Particle Swarm Optimization Algorithm Research Articles

Application of Immunological and Swarm Intelligence Learning-Based Algorithm for Industrial Grade Computer Sales Prediction

ABSTRACT This paper strives to raise the imitating effectiveness of radial basis function-based neural network (RNNet) through biological learning (BL) and swarm intelligence (SI) optimization algorithms. Latter, the artificial immune system (AIS) and particle swarm optimization (PSO) algorithms are utilized for RNNet to regulate. The proposed synthesis of AIS-inspired and PSO-inspired (SAIPS) algorithm incorporates the complementary development and prospecting abilities to realize optimized resolution. The attribute of population variation has shown high frequency to meet the global optimum to replace local optimum being restricted and outperforms in five standard nonlinear trial functions. The experimental results have represented that the consolidation of AIS-inspired and PSO-inspired algorithms is an outstanding approach and therefore a hybrid algorithm is proposed, which aims to obtain an expression that can cultivate optimum precision among related algorithms in this research. The algorithm then evaluates results from five standard inspections and an empirical industrial grade computer (IgC) sales prediction instance in Taiwan, which reveals that the proposed SAIPS algorithm exceeds the performance among related algorithms as well as the relevant auto-regressive integrated moving average (ARIMA) models in terms of accuracy and time spent.

Lateral collision avoidance stability control study of intelligent vehicles on low adherence roads

To enhance lateral collision avoidance safety for intelligent vehicles on various road surfaces, this paper proposes a collision avoidance algorithm specifically designed for low-adhesion roads. The study begins by a fuzzy control lane change decision model that considers the relative state information between the ego vehicle and the front vehicle. Next, the environment risk potential field is modeled, with multiple lane change paths of varying parameters considered as candidate trajectories. Critical risks during vehicle steering are analyzed, leading to the design of a dynamic instability boundary risk point potential field. Trajectory optimization is then performed using the Particle Swarm Optimization (PSO) algorithm. Finally, a fuzzy PID controller is developed to track the optimal trajectory. Simulation results demonstrate that, compared to the traditional artificial potential field method, the proposed collision avoidance algorithm provides better lateral stability in the planned paths. Additionally, the fuzzy PID controller designed in this study outperforms the Quantitative Feedback Theory (QFT) controller in terms of tracking accuracy.

Overlapping Channel Allocation Method for Wireless Communication Network Based on Discrete Particle Swarm Optimization Algorithm

To address the challenges of interference between network channels and low-frequency resource utilization efficiency, and to improve the overall performance and resource allocation fairness of the network, this study proposes a wireless communication network overlapping channel allocation strategy based on a discrete particle swarm optimization algorithm. First, the overlapping channel allocation problem in wireless networks is abstracted as a linear programming model with specific constraints, with the core objective of minimizing weighted interference between links. To solve this model, a discrete particle swarm optimization algorithm is introduced. In this algorithm, each particle’s position corresponds to a feasible overlapping channel allocation scheme, which is the potential solution to the optimization problem; The velocity of particles reflects the process of transitioning from the current channel configuration to the target channel configuration. Subsequently, based on the characteristics of discrete variables, corresponding operation rules and particle motion equations were defined. In addition, when particle swarm aggregation occurs, an optimal asymptotic mutation operator is introduced, which not only enhances the local search ability of the algorithm but also strengthens its global search ability. During algorithm execution, this mutation operator is applied based on a certain probability of mutation. The experimental results show that when using this strategy for overlapping channel allocation in wireless communication networks, even with an increase in the number of traffic flows, the average network throughput can remain stable, demonstrating a high network carrying capacity. Meanwhile, wireless networks exhibit a higher fairness factor, indicating a more balanced allocation of network resources among different users. In the process of optimal channel allocation, the packet loss rate reaches the lowest level, ensuring the reliability of data transmission.

Autonomous Decision-Making for Air Gaming Based on Position Weight-Based Particle Swarm Optimization Algorithm

As the complexity of air gaming scenarios continues to escalate, the demands for heightened decision-making efficiency and precision are becoming increasingly stringent. To further improve decision-making efficiency, a particle swarm optimization algorithm based on positional weights (PW-PSO) is proposed. First, important parameters, such as the aircraft in the scenario, are modeled and abstracted into a multi-objective optimization problem. Next, the problem is adapted into a single-objective optimization problem using hierarchical analysis and linear weighting. Finally, considering a problem where the convergence of the particle swarm optimization (PSO) is not enough to meet the demands of a particular scenario, the PW-PSO algorithm is proposed, introducing position weight information and optimizing the speed update strategy. To verify the effectiveness of the optimization, a 6v6 aircraft gaming simulation example is provided for comparison, and the experimental results show that the convergence speed of the optimized PW-PSO algorithm is 56.34% higher than that of the traditional PSO; therefore, the algorithm can improve the speed of decision-making while meeting the performance requirements.

A Reliable and Load Balancing Controller Placement Method in Software‐Defined Networks

ABSTRACTSoftware‐defined networking (SDN) achieves the programmability of the control plane by separating it from the data forwarding plane to provide flexible management of the network resources. The multicontroller architecture is required to be deployed to enhance the scalability and reliability of the control plane with the network traffic growth. However, the controller placement problem (CPP) is considered an important challenge in software‐defined networking, which should be addressed. The number of required controllers and their locations are the important challenges that affect various aspects of the separated controller plane such as the performance metrics, and ability to respond to failures. Also, unappropriated subdomain partitioning of the software‐defined network by multicontrollers may cause the unbalanced distribution of controller loads resulting in the reduction of communication performance of the network. In this paper, an optimization subdomain partitioning method based on the particle swarm optimization (PSO) algorithm is presented for deploying the CPP and allocating switches to controllers. The proposed control placement method aims to minimize the cost of the network known as the number of required controllers, to minimize the maximum load imbalance between controllers, and to improve resilience against a failure between each switch and its mapping controller. The presented method is evaluated using two widely used networks from the Internet Topology Zoo such as Aarnet, Oxford, Chinanet, Interoute, and ION topologies to show the scalability of the proposed method. The results show that the proposed method achieves better performance in the required number of controllers, propagation delay, and load balancing among controllers when compared to the controller placement methods based on the Varna, clustering‐based network partition algorithm (CNPA), and K‐means. Moreover, the proposed method improves load balancing when compared to the controller placement methods based on the Varna, CNPA, and K‐means, respectively. The proposed controller placement based on the PSO outperforms nearly 20% and 17% decline in the number of required controllers in comparison with the Varna‐based heuristic controller placement method and the CNPA for different scales of topologies, respectively. Moreover, the proposed controller placement method based on the particle swarm optimization enhances the load balancing metric by nearly 6% compared to the Varna‐based controller placement method in the case of load balancing scenario in the Interoute and ION topologies, which shows the improvement of the proposed method based on the PSO compared to the Varna‐based method. Also, in the proposed controller placement method based on the PSO, the load balancing scenario outperforms the load balancing metric among the assigned controllers by nearly 14%, 22%, 13%, and 18% compared to the K‐means‐based method in the ION, Interoute, Chinanet, Oxford, and Aarnet topologies. Furthermore, the proposed method achieves nearly 9%, 5%, and 15% decline in the average propagation delay compared to the Varna‐, CNPA‐, and K‐means‐based controller placement methods for different topologies. Furthermore, the proposed scheme achieves a higher resilience against controller failures compared to the existing approaches.

Temperature Control Strategy for Hydrogen Fuel Cell Based on IPSO-Fuzzy-PID

Hydrogen fuel cell water-thermal management systems suffer from slow response time, system vibration, and large temperature fluctuations of load current changes. In this paper, Logistic chaotic mapping, adaptively adjusted inertia weight and asymmetric learning factors are integrated to enhance the particle swarm optimization (PSO) algorithm and combine it with fuzzy control to propose an innovative improved particle swarm optimization-Fuzzy control strategy. The use of chaotic mapping to initialize the particle population effectively enhances the variety within the population, which subsequently improves the ability to search globally and prevents the algorithm from converging to a local optimum solution prematurely; by improving the parameters of learning coefficients and inertia weight, the global and local search abilities are balanced at different stages of the algorithm, so as to strengthen the algorithm’s convergence certainty while reducing the dependency on expert experience in fuzzy control. In this article, a fuel cell experimental platform is constructed to confirm the validity and efficiency of the recommended strategy, and the analysis reveals that the improved particle swarm optimization (IPSO) algorithm demonstrates better convergence performance than the standard PSO algorithm. The IPSO-Fuzzy-PID management approach is capable of providing a swift response and significantly diminishing the overshoot in the system’s performance, to maintain the system’s safe and stable execution.

A Noise Reduction Method for Signal Reconstruction and Error Compensation of a Maglev Gyroscope Under Persistent External Interference

To eliminate the noise interference caused by continuous external environmental disturbances on the rotor signals of a maglev gyroscope, this study proposes a noise reduction method that integrates an adaptive particle swarm optimization variational modal decomposition algorithm with a strategy for error compensation of the trend term in reconstructed signals, significantly improving the azimuth measurement accuracy of the gyroscope torque sensor. The optimal parameters for the variational modal decomposition algorithm were determined using the adaptive particle swarm optimization algorithm, allowing for the accurate decomposition of noisy rotor signals. Additionally, using multi-scale permutation entropy as a criterion for discriminant, the signal components were filtered and summed to obtain the denoised reconstructed signal. Furthermore, an empirical mode decomposition algorithm was employed to extract the trend term of the reconstructed signal, which was then used to compensate for the errors in the reconstructed signal, achieving significant noise reduction. On-site experiments were conducted on the high-precision GNSS baseline of the Xianyang Yuan Tunnel in the second phase of the project to divert water from the Han River to the Wei River, where this method was applied to process and analyze multiple sets of rotor signals. The experimental results show that this method effectively suppresses continuous external environmental interference, reducing the average standard deviation of the compensated signals by 46.10% and the average measurement error of the north azimuth by 45.63%. Its noise reduction performance surpasses that of the other four algorithms.

Prediction of the Dissolved Oxygen Content in Aquaculture Based on the CNN-GRU Hybrid Neural Network

The dissolved oxygen (DO) content is one of the important water quality parameters; it is crucial for assessing water body quality and ensuring the healthy growth of aquatic organisms. To enhance the prediction accuracy of DO in aquaculture, we propose a fused neural network model integrating a convolutional neural network (CNN) and a gated recurrent unit (GRU). This model initially employs a CNN to extract primary features from water quality parameters. Subsequently, the GRU captures temporal information and long-term dependencies, while a temporal attention mechanism (TAM) is introduced to further pinpoint crucial information. By optimizing model parameters through an improved particle swarm optimization (IPSO) algorithm, we develop a comprehensive IPSO-CNN-GRU-TAM prediction model. Experiments conducted using water quality datasets collected from Eagle Mountain Lake demonstrate that our model achieves a root mean square error (RMSE) of 0.0249 and a coefficient of determination (R2) of 0.9682, outperforming other prediction models with high precision. The model exhibits stable performance across fivefold cross-validation and datasets of varying depths, showcasing robust generalization capabilities. In summary, this model allows aquaculturists to precisely regulate the DO content, ensuring fish health and growth while achieving energy conservation and carbon reduction, aligning with the practical demands of modern aquaculture.

Tethered Balloon Cluster Deployments and Optimization for Emergency Communication Networks

Natural disasters can severely disrupt conventional communication systems, hampering relief efforts. High-altitude tethered balloon base stations (HATBBSs) are a promising solution to communication disruptions, providing wide communication coverage in disaster-stricken areas. However, a single HATBBS is insufficient for large disaster zones, and limited resources may restrict the number and energy capacity of available base stations. To address these challenges, this study proposes a cluster deployment of tethered balloons to form flying ad hoc networks (FANETs) as a backbone for post-disaster communications. A meta-heuristic-based multi-objective particle swarm optimization (MOPSO) algorithm is employed to optimize the placement of balloons and power control to maximize target coverage and system energy efficiency. Comparative analysis with a stochastic algorithm (SA) demonstrates that MOPSO converges faster, with significant advantages in determining optimal balloon placement. The simulation results show that MOPSO effectively improves network throughput while reducing average delay and packet loss rate.

System identification-based tuning of PID algorithm parameters by using PSO and genetic algorithms

The PID control algorithm is the most used industrial control method. This is due to its simplicity, ease of use, and because it yields stable results. PID parameters vary for each controller; therefore, finding the optimal parameters is crucial for obtaining good results. However, tuning PID parameters for complex systems is not a trivial task and is also somewhat difficult as conventional techniques rely on time and effort manual tuning or rely on simplified statistical estimation techniques of the system’s model yielding sub- optimal results. In this project, we propose to use machine learning for PID parameter tuning on proprietary historical time series operating process control data. The data is processed with the help of computational and machine-learning techniques to better identify the process model and predict the optimal PID parameters. The research methodology consists of three main steps. First, process-model identification is done by using a Radial Basis Function (RBF) neural network. Secondly, Particle Swarm Optimization (PSO) hybrid with Genetic Algorithms (GA) is used for finding the optimal PID parameters. The PID values predicted by PSO, will be fed to GA optimization process as an initial starting point. The final step consists of integrating the identified process model with the PID optimization algorithm in a computer-based simulation environment (Simulink). The experimental simulations are done for various study cases. Results showed that the predicted PID parameters error rate and standard deviation for PID control and process are decreased, which enhances the process controlling and stability.

Grounding Grids Corrosion Evaluation of Electrified Railway Based on Two‐Stage Optimization Algorithm

AbstractDue to the complex soil environment and continuous traction ground return, the electrified railway grounding grids (ERGG) are easily susceptible to corrosion, which seriously threatens the safe operation of the traction power supply system (TPSS). In this paper, the corrosion evaluation model is presented based on electrical network theory. In order to solve the equations with multi‐variable and high‐dimension characteristics of the model, a two‐stage optimization (TSO) algorithm is proposed. In the first stage, the CPLEX solver (CPLEX optimizer produced by IBM) is adopted to obtain the resistance value of multiple branches, which provides the initial solver and search scope for the next step. In the second stage, the particle swarm optimization (PSO) algorithm is used to determine the optimal solution so as to minimize the node potential difference. Finally, a corrosion experimental system is designed and established to verify the effectiveness of the above algorithm. The results show that TSO algorithm can accurately diagnose the corrosion state of grounding conductors and adapt to the various scenarios of ERGG. © 2024 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

Design of a Deep Learning-Based Metalens Color Router for RGB-NIR Sensing.

Metalens can achieve arbitrary light modulation by controlling the amplitude, phase, and polarization of the incident waves and have been applied across various fields. This paper presents a color router designed based on metalens, capable of effectively separating spectra from visible light to near-infrared light. Traditional design methods for meta-lenses require extensive simulations, making them time-consuming. In this study, we propose a deep learning network capable of forward prediction across a broad wavelength range, combined with a particle swarm optimization algorithm to design metalens efficiently. The simulation results align closely with theoretical predictions. The designed color router can simultaneously meet the theoretical transmission phase of the target spectra, specifically for red, green, blue, and near-infrared light, and focus them into designated areas. Notably, the optical efficiency of this design reaches 40%, significantly surpassing the efficiency of traditional color filters.

Prediction of Air Quality Index Based on Support Vector Machine and Improved Butterfly Optimization Algorithm

Air pollution is an increasingly serious problem. Air pollution has been become a main cause of environmental degradation and health effects. Accurate prediction of air quality can help improve environmental quality and human living conditions. The traditional methods for predicting the air quality index have the problem of low accuracy and efficiency. To solve this problem, this paper proposes a novel support vector machine prediction model based on improved butterfly optimization algorithm, which is called IBOA-SVM model. In the improved butterfly optimization algorithm, the sigmoid function is used to optimize the update of the parameter c, which increases the search diversity and improves the convergence speed. The performance of the improved butterfly optimization algorithm is verified using eight benchmark functions. Compared with performances of the traditional butterfly optimization algorithm and the particle swarm optimization algorithm, the improved butterfly optimization algorithm has strong competitiveness in accuracy and stability. We establish the IBOA-SVM prediction model for forecasting air quality based on the improved butterfly optimization algorithm. The performance of our proposed model is compared with other predicting models. The experimental results show that our proposed model has higher accuracy and efficiency in predicting the air quality index of four cities in southern China.

Revising the Motion Control Parameter Optimization Research of a Two-Wheel Differential Car

This paper proposes a solution based on the particle swarm optimization algorithm to address the issue of Proportional Integral Derivative parameter selection in the motion control of a two-wheel differential car. The mathematical motion model is established based on the driving principle of a two-wheel differential car. The transfer function of the DC motor is derived in detail, based on Kirchhoff’s law and the Laplace transform. The pose renewal equation and error renewal equation of the car are based on the mathematical motion model. Finally, a numerical simulation and experimental analysis were conducted using MATLAB R2022a, Simulink 9.1 (part of R2018a), VOFA 1.3.10 software, an STM32 microcontroller, an L298N driver chip, and other hardware components. The results indicate that the particle swarm optimization algorithm enables the rapid acquisition of optimal Proportional Integral Derivative parameters. The optimized parameter of the motor speed convergence time is set to 10 ms, with an overshoot of 1 r/min and an enhanced anti-interference ability. The optimized parameters effectively regulate the car’s motion, ensuring a maximum error control of approximately 0.003 m.

Sports Image Feature Extraction Based on Machine Learning and Global Search Algorithm

This study utilizes a combination of machine learning and a global search algorithm to enhance the quality of feature extraction in sports images. A deep residual generative adversarial network is employed to deblur images and sharpen their clarity, while the optimized particle swarm optimization algorithm is utilized to extract image features with precision and identify critical information. According to the experimental results, the research method improves the peak signal-to-noise ratios in ball sports image deblurring by 12.45%, 13.91%, and 17.18%, respectively, and in track and field sports image deblurring by 11.71%, 12.91%, and 21.61%, respectively, when compared with the generative adversarial network, generative adversarial network incorporating the attention mechanism, and multi-scale convolution-based algorithm. The accuracy-recall curve of the particle swarm algorithm that has been optimised for research completely encircles the accuracy-recall curves of the other four algorithms, which verifies the efficacy of the research methodology. The research will offer a more comprehensive perspective on sports image processing.

Cross-social-network user alignment research based on multi-dimensional user features

Accurately aligning the same users on different flat social networks to merge user information and create more nuanced user profiles is critical. However, the current research in this area faces challenges related to low efficiency and inadequate alignment accuracy. To address these challenges, we introduce a cross-social network user alignment model based on multi-dimensional user features (MDUF). First, inspired by the principles of entity recognition and Hartley's association method, we employed a block matrix association algorithm to project the original dataset into different high-dimensional spaces. Second, we proposed a new inertia weight calculation method to improve the convergence speed from linear to nonlinear transformations. This method improves the performance of traditional particle swarm optimization algorithms. Finally, we utilize improved particle swarm optimization and residual connection techniques to optimize bidirectional long short-term memory networks. The experimental results show that our proposed model significantly outperforms traditional alignment models in terms of alignment efficiency and accuracy, which is highly practical and has the potential to inspire further research on social network user alignment.

Research on Vehicle Path Planning Method with Time Windows in Uncertain Environments

With the growing complexity of logistics and the demand for sustainability, the vehicle routing problem (VRP) has become a key research area. Classical VRPs now incorporate practical challenges such as time window constraints and carbon emissions. In uncertain environments, where many factors are stochastic or fuzzy, optimization models based on uncertainty theory have gained increasing attention. A single-objective optimization model is proposed in this paper to minimize the total cost of VRP in uncertain environments, including fixed costs, transportation costs, and carbon emission costs. Practical constraints like time windows and load capacity are incorporated, and uncertain variables, such as carbon emission factors, are modeled using normal distributions. Two uncertainty models, based on the expected value and chance-constrained criteria, are developed, and their deterministic forms are derived using the inverse distribution method. To solve the problem effectively, a hybrid ant colony–zebra optimization algorithm is proposed, integrating ant colony optimization, zebra optimization, and the 3-opt algorithm to enhance global search and local optimization. Numerical experiments demonstrate the superior performance of the hybrid algorithm, achieving lower total costs compared to standalone ant colony, zebra optimization, genetic algorithm, and particle swarm optimization algorithms. The results highlight its robustness and efficiency in addressing complex constraints.

Degradation-considering adaptive equivalent consumption minimization strategy for fuel cell electric vehicles

In order to improve the economy and durability of fuel cell vehicle, a degradation-considering adaptive equivalent consumption minimization strategy (D-ECMS) was proposed for fuel cell/lithium battery hybrid power system. Aiming at enhancing the adaptability of the equivalent consumption minimum strategy, four different vehicle speed conditions were considered including low speed, medium speed, high speed, and ultra-high speed. Particle swarm optimization (PSO) algorithm was used to optimize the penalty factor under different vehicle speed conditions, and BP neural network was used to identify real-time conditions to form an adaptive equivalent consumption minimum strategy (A-ECMS). The voltage decay model of the fuel cell was established according to the voltage decay rate of the fuel cell under the three adverse conditions of idle speed, high power and dynamic load cycle. The voltage decay of the fuel cell was transformed to hydrogen consumption in the cost function of the A-ECMS strategy to obtain the D-ECMS considering durability degradation. The simulation and the hardware-in-the-loop (HIL) test based on the dSPACE real-time platform was carried out. The results show that: compared with A-ECMS, the hydrogen consumption of the D-ECMS increased by 5.2%, 9.7%, 10.2%, and 0.2% respectively under WLTC, NEDC, UDDS, and HWFET cycles. However, D-ECMS can reduce the voltage decay by 38.3%, 59.7%, 35.2%, and 26% under the above four test conditions, and their total comprehensive cost loss is reduced by 8.7%, 6.7%, 9.6%, and 6.3% respectively. As result, the D-ECMS can reduces the voltage attenuation of fuel cells and improve the durability of fuel cells significantly with a small hydrogen consumption increase,which can reduce the operating cost of fuel cells finally. Furthermore, the average error between the results of HIL test and offline simulation results is less than 5%, which validates the adaptability and real-time application feasibility of D-ECMS.

Low cost of smart unilateral street lighting system with photovoltaic power plant using particle swarm optimization

The conventional street lighting system has played an important role in our modern life to keep the buildings and roads safe and active using constant light mode during all the night from the sunset to sunrise; however its high energy consumption, and maintenance cost are considered the huge challenges facing the country’s economy. The smart street light system is currently used as the best option to solve these problems. In our project a smart unilateral street light system was implemented using advanced technology in our experimental site in the south-west of Algeria. The prototype significantly decreased the consumed energy compared to that of the conventional street light system. A Standalone Photovoltaic Plant (SPP) is designed and sized to meet various energy requirements such as street light and photovoltaic pumping in order to isolate the whole system from the power grid. The Particle Swarm Optimization (PSO) algorithm is used to study the cost function to improve the economic feasibility.

Research on optimal scheduling of minor and moderate floods for cascade reservoirs in the lower reaches of the jinsha river considering power generation

The comprehensive cascade reservoir system is extensively used for flood control and power generation. Traditional reservoir scheduling often treats these tasks as competing objectives, prioritizing flood control during the flood season and neglecting power generation benefits. This approach, primarily applied to large floods, leads to hydraulic resource wastage and power generation loss during minor and moderate floods. The lower Jinsha River cascade reservoirs have significant flood control storage capacity, allowing part of the capacity to be used for maintaining higher water levels without compromising safety under minor and moderate floods within a 20-year return period. This paper proposes an optimal scheduling model that considers both flood control and power generation tasks for the lower Jinsha River cascade reservoirs. The Multi-objective Particle Swarm Optimization (MOPSO) algorithm was used to find non-inferior solution sets. An evaluation index system was developed to select optimal solutions under different flood frequencies, using the Minimum Discriminant Information Principle and VIKOR model. The results indicate that the optimal scheduling scheme under 20-year, 10-year, and 5-year return period floods can enhance power generation by 1.64, 1.71, and 1.35 billion kWh, respectively, compared to conventional scheduling. This approach supports coordinated flood control and power generation scheduling, contributing to the high-quality development of the Yangtze River Economic Belt.