Particle Swarm Research Articles

Jet mixing optimization using a bio-inspired evolution of hardware and control

Jet mixing is a critical factor in various engineering applications, influencing pollutant dispersion, chemical processes, medical treatments, and combustion enhancement. Hitherto, jet mixing has typically been optimized by either passive or active control techniques. In this experimental study, we combine simultaneous optimization of active control with 12 inward-pointing minijets and a tuneable nozzle exit shape commanded by 12 stepper motors. Jet mixing is monitored at the end of the potential core with an array of 7 \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ imes$$\\end{document} 7 Pitot tubes. This high-dimensional actuation space is conquered with Particle Swarm Optimization through Targeted, Position-Mutated Elitism. Our results underscore the significant impact of combining control techniques, illustrating the complex interactions of both passive and active control on jet flow dynamics. The mixing area of the combined control optimization is 4.5 times larger than the area of the unforced state. This mixing increase significantly outperforms the effect of shape optimization of the nozzle alone. Our study points at the potential of optimization in high-dimensional design spaces for shapes as well as passive and active control—leveraging the rapid development of flow control hardware and the increasingly powerful tools of artificial intelligence for optimization.

Optimal Design of State Variable Filter using Partition-bound Particle Swarm Optimization

Because of manufacturing constraints, designing analog active filters is highly challenging. Evolutionary computing is an effective method for automatically selecting the component values like resistors and capacitors. This work describes the partition-bound Particle Swarm Optimization (PB-PSO) for efficiently designing second-order active low-pass state variable filter (SVF) considering different manufacturing series. PB-PSO is responsible for efficiently picking components and minimizing total design error. The filter components are chosen to be compatible with the E12/ E24/ E96 series. Compared to earlier optimization strategies, the simulation findings show that PB-PSO reduces the overall design error.

An Assessment of the Visibility of Particular Swarm Intelligence Technologies in the Resolution of the Object Classification Problem

This article assesses the visibility of five swarm intelligence algorithms in resolving the object classification problem explicitly particle swarm optimization, artificial bee colonies, ant colony optimization, bacterial foraging optimization, and the Social Spider Optimization. 58 articles in total were reviewed and used as the ground on which this assessment was based on. Primarily articles were grouped into two categories namely the articles which directly resolve the object classification problem and those which in directly resolve the object classification problem followed by a further grouping to indicate articles that were directly linked to the object classification problem through swarm technology and finally grouped by the aim. Three aims were observable which are to modify, to improve and to investigate. More than 70% of the articles aimed at either modifying or improving already existing swarm intelligence algorithms. PSO was the most dominant algorithm of the five technologies assessed. Interesting to note was that although all these algorithms were applied there is no formal representation of knowledge in this domain.

Enhanced Edge Detection through Binary Particle Swarm Optimization and L0 Guided Filtering

Detecting edges holds significant importance in image processing, serving as a fundamental step in numerous computer vision applications. This paper presents an innovative method for performing edge detection by combining Binary Particle Swarm Optimization (BPSO) with L0 Guided Filtering. The proposed method aims to address the challenge of accurately detecting edges in noisy and complex images by leveraging the benefits of both BPSO and L0 guided filtering. The process begins with the initialization of the BPSO algorithm, where binary particles traverse the solution space to optimize parameters critical for edge detection. These optimized parameters are subsequently employed in the L0 guided filtering framework, a sophisticated edge preserving filter known for its ability to maintain fine details while effectively reducing noise. The synergy of BPSO and L0 guided filtering demonstrates improved adaptability to diverse image characteristics, enhancing the overall robustness of edge detection. The binary nature of BPSO allows for efficient exploration of the solution space, facilitating faster convergence to optimal parameters. Concurrently, the L0 guided filtering ensures edge preserving smoothing, contributing to the suppression of unwanted artifacts. Experimental evaluations on benchmark datasets showcase the effectiveness of the proposed method compared to traditional edge detection techniques. The results indicate superior edge localization and reduced sensitivity to noise, highlighting the potential of the BPSO Based Edge Detection under L0 Guided Filtering in real world applications. The presented approach offers a valuable contribution to the advancement of edge detection methodologies, demonstrating its potential for enhancing the performance of computer vision systems in various domains.

Designing broadband cross-polarization conversion metasurfaces using binary particle swarm optimization algorithm

Owing to their disadvantages of broadband discontinuity, infinite value, or incapability of distinguishing cross-polarization conversions from co-polarization conversions, the traditional linear polarization discrimination methods relating to the phase difference Δφ, the ellipticity angle β, or the axial ratio are inapplicable to the software-based optimal design of broadband cross-polarization conversion metasurfaces (CPMs) consisting of subblocks. Therefore, we introduce the pseudo ellipticity angle β' to characterize the cross-polarization conversion. And based upon the coding metasurfaces and β', this work first employs the Python-programmed binary particle swarm optimization algorithm to accomplish the optimal design of reflective broadband CPMs. Then, the obtained CPM is further simplified by reprocessing the relatively isolated subblocks, which results in the final CPM. The simulated results show that the final CPM realizes the broadband cross-polarization conversion with β' ≥ 0.4π and a relative bandwidth of 36.1 % from 11.66 GHz to 16.79 GHz. Moreover, the measured results are in excellent agreements with the simulated ones, which validates the proposed design method. It advances the broadband CPMs design from manual to automated method.

Optimizing actual PID control for walking quadruped soft robots using genetic algorithms

The construction of soft robots’s models and controllers remains a significant challenge. In this paper, we propose a new walking control method for the quadruped soft robot named genetic algorithm-optimized PID. First, we construct the control model correlating valve voltage with leg bending based on the geometrical analysis. This modeling approach leverages the characteristics of novel leg structure and bend sensor, thereby streamlining the control model for locomotion of quadruped soft robotic. Moreover, We apply the genetic algorithm to automatically tune parameters and optimize PID controllers, aiming to enhance control performance. The application of the proposed method to the walking control has been uniquely demonstrated on a real 3D-printed quadruped soft robot. Experimental results indicate that the genetic algorithm-optimized PID controller significantly improves trajectory tracking compared to the Ziegler-Nichols tuning method. This optimization increases the robot’s walking speed from 5 mm/s to 8 mm/s, reduces the error rate by 2.4064%, decreases overshoot by 12.55%, and shortens response time by 0.5 s, substantially enhancing the controller’s overall performance. Additionally, compared to particle swarm optimization, the proposed method further improves performance by reducing the error rate by 0.4079%, overshoot by 8.4%, and response time by 1.0 s.

A novel energy efficient QoS secure routing algorithm for WSNs

Quality of Service (QoS) routing protocol is a hot topic in the research field of wireless sensor networks (WSNs). However, the task of identifying an optimal path that simultaneously meets multiple QoS constraints is acknowledged as an NP-hard problem, with its complexity intensifying in proportion to the network’s nodal count. Therefore, a novel heuristic multi-objective trust routing method, the Levy Chaos Adaptive Snake Optimization-based Multi-Trust Routing Method (LCASO-MTRM), is proposed, aiming to enhance link bandwidth while simultaneously reducing latency, packet loss, and energy consumption. The proposed method incorporates innovative chaos and adaptive operators within the LCASO framework. The chaos operator enhances population diversity, expands the solution space, and accelerates the search process. Meanwhile, the adaptive operator improves convergence, enhances robustness, and effectively prevents stagnation. Additionally, this paper introduces a novel multi-objective QoS routing model that integrates a link trust mechanism, allowing for a more accurate assessment of link trust levels and a precise reflection of the current link status. The effectiveness of LCASO-MTRM is demonstrated through simulation comparisons with the Improved Particle Swarm Optimization (IPSO), Improved Artificial Bee Colony Algorithm (IABC), and Cloned Whale Optimization Algorithm (CWOA). Simulation results demonstrate that LCASO-MTRM significantly reduces energy consumption by 49.53%, latency by 22.56%, and packet loss by 40.21%, while increasing bandwidth by 6.13%, outperforming the other algorithms.

State of Health Estimation of Lithium-Ion Batteries Using Fusion Health Indicator by PSO-ELM Model

The accurate estimation of the State of Health (SOH) of lithium-ion batteries is essential for ensuring their safe and reliable operation, as direct measurement is not feasible. This paper presents a novel SOH estimation method that integrates Particle Swarm Optimization (PSO) with an Extreme Learning Machine (ELM) to improve prediction accuracy. Health Indicators (HIs) are first extracted from the battery’s charging curve, and correlation analysis is conducted on seven indirect HIs using Pearson and Spearman coefficients. To reduce dimensionality and eliminate redundancy, Principal Component Analysis (PCA) is applied, with the principal component contributing over 94% used as a fusion HI to represent battery capacity degradation. PSO is then employed to optimize the weights (ε) between the input and hidden layers, as well as the hidden layer bias (u) in the ELM, treating these parameters as particles in the PSO framework. This optimization enhances the ELM’s performance, addressing instability issues in the standard algorithm. The proposed PSO-ELM model demonstrates superior accuracy in SOH prediction compared with ELM and other methods. Experimental results show that the mean absolute error (MAE) is 0.0034, the mean absolute percentage error (MAPE) is 0.467%, and the root mean square error (RMSE) is 0.0043, providing a valuable reference for battery safety and reliability assessments.

Multi-Static Radar System Deployment Within a Non-Connected Region Utilising Particle Swarm Optimization

This paper is mainly devoted to studying the deployment problem of a multi-static radar system (MSRS) within a non-connected deployment region using multi-objective particle swarm optimization (MOPSO). By modeling and reformulating the problem, it can be represented as a multi-objective mixed integer programming (MOMIP), which eliminates the need for additional constraints. To enhance the algorithm performance, integer variables and continuous ones are treated separately employing multiple velocity formulas. The velocity formulas for integer variables are modified using the sigmoid function and genetic operation, leading to the proposal of two MSRS deployment algorithms, namely MOPSO-Sigmoid and MOPSO-Gene. To evaluate the performance of the proposed algorithms, they are compared with two existing MOPSO-based algorithms. The first algorithm is the MSRS deployment algorithm for the non-connected deployment region that addresses the additional constraint problem model. The second algorithm is based on an existing conventional MOPSO algorithm and addresses the equivalent MOMIP problem model. A numerical study demonstrates that MOPSO-Sigmoid and MOPSO-Gene exhibit promising efficiency and effectiveness.

Fuzzy hybrid approach for advanced teaching learning technique with particle swarm optimization in the diagnostic of dengue disease

Dengue fever is a serious public health issue worldwide, particularly in tropical and subtropical areas. Early detection and accurate diagnosis are essential for effective management and control of the disease. In this study, we present a fuzzy hybrid approach (F-TLBO-APSO) for the detection and diagnosis of dengue disease using an advanced teaching-learning technique with adaptive particle swarm optimization. The proposed method combines the strengths of fuzzy logic, teaching learning-based optimization (TLBO), and adaptive particle swarm optimization (APSO) to improve the accuracy and efficiency of dengue detection based on symptoms. A key challenge addressed is the management of uncertain information existing in the problem. To validate the proposed technique, we applied it to a case study, demonstrating its robustness. The results indicate the versatility of the F-TLBO-APSO algorithm and highlight its value in detecting dengue based on symptoms. Our numerical computations reveal the advantages of the F-TLBO-APSO algorithm compared to TLBO and APSO.

Leveraging the Interplanetary Superhighway for Propellant–Optimal Orbit Insertion into Saturn–Titan System

This paper presents an innovative approach using Dynamical Systems Theory (DST) for interplanetary orbit insertion into Saturn−Titan three−body orbits. By leveraging DST, this study identifies invariant manifolds guiding a spacecraft into Titan−centered Distant Retrograde Orbits (DROs), strategically selected for their scientific significance. Subsequently, Particle Swarm Optimization (PSO) is employed to fine−tune the insertion parameters, thereby minimizing ΔV. The results demonstrate that the proposed method allows for a reduction in ΔV of over 70% compared to conventional approaches like patched conics−based flybys (2.68 km/s vs. 9.23 km/s), albeit with an extended time of flight, which remains notably faster than weak stability boundary transfers. This paper serves as an interplanetary mission planning methodology to optimize spacecraft trajectories for the exploration of the Saturn−Titan system.

Intelligent Control Algorithms for Enhanced Frequency Stability in Single and Interconnected Power Systems

Ensuring stable power system performance is crucial for reliable grid operation. This study assesses various Load Frequency Control (LFC) strategies, including conventional PID, pole placement, Genetic Algorithm (GA)-optimized PID, Particle Swarm Optimization (PSO)-optimized PID, and an Artificial Neural Network (ANN)-based controller, in single and interconnected power grids. The results reveal that GA- and PSO-optimized PID outperform conventional methods, offering minimal overshoot and fast settling times. Pole placement strikes a balance between response time and stability, while the ANN controller demonstrates adaptability and quick rise times but exhibits higher overshoot and longer settling times compared to the optimization techniques. Tie-line bias control aids in frequency stabilization but presents challenges with overshoot and prolonged settling times. Notably, PSO-optimized PID emerges as a promising solution, effectively mitigating overshoot and achieving rapid frequency recovery. This study underscores the importance of tailored control strategies for optimal LFC, which are essential for enhancing power system stability and efficiency. Future research should explore the potential of advanced techniques, such as deep learning and reinforcement learning, to further improve control performance.

Enhancing federated learning with dynamic weight adjustment based on particle swarm optimization

Federated learning (FL) stands as a promising distributed machine learning approach today, allowing model training on local clients without data sharing. However, varied contributions among clients often impact the global model’s performance, while FL confronts challenges like communication overhead, data heterogeneity, and privacy concerns. In this paper, we introduce a novel federated learning server aggregation algorithm: the Federated Learning Algorithm with Optimized Weight Aggregation via Particle Swarm Optimization Algorithm (AdpFedPSO). This approach dynamically adjusts client model contribution weights based on their performance and stability, aiming to enhance global model accuracy, convergence speed, and make model aggregation smarter and more adaptable. Through experimental validation on real datasets, we find that AdpFedPSO enhances accuracy by about 15% on the 0.6-Dirichlet MNIST dataset, 7.3% on the FashionMNIST dataset, and 13.4% on the CIFAR-10 dataset compared to the traditional FL aggregation method, FedAvg. These results indicate that AdpFedPSO not only enhances the accuracy of the global model but also expedites the model’s convergence speed. Additionally, it demonstrates resilience across various levels of client numbers, participation rates, and client heterogeneity, providing valuable reference and guidance for the further development of FL technology. Moreover, the concept of employing the particle swarm optimization algorithm for FL model aggregation also offers new insights and directions for future research.

Comparative Sensitivity Analysis of GA, PSO, and PSO-GA Algorithms for Carbon Environment Development and Sustainability

<p style="text-align:justify;"><span style="font-family:"Times New Roman",serif;" lang="EN-US">To effectively address the CO<sub>2</sub> emissions in the tourism place, in this study focuses a Sanshan scenic spot for precise prediction. The main factors contributing CO<sub>2</sub> emission as, accommodation, transportation and catering services which intends to improve the need of carbon emission forecasting methods. To address this issue, we proposed an innovatively integrating the optimization algorithms Genetic Algorithm and Particle Swarm Optimization for accurately predicting the CO<sub>2</sub> emissions. Initially, this system taken input from the CO2 emissions occurred from the year of 2010 to 2020. The optimization model contains the input variables, initializing populations, evaluating fitness values, and iterating until convergence to found best prediction model. The experimental results of the proposed PSO-GA system attains 9.86 highest sensitivity showcases best performance in predicting CO<sub>2 </sub>emissions, as evidenced by its superior Adjusted Rand Index value of 1 after 160 iterations. The predicted and measured values of CO<sub>2</sub> emissions using this algorithm remains significant and advanced carbon environment development at scenic spots. The PSO-GA was used to calculate the carbon emissions of three mountainous scenic spots over the next five years, and the medium and high carbon levels should change to a medium carbon development stage by 2024.</span></p>

Geometric optimization to improve the performance of a new design of concentration‐based solar distiller using particle swarm optimization algorithm

AbstractSolar distillers are one of the oldest and simplest technologies for desalinating salt water using solar energy. The main problem is the low freshwater production compared to the amount of energy input from the sun. To overcome this problem, a solar distiller was developed using a parabolic trough concentrator to increase the freshwater yield. The geometry optimization of various parameters of the developed system was also studied using the particle swarm optimization (PSO) algorithm to obtain the optimal design and give the improved freshwater yield. The simulation is carried out based on the algorithm using the climatic condition data of Rabat City, Morocco. Numerical resolution of the energy balance equations was performed using MATLAB code. In terms of flexibility, speed, and global convergence, this method's final results were satisfactory. The maximum optimized freshwater yield using the PSO approach is 12.83 kg/m2 and the maximum optimized energy and exergy efficiency were about 204.7% and 34.3%, respectively. Furthermore, the results showed that the absorber basin accounts for the highest exergy destruction, 88.7% of the total optimized exergy destruction was found from exergy analysis.

Multi-objective optimization of switched reluctance motor driving electric vehicles based on vehicle operating condition

To improve the comprehensive performance of the switched reluctance motor (SRM) for electric vehicles (EVs) under actual driving conditions, a novel geometrical optimization method based on the time-varying phase current characteristics and the comprehensive indicator with justified weight coefficients is proposed in this paper. First, based on the established dynamic models of EV and SRM, the actual operating phase currents of the motor working under the New European Driving Cycle (NEDC) are extracted and classified to develop the representative optimization conditions. Then, considering the requirements of EV, five indicators are defined to evaluate the comprehensive performance of SRM, and a new multi-objective synchronization optimization function is proposed with five weight coefficients obtained by the statistical analysis method for the sample data of the indicators. Furthermore, the geometrical optimization for a four-phase 8/6 SRM is carried out by the combination of Particle Swarm Optimization (PSO) and BP neural network algorithms, after analyzing the effects of key geometrical parameters on the indicators. Finally, the work performance of the optimized SRM is evaluated and compared with that of the initial prototype, and the results show that the optimized SRM with the proposed multi-objective synchronization optimization method can improve the comprehensive indicator by 59.59%, and can significantly enhance the vehicle actual working economy by 50.74% under NEDC cycle.

An Analysis of and Improvements in the Gear Conditions of the Automated Mechanical Transmission of a Battery Electric Vehicle Considering Energy Consumption and Power Performance

The design of the gear quantity and transmission parameters of a vehicle has large effects on its economical and power performance. This paper mainly researches the gear conditions (including the gear quantity and each gear’s transmission parameters) of two-gear and three-gear AMT (Automated Mechanical Transmission). This research uses Cruise software to build a multi-gear simulation model of a BEV (Battery Electric Vehicle) and adopts the LHS (Latin hypercube sampling) method to design an experiment plan and conduct a simulation experiment. This paper proposes a systematic method for influencing factor analyses and the optimization of transmission parameters, combining fuzzy theory, multiple regression, and particle swarm optimization. The research results show that the gear quantity allowing for optimal overall performance is three. The highest score obtained in the results of the simulation experiment for three-gear AMT is 11.15% higher than that of the two-gear AMT. The optimal design plan for the two-gear AMT is a small ig1 with a big k1, in which case the highest score of the regression model increases by 2.67% compared with that before modeling. The optimal design plan for the three-gear AMT is a big k1 with a big k2, in which case the highest score of the regression model increases by 12.78% compared with that before modeling. Then, this research uses PSO (particle swarm optimization) to further optimize the regression models and compares the difference between the highest scores in the results of the simulation experiment. The difference between the highest scores of the three-gear and two-gear AMT further increases to 21.95% after optimization. As shown in the results, the key factor influencing the performance of two-gear and three-gear AMT is gear quantity.

Optimizing Hydrogen Refueling Station Recommendations: A Comparative Analysis Between Genetic Algorithm and Particle Swarm Optimization

Optimizing Hydrogen Refueling Station Recommendations: A Comparative Analysis Between Genetic Algorithm and Particle Swarm Optimization

Forecasting the trend of tuberculosis incidence in Anhui Province based on machine learning optimization algorithm, 2013–2023

Tuberculosis has been one of the most common communicable diseases raising global concerns. Accurately predicting the incidence of Tuberculosis remains challenging. Here we constructed a time-series analysis and fusion tool using multi-source data, and aimed to more accurately predict the incidence trend of tuberculosis of Anhui Province from 2013 to 2023. Random forest algorithm (RF), Feature Recursive Elimination (RFE) and Least absolute shrinkage and selection operator (LASSO) were implemented to improve the derivation of features related to infectious diseases and feature work. Based on the characteristics of infectious disease data, a model of RF-RFE-LASSO integrated particle swarm optimization multiple inputs long short term memory recurrent neural network (RRL-PSO-MiLSTM) was created to perform more accurate prediction. Results showed that the PSO-MiLSTM achieved excellent prediction results compared with common single-input and multi-input time-series models (test set MSE:42.3555, MAE: 59.3333, RMSE: 146.7237, MAPE: 2.1133, R2: 0.8634). PSO-MiLSTM enriches and complements the methodological research content of calibrating the time-series predictive analysis of infectious diseases using multi-source data, and can be used as a brand-new benchmark for the analysis of influencing factors and trend prediction of infectious diseases at the public health level in the future, as well as providing a reference for incidence rate prediction of infectious diseases.

Estimating Ross 308 Broiler Chicken Weight Through Integration of Random Forest Model and Metaheuristic Algorithms.

For accurate estimation of broiler chicken weight (CW), a novel hybrid method was developed in this study where several benchmark methods, including Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Ant Colony Optimization (ACO), Differential Evolution (DE), and Gravity Search Algorithm (GSA), were employed to adjust the Random Forest (RF) hyperparameters. The performance of the RF models was compared with that of classic linear regression (LR). With this aim, data (temperature, relative humidity, feed consumption, and CW) were collected from six poultry farms in Samsun, Türkiye, covering both the summer and winter seasons between 2014 and 2021. The results demonstrated that PSO and ACO significantly enhanced the performance of the standard RF model in all periods. Specifically, the RF-PSO model achieved a significant improvement by reducing the Mean Absolute Error (MAE) by 5.081% to 60.707%, highlighting its superior prediction accuracy and efficiency. The RF-ACO model also showed remarkable MAE reductions, ranging from 3.066% to 43.399%, depending on the input combinations used. In addition, the computational time required to train the RF models with PSO and ACO was considerably low, indicating their computational efficiency. These improvements emphasize the effectiveness of the PSO and ACO algorithms in achieving more accurate predictions of CW.