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VEHICLES 2025年第1期7卷 14-14页

作者： Khatory, Salma Chafouk, Houcine Mellouli, El Mehdi Natl Sch Appl Sci LISA Lab Fes 30050 Morocco Rouen Normandie Univ ESIGELEC IRSEEM Lab F-76000 Rouen France

Sliding Mode Control (SMC) has gained significant attention due to its simplicity, robustness, and rapid response in ensuring system stability, particularly with the Lyapunov approach. Despite its advantages, SMC faces challenges such as chattering near equilibrium, sensitivity to parameter variations, and delayed convergence. To address these issues, advanced techniques like Terminal Sliding Mode Control (TSMC) and Integral Terminal Sliding Mode Control (ITSMC) have been proposed. TSMC ensures finite-time convergence while mitigating chattering, while ITSMC further handles singularities and disturbances. Additionally, Adaptive Switching Control (ASC) based on particle swarm optimization (PSO) is applied to achieve faster convergence, suppress chattering, and enhance system robustness. The adaptive control law, utilizing a Lyapunov-based approach, is employed to estimate and compensate for external disturbances, further improving system performance under uncertainties. Gain tuning, essential for optimizing system performance and reducing tracking errors, is achieved using the efficient Teaching-Learning-Based optimization (TLBO) algorithm. This study applies TSMC, ITSMC, and ASC-based PSO to an Anti-Lock Braking System (ABS), aiming to enhance robustness, stability, and finite-time convergence while reducing chattering. Stability is analyzed through the Lyapunov theory, ensuring rigorous validation. MATLAB simulations demonstrate the effectiveness of the proposed methods in improving ABS performance, offering a valuable contribution to robust control techniques for systems operating under dynamic and uncertain conditions.

关键词： terminal sliding mode control integral TSMC adaptive control law anti-lock braking system gain-tuning particle swarm optimization algorithm finite-time convergence chattering reduction Lyapunov

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Grid Task Scheduling Strategy Based on particle swarm optimizationand Ant Colony optimization algorithm

Grid Task Scheduling Strategy Based on Particle Swarm Optimi...

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2010 International Conference on Advanced Measurement and Test (AMT 2010)

作者： Wei Pengcheng 1,2,a,Shi xi 2,b 1 Computer Science and Engineering College,Chongqing University,Chongqing,China 2 Department of Computer Science,Chongqing Education College Chongqing,China

Based on particle swarm optimization algorithm,this paper presents a grid scheduling optimization algorithm combing the advantages of Ant Colony optimization *** algorithm processes task scheduling through particle swarm optimization algorithm to get a group of relatively optimal solutions,and then conducts small-area local search with Ant Colony optimization *** analysis and results of the simulation experiments show that this scheduling algorithm effectively achieves load balancing of resources with comprehensive advantages in time efficiency and solution accuracy compared to the traditional Ant Colony optimization algorithm and particle swarm optimizationalgorithm,and can be applied to task scheduling in grid computing.

关键词： grid computing task scheduling particle swarm optimization algorithm Ant Colony optimization algorithm

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Instrumentation Amplifier Input Impedance Calibration With Machine Learning-Based optimizations

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS 2025年第2期72卷 394-398页

作者： Abdelfattah, Safaa Hussein, Hussein M. E. Shrivastava, Aatmesh Onabajo, Marvin Northeastern Univ Dept Elect & Comp Engn Boston MA 02115 USA

This brief introduces a digital calibration technique to boost the input impedance of instrumentation amplifiers (IAs) with digitally tunable input impedance. The technique employs two machine learning-driven optimization algorithms, the genetic algorithm (GA) and the particle swarm optimization (PSO) algorithm, to efficiently control integrated capacitor banks within the IA for the determination of the optimal input impedance. These algorithms offer a significant time reduction compared to a calibration with an exhaustive search, reducing calibration time by a factor of over 10(6) (with four 9-bit digital control words) while conserving computational resources. A prototype platform was developed to automatically optimize a fabricated IA test chip designed with 65-nm CMOS technology, which allows to test the machine learning algorithms using a microcontroller to control the digitally tunable input impedance. With an extra input capacitance of 100 pF, the GA algorithm achieved an input impedance of 1.75 G Omega after four generations (iterations), while the PSO algorithm achieved 1.27 G Omega with five iterations.

关键词： Impedance optimization Genetic algorithms Capacitors Calibration Capacitance Machine learning algorithms Cables particle swarm optimization Impedance measurement Input impedance calibration instrumentation amplifier genetic algorithm particle swarm optimization algorithm negative capacitance generation feedback (NCGFB)

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optimization process of designing and manufacturing of cylinder block system in cast iron industry using metaheuristic computing technique

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INTERNATIONAL JOURNAL OF INTERACTIVE DESIGN AND MANUFACTURING - IJIDEM 2025年第3期19卷 2203-2222页

作者： Urvashi, Shikha Bansal, Shikha SRM Inst Sci & Technol Dept Math Delhi NCR Campus Ghaziabad 201204 India

A cylinder block system is a complicated component within a cast iron manufacturing structure, involving several interaction subsystems throughout the production process. Given the increasing need for efficient automotive components, optimizing the design and manufacturing processes of this system to enhance its performance and production efficiency is imperative. The primary aim of this work is to design and enhance the performance of the cylinder block system utilizing a hybrid metaheuristic approach, specifically the genetic algorithm (GA) and particle swarm optimization (PSO) algorithm. The algorithms are executed in MATLAB-R2022 software for the computation of numerical results for the performance of the system. The system's mathematical model is constructed using the Markov birth-death process, and the differential associated with that model is generated from the system's state transition diagram. The Markov birth-death technique examines the system's performance and determines which subsystem is more critical and needs greater maintenance. The system's performance is enhanced through the utilization of the PSO and GA algorithms. The system's optimism parameters value is determined using these algorithms, varying both population size and generation size;these optimal parameters help the casting production industry to enhance system performance.

关键词： Design Genetic algorithm Maintenance Markov approach Performance particle swarm optimization algorithm Steady-state availability

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Pricing and Replenishment Decisions for Vegetable Commodities Based on ARIMA Prediction and particle swarm optimization algorithms

Pricing and Replenishment Decisions for Vegetable Commoditie...

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2023 IEEE International Conference on Electrical, Automation and Computer Engineering, ICEACE 2023

作者： Wang, Xiaodi Hu, Zhenshuo Wang, Zhaokun Ludong University Yantai China

ISBN: (纸本)9798350309614

Supermarkets need to make replenishment decisions for the vegetable category of the day without knowing exactly what the specific individual items and the price of the stock are. Accurate market demand analysis is crucial for replenishment and pricing decisions. In this paper, for the problems in automatic pricing and replenishment of vegetables, based on the idea of cost-plus pricing and linear programming, a model is established by determining the indicators of sales volume, selling price, and relevance of vegetables, with the goal of finding the relevance of each part and obtaining the maximum revenue, and the model is solved using the particle swarm algorithm. Firstly, considering the different selling prices and sales volumes of vegetable categories in different time periods, two-by-two correlation analysis is carried out by calculating the Pearson correlation coefficient, and the correlation heat map is used to reflect the correlation problem between each vegetable category. Then, the ARIMA model is established to predict the cost of each vegetable category, and the quadratic programming method is used to predict the total daily replenishment and pricing strategy, which gives the replenishment volume and pricing strategy of each category with the greatest benefit in the coming week. Finally, by establishing a planning model for single product selection and using particle swarm algorithm to solve the problem, different profit margins are chosen to calculate different single product pricing, and the replenishment quantity and pricing strategy for single product with the maximum revenue are formulated. The proposed model is fully integrated with reality and has the advantages of simplicity and easy to understand. © 2023 IEEE.

关键词： ARIMA analysis correlation coefficient linear programming particle swarm optimization algorithm

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MICE-PSO-RF Model for Predicting Coal Spontaneous Combustion Temperature Based on Multiple Imputation by Chained Equations

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COMBUSTION SCIENCE AND TECHNOLOGY 2025年

作者： Zheng, Xuezhao Li, Peihua Cai, Guobin Guo, Jun Liu, Yin Xian Univ Sci & Technol Coll Safety Sci & Engn 58 Yanta Mid Rd Xian 710054 Shaanxi Peoples R China Shaanxi Xikuang Zhitong Technol Co Ltd Xian Peoples R China Key Lab Urban Secur & Emergency Rescue Higher Educ Xian Peoples R China

To address the issue of missing indicator gas data in coal spontaneous combustion temperature prediction models in practical applications, a novel prediction model is proposed, which integrates the MICE (Multiple Imputation by Chained Equations) method with PSO (particle swarm optimization)-optimized RF (Random Forest) algorithm. A coal spontaneous combustion heating experiment was conducted, selecting O2, CO, CO2, CH4, and C2H6 as characteristic gases for coal combustion. Five sets of data with randomly introduced missing values were designed, and four imputation methods - KNN, RF, PSO-SVR, and MICE - were used to impute the missing values. The imputed data were then used to train three temperature prediction models (BP, RF, and SVR), with parameters optimized using PSO. The imputation and prediction results were compared. The results indicate that, across five different missing data rates, the MICE method outperforms the other imputation techniques. Without parameter tuning, all three models showed overfitting. However, after PSO optimization, the PSO-RF model achieved the highest prediction accuracy and demonstrated good stability. In practical applications, the MICE-PSO-RF model yielded an average temperature prediction error of 2.69 degrees C across 15 test sets, demonstrating reliable performance in predicting coal temperature.

关键词： Coal spontaneous combustion temperature prediction data imputation multiple imputation method random forest particle swarm optimization algorithm

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An improved retrieval method for liquid water content of the Antarctic ice sheet using SMOS data

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GEOCARTO INTERNATIONAL 2025年第1期40卷

作者： Zhou, Yi Feng, Yongjiu Cao, Yuze Chen, Shurui Wang, Rong Wang, Xiaofeng Xiao, Changjiang Ye, Zhen Tong, Xiaohua Tongji Univ Coll Surveying & Geoinformat Shanghai Peoples R China Tongji Univ Shanghai Key Lab Planetary Mapping & Remote Sensin Shanghai Peoples R China Tongji Univ Shanghai Res Inst Intelligent Autonomous Syst Shanghai Peoples R China

With global climate warming, Antarctic ice sheet melting has garnered increasing attention, as changes in liquid water content (LWC) significantly affect sea level rise and regional climate. This study integrates SMOS L-band passive microwave data with the LS-MEMLS microwave emission model and employs the particle swarm optimization algorithm to retrieve the surface LWC of the Antarctic ice sheet and its spatiotemporal variations. We analyzed LWC, surface density, and melt days across different Antarctic regions, focusing on the trends in LWC and its relationship with multi-source remote sensing products. The results indicate a rising trend in LWC and melting of the Antarctic Peninsula and coastal ice shelves from 2018 to 2020, with a notable peak in 2020, potentially related to the anomalous climatic events. This research provides new methodological and theoretical insights into Antarctic ice sheet dynamics melt and their implications for the global climate system.

关键词： Antarctic ice sheet liquid water content SMOS melt detection particle swarm optimization algorithm

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Prediction of cardiovascular disease based on multiple feature selection and improved PSO-XGBoost model

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SCIENTIFIC REPORTS 2025年第1期15卷 1-12页

作者： Cao, Kerang Liu, Chang Yang, Siqi Zhang, Yuxin Li, Lili Jung, Hoekyung Zhang, Shuo Shenyang Univ Chem Technol Coll Comp Sci & Technol Shenyang 110142 Peoples R China Key Lab Intelligent Technol Chem Proc Ind Liaoning Shenyang 110142 Peoples R China Shenyang Matern & Child Hlth Hosp Shenyang 110014 Peoples R China Paichai Univ Comp Engn Dept Daejeon 35345 South Korea

Cardiovascular disease is a common disease that threatens human health. In order to predict it more accurately, this paper proposes a cardiovascular disease prediction model that combines multiple feature selection, improved particle swarm optimization algorithm, and extreme gradient boosting tree. Firstly, the dataset is preprocessed, and an XGBoost cardiovascular disease prediction model is constructed for model training and compare it with other algorithms. Then, combined with two factor Pearson correlation analysis and feature importance ranking, multiple feature selection is performed, with the optimal feature subset as the feature input. Finally, the improved particle swarm optimization algorithm is used to adjust the hyperparameters of the extreme gradient boosting tree algorithm, and selecting the optimal hyperparameter combination to construct the MFS-DLPSO-XGBoost model. The recall, precision, accuracy, F1 score, and area under the ROC curve (AUC) of the MFS-DLPSO-XGBoost model reached 71.4%, 76.3%, 74.7%, 73.6%, and 80.8%, respectively, which increased by 3.6%, 3.2%, 2.7%, 3.2%, and 2.3% compared to XGBoost. The results indicate that the model proposed in this article has good classification performance and can provide assistance for doctors and patients in predicting and preventing heart disease.

关键词： Cardiovascular disease Machine learning XGBoost algorithm Multi feature selection particle swarm optimization algorithm Model prediction

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Economic optimization of business administration resources: Multi-objective scheduling method based on improved PSO

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JOURNAL OF COMPUTATIONAL METHODS IN SCIENCES AND ENGINEERING 2025年

作者： Li, Sizheng Jiangxi Vocat Coll Mech & Elect Technol Sch Management Engn 168 Fenglin Ave Changbei Econ Dev Zone Nanchang 330000 Peoples R China

In the context of increasing environmental challenges and the demand for sustainable development, traditional resource scheduling models in business management often fail to balance economic efficiency with environmental constraints. To address this gap, this study proposes an enhanced particle swarm optimization (PSO) algorithm, termed OBLPSO, which integrates Opposition-Based Learning (OBL) and a perturbation mechanism. First, OBL generates a high-quality initial population to improve solution diversity, while a cosine curve adaptive strategy dynamically adjusts inertia weights to balance global exploration and local exploitation. Additionally, a perturbation mechanism expands the search range, preventing premature convergence. A multi-objective optimization model is established, incorporating task time, economic cost, and environmental impact (e.g., energy consumption and pollutant emissions) to maximize resource utilization and minimize ecological harm. Experimental results demonstrate that OBLPSO reduces task processing time by 29.7% and energy consumption by 16.1% compared to benchmark algorithms (e.g., ACO, GA, and standard PSO) under large-scale tasks (2000 tasks). The proposed method provides a robust solution for sustainable resource scheduling in an enterprise management environment with economic constraints.

关键词： Business management resource scheduling particle swarm optimization algorithm inverse learning perturbation mechanism resource mapping

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Research on the collaborative mechanism between site selection optimization model and control of green space planning under the background of urban renewal

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DISCOVER SUSTAINABILITY 2025年第1期6卷 1-22页

作者： Li, Xiangyun Dai, Desheng Dong, Jingwen Hu, Kaili Nanjing Municipal Design & Res Inst Co Ltd Nanjing 210018 Peoples R China Nanjing Inst Technol Sch Civil Engn & Architecture Nanjing 211167 Peoples R China Natl Engn Res Ctr Water Resources Efficient Utiliz Nanjing 210024 Peoples R China

As a part of integrated urban ecosystems, green space is an important target of urban renewal planning. In the process of urban renewal of green space, it is difficult to control the special planning for green space systems, and the scale of urban public space is insufficient. From the perspective of spatial governance, it is of great practical importance to study the relationship between site selection optimization and the control of special planning for green space systems. This paper proposes a method of green space evaluation and site selection optimization based on Geographic Information System (GIS) modelling and particle swarm optimization algorithm. It optimizes the green space allocation by using the model constraints. Taking the special planning for green space systems in Gaochun District, Nanjing City as an example, it analyzes the constraints of the special planning for green space systems in the site selection optimization model from two perspectives: indicator conditional constraints and space requirement constraints. The GIS model of optimal site selection is introduced into the special planning for green space systems' adjustment process in the context of urban renewal. This approach can bring a variety of positive benefits to meet the needs of green space sharing and people's well-being.

关键词： Green space planning Site selection optimization Urban renewal particle swarm optimization algorithm

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