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Research on millet origin identification model based on improved parrot optimizer optimized regularized extreme learning machine

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JOURNAL OF FOOD COMPOSITION AND ANALYSIS 2025年 141卷

作者： Gao, Peng Wang, Na Lu, Yang Liu, Jinming Wang, Guannan Hou, Rui Heilongjiang Bayi Agr Univ Coll Informat & Elect Engn Daqing 163319 Heilongjiang Peoples R China

To achieve nondestructive identification of millet origins, near-infrared spectroscopy technology was employed to obtain the original spectral data of millet. By integrating the Parrot Optimizer (PO) with the Regularized Extreme Learning Machine (RELM), the model achieved an accuracy of 83.67 % in millet origin identification. To further enhance model performance, this study incorporated strategies such as chaotic mapping and adaptivity into PO, resulting in the Improved Parrot Optimizer (IPO). The IPO was then combined with RELM to construct the IPO-RELM model, which significantly improved the model's generalization capability and robustness. Experimental results demonstrated that the IPO-RELM model outperformed the RELM model, achieving an accuracy of 98.33 %, a precision of 98.49 %, a recall of 98.33 %, an F1 score of 98.41 %, and a Kappa coefficient of 98 %, representing respective improvements of 11.32 %, 7.92 %, 11.32 %, 9.62 %, and 13.90 % over the traditional RELM model. Furthermore, the performance of the IPO-RELM model was validated using two publicly available datasets, confirming its superiority over the conventional RELM model. Compared to the PO algorithm, the IPO algorithm exhibited enhanced global search and local optimization capabilities with faster convergence speed. The IPO-RELM model accurately and efficiently identified millet origin information, providing robust support for ensuring millet quality and safety, while also contributing to the protection of the uniqueness and market value of geographically indicated agricultural products.

关键词： Near-infrared spectroscopy Millet Parrot optimizer RELM Origin identification intelligent optimization algorithm

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CORPORATE FRAUD DETECTION BASED ON IMPROVED BP NEURAL NETWORK

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COMPUTING AND INFORMATICS 2024年第3期43卷 611-632页

作者： Liu, Wei Liu, MingMing Yan, Chun Qi, Man Zhang, LuLu Shandong Univ Sci & Technol Coll Comp Sci & Engn Qingdao 266590 Peoples R China Shandong Univ Sci & Technol Coll Math & Syst Sci Qingdao 266590 Peoples R China Canterbury Christ Church Univ Sch Engn Technol & Design Canterbury CT1 1QU England

Corporate fraud risk detection is a branch of fraud. It may exist in various industries and cause economic problems. Effective identification of corporate fraud can protect the safety of funds for investors in some sense. This paper proposes a classifier model of a fractional -order immune BP neural network based on the self -attention mechanism to improve efficiency. The improved artificial immune algorithm with dynamic region contraction strategy is used to optimize the initialization process of the BP neural network. Furthermore, it combines the self -attention mechanism to design the input layer. Finally, Caputo fractional noncausal calculus is used to optimize the parameter updating process in BP neural network. The experiment results indicate that our model has fast convergence rate and powerful capacity of detection, and performs efficiently in detecting fraud behaviors.

关键词： intelligent optimization algorithm self-attention mechanism BP neural network fractional-order fraud detection

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Particle Swarm-Grey Wolf Cooperation algorithm Based on Microservice Container Scheduling Problem

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IEEE ACCESS 2023年 11卷 16667-16682页

作者： Chen, Xinying Wu, Yuefeng Xiao, Siyi Dalian Jiaotong Univ Sch Comp & Commun Engn Dalian 116600 Liaoning Peoples R China

In recent years, microservices have been very widely used as a new application development technology in edge computing, IoT, and cloud computing. Application containerization technology is one of its core technologies, which allows multiple containers to be deployed within the same physical node. Then a single physical node could provide different services to user. How to rationally deploy containers on a cluster of physical nodes is one of the main research directions nowadays. Although a number of researchers have modeled the microservice container scheduling problem and proposed effective solutions, there are still shortcomings, for example, the slow speed of finding the optimal solution and the tendency of the algorithm to fall into local optimality. This paper propose a Particle Swarm - Grey Wolf Cooperation algorithm based on Microservice Container Scheduling Problem (PS-GWCA) by using particle swarm optimization algorithm (PSO) and grey wolf algorithm (GWO) in a multi-core parallel way, which enables the two algorithms to complement each other in the whole search process through the information exchange between populations. In the early of the search stage, the GWO can use its global search capability to guide the PSO to jump out of the local optimum to avoid premature convergence, and in the late of the search stage, the PSO can enhance the search capability of the GWO on the pareto optimal frontier. The experimental results show that compared with the other three algorithms, the algorithm optimizes 18.07% in network transmission cost, 14.67% in local load balancing, 20.66% in global load balancing, and 7.5% in search speed, and 5.69% in service reliability.

关键词： intelligent optimization algorithm PSO microservice container scheduling GWO pareto optimal

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Artificial Intelligence Methods Applied to Catalytic Cracking Processes

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Big Data Mining and Analytics 2023年第3期6卷 361-380页

作者： Fan Yang Mao Xu Wenqiang Lei Jiancheng Lv College of Computer Science Sichuan UniversityChengdu 610041China Algorithm and Big Data Center New Hope Liuhe Co LtdChengdu 610000China. Data Intelligence Lab New Hope Liuhe Co LtdChengdu 610000China. College of Computer Science Sichuan UniversityChengdu 610041China.

Fluidic Catalytic Cracking(FCC)is a complex petrochemical process affected by many highly non-linear and interrelated *** yield analysis,flue gas desulfurization prediction,and abnormal condition warning are several key research directions in *** paper will sort out the relevant research results of the existing Artificial Intelligence(AI)algorithms applied to the analysis and optimization of catalytic cracking processes,with a view to providing help for the follow-up *** with the traditional mathematical mechanism method,the AI method can effectively solve the difficulties in FCC process modeling,such as high-dimensional,nonlinear,strong correlation,and large *** methods applied in product yield analysis build models based on massive *** fitting the functional relationship between operating variables and products,the excessive simplification of mechanism model can be avoided,resulting in high model *** methods applied in flue gas desulfurization can be usually divided into two stages:modeling and *** the modeling stage,data-driven methods are often used to build the system model or rule base;In the optimization stage,heuristic search or reinforcement learning methods can be applied to find the optimal operating parameters based on the constructed model or rule *** methods,including data-driven and knowledge-driven algorithms,are widely used in the abnormal condition ***-driven methods have advantages in interpretability and generalization,but disadvantages in construction difficulty and prediction *** the data-driven methods are just the ***,some studies combine these two methods to obtain better results.

关键词： neural networks intelligent optimization algorithm catalytic cracking lumped kinetics

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Kinematic parameters calibration of industrial robot based on RWS-PSO algorithm

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE 2023年第14期237卷 3210-3220页

作者： Li, Hang Hu, Xiaobing Zhang, Xuejian Wei, Shangyun Luo, Qingyi Sichuan Univ Sch Mech Engn Chengdu Peoples R China Sichuan Univ Yibin R&D Pk Yibin Peoples R China Sichuan Univ Sch Mech Engn 24South Sect 1First Ring Rd Chengdu 610065 Peoples R China

The positioning accuracy of an industrial robot has a significant impact on its application in precision manufacturing, and it is necessary to calibrate robot kinematic parameters. Previous studies have established numerous nonlinear equations to solve the kinematic parameters, which are complicated and time consuming. A standard particle swarm optimization (PSO) algorithm is limited by long running time and low solution efficiency. Therefore, in this study, a dynamic particle swarm optimization algorithm based on roulette wheel selection (RWS-PSO) is proposed to realize the kinematic parameters calibration. First, a kinematics model is constructed using the standard Denavit-Hartenberg (D-H) method, and the theoretical and actual values of the spatial position of the robot endpoint are obtained via forward kinematics and a Laser Tracker, respectively. Next, the kinematic parameters calibration problem is transformed into a solution of a high-dimensional nonlinear equation using the proposed RWS-PSO algorithm. In the proposed RWS-PSO algorithm, the inertia factor is considered as linearly decreasing and the number of particles is selected by the roulette wheel selection (RWS) to improve its computational efficiency. The proposed RWS-PSO and standard PSO algorithms are compared based on various indices by simulation. The results reveal that the time cost of the RWS-PSO algorithm is much lower than that of the standard PSO algorithm on the basis of high precision and a reduced running time of approximately 53%. Finally, the kinematic parameter errors obtained by the two algorithms are compensated. According to the experimental results, the positioning accuracy of the robot in three (x-, y-, and z-) directions is improved by 78%, 46%, and 67%, respectively, compared to that of before compensation, which proves that the RWS-PSO algorithm is effective and practical for kinematic parameters calibration.

关键词： Industrial robot positioning accuracy kinematic parameters calibration intelligent optimization algorithm laser tracker

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optimization Modeling for Vibration of Composite Conical Shell Suitable for A Wide Range of Complex Boundaries: Used for Discontinuous Winding Structure

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JOURNAL OF VIBRATION ENGINEERING & TECHNOLOGIES 2024年第SUPPL 1期12卷 531-556页

作者： Wang, Ting Fan, Ye Zhao, Yang Li, Xinghong Wang, Hui Lu, Sheng Chongqing Univ Posts & Telecommun Inst Adv Sci Chongqing 400065 Peoples R China Chongqing Univ Posts & Telecommun Key Lab Ind Internet Things & Networked Control Minist Educ Chongqing 400065 Peoples R China Chongqing Univ Posts & Telecommun Coll Adv Mfg Engn Chongqing 400065 Peoples R China Xi An Jiao Tong Univ State Key Lab Strength & Vibrat Mech Struct Xian 710049 Peoples R China

BackgroundThe study of shell vibration also has clear engineering significance and scientific *** practical engineering, the spring stiffness configuration of the classical boundary is difficult to simulate the actual boundary situation, which in turn requires the use of elastic boundaries for *** this, this paper is a pioneering proposal to optimize the spring stiffness setting by introducing an intelligent optimization algorithm. Firstly, the vibration model of an isotropic truncated conical shell is established by applying a modified Fourier series method. Then the new characteristic equation is derived by setting the artificial spring stiffness as the parameter to be optimized. The objective function is then obtained from the deviations of the modal parameters to build the optimization model. Subsequently, the optimized model is optimized using an improved particle swarm algorithm and an immune algorithm. Finally, the optimized semi-analytical solution is compared with the finite element analysis results for *** results show that the method in this paper can find a better spring stiffness configuration to simulate complex boundary *** optimization algorithm can be applied to the cone, column, spherical shell, and other shells and combination shells. The stator end winding is used as an example. Similarly, and characteristic equation under the theory of composite materials are innovatively established;the objective function is extended to the optimization model of characteristic equation for stator-winding. To solve the vibration optimization model of complex structural constraints, the pathological characteristics of data introduced by the numerical difference between the small vibration and the order of magnitude of constraint stiffness are solved. The spring stiffness of small-diameter end of the boundary spring for the stator-winding model has been optimized.

关键词： Energy method Conical shell Complex boundary intelligent optimization algorithm Artificial spring Stator winding

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An intelligent CPSOGSA-based mixed H2/H∞ robust controller for the multi-hydro-turbine governing system with sharing common penstock

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RENEWABLE ENERGY 2023年第1期206卷 481-497页

作者： Dao, Fang Zou, Yidong Zeng, Yun Qian, Jing Li, Xiang Kunming Univ Sci & Technol Faulty Met & Energy Engn Kunming 650093 Peoples R China Wuhan Univ Sch Power & Mech Engn Wuhan 430072 Peoples R China Kunming Univ Sci & Technol Kunming 650093 Peoples R China

In order to improve the stability of the multi-hydro-turbine governing system with sharing one penstock (MGSSP) during operation, an intelligent CPSOGSA-based mixed H2/H infinity robust controller with an integrator is proposed. The excellent control performance of the H2/H infinity robust controller is used to stabilize the system. Firstly, the mathematical model of the MGSSP is derived from the transfer function. Secondly, the robust controller is designed based on the linear matrix inequality (LMI), considered the optimal and robust performance. The modified hybrid PSOGSA with chaotic maps algorithm (CPSOGSA) is also introduced in the design process to ensure the controller is optimal. The designed robust controller is simple in structure, low in order, and easy to implement. Simulation experiments show that the CPSOGSA can complete the optimization of the controller with less than ten iterations. The designed robust controller can stabilize the system in less than 10 s for different operating conditions. The robust controller enables the controlled object to exhibit better dynamic performance than the conventional controller. The research results can provide a reference for the control application of the extended operation of the hydro-turbine governing system (HTGS).

关键词： Multi-hydro-turbine governing system with sharing one penstock (MGSSP) H2 H? robust controller intelligent optimization algorithm Linear matrix inequality (LMI)

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Tool remaining useful life prediction and parameters optimization in milling 508III steel

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2023年第3-4期129卷 1741-1757页

作者： Gai, Xiaoyu Cheng, Yaonan Guan, Rui Jin, Yingbo Lu, Mengda Zhou, Shilong Xue, Jing Harbin Univ Sci & Technol Key Lab Adv Mfg & Intelligent Technol Minist Educ Harbin 150080 Peoples R China Harbin Univ Sci & Technol Sch Mech & Power Engn 52 Xuefu RdPOB 92 Harbin 150080 Peoples R China

Tool remaining useful life prediction (RUL) and parameters optimization are very important for the normal operation of the machining system, the full use of the tool excellent performance, and the tool health management. This paper proposes a RUL prediction based on WOA-optimized XGBoost with multidomain feature fusion. Based on the milling 508III experiment, Kernel principal component analysis (KPCA) is used for multi-domain sensitive feature dimensionality reduction fusion. The whale optimization algorithm (WOA) is used to optimize the extreme gradient lifting integrated learning (XGBoost) method, and a WOA-XGBoost correlation model integrating features and tool life evolution is established to predict the RUL. The effectiveness and reliability are verified by comparison with other methods and milling experiments. A parameter optimization method combining multi-objective genetic algorithm and multi-attribute decision-making is proposed. Based on tool life and cutting force experiments, the multi-objective cutting parameter optimization problem is modeled to provide a theoretical basis for cutting parameter optimization. Taking tool life, cutting efficiency, and cutting force as evaluation indicators, the optimal cutting parameter combination that can be accepted by all optimization objectives is obtained. Results show that the prediction performance of the RUL prediction method proposed is better compared with other ensemble learning methods adopted in this paper. Based on the limited experimental data, the RUL can be predicted effectively and reliably. Through the parameter optimization method proposed, the acceptable optimal combination is: the cutting speed is 284.78 m/min, feed per tooth is 0.3 mm/z, and axial cutting depth is 1.999 mm. These findings may provide theoretical basis and technical support for promoting the intelligent development of tool status warning and cutting process optimization in machining.

关键词： Remaining useful life prediction Parameters optimization Milling 508III steel experiment Integrated learning intelligent optimization algorithm

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Dynamic spiral updating whale optimization algorithm for solving optimal power flow problem

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JOURNAL OF SUPERCOMPUTING 2023年第17期79卷 19959-20000页

作者： Wang, Fengxian Feng, Shaozhi Pan, Youmei Zhang, Huanlong Bi, Senlin Zhang, Jiaxiang Zhengzhou Univ Light Ind Coll Elect & Informat Engn Ke Xue Ave Zhengzhou 450002 Henan Peoples R China

To address the problems of slow convergence of the traditional whale optimization algorithm and the tendency to fall into local optimal solutions, an improved algorithm based on a combination of elite disturbance opposition-based learning and dynamic spiral updating (OWOA) is proposed, which improves the convergence speed of the algorithm and increases the probability of the algorithm jumping out of the local optimum to find the global optimum. First, an elite disturbance opposition-based learning strategy is used to initialize the whale population to ensure diversity of the population, and then, random perturbation of the elite whales increases the exploratory capacity of the population to avoid falling into local optimal solutions. Secondly, a dynamic spiral update strategy is used to dynamically adjust the shape of the spiral with the number of iterations, avoiding the algorithm from falling into a local optimum at the beginning of the iteration and improving the convergence speed of the algorithm at a later stage, which improves the ability of the algorithm to find the global optimum. The experimental results show that the OWOA algorithm, due to the introduction of the lite disturbance opposition-based learning strategy and dynamic spiral updating strategy, has higher convergence speed and accuracy on single-peak functions, multi-peak functions and multi-dimensional functions compared to other algorithms. Furthermore, good results have been obtained in the practical application of OPF. This shows that our improvements are effective.

关键词： Whale optimization algorithm Opposition-based learning Dynamic spiral updating intelligent optimization algorithm Optimum power flow

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A combined prediction model based on secondary decomposition and intelligence optimization for carbon emission

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APPLIED MATHEMATICAL MODELLING 2023年第1期121卷 484-505页

作者： Yang, Hong Wang, Maozhu Li, Guohui Xian Univ Posts & Telecommun Sch Elect Engn Xian 710121 Shaanxi Peoples R China

Accurate prediction of carbon emission is critical for the development of low-carbon economy. However, most carbon emission prediction studies use a single model with low prediction accuracy, and do not consider the instability of carbon emission. Therefore, this paper proposes a combined prediction model of carbon emission. Firstly, the original data is decomposed by singular spectrum decomposition to obtain a limited amount of singular spectrum components. Secondly, high complexity components are secondarily decomposed by variational mode decomposition. Then, chameleon swarm algorithm and carnivorous plant algorithm are used to train the regularization coefficients and kernel parameters of kernel extreme learning machine and least squares support vector machine respectively, and the trained model is used to predict the decomposition components. Finally, induced ordered weighted averaging operator is used to calculate the weight of single model, and error correction is introduced to further promote the prediction accuracy. The carbon emission data of China and the United States is used to make a prediction experiment. The results indicate that the proposed model is superior to other comparative models in different indexes, which provides a new idea for carbon emission prediction. & COPY;2023 Elsevier Inc. All rights reserved.

关键词： Carbon emission Secondary decomposition intelligent optimization algorithm Weighted combination Error correction

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