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Industrial defect detection and location based on greedy particle swarm optimization algorithm

INTERNATIONAL JOURNAL OF PARALLEL EMERGENT AND DISTRIBUTED SYSTEMS 2025年第3期40卷 214-221页

作者： Tang, Yaorui Yang, Bo Peng, Hong Luo, Xiaohui Xihua Univ Sch Comp & Software Engn Chengdu Peoples R China

Particle swarm optimization algorithm is a branch of evolutionary computing and can search for a better solution in a given feature space. This paper introduces the particle swarm optimization algorithm and greedy strategy for the defect detection and location of industrial components and then proposes a greedy particle swarm optimization algorithm. This paper employs sparse random projection to map the vast high-dimensional information to the low-dimensional space while maintaining the relative distance between the data. This employment helps to accelerate the training and prediction speed of the model and remove some unimportant features or noise. This algorithm first adopts particle swarm optimization (PSO) to initialize the cluster centers in an effort to minimize the maximum distance between all unlabeled data points and these centers. The algorithm then utilizes the greedy strategy to select a batch of data points to represent the corresponding features of the normal image, thereby improving the coverage of the model to the data. Experiments have shown that the results of most categories of data sets are close to or better than the current existing methods, especially in defect detection. In terms of defect localization for object categories, our method achieves a pixel-level anomaly localization index (AUROC) of 98.3% on the MVTec AD dataset. [GRAPHICS]

关键词： Evolutionary computing optimization algorithm feature extraction greedy strategy industrial defect detection and location

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Estimation of suspended sediment load utilizing a super-optimized deep learning approach informed by the red fox optimization algorithm

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EARTH SCIENCE INFORMATICS 2025年第3期18卷 1-17页

作者： Malekpour, Mohammad Mahdi Ahmadi, Mohammad Mehdi Gugliotta, Marcello Tabari, Mahmoud Mohammad Rezapour Qaderi, Kourosh Shahid Bahonar Univ Kerman Fac Agr Dept Water Engn Kerman Iran Martin Luther Univ Halle Wittenberg Inst Geosci & Geog Halle An Der Saale Germany Univ Mazandaran Fac Engn & Technol Dept Civil Engn Babolsar Mazandaran Iran

The suspended sediment load transported by rivers can be estimated using various methodologies, including those based on artificial intelligence. In this study, we employed the Long Short-Term Memory (LSTM) model to estimate the suspended sediment concentration in the Mississippi River, United States of America. The input variables for the LSTM model included river discharge, water depth, suspended sediment load, and flow velocity. To enhance the model's performance, the input data and initial parameters were optimized using the Red Fox optimization (RFO) algorithm, resulting in a super-optimized LSTM model (SLSTM-RFO) developed through a two-phase optimization process. Additionally, sediment load estimations were conducted using alternative models, specifically the Artificial Neural Network (ANN) and Generalized Regression Neural Network (GRNN) models. The performance of these models was assessed using five performance indicators, the correlation coefficient (R2), root mean square error (RMSE), mean absolute error (MAE), Nash and Sutcliffe efficiency (NS), and RMSE observations standard deviation ratio (RSR), demonstrating that the SLSTM-RFO model significantly outperformed the other models. Specifically, the SLSTM-RFO yielded improved estimation results, achieving reductions in error (RMSE) of 73.30%, 81.50%, and 82.56% compared to the LSTM, ANN, and GRNN models, respectively.

关键词： Artificial neural network Long short-term memory (LSTM) optimization algorithm Mississippi River Sedimentation concentration

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Enhancing neurological disease diagnostics: fusion of deep transfer learning with optimization algorithm for acute brain stroke prediction using facial images

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SCIENTIFIC REPORTS 2025年第1期15卷 1-21页

作者： Alrowais, Fadwa Alqahtani, Mohammed Khan, Jahangir Ben Miled, Achraf Albalawneh, Da'ad Alkharashi, Abdulwhab Al Zanin, Samah Marzouk, Radwa Princess Nourah Bint Abdulrahman Univ Coll Comp & Informat Sci Dept Comp Sci POB 84428 Riyadh 11671 Saudi Arabia Univ Bisha Coll Comp & Informat Technol Dept Informat Syst & Cyber Secur Bisha 61922 Saudi Arabia King Khalid Univ Appl Coll Mahayil Dept Comp Sci Abha Saudi Arabia Northern Border Univ Coll Sci Dept Comp Sci Ar Ar 73213 Saudi Arabia Univ Tabuk Univ Coll Umluj Dept Comp Sci Tabuk Saudi Arabia Saudi Elect Univ Coll Comp & Informat Dept Comp Sci Riyadh Saudi Arabia Prince Sattam Bin Abdulaziz Univ Coll Comp Engn & Sci Dept Comp Sci Kharj Saudi Arabia Princess Nourah bint Abdulrahman Univ Coll Comp & Informat Sci Dept Informat Syst Riyadh Saudi Arabia

Stroke is a main risk to life and fitness in current society, particularly in the aging population. Also, the stroke is recognized as a cerebrovascular accident. It contains a nervous illness, which can result from haemorrhage or ischemia of the brain veins, and regular mains to assorted motor and cognitive damages that cooperate with functionality. Screening for stroke comprises physical examination, history taking, and valuation of risk features like age or certain cardiovascular illnesses. Symptoms and signs of stroke include facial weakness. Even though computed tomography (CT) and magnetic resonance imaging (MRI) are standard diagnosis techniques, artificial intelligence (AI) systems have been constructed based on these methods, which deliver fast detection. AI is gaining high attention and is being combined into numerous areas with medicine to enhance the accuracy of analysis and the quality of patient care. This paper proposes an enhancing neurological disease diagnostics fusion of transfer learning for acute brain stroke prediction using facial images (ENDDFTL-ABSPFI) method. The proposed ENDDFTL-ABSPFI method aims to enhance brain stroke detection and classification models using facial imaging. Initially, the image pre-processing stage applies the fuzzy-based median filter (FMF) model to eliminate the noise in input image data. Furthermore, fusion models such as Inception-V3 and EfficientNet-B0 perform the feature extraction. Moreover, the hybrid of convolutional neural network and bidirectional long short-term memory (CNN-BiLSTM) model is employed for the brain stroke classification process. Finally, the multi-objective sailfish optimization (MOSFO)-based hyperparameter selection process is carried out to optimize the classification outcomes of the CNN-BiLSTM model. The simulation validation of the ENDDFTL-ABSPFI technique is investigated under the Kaggle dataset concerning various measures. The comparative evaluation of the ENDDFTL-ABSPFI technique portra

关键词： Fusion of transfer learning Acute brain stroke prediction Facial images optimization algorithm Image pre-processing

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Enhanced COVID-19 optimization algorithm for Solving Multi-Objective Optimal Power Flow Problems with Uncertain Renewable Energy Sources: A Case Study of the Iraqi High-Voltage Grid

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ENERGIES 2025年第3期18卷 478-478页

作者： Albaaj, Basim Kaplan, Orhan Gazi Univ Dept Elect & Elect Engn TR-06570 Ankara Turkiye

The optimal power flow (OPF) problem is a critical component in the design and operation of power transmission systems. Various optimization algorithms have been developed to address this issue. This paper expands the use of the coronavirus disease optimization algorithm (COVIDOA) to solve a multi-objective OPF problem (MO-OPF), incorporating renewable energy sources as distributed generation (DG) across multiple scenarios. The main objective is to minimize fuel costs, emissions, voltage deviations, and power losses. Due to its non-convex nature and computational complexity, OPF poses significant challenges. While COVIDOA has been utilized to solve engineering problems, it faces difficulties with non-linear and non-convex issues. This paper introduces an enhanced version, the enhanced COVID-19 optimization algorithm (ENHCOVIDOA), designed to improve the performance of the original method. The effectiveness of the proposed algorithm is validated through testing on IEEE 30-bus, 57-bus, and 118-bus systems, as well as a real-world 28-bus system representing Iraq's standard Iraq super grid high voltage (SISGHV 28-bus). The two-point estimation method (TPEM) is also applied to manage uncertainties in renewable energy sources in some cases, leading to cost reductions and annual savings of ($70,909.344, $817,676.64, and $5,608,782.144) for the IEEE 30-bus, 57-bus, and reality 28-bus systems, respectively. Thirteen different cases were analyzed, and the results demonstrate that ENHCOVIDOA is notably more efficient and effective than other optimization algorithms in the literature.

关键词： optimization algorithm multi-objective OPF problem distributed generation TPEM standard Iraq super grid high voltage 28-bus

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Framework design and empirical analysis of intelligent scheduling system for high-altitude photovoltaic power generation based on mixed optimization of long-nosed raccoon optimization algorithm and black winged kite optimization algorithm (COA-BKA)

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DISCOVER COMPUTING 2025年第1期28卷 1-33页

作者： Hu, Heng Xiong, Xiaoming Wang, Shuang Yan, Taidong Zhang, Yuancheng Gan, Shuang Three Gorges Grp Sichuan Daofu Renewable Energy Co Chengdu 626400 Sichuan Peoples R China Three Gorges Grp Sichuan ABa Renewable Energy Co L Chengdu 624600 Sichuan Peoples R China

This study proposes an intelligent scheduling system for high-altitude photovoltaic power generation, utilizing a hybrid optimization approach that combines the Long-nosed Raccoon optimization algorithm (COA) and the Black-winged Kite optimization algorithm (BKA) (COA-BKA). The goal is to enhance scheduling accuracy, stability, and response speed under the unique environmental conditions of high-altitude regions, such as fluctuating light intensity, extreme temperatures, and dynamic load demands. In experimental comparisons with traditional algorithms like Particle Swarm optimization (PSO) and Genetic algorithm (GA), COA-BKA achieved a scheduling accuracy of 0.98, outperforming PSO (0.92) and GA (0.90). COA-BKA also demonstrated superior convergence speed, reaching the optimal solution by the 50th iteration, while PSO and GA required more iterations (80 and 100, respectively). Additionally, COA-BKA completed scheduling in just 4.5 s, significantly faster than PSO (6.3 s) and GA (7.2 s). The system effectively handled fluctuating light intensity and load demand changes, showcasing its robust adaptability. These results suggest that COA-BKA provides a highly efficient and stable solution for intelligent scheduling in high-altitude photovoltaic power systems, improving operational efficiency and reducing costs, while offering significant advancements for real-time optimization in smart grids.

关键词： Plateau photovoltaic power generation Intelligent scheduling Long nosed raccoon optimization algorithm Black winged kite optimization algorithm Hybrid optimization COA-BKA optimization algorithm

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Dual-function waveform optimization algorithm of joint transmitter and receiver via W-ADPM

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SIGNAL PROCESSING 2025年 235卷

作者： Ji, Wenshuai Lin, Hangyu Liu, Tao Sun Yat Sen Univ Sch Elect & Commun Engn Shenzhen Campus Shenzhen 518107 Peoples R China

This paper proposes a novel algorithm for joint transmitter and receiver dual-function radar communication (DFRC) systems based on orthogonal frequency division multiplexing (OFDM) waveforms. The algorithm achieves a better detection performance while containing communication bit error rate (BER) performance, modulating communication information into the phase of the OFDM waveform using M-phase-shift keying (MPSK) schemes such as Binary-PSK (BPSK) and Qinary-PSK (QPSK). Subsequently, the waveform minimizes the weighted peak sidelobe level (WPSL) of the transmit waveform and the receive mismatch filter while ensuring the bit error rate (BER) condition to reduce sidelobes. Additionally, constraints are placed on constant amplitude, BER, mainlobe energy, and signal-to-noise ratio (SNR) loss. This paper employs a Weight Alternating Direction Method of Penalty (W-ADPM) network-based approach to simultaneously optimize the transmit waveform and receive mismatched filters to address these issues, achieving the desired effect. The simulation experiments demonstrate that the proposed algorithm has better convergence performance for the DFRC OFDM waveform compared to the Alternating Direction Method of Multipliers (ADMM) algorithm. Besides, the simulation experiments show that, compared to traditional matched filters, the jointly transmitted and received mismatched filters proposed in this paper provide better ISL cross-correlation performance while ensuring the BER.

关键词： Dual-function radar communication (DFRC) Orthogonal frequency-division multiplexing (OFDM) Weight peak sidelobe level (WPSL) Alternating direction method of penalty (ADPM) optimization algorithm

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optimization algorithm-based Elman neural network controller for continuous stirred tank reactor process model

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CURRENT SCIENCE 2021年第8期120卷 1324-1333页

作者： Baranilingesan, I KPR Inst Engn & Technol Dept Elect & Elect Engn Coimbatore 641407 Tamil Nadu India

A continuous stirred tank reactor (CSTR) is a standout nonlinear system among the most essential units of chemical industries. In this article, an Elman neural network is designed to analyse the characteristics of nonlinear behaviour of the CSTR system. The data generated employing the state-space model of CSTR are used to train the designed Elman neural network controller and the controller parameters are optimally tuned by the proposed hybrid swarm intelligence-based optimization algorithm. Two different hybridizations have been developed, including DPSO, DGSA and hybrid DPSO-DGSA and successfully employed in controller tuning. The significance of the proposed controller is validated by a comparative analysis made with conventional methods and the performance is experimentally demonstrated using MATLAB software.

关键词： Continuous stirred tank reactor neural network controller nonlinear behaviour optimization algorithm process control

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optimization algorithm based on kinetic-molecular theory

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Journal of Central South University 2013年第12期20卷 3504-3512页

作者：范朝冬欧阳红林张英杰艾朝阳 College of Electrical and Information Engineering Hunan University College of Information Science and Engineering Hunan University Institute of Cognitive Science Hunan University

Traditionally, the optimization algorithm based on physics principles has some shortcomings such as low population diversity and susceptibility to local extrema. A new optimization algorithm based on kinetic-molecular theory(KMTOA) is proposed. In the KMTOA three operators are designed: attraction, repulsion and wave. The attraction operator simulates the molecular attraction, with the molecules moving towards the optimal ones, which makes possible the optimization. The repulsion operator simulates the molecular repulsion, with the molecules diverging from the optimal ones. The wave operator simulates the thermal molecules moving irregularly, which enlarges the searching spaces and increases the population diversity and global searching ability. Experimental results indicate that KMTOA prevails over other algorithms in the robustness, solution quality, population diversity and convergence speed.

关键词： optimization algorithm heuristic search algorithm kinetic-molecular theory diversity convergence

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optimization algorithm for Reduction the Size of Dixon Resultant Matrix:A Case Study on Mechanical Application

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Computers, Materials & Continua 2019年第2期58卷 567-583页

作者： Shang Zhang Seyedmehdi Karimi Shahaboddin Shamshirband Amir Mosavi College of Computer and Information Technology China Three Gorges UniversityYichangChina Department of Mathematics Jouybar branchIslamic Azad UniversityJouybarIran Department for Management of Science and Technology Development Ton Duc Thang UniversityHo Chi Minh CityVietnam Faculty of Information Technology Ton Duc Thang UniversityHo Chi Minh CityVietnam Department Institute of Structural Mechanics Bauhaus University of WeimarGermany Institute of Automation Kando Kalman Faculty of Electrical EngineeringObuda University1431 BudapestHungary

In the process of eliminating variables in a symbolic polynomial system,the extraneous factors are referred to the unwanted parameters of resulting *** paper aims at reducing the number of these factors via optimizing the size of Dixon *** optimal configuration of Dixon matrix would lead to the enhancement of the process of computing the resultant which uses for solving polynomial *** do so,an optimization algorithm along with a number of new polynomials is introduced to replace the polynomials and implement a complexity ***,the monomial multipliers are optimally positioned to multiply each of the ***,through practical implementation and considering standard and mechanical examples the efficiency of the method is evaluated.

关键词： Dixon resultant matrix symbolic polynomial system elimination theory optimization algorithm computational complexity

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OPTIMAL CONTROL AND optimization algorithm OF NONLINEAR IMPULSIVE DELAY SYSTEM PRODUCING 1,3-PROPANEDIOL

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JOURNAL OF APPLIED MATHEMATICS AND COMPUTING 2007年第1-2期24卷 387-397页

作者： Li, Kezan Feng, Enmin Xiu, Zhilong Dalian Univ Technol Dept Appl Math Dalian 116023 Peoples R China Dalian Univ Technol Dept Biotechnol Dalian 116023 Peoples R China

According to the controllability of pulse times and the amount of jumps in the states at these times in the process of fed-batch culture producing 1,3-propanediol, this paper proposes a terminal optimal control model, whose constraint condition is the nonlinear impulsive delay system. The existence of optimal control is discussed and an optimization algorithm which is applied to each subinternal over one cycle for this optimal control problem is constructed. Finally, the numerical simulations show that the terminal intensity of producing 1,3-propanediol has been increased obviously.

关键词： Fed-batch culture impulsive delay system optimal control optimization algorithm

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