Li, Xingjun Xijing Univ Sch Sci Xian 710123 Peoples R China

With the development of the times, people's demand for oil and gas has been increasing;this trend has also led to the progress and development of oil and gas exploration and testing technology. As a new and scientific theory, pre-stack AVO inversion technique is of great significance for oil and gas exploration, and the intelligent optimization algorithm also provides some opportunities for the development and progress of the technology. In view of the current situation of the increasing use of oil and gas in China and the not mature application of pre-stack AVO inversion technology, the technology was analyzed in this paper. The results show that the pre-stack AVO inversion technique is of positive significance to oil and gas exploration.

关键词： intelligent optimization algorithm pre-stack AVO inversion nonlinear geological exploration

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A novel variable selection method based on combined moving window and intelligent optimization algorithm for variable selection in chemical modeling

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SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2021年 246卷 118986-118986页

作者： Zhang, Pengfei Xu, Zhuopin Wang, Qi Fan, Shuang Cheng, Weimin Wang, Haiping Wu, Yuejin Chinese Acad Sci Hefei Inst Phys Sci Hefei 230031 Peoples R China Univ Sci & Technol China Hefei 230026 Peoples R China

We propose a new wavelength selection algorithm based on combined moving window(CMW) and variable dimension particle swarm optimization (VDPSO) algorithm. CMW retains the advantages of the moving window algorithm, and different windows can overlap each other to realize automatic optimization of spectral interval width and number. VDPSO algorithms improve the PSO algorithm. They can search the data space in different dimensions, and reduce the risk of limited local extrema and over fitting. Four different high-performance variable selection algorithms-BOSS, VCPA, iVISSA and IRF-are compared in three NIR data sets (corn, beer and fuel). The results show that VDPSO-CMW has better performance. The Matlab codes for implementing PSO-CWM and VDPSO-CMW are freely available on the website: https://***/matlabcentral/fileexchange/ 75828-a-variable-selection-method. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Variable selection Multivariate calibration intelligent optimization algorithm Particle swarm optimization Near-infrared spectroscopy

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A novel deep learning framework for landslide susceptibility assessment using improved deep belief networks with the intelligent optimization algorithm

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COMPUTERS AND GEOTECHNICS 2024年 167卷

作者： Meng, Shaoqiang Shi, Zhenming Li, Gang Peng, Ming Liu, Liu Zheng, Hongchao Zhou, Changshi Tongji Univ Shanghai Res Inst Intelligent Autonomous Syst Shanghai 200092 Peoples R China Tongji Univ Key Lab Geotech & Underground Engn Minist Educ Shanghai 200092 Peoples R China Tongji Univ Coll Civil Engn Dept Geotech Engn Shanghai 200092 Peoples R China Chinese Acad Sci Inst Rock & Soil Mech State Key Lab Geomech & Geotech Engn Wuhan 430071 Peoples R China

This research proposed a novel deep learning framework that combines the Laplace function sparse regularized continuous deep belief network (LSCDBN) and the Gray Wolf optimization algorithm (GWO) and the Whale optimization algorithm (WOA), to perform landslide susceptibility assessment. This framework mitigates the challenges of feature homogenization for continuous input variables for landslide condition factors, limited landslide samples, and local optima in the training process. To facilitate this investigation, a meticulous compilation of existing landslide occurrences was used to create a database comprising 18 landslide conditioning factors. To compare the performance of the model, a set of statistical indicators was employed. The results demonstrate the superior performance of both the LSCDBN-GWO model (AUC = 0.952, RMSE = 0.182) and LSCDBN-WOA model (AUC = 0.964, RMSE = 0.174) when compared to the alone LSCDBN model (AUC = 0.913, RMSE = 0.291). It is noteworthy that the performance of the LSCDBN model outperformed that of lone machine learning models (SVM, BP, RF, and LR), lone deep learning models (RNN and CNN), and hybrid deep learning models (CNN-GWO and CNN-WOA). Therefore, it is evident that the proposed LSCDBN-WOA framework can generate models that are optimally suited for landslide susceptibility assessment.

关键词： Landslide susceptibility assessment Deep learning Deep belief network Frequency ratio intelligent optimization algorithm

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Research on the Application of intelligent optimization algorithm in Mechanical Design

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Mechanical Engineering Science 2024年第1期6卷 26-29页

作者： Donglai LUAN University of Shanghai for Science and Technology Shanghai200093China

intelligent optimization algorithm belongs to a kind of emerging technology,show good characteristics,such as high performance,applicability,its algorithm includes many contents,including genetic,particle swarm and artificial neural network algorithm,compared with the traditional optimization way,these algorithms can be applied to a variety of situations,meet the demand of solution,in the mechanical design industry has wide application *** paper analyzes the application of the algorithm in mechanical design and the comparison of the results to verify the significance of the intelligent optimization algorithm in mechanical design.

关键词： intelligent optimization algorithm mechanical design application

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Research on Community intelligent Logistics UAV Scheduling Based on intelligent optimization algorithm

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电脑学刊 2022年第6期33卷 61-71页

作者： Ya-Ping Li Shang-Cai Chi

"The last mile" is a key problem that needs to be solved urgently by major e-commerce companies, and also an important means to improve their competitiveness in e-commerce activities. In this paper, aiming at the problems existing in the current community intelligent logistics, from the perspective of the entire logistics system, combined with the actual distribution of logistics sites and distribution methods, we studied the scheduling problem of UAVs using different strategies when returning to the site after distribution. First, the UAV distribution time model is established, and then the model is solved with the minimum completion time of goods distribution as the goal. Finally, the feasibility of the algorithm is verified through simulation experiments, and the optimal scheduling strategy of UAV is given in combination with the actual operation, thus guiding e-commerce to build a reasonable intelligent logistics system.

关键词： unmanned aerial vehicle scheduling strategy intelligent optimization algorithm

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Multi-threshold segmentation with exponential entropy based on co-occurrence matrix and intelligent optimization algorithm 23

Multi-threshold segmentation with exponential entropy based ...

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Proceedings of the 2023 6th International Conference on Artificial Intelligence and Pattern Recognition

作者： Jinjing Ai Jiulun Fan Xi'an University of Posts and Telecommunications China

ISBN: (纸本)9798400707674

The co-occurrence matrix is a way to describe the spatially relevant information of an image. The image thresholding method based on the co-occurrence matrix has its advantages over the thresholding method based on 2D grey-scale histogram. The traditional 2D grey-scale histogram requires two thresholds for binary segmentation, whereas the grey-scale co-occurrence matrix requires only one threshold for binary segmentation. When performing multi-threshold segmentation of an image, using co-occurrence matrix reduces the number of thresholds by half compared to using two-dimensional grey-scale histogram. In this paper, exponential entropy is used to implement co-occurrence matrix threshold selection, and swarm intelligent optimization algorithms is used to reduce the computational effort of multi-threshold selection. This method is compared with the 2D histogram-based method and the traditional log-entropy-based threshold segmentation method on BSD500 images, and the metrics of PSNR, FSIM, and SSIM are analysed in conjunction with the three intelligent optimization algorithms, and the methods in this paper are all superior to other comparative methods.

关键词： co-occurrence matrix exponential entropy image segmentation intelligent optimization algorithm multi-level thresholding

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NONINVASIVE ELECTROMAGNETIC SOURCE IMAGING BASED ON IMPROVED intelligent optimization algorithm

NONINVASIVE ELECTROMAGNETIC SOURCE IMAGING BASED ON IMPROVED...

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第十六届国际体视学与图像分析学术会议

作者： Yan LIU Xiaolin LI Siqi ZHANG Bo PENG Wangcheng ZHOU Yakang DAI Suzhou Institute of Biomedical Engineering and Technology Chinese Academy of Science Harbin University of Science and Technology

Electrophysiological/Magnetoencephalography source imaging(E/MSI) is widely used to noninvasively reconstruct brain sources from scalp measurements with high temporal *** of the key points of E/MSI is solving ill-conditioned equations,which highly depends on optimization *** optimization algorithms are more sensitive to the initial iteration points and their results are more influenced by the initial *** Particle Swarm optimization(QPSO) is an intelligent optimization algorithm with strong global optimization ability due to the randomness of particle *** this paper,the concept of population diversity was introduced,to control the population diversity of the QPSO,and then the DQPSO(QPSO with population diversity control) algorithm was *** maintaining a high population diversity in the whole iterative process of DQPSO,the global search capability was further *** the test and application of the algorithm were carried *** of all,the superiority of DQPSO algorithm in solving ill-conditioned equations was preliminarily verified through solving Hilbert ***,the DQPSO was used for the localization of simulated epileptic foci to deal with the ill-condition of the E/*** with the head structure information of high spatial resolution provided by MRI of brain structure,the personalized source imaging and localization with high spatial and temporal resolution was realized based on DQPSO algorithm in this *** results show that the loss of population diversity in the late iteration is avoided through the *** higher population diversity endows the DQPSO with a more powerful global traversal *** improves the accuracy and stability of the source imaging and localization compared with other algorithms,providing a more suitable tool for realizing E/MSI.

关键词： quantum particle swarm optimization intelligent optimization algorithm EEG/MEG source imaging real head model Ill-conditioned problem

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Research Progress of intelligent optimization algorithm in Big Data Background

Research Progress of Intelligent Optimization Algorithm in B...

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2nd International Conference on Artificial Intelligence and Engineering Applications(AIEA 2017)

作者： DUO ZHANG College of Management and Economics Tianjin University

Big data is the inevitable outcome of the rapid development of modern information technology, effective analysis and processing of big data will not only bring great economic value, but will also promote social development. Under the big data environment, the data scale, speed of emergence and its difficulty make optimizing issues very complex. In recent decades, genetic algorithm(GA), particle swarm optimization(PSO), ant colony optimization(ACO), Artificial Fish School algorithm, Bacteria Foraging optimization algorithm(BFOA), artificial neural networks(ANNs) and other multi-population intelligent algorithms appeared. In this paper, several typical intelligent optimization algorithms are introduced, including genetic algorithm, particle swarm optimization algorithm, ant colony algorithm, artificial fish swarm algorithm and bacterial foraging algorithm. The basic principles of five algorithms are described respectively, along with the direction of improvement and feasible applications.

关键词： Big data intelligent optimization algorithm algorithm optimization genetic algorithm

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Design and Realization of College Student Employment Management System Based on intelligent optimization algorithm 21

Design and Realization of College Student Employment Managem...

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2021 2nd Artificial Intelligence and Complex Systems Conference

作者： Jie Li ZhaoYi Xu Wuhan Business University China

ISBN: (纸本)9781450384100

Colleges and universities not only shoulder the major task of talent training, but also are responsible for recommending the trained talents to suitable positions. In the face of fierce competition for talents, how to effectively provide students with suitable jobs or employment information has become an urgent problem facing the employment departments of major universities. This article focuses on the research on the design and implementation of the employment management system for college students based on intelligent optimization algorithms. This article first investigated the needs of college students for employment services provided by the employment guidance center, summarized the functional needs of the employment management system, divided the system according to user roles and designed the system with corresponding functional modules. Finally, through the system function test and performance test, the reliability of the system is verified. Experimental data shows that when the number of users is 300, the response time for logging in to the system, information entry, and information query is 1.60s, 2.46s, and 1.66s, respectively. It can be seen that the system responds quickly and meets the needs of users.

关键词： College graduates Employment management Function analysis intelligent optimization algorithm

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Optimal Synchronization in Dynamically Coupled Fractional-order Chaotic Systems Based on intelligent optimization algorithm with Simulink

Optimal Synchronization in Dynamically Coupled Fractional-or...

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第35届中国控制与决策会议

作者： Huiling Xi Ruixia Zhang School of Mathematics North University of China

In this paper,for the synchronization control of the dynamically coupled fractional-order Rossler system,by reasonably selecting the optimization objective function and using the efficient global optimization ability of the intelligent optimization algorithm,the optimal values of the error system parameters are directly determined;besides,a fractional integral optimal sliding mode control laws is designed,so that the parameters are changed from unknown to known,which avoids the construction of Lyapunov function according to the Lyapunov stability principle and the large amount of calculation of trial and error method,and provides an idea for us to determine the parameters in the system or *** the help of MATLAB Simulink,the fractional differential solver is obtained and the Simulink block diagrams of the systems is *** fast synchronization of the dynamically coupled drive and response system can be achieved by substituting the optimal parameter value into the *** numerical simulation results verify the feasibility and effectiveness of the proposed method.

关键词： Dynamically coupled fractional-order chaotic system intelligent optimization algorithm Matlab Simulink Optimal synchronization Sliding mode control

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