Particle Swarm Research Articles

Background: Coronary Artery Disease (CAD) is one of the biggest causes of mortality worldwide. Risk stratification for early detection is essential for the primary prevention of CAD. QRISK3 is known to overestimate future CAD risk in some populations, resulting in unnecessary preventive treatment and reduced cost-effectiveness and safety. Combining machine learning model with the metaheuristic optimisation approach using the PSO algorithm may outperform QRISK3 in predicting CAD. It improves performance by selecting the best-performing subset of features related to clinical outcomes. Research Question: Does the performance of Machine Learning Models Combined with PSO Algorithm for Feature Selection as a Metaheuristic Optimisation Approach in Predicting Coronary Arterial Disease using the UK Biobank dataset outperform the QRISK3 calculator? Aims: This study is to assess the accuracy of QRISK3 in predicting CAD using the UK Biobank dataset. It aims to evaluate the efficacy of machine learning models on the identical dataset for predicting CAD. The work utilises the PSO algorithm for feature selection to identify the optimal subset of features from the UK Biobank dataset. Methods: This study utilises data from the UK Biobank. The dataset consists of 348,015 participants aged 24-84 with no prior diagnosis of CAD. The performance of both QRISK3 and machine learning models was evaluated separately using ROC analysis. Several machine learning models were employed: Logistic Regression, Decision Tree, Random Forest, Naïve Bayes, and Gradient Boosting. The dataset was split into training and test sets with a ratio of 4:1 for the machine learning models. Each model has been developed by adding a PSO algorithm to enhance the model's classification accuracy. Results: Out of the total 348,015 participants, 23,136 individuals (6.64%) were diagnosed with CAD within 10 years following their first visit, while 324,879 individuals (93.4%) did not develop CAD. The AUC value of the QRISK3 prediction is 0.6113, while the combined machine learning models using the PSO algorithm using the gradient boosting model achieve an AUC of 0.7258, showing better performance. Conclusions: This study shows hybrid machine learning models optimised with the PSO algorithm can better predict CAD than QRISK3. These ML models can effectively identify high-risk CAD patients, allowing for more personalised preventative strategies and supporting policymakers in implementing lifestyle change recommendations.

This study proposes an optimization-based methodology for predicting cutting forces in milling by eliminating the need for traditional offline calibration procedures. A mechanistic force model is employed, in which cutting force coefficients are identified using population-based metaheuristic algorithms, namely Genetic Algorithm (GA), Differential Evolution (DE), and Particle Swarm Optimization (PSO). Cutting force data collected during machining are utilized to optimize the model parameters directly. The performance of each algorithm is systematically evaluated through 30 independent trials to ensure statistical reliability. The DE algorithm demonstrated the best performance, converging in all 30 runs with an average of 197 iterations and 5.4 s, followed by PSO (363 iterations, 9.8 s), while GA exhibited lower reliability (18 successful runs, 2108 iterations, 62.9 s). The optimized coefficients were validated against experimental data, yielding mean prediction errors of 2.82 N (F x ) and 4.35 N (F y ). The proposed method offers a fast, accurate, and scalable solution for cutting force prediction, supporting adaptive process control, and contributing to the development of intelligent manufacturing systems.

Abstract This paper presents the optimization of hybrid energy storage system for an electric vehicle, by using particle swarm optimization and genetic algorithm techniques including the design of conventional controller. It utilizes the steady-state filtered power as the reference output power of the battery. To regulate the steady-state current output of the battery, the output power of the ultracapacitor is adjusted dynamically with help of a proportional-integral-derivative controller, such that the power difference controlled structure is obtained. The PID controller parameters are optimized through the particle swarm optimization algorithm, and genetic algorithm. The output is compared with the federal test procedure drive cycle, and the optimized power output and state of charge of the battery is obtained. In this paper the result obtained shows that the SoC of battery is increased by 30.12% with the help of PSO-PID and 0.1% with the help of GA-PID and almost similar results were obtained using conventional PID controller. The battery power ratio in the total power demand is 0.6667 with PSO-PID, 0.8871 with GA-PID and 0.9109 with conventional PID controller. The results obtained shows that the proposed control strategy PSO-PID is capable of eliminating the deviation in power quickly and accordingly achieving the best suited global optimization of EV. In comparison with the optimized PID strategy the result obtained by the proposed strategy shows improvement in the energy consumption and battery life of EV.

To solve the problems of the lack of economic efficiency and the short driving range of electric commercial vehicles, a hybrid system was developed in this work that uses fuel cells as a range extender. In addition, a method to solve the problem of multi-power energy management was proposed using the model predictive control as a framework. In the state of charge maintenance interval, a quadratic utility function was used to calculate the output power of the fuel cell and battery. The unknown parameters in the quadratic utility function were solved using the model prediction control. Speed prediction was performed using long short-term memory and particle swarm optimization. The demanded power sequence within the prediction horizon was calculated based on the predicted speed. The dynamic programming algorithm was used to solve the power demand sequence within the prediction horizon length, and the unknown parameters in the utility function were deduced inversely. The simulation results show that the proposed energy management strategy (EMS) is superior to conventional EMS in improving component durability and vehicle economy.

The illegal use of β-agonists like clenbuterol (CLB) and ractopamine (RAC) as livestock growth promoters poses severe threats to human health. To address this, we developed a novel electrochemical sensor based on violet phosphorene (VP) and carboxylated multi-walled carbon nanotubes (COOH-MWCNTs) with Nafion-isopropanol mixed solution for simultaneous voltammetric detection of CLB and RAC in beef. The sensing platform was optimized through response surface methodology coupled with particle swarm optimization, enabling multi-objective parameter (VP concentration, COOH-MWCNTs concentration, and pH value) refinement that enhanced sensitivity while minimizing experimental trials. This design leveraged the synergistic properties of VP (high carrier mobility) and COOH-MWCNTs (electrocatalytic activity), yielding a sensor with ultra-low detection limits of 5.8nM for CLB and 6.4nM for RAC, excellent selectivity with negligible interference from biological matrices, and high stability. Support vector machine (SVM) nonlinear models achieved robust quantification (R2 > 0.9948 and RPD > 7.09 for both analytes), validated by high recoveries (98.4-103.1% for CLB and 96.4-103.7% for RAC) in beef samples. This work establishes a rapid, cost-effective platform for monitoring β-agonist residues, advancing food safety surveillance.

Gas foil journal bearings (GFJBs) are critical components in high-speed turbomachinery due to their advantages such as increased load capacity, reduced friction, and improved rotordynamic stability. This study presents a comprehensive numerical analysis comparing the static and dynamic performance of herringbone groove textures—namely single, double, and multi-structured patterns—applied to the top foil of GFJBs. The pressure distribution is obtained by solving the Reynolds equation using the finite difference method, and key performance metrics such as load-carrying capacity, frictional torque, power loss, effective stiffness, damping, and system stability are systematically evaluated. Among the patterns, the multi-herringbone texture (Texture 3) consistently outperformed other configurations. To further enhance performance, a multi-objective Particle Swarm Optimization (PSO) algorithm was employed to optimize key groove parameters. The optimized configuration yielded improved static and dynamic behavior, including higher stiffness and damping values and enhanced system stability. Notably, the optimized textured GFJB demonstrated a 26.9% increase in load-carrying capacity (LCC) compared to the plain GFJB. This research highlights the effectiveness of surface textures in enhancing GFJB performance and demonstrates the utility of PSO as a powerful tool for multi-objective optimization in high-speed bearing design.

Abstract Accurate prediction of the SOH(State of Health) of lithium-ion batteries is essential for ensuring the safety and efficiency of new energy vehicles. To overcome the limitations of existing methods that rely on single-category HFs(Health features), this study proposes an SOH prediction approach based on feature-type analysis and a GAPSO-GCRN neural network. Multi-dimensional HFs are extracted from voltage, IC(Incremental Capacity), and DTV(Differential Thermal Voltammetry) curves during charging and discharging, covering voltage, time, temperature, and capacity dimensions. The Pearson–Spearman mixed correlation analysis, combined with feature evolution trends during aging, identifies three optimal indicators: voltage inflection point, capacity entropy change rate, and temperature rise rate. A GCRN(Graph Convolutional Recurrent Network) model is then developed, with a GAPSO(Genetic Algorithm–Particle Swarm Optimization) hybrid strategy employed for global hyperparameter optimization. Experimental results on the Oxford Battery Degradation Dataset show that the proposed method achieves MAE(Mean Absolute Error) and RMSE(Root Mean Square Error) within 0.4\% for SOH prediction, demonstrating high accuracy, robustness, and strong generalization capability.

Abstract This paper introduces a rotational quantum particle swarm optimizer (RQPSO) that updates particles via quantum rotation–gate dynamics on a compact phase representation. Real-valued amplitudes decoded from phases are mapped feasibly by construction into the decision space, removing the need for bound repair. Lightweight π-flip perturbations and brief stagnation-triggered reseeding sustain diversity, and a short local polish consolidates the incumbent near termination. RQPSO is benchmarked against PSO, QPSO, GWO, MA, jDE, and CMA-ES under a common protocol of 3000 function evaluations with 30 independent runs per problem. Reporting uses median [IQR] as the primary statistic with Friedman/Nemenyi global tests and Holm-corrected Wilcoxon pairwise tests; effect sizes are summarized by Cliff’s δ. Experiments cover 23 classical functions and 10 CEC-2019 functions. On the classical suite, RQPSO attains the best or tied-best median on a majority of functions and achieves a leading global rank under the fixed budget. On CEC-2019, it records three best medians (including one tie) and a top mean rank; post-hoc tests show significant gains over MA and jDE and broadly comparable performance to PSO, QPSO, GWO, and CMA-ES. A combined economic–emission dispatch (CEED) study on a six-unit system with cubic cost and emission models further demonstrates budget-efficient performance. The rotational-gate RQPSO attains the lowest mean operating cost and the smallest dispersion at all loads versus PSO and QPSO. A percentage-recovery repair enforces generator limits and power balance without penalty functions by proportionally rescaling outputs. Together, the rotation-gate updates, feasibility-preserving decoding, and proportional repair provide a robust alternative to classical swarms for both benchmark and power-system optimization under tight evaluation budgets.

In response to the significant waste of agricultural irrigation resources and the inaccuracy of water demand predictions, this study aims to develop an automated irrigation system that can reduce fluctuations in water volume and enable precise control. Against the backdrop of current water scarcity and low agricultural water efficiency, improving irrigation precision is of great significance for ensuring food security and promoting sustainable agricultural development. This study combines particle swarm optimization algorithm with extreme learning machine and integrates it into a microcontroller to construct a new intelligent irrigation system. This technology can solve the problem of inaccurate crop water demand predictions in existing technologies and promote the transformation of intelligent agriculture from empirical to data-driven. This technology uses a LoRa based wireless sensor network to collect data and is controlled by a microcontroller. The particle swarm algorithm optimizes the initial parameters of the extreme learning machine, improving the accuracy with which it predicts farmland water demand. The results showed that the proposed method had the lowest root mean square error value, with an average of only 0.1025, indicating that the algorithm had the most accurate irrigation prediction effect. The automatic water-saving irrigation technology proposed in this study required less water compared to traditional irrigation techniques, with a minimum water consumption of 3015 m 3 /hm 2 and a maximum water consumption of only 5268.3 m 3 /hm 2 . The system’s accuracy in predicting crop irrigation water demand could reach over 98%. The method proposed in this study can accurately control irrigation water. It can also maximize irrigation water conservation. This brings new research directions for the knowledge system of automated water-saving irrigation technology in farmland. It also provides new technical ideas for the development of intelligent agricultural irrigation technology.

Numerous studies on the statistical inferences of the Weibull distribution’s parameters have been performed because it is among the most well-known and widely applied distributions in several fields, including lifetime studies and reliability. Although maximum likelihood is a widely used method in the estimation process of unknown parameters, estimating the parameters by maximizing the likelihood function is very challenging for some distributions, like the three-parameter Weibull distribution. The Particle Swarm Optimization (PSO) algorithm is examined in order to address this issue and achieve improved outcomes. However, different parameter values for the algorithm need to be adjusted to achieve good results and increase the performance of PSO. In this context, it is very important to determine the inertia weight, which significantly affects the search process. As a novelty in this paper, chaotic maps for the inertia weight, which is the factor affecting the convergence of the PSO, are examined in detail for the estimation of different parameter values of the three-parameter Weibull distribution. The effectiveness of the suggested method is investigated by a thorough Monte-Carlo simulation analysis. The simulation findings demonstrate that the proposed chaotic map approach outperforms the classic linear decreasing inertia weights.

The increasing complexity of urban energy systems requires decentralized, sustainable, and scalable solutions. The paper presents a new multi-layered framework for smart energy management in microgrids by bringing together advanced forecasting, decentralized decision-making, evolutionary optimization and blockchain-based coordination. Unlike previous research addressing these components separately, the proposed architecture combines five interdependent layers that include forecasting, decision-making, optimization, sustainability modeling, and blockchain implementation. A key innovation is the use of Temporal Fusion Transformer (TFT) for interpretable multi-horizon forecasting of energy demand, renewable generation, and electric vehicle (EV) availability which outperforms conventional LSTM, GRU and RNN models. Another novelty is the hybridization of Genetic Algorithms (GA) and Particle Swarm Optimization (PSO), to simultaneously support discrete and continuous decision variables, allowing for dynamic pricing, efficient energy dispatching and adaptive EV scheduling. Multi-Agent Reinforcement Learning (MARL) which is improved by sustainability shaping by including carbon intensity, renewable utilization ratio, peak to average load ratio and net present value in agent rewards. Finally, Ethereum-based smart contracts add another unique contribution by providing the implementation of transparent and tamper-proof peer-to-peer energy trading and automated sustainability incentives. The proposed framework strengthens resilient infrastructure through decentralized coordination and intelligent optimization while contributing to climate mitigation by reducing carbon intensity and enhancing renewable integration. Experimental results demonstrate that the proposed framework achieves a 14.6% reduction in carbon intensity, a 12.3% increase in renewable utilization ratio, and a 9.7% improvement in peak-to-average load ratio compared with baseline models. The TFT-based forecasting model achieves RMSE = 0.041 kWh and MAE = 0.032 kWh, outperforming LSTM and GRU by 11% and 8%, respectively.

Valve internal leakage in thermal power stations exhibits a strong concealed nature. If it cannot be discovered and predicted of development trend in time, it will affect the safe and economical operation of plant equipment. This paper proposed an intelligent identification method for valve internal leakage that integrated an Improved Kepler Optimization Algorithm (IKOA) with Support Vector Regression (SVR). The Kepler Optimization Algorithm (KOA) was improved using the Sobol sequence and an adaptive Gaussian mutation strategy to achieve self-optimization of the key parameters in the SVR model. A multi-step sliding cross-validation method was employed to train the model, ultimately yielding the IKOA-SVR intelligent identification model for valve internal leakage quantification. Taking the main steam drain pipe valve as an example, a simulation case validation was carried out. The calculation example used Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and determination coefficient (R2) as performance evaluation metrics, and compared and analyzed the training and testing dataset using IKOA-SVR, KOA-SVR, Particle Swarm Optimization (PSO)-SVR, Random Search (RS)-SVR, Grid Search (GS)-SVR, and Bayesian Optimization (BO)-SVR methods, respectively. For the testing dataset, the MSE of IKOA-SVR is 0.65, RMSE is 0.81, MAE is 0.49, and MAPE is 0.0043, with the smallest values among the six methods. The R2 of IKOA-SVR is 0.9998, with the largest value among the six methods. It indicated that IKOA-SVR can effectively solve problems such as getting stuck in local optima and overfitting during the optimization process. An Out-Of-Distribution (OOD) test was conducted for two scenarios: noise injection and Region-Holdout. The identification performance of all six methods decreased, with IKOA-SVR showing the smallest performance decline. The results show that IKOA-SVR has the strongest generalization ability and robustness, the best effect in improving fitting ability, the smallest identification error, the highest identification accuracy, and results closer to the actual value. The method presented in this paper provides an effective approach to solve the problem of intelligent identification of valve internal leakage in thermal power station.

The increasing adoption of high-performance DC motor control in embedded systems has driven the development of cost-effective solutions that extend beyond traditional software-based optimization techniques. This work presents a refined hardware-centric approach implementing real-time particle swarm optimization (PSO) directly executed on STM32 microcontroller for DC motor speed control, departing from conventional simulation-based parameter-tuning methods. Novel hardware-optimized composition of an interval type-2 fuzzy logic controller (FLC) and a PID controller is developed, designed for resource-constrained embedded systems and accounting for processing delays, memory limitations, and real-time execution constraints typically overlooked in non-experimental studies. The hardware-in-the-loop implementation enables real-time parameter optimization while managing actual system uncertainties in controlling DC micro-motors. Comprehensive experimental validation against conventional PI, PID, and PIDF controllers, all optimized using the same embedded PSO methodology, reveals that the proposed FT2-PID controller achieves superior performance with 28.3% and 56.7% faster settling times compared to PIDF and PI controllers, respectively, with significantly lower overshoot at higher reference speeds. The proposed hardware-oriented methodology bridges the critical gap between theoretical controller design and practical embedded implementation, providing detailed analysis of hardware–software co-design trade-offs through experimental testing that uncovers constraints of the low-cost microcontroller platform.

Rapid detection of maize seed germination using near-infrared spectroscopy combined with Gaussian process regression.

A hybrid global maximum power point tracking control method based on particle swarm optimization (PSO) and perturbation and observation (P&O)

Machine learning for predicting and optimizing the performance of a commercial-scale anaerobic digester with diverse feedstocks and operating conditions.

Day-ahead residential power load forecasting using adaptive online learning and Particle Swarm Optimization

Qualitative and quantitative pesticide residue analysis in Allium tuberosum using an electronic nose with multivariate analysis.

An integrated optimization and deep learning pipeline for predicting live birth success in IVF using feature optimization and transformer-based models.

Resource-efficient automatic software vulnerability assessment via knowledge distillation and particle swarm optimization