Search

Manual forestland classification methods, which rely on predetermined scoring criteria and subjective interpretation, are commonly used but suffer from limitations such as high labor costs, complexity, and lack of scalability. This study proposes an innovative machine learning-based approach to forestland classification, utilizing a Support Vector Machine (SVM) model to automate the classification process and enhance both efficiency and accuracy. The main contributions of this work are as follows: A machine learning model was developed using integrated data from the Third National Land Survey of China, including forestry, grassland, and wetland datasets. Unlike previous approaches, the SVM model is optimized with Grid Search (GS), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO) to automatically determine classification parameters, overcoming the limitations of manual rule-based methods. The performance of the SVM model was evaluated using confusion matrices, classification accuracy, and Matthews Correlation Coefficient (MCC). A comprehensive comparison under different optimization techniques revealed significant improvements in classification accuracy and generalization ability over manual classification systems. The experimental results demonstrated that the GA-SVM model achieved classification accuracies of 98.83% (test set) and 99.65% (overall sample), with MCC values of 0.9796 and 0.990, respectively, outpacing other optimization algorithms, including Grid Search (GS) and Particle Swarm Optimization (PSO). The GA-SVM model was applied to classify public welfare forestland in Kunyu City, yielding detailed classifications across various forestland categories. This result provides a more efficient and accurate method for large-scale forestland management, with significant implications for future land use assessments. The findings underscore the advantages of the GA-SVM model in forestland classification: it is efficient, accurate, and easy to operate. This study not only presents a more reliable alternative to conventional rule-based and manual scoring methods but also sets a precedent for using machine learning to automate and optimize forestland classification in future applications.

With the wide application of Residence Time Difference (RTD) fluxgate sensors in Unmanned Aerial Vehicle (UAV) aeromagnetic measurements, the requirements for their measurement accuracy are increasing. The core characteristics of the RTD fluxgate sensor limit its sensitivity; the high-permeability soft magnetic core is especially easily interfered with by the input noise. In this paper, based on the study of the excitation signal and input noise characteristics, the stochastic resonance is proposed to be realized by adding feedback by taking advantage of the high hysteresis loop rectangular ratio, low coercivity and bistability characteristics of the soft magnetic material core. Simulink is used to construct the sensor model of odd polynomial feedback control, and the Particle Swarm Optimization (PSO) algorithm is used to optimize the coefficients of the feedback function so that the sensor reaches a resonance state, thus reducing the noise interference and improving the sensitivity of the sensor. The simulation results show that optimizing the odd polynomial feedback coefficients with PSO enables the sensor to reach a resonance state, improving sensitivity by at least 23.5%, effectively enhancing sensor performance and laying a foundation for advancements in UAV aeromagnetic measurement technology.

This paper addresses the Capacitated Location Tree Problem with Interconnections, a new combinatorial optimization problem with applications in network design. In this problem, the required facilities picked from a set of potential facilities must be opened to serve customers using a tree-shaped network. Costs and capacities are associated with the opening of facilities and the establishment of network links. Customers have a given demand that must be satisfied while respecting the facilities and link capacities. The problem aims to minimize the total cost of designing a distribution network while considering facility opening costs, demand satisfaction, capacity constraints, and the creation of interconnections to enhance network resilience. A valid mixed-integer programming was proposed and an exact solution method based on the formulation was used to solve small- and medium-sized instances. To solve larger instances two metaheuristic approaches were used. A specific decoder procedure for the metaheuristic solution approaches was also proposed and used to help find solutions, especially for large instances. Computational experiments and results using the three solution approaches are also presented. Finally, a case study on the design of electrical transportation systems was presented and solved.

This study employs four metaheuristic optimization methods to optimize the dimensional synthesis of Ackermann steering mechanisms for three-axle, six-wheeled vehicles with front-axle steering mode and reverse-phase steering mode. The employed optimization methods include Particle Swarm Optimization (PSO), Hybrid Particle Swarm Optimization (HPSO), Differential Evolution with golden ratio (DE-gr), and Linearly Ensemble of Parameters and Mutation Strategies in Differential Evolution (L-EPSDE). With a front-wheel steering angle range of 70 degrees, two hundred optimization experiments were conducted for each method, and statistical analyses revealed that DE-gr and L-EPSDE methods outperformed PSO and HPSO methods in terms of standard deviation, mean value, and minimum error. These two methods exhibited superior convergence stability, faster convergence, and higher accuracy compared to PSO and HPSO. Reverse-phase (K = 1) steering mode outperformed front-axle steering mode, delivering reduced steering errors and turning radii. Considering the transmission ratio of front to rear axle (K) as a design variable in reverse-phase steering mode increased design flexibility and significantly lowered steering errors for the front and rear axle steering mechanisms. However, this comes with a slight increase in the turning radius of the vehicle’s front part compared to when K = 1. The optimized mechanism, designed using the DE-gr method, was validated through kinematic simulations and steering analyses using MSC-ADAMS v2015 software, further confirming the effectiveness and reliability of the proposed design.

Current trends in the use of energy storage, electric mobility, and the integration of a large number of distributed generations at the distribution level can have positive effects on reducing loads and losses in the network. An excessive and uncontrolled level of integration of the above trends leads to various problems related to the power quality. Distortion of the voltage and current waveforms caused by nonlinear loads is manifested through harmonics and can be classified as one of the most essential parameters of electricity quality. Reducing harmonics thus becomes the primary goal for improving the quality of electricity at the distribution level. This paper, along with a detailed analysis of the literature, provides an overview of different views on the problems of optimal allocation of active filters and emphasizes the importance that the problem of optimal allocation of active filters should be based on the variability of the harmonic spectrum as a function of time. Installing devices for reducing harmonics in the network, in terms of improving the quality of electricity, is one of the essential elements from both a technical and an economic point of view and can solve these challenges. Therefore, it is necessary to develop methods for solving the problem of determining the position, size and parameters of filters, as well as the number of buses on which such devices can be integrated. Applying optimization techniques enables the development of more realistic models for applying active power filters. The research in this paper is directed toward developing a co-simulation optimization model to determine optimal settings of the parallel APF due to harmonic reduction in a real low-voltage network using particle swarm optimization for 24 h intervals. The research in this paper was conducted on a real radial low-voltage feeder, where at each node, the variability of production and/or consumption is taken, which is obtained on the basis of real measurements and tests. Based on this, the position and dimensioning of the shunt active power filters (APFs) depend on the load range within a 24 h interval at all nodes in the observed time interval. Furthermore, the paper emphasizes the importance of observing Voltage Total Harmonic Distortion (THDV) on the busbars in the depth of the feeder as well as the importance of observing THDV in each phase.

Recent advances in emotion recognition through Artificial Intelligence (AI) have demonstrated potential applications in various fields (e.g., healthcare, advertising, and driving technology), with electroencephalogram (EEG)-based approaches demonstrating superior accuracy compared to facial or vocal methods due to their resistance to intentional manipulation. This study presents a novel approach to enhance EEG-based emotion estimation accuracy by emphasizing temporal features and efficient parameter space exploration. We propose a model combining Long Short-Term Memory (LSTM) with an attention mechanism to highlight temporal features in EEG data while optimizing LSTM parameters through Particle Swarm Optimization (PSO). The attention mechanism assigned weights to LSTM hidden states, and PSO dynamically optimizes the vital parameters, including units, batch size, and dropout rate. Using the DEAP and SEED datasets, which serve as benchmark datasets for emotion estimation research using EEG, we evaluate the model’s performance. For the DEAP dataset, we conduct a four-class classification of combinations of high and low valence and arousal states. We perform a three-class classification of negative, neutral, and positive emotions for the SEED dataset. The proposed model achieves an accuracy of 0.9409 on the DEAP dataset, surpassing the previous state-of-the-art accuracy of 0.9100 reported by Lin et al. The model attains an accuracy of 0.9732 on the SEED dataset, recording one of the highest accuracies among the related research. These results demonstrate that integrating the attention mechanism with PSO significantly improves the accuracy of EEG-based emotion estimation, contributing to the advancement of emotion recognition technology.

Generally, nonlinear systems have dynamic uncertainties, and under certain conditions, the systems exhibit different chaos intensities. Therefore, it is an important consideration for designers to realize the suppression and enhancement of chaos intensity under nonlinear factors according to the actual situation, but there are few research results on this problem. To investigate the dynamic performance and chaotic intensity of a nonlinear mechanism, a planar closed-chain under-actuated mechanism, which has not been extensively studied before, is taken as an example. It is worth noting that a small change in the parameters of a nonlinear system will cause a large change in the motion state of the system and even the mutual transformation between chaotic phenomena and periodic phenomena. To solve this problem, uniformity is used to evaluate the chaos intensity of the system. Finally, based on uniformity, the particle swarm optimization algorithm successfully achieves the suppression and enhancement of the chaos intensity of the closed-chain under-actuated five-bar mechanism by optimizing its linkage length and driving speed, and the results are verified by the experimental platform.

This study introduces a novel method that integrates artificial neural networks (ANNs) with the Particle Swarm Optimization (PSO) algorithm to enhance the efficiency and precision of parameter optimization for the small-signal equivalent model of dual-field-plate GaN HEMT devices. We initially train an ANN model to predict the S-parameters of the device, and subsequently utilize the PSO algorithm for parameter optimization. Comparative analysis with the NSGA2 and DE algorithms, based on convergence speed and accuracy, underscores the superiority of the PSO algorithm. Ultimately, this ANN-PSO approach is employed to automatically optimize the internal parameters of a 4 × 250 μm dual-field-plate GaN HEMT equivalent circuit model within the frequency range of 1–18 GHz. The method’s effectiveness under varying bias conditions is validated through comparison with traditional physical formula analysis methods. The results demonstrate that the ANN-PSO method significantly enhances the automation and efficiency of parameter optimization while maintaining model accuracy, providing a reference for the optimization of other device models.

Accurate prediction of the Remaining Useful Life (RUL) of lithium-ion batteries is essential for enhancing energy management and extending the lifespan of batteries across various industries. However, the raw capacity data of these batteries is often noisy and exhibits complex nonlinear degradation patterns, especially due to capacity regeneration phenomena during operation, making precise RUL prediction a significant challenge. Although various deep learning-based methods have been proposed, their performance relies heavily on the availability of large datasets, and satisfactory prediction accuracy is often achievable only with extensive training samples. To overcome this limitation, we propose a novel method that integrates sequence decomposition algorithms with an optimized neural network. Specifically, the Complementary Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) algorithm is employed to decompose the raw capacity data, effectively mitigating the noise from capacity regeneration. Subsequently, Particle Swarm Optimization (PSO) is used to fine-tune the hyperparameters of the Bidirectional Gated Recurrent Unit (BiGRU) model. The final BiGRU-based prediction model was extensively tested on eight lithium-ion battery datasets from NASA and CALCE, demonstrating robust generalization capability, even with limited data. The experimental results indicate that the CEEMDAN-PSO-BiGRU model can reliably and accurately predict the RUL and capacity of lithium-ion batteries, providing a promising and reliable method for RUL prediction in practical applications.

In the domain of bionic walking control for biped robots, optimizing the parameters of the central pattern generator (CPG) presents a formidable challenge due to its high-dimensional and nonlinear characteristics. The traditional particle swarm optimization (PSO) algorithm often converges to local optima, particularly when addressing CPG parameter optimization issues. To address these challenges, one improved particle swarm optimization algorithm aimed at enhancing the stability of the walking control of biped robots was proposed in this paper. The improved PSO algorithm incorporates a spiral function to generate better particles, alongside optimized inertia weight factors and learning factors. Evaluation results between the proposed algorithm and comparative PSO algorithms were provided, focusing on fitness, computational dimensions, convergence rates, and other metrics. The biped robot walking validation simulations, based on CPG control, were implemented through the integration of the V-REP (V4.1.0) and MATLAB (R2022b) platforms. Results demonstrate that compared with the traditional PSO algorithm and chaotic PSO algorithms, the performance of the proposed algorithm is improved by about 45% (two-dimensional model) and 54% (four-dimensional model), particularly excelling in high-dimensional computations. The novel algorithm exhibits a reduced complexity and improved optimization efficiency, thereby offering an effective strategy to enhance the walking stability of biped robots.

There has been very limited research conducted to predict rental prices in the German real estate market using an AI-based approach. From a general perspective, conventional approaches struggle to handle large amounts of data and fail to consider the numerous elements that affect rental prices. The absence of sophisticated, data-driven analytical tools further complicates this situation, impeding stakeholders, such as tenants, landlords, real estate agents, and the government, from obtaining the accurate insights necessary for making well-informed decisions in this area. This paper applies novel machine learning (ML) approaches, including ensemble techniques, neural networks, linear regression (LR), and tree-based algorithms, specifically designed for forecasting rental prices in Munich. To ensure accuracy and reliability, the performance of these models is evaluated using the R2 score and root mean squared error (RMSE). The study provides two feature sets for model comparison, selected by particle swarm optimisation (PSO) and CatBoost. These two feature selection methods identify significant variables based on different mechanisms, such as seeking the optimal solution with an objective function and converting categorical features into target statistics (TSs) to address high-dimensional issues. These methods are ideal for this German dataset, which contains 49 features. Testing the performance of 10 ML algorithms on two sets helps validate the robustness and efficacy of the AI-based approach utilising the PyTorch framework. The findings illustrate that ML models combined with PyTorch-based neural networks (PNNs) demonstrate high accuracy compared to standalone ML models, regardless of feature changes. The improved performance indicates that utilising the PyTorch framework for predictive tasks is advantageous, as evidenced by a statistical significance test in terms of both R2 and RMSE (p-values < 0.001). The integration results display outstanding accuracy, averaging 90% across both feature sets. Particularly, the XGB model, which exhibited the lowest performance among all models in both sets, significantly improved from 0.8903 to 0.9097 in set 1 and from 0.8717 to 0.9022 in set 2 after being combined with the PNN. These results showcase the efficacy of using the PyTorch framework, enhancing the precision and reliability of the ML models in predicting the dynamic real estate market. Given that this study applies two feature sets and demonstrates consistent performance across sets with varying characteristics, the methodology may be applied to other locations. By offering accurate projections, it aids investors, renters, property managers, and regulators in facilitating better decision-making in the real estate sector.

This study compares bio-inspired optimization algorithms for enhancing an ANN-based Maximum Power Point Tracking (MPPT) forecast system under partial shading conditions in photovoltaic systems. Four algorithms—grey wolf optimizer (GWO), particle swarm optimization (PSO), squirrel search algorithm (SSA), and cuckoo search (CS)—were evaluated, with the dataset augmented by perturbations to simulate shading. The standard ANN performed poorly, with 64 neurons in Layer 1 and 32 in Layer 2 (MSE of 159.9437, MAE of 8.0781). Among the optimized approaches, GWO, with 66 neurons in Layer 1 and 100 in Layer 2, achieved the best prediction accuracy (MSE of 11.9487, MAE of 2.4552) and was computationally efficient (execution time of 1198.99 s). PSO, using 98 neurons in Layer 1 and 100 in Layer 2, minimized MAE (2.1679) but had a slightly longer execution time (1417.80 s). SSA, with the same neuron count as GWO, also performed well (MSE 12.1500, MAE 2.7003) and was the fastest (987.45 s). CS, with 84 neurons in Layer 1 and 74 in Layer 2, was less reliable (MSE 33.7767, MAE 3.8547) and slower (1904.01 s). GWO proved to be the best overall, balancing accuracy and speed. Future real-world applications of this methodology include improving energy efficiency in solar farms under variable weather conditions and optimizing the performance of residential solar panels to reduce energy costs. Further optimization developments could address more complex and larger-scale datasets in real-time, such as integrating renewable energy sources into smart grid systems for better energy distribution.

Traffic lights play an important role in efficient traffic management, especially in crowded cities. Optimizing traffic helps to reduce crowding, save time, and ensure the smooth flow of traffic. Metaheuristic algorithms have a proven ability to optimize smart traffic management systems. This paper investigates the effectiveness of two metaheuristic algorithms: particle swarm optimization (PSO) and grey wolf optimization (GWO). In addition, we posit a hybrid PSO-GWO method of optimizing traffic light control using IoT-enabled data from sensors. In this study, we aimed to enhance the movement of traffic, minimize delays, and improve overall traffic precision. Our results demonstrate that the hybrid PSO-GWO method outperforms individual PSO and GWO algorithms, achieving superior traffic movement precision (0.925173), greater delay reduction (0.994543), and higher throughput improvement (0.89912) than standalone methods. PSO excels in reducing wait times (0.7934), while GWO shows reasonable performance across a range of metrics. The hybrid approach leverages the power of both PSO and GWO algorithms, proving to be the most effective solution for smart traffic management. This research highlights using hybrid optimization techniques and IoT (Internet of Things) in developing traffic control systems.

The paper presents the process of optimizing the duty cycle of a rotary crane. The minimization of the carried load’s trajectory was chosen as the objective function. The research was conducted using the genetic algorithm and the particle swarm algorithm. The influence of particular algorithm parameters on the obtained optimal solution was characterized. For the obtained best case, the inverse kinematics problem was solved, allowing us to determine the control functions of individual crane members. The presented redundant system was solved with the use of an algorithm for temporarily limiting the movement of specific kinematic pairs. On the basis of the obtained results, it was determined which of the algorithms used is more favorable, taking into account the crane’s operational safety and lifting capacity.

High-voltage direct current (HVDC) transmission systems are widely used today due to their several advantages over high-voltage alternative current (HVAC) systems. They are primarily utilized in connecting different large grids or smaller independent networks, especially in submarine interconnections. Many studies have been conducted globally on the optimal planning, operation, and control of these power systems. Understanding the transient stability of these systems during significant disturbances is also of great importance. This paper specifically focuses on power systems with HVDC connections and high penetration of renewable energy sources. Analytical models were developed for power system components like HVDC converters, DC lines, and full converter wind generators. A simulation of a test-case power system was conducted, including various significant disturbances such as HVDC line trips, short circuits, power unit failures, and major changes in load. The voltage and frequency stability of the system under specific operational scenarios was also examined. The results obtained indicate technical limitations and operating guidelines to avoid undesirable conditions and system failure. In conclusion, adopting short-term stability tests the optimal operation point of the system, and the limitations in the penetration by RES units can be decided.

Range extender hybrid vehicles have the advantages of better dynamics and longer driving range while reducing pollution and fuel consumption. This work focuses on the control strategy of an Auxiliary Power Unit (APU) operating in power generation mode for a range-extender mixer truck. When an operating point is switched, the engine speed and generator torque of the APU will switch accordingly. In order to ensure APU fast and stable adjustment to meet the power demand of the vehicle as well as operate at the lowest fuel consumption, a fuzzy adaptive PID coordination control strategy based on particle swarm optimization (PSO) is proposed to control the APU. The optimal operating curve of APU is calculated by coupling the engine and generator first. Then, the adaptive PID algorithm is used to control the speed and torque of the APU in a dual closed loop. The PSO is used to optimize the PID control parameter. Through hardware-in-the-loop tests under different working conditions, the control strategy is verified to be effective and real-time. The results show that the proposed control strategy can coordinate the operating of engine and generator and control the APU to track target power stably and quickly under minimum fuel consumption. Compared with traditional PID control strategy, the overshoot, regulation time and steady-state error are reduced by 55.1%, 11.1% and 77.3%, respectively.

This work explores efficiency improvements in the copper flotation stage, a complex nonlinear, multivariable process subject to numerous perturbations. The primary objective is to design a fractional-order PID (FOPID) control strategy and a fractional-order model reference adaptive control (FOMRAC) system. The parameters for these controllers are optimized using the particle swarm optimization (PSO) algorithm with an objective function tailored to the control goals. This study employs models of both a bank series of five flotation cells and a flotation column. Their performance results are compared against traditional controllers, such as an integer-order PID and MRAC. The findings reveal that fractional-order controllers offer notable advantages over their integer-order counterparts, showing improved performance metrics with minimal changes to the existing control framework. This research highlights the effectiveness of fractional control in enhancing flotation processes and introduces a novel application of fractional control techniques in this area.

Localization methods of unknown emitters are used for the monitoring of illegal radio waves. The localization methods using ground-based sensors suffer from a degradation of localization accuracy in environments where the distance between the emitter and the sensor is non-line-of-sight (NLoS). Therefore, research is being conducted to improve localization accuracy by utilizing Unmanned Aerial Vehicles (UAVs) as sensors to ensure a line-of-sight (LoS) condition. However, UAVs can fly freely over the sky, making it difficult to optimize flight paths based on particle swarm optimization (PSO) for efficient and accurate localization. This paper examines the optimization of UAV flight paths to achieve highly efficient and accurate outdoor localization of unknown emitters via two approaches, a circular orbit and free-path trajectory, respectively. Our numerical results reveal the improved localization estimation error performance of our proposed approach. Particularly, when evaluating at the 90th percentile of the error’s cumulative distribution function (CDF), the proposed approach can reach an error of 28.59 m with a circular orbit and 12.91 m with a free-path orbit, as compared to the conventional fixed sensor case whose localization estimation error is 55.02 m.

The integration of nonconventional energy sources such as solar, wind, and fuel cells into electrical power networks introduces significant challenges in maintaining frequency stability and consistent tie-line power flows. These fluctuations can adversely affect the quality and reliability of power supplied to consumers. This paper addresses this issue by proposing a Proportional–Integral–Derivative (PID) controller optimized through a hybrid Particle Swarm Optimization–Artificial Hummingbird Algorithm (PSO-AHA) approach. The PID controller is tuned using the Integral Time Absolute Error (ITAE) as a fitness function to enhance control performance. The PSO-AHA-PID controller’s effectiveness is evaluated in two networks: a two-area thermal tie-line interconnected power system (IPS) and a one-area multi-source power network incorporating thermal, solar, wind, and fuel cell sources. Comparative analyses under various operational conditions, including parameter variations and load changes, demonstrate the superior performance of the PSO-AHA-PID controller over the conventional PSO-PID controller. Statistical results indicate that in the one-area multi-source network, the PSO-AHA-PID controller achieves a 76.6% reduction in overshoot, an 88.9% reduction in undershoot, and a 97.5% reduction in settling time compared to the PSO-PID controller. In the dual-area system, the PSO-AHA-PID controller reduces the overshoot by 75.2%, reduces the undershoot by 85.7%, and improves the fall time by 71.6%. These improvements provide a robust and reliable solution for enhancing the stability of interconnected power systems in the presence of diverse and variable energy sources.

Orifice Hollow Cathodes are electric devices necessary for the functioning of common plasma thrusters for space applications. Their reliability mainly depends on the success of a spacecraft’s mission equipped with electric propulsion. The development of plasma models is crucial in the evaluation of plasma properties within the cathodes that are difficult to measure due to the small dimensions. Many models, based on non-linear systems of plasma equations, have been proposed in the openiterature. These are solved commonly by means of iterative procedures. This paper investigates the possibility of solving them by means of the Particle Swarm Optimization method. The results of the validation tests confirm the expected trends for all the unknowns; the confidence bound of the discharge current as a function of mass flow rate is very narrow (2 ÷ 5) V); moreover, the results match very well the experimental data except at theowest mass flow rate (0.08 mg/s) and discharge current (1A), where the computations underpredict the discharge current to the utmost by 40%. The highest data dispersion regards the plasma density in the emitter region (±20% of the average value) and the wall temperatures (±50 K with respect to the average values) of the orifice and insert; those of the others variables are very tiny.

This research aims to implement an already developed algorithm to obtain the maximum power transfer of a solar generation field based on a dynamic approach. The study addresses the sizing of the load to be supplied, which is a residential building. On the other hand, it also considers the field sizing as a function of the load and the operating characteristics of the selected inverter. The irradiance data correspond to the hourly record of a station that is part of the network of meteorological stations in Quito. Quito was chosen as the location for this research due to the optimization algorithm’s practical application and the availability of experimental equipment. The demand sizing is based on the regulations of the distribution company with jurisdiction in the area, which makes it a suitable test bed for the algorithm. The optimization algorithm is developed using Python (version 3.9), and the analysis of the behavior of the solar panels is performed by dynamic modeling using the Vensim software (version 10.1.2). Finally, comparative results are presented between using and not using the investigated circuit and algorithm in the photovoltaic system, obtaining an improvement in the generation over a system without the use of these improvements, validating these results by implementing them in a test system, obtaining ranges higher than 10% of the initially generated power.

Efficient healthcare facility design is crucial for providing high-quality healthcare services. This study introduces an innovative approach that integrates artificial intelligence (AI) algorithms, specifically particle swarm optimization (PSO), with building information modeling (BIM) and digital twin technologies to enhance facility layout optimization. The methodology seamlessly integrates AI-driven layout optimization with the robust visualization, analysis, and real-time capabilities of BIM and digital twins. Through the convergence of AI algorithms, BIM, and digital twins, this framework empowers stakeholders to establish a virtual environment for the streamlined exploration and evaluation of diverse design options, significantly reducing the time and manual effort required for layout design. The PSO algorithm generates optimized 2D layouts, which are seamlessly transformed into 3D BIM models through visual programming in Dynamo. This transition enables stakeholders to visualize, analyze, and monitor designs comprehensively, facilitating well-informed decision-making and collaborative discussions. The study presents a comprehensive methodology that underscores the potential of AI, BIM, and digital twin integration, offering a path toward more efficient and effective facility design.

Path planning is one of the core issues in the autonomous navigation of an Unmanned Surface Vehicle (USV), as the accuracy of the results directly affects the safety of the USV. Hence, this paper proposes a USV path planning algorithm that integrates an improved Particle Swarm Optimisation (PSO) algorithm with a Dynamic Window Approach (DWA). Firstly, in order to advance the solution accuracy and convergence speed of the PSO algorithm, a nonlinear decreasing inertia weight and adaptive learning factors are introduced. Secondly, in order to solve the problem of long path and path non-smoothness, the fitness function of PSO is modified to consider both path length and path smoothness. Finally, the International Regulations for Preventing Collisions at Sea (COLREGS) are utilised to achieve dynamic obstacle avoidance while complying with maritime practices. Numerical cases verify that the path planned via the proposed algorithm is shorter and smoother, guaranteeing the safety of USV navigation while complying with the COLREGS.

To accurately identify the deflection data collected by a traffic speed deflectometer (TSD) and eliminate the noise in the measured signals, a TSD signal denoising method based on the partial swarm optimization–variational mode decomposition (PSO–VMD) method is proposed. Initially, the VMD algorithm is used for modal decomposition, calculating the correlation coefficients between each decomposed mode and the original signal for modal selection and signal reconstruction; Then, the particle swarm optimization algorithm is utilized to optimize the number of modes K and the value α for the VMD algorithm, adopting fuzzy entropy as the affinity function to circumvent effects from sequence decomposition and forecasting accuracy, thus identifying the optimal combination of hyperparameters. Finally, the analysis on simulated signals indicates that the PSO–VMD method secures the best parameters, showing a clear advantage in denoising. Denoising real TSD data validates that the approach proposed herein achieves commendable outcomes in TSD deflection noise reduction, offering a feasible strategy for TSD signal denoising.

Currently, complex scene classification strategies are limited to high-definition image scene sets, and low-quality scene sets are overlooked. Although a few studies have focused on artificially noisy images or specific image sets, none have involved actual low-resolution scene images. Therefore, designing classification models around practicality is of paramount importance. To solve the above problems, this paper proposes a two-stage classification optimization algorithm model based on MPSO, thus achieving high-precision classification of low-quality scene images. Firstly, to verify the rationality of the proposed model, three groups of internationally recognized scene datasets were used to conduct comparative experiments with the proposed model and 21 existing methods. It was found that the proposed model performs better, especially in the 15-scene dataset, with 1.54% higher accuracy than the best existing method ResNet-ELM. Secondly, to prove the necessity of the pre-reconstruction stage of the proposed model, the same classification architecture was used to conduct comparative experiments between the proposed reconstruction method and six existing preprocessing methods on the seven self-built low-quality news scene frames. The results show that the proposed model has a higher improvement rate for outdoor scenes. Finally, to test the application potential of the proposed model in outdoor environments, an adaptive test experiment was conducted on the two self-built scene sets affected by lighting and weather. The results indicate that the proposed model is suitable for weather-affected scene classification, with an average accuracy improvement of 1.42%.

The growing deployment of distributed resources significantly affects the distribution grid performance in most countries. The optimal sizing and placement of these resources have become increasingly crucial to mitigating grid issues and reducing costs. Particle Swarm Optimization (PSO) is widely used to address such problems but faces computational inefficiency due to its numerical convergence behavior. This limits its effectiveness, especially for power system problems, because the numerical distance between two nodes in power systems might be different from the actual electrical distance. In this paper, a scalable bilevel optimization problem with two novel algorithms enhances PSO’s computational efficiency. While the resistivity-driven algorithm strategically targets low-resistivity regions and guides PSO toward areas with lower losses, the connectivity-driven algorithm aligns solution spaces with the grid’s physical topology. It prioritizes actual physical neighbors during the search to prevent local optima traps. The tests of the algorithms on the IEEE 33-bus and the 69-bus and Norwegian networks show significant reductions in power losses (up to 74% for PV, wind, and storage) and improved voltage stability (a 21% reduction in mean voltage deviation index) with respect to the results of classical PSO. The proposed network-oriented PSO outperforms classical PSO by achieving a 2.84% reduction in the average fitness value for the IEEE 69-bus case with PV, wind, and storage deployment. The Norwegian case study affirms the effectiveness of the proposed approach in real-world applications through significant improvements in loss reduction and voltage stability.

According to standards, the harmonic content should be less than 5% at the time of calibration, while the calibration index proposed in this paper stipulates that the primary current amplitude of 1%~120% of the full range of the accuracy level reaches 0.05 S level. In view of the above problems and indexes, this paper adopts the active regulation technology of primary current; firstly, the harmonics of primary current are extracted, and then the opposite harmonic currents are injected by the penetrating wire to offset the harmonics, and finally, charged calibration is carried out. The inductive current transformer charged calibration device was first designed, and the particle swarm algorithm was used to optimize the offset performance of the key link of the background current offset suppression. The transfer function of the offset loop was derived, and it was theoretically analyzed that it can meet the offset requirements up to the 200th harmonic. The results show that the optimized calibration method can realize the full-range calibration of low-voltage inductive current transformers under charged conditions by making a prototype of the device and constructing a test circuit for experiments, and measuring the data and analyzing them.

To further enhance the precision and the adaptability of path tracking control, and considering that most of the research is focused on front-wheel steering, an adaptive parametric model predictive control (MPC) was proposed for rear-wheel-steering agricultural machinery. Firstly, the kinematic and dynamic models of rear-wheel-steering agricultural machinery were established. Secondly, the influence laws of curvature and velocity on the prediction horizon Np, control horizon Nc, and preview value Npre were obtained by simulating and analyzing the factors influencing the MPC tracking effect. The results revealed that raising Npre can improve curve tracking performance. Np was correlated negatively with the curvature change, whereas Nc and Npre were positively connected. Np, Nc, and Npre were correlated positively with the velocity change. Then, the parameters for self-adaptation of Np, Nc, and Npre were accomplished via fuzzy control (FC), and particle swarm optimization (PSO) was utilized to optimize the three parameters to determine the optimal parameter combination. Finally, simulation and comparative analysis were conducted to assess the tracking effects of the manual tuning MPC, the FC_MPC, and the PSO_MPC under U-shaped and complex curve paths. The results indicated that there was no significant difference and all three methods achieved better tracking effects under no disturbance, with the mean absolute value of lateral error ≤0.18 cm, standard deviation ≤0.37 cm, maximum deviation of U-shaped path

The coefficients of friction (COFs) is one of the most important parameters used to evaluate the braking performance of a friction brake. Many indicators that affect the safety and comfort of automobiles are associated with brake COFs. The manufacturers of friction brakes and their components are required to spend huge amounts of time and money to carry out experimental tests to ensure the COFs of a newly developed braking system meet the required standards. In order to save time and costs for the development of new friction brake applications, the GRU (Gate Recurrent Unit) algorithm optimized by the improved PSO (particle swarm optimization) global optimization method is employed in this work to predict brake COFs based on existing experimental data obtained from friction braking dynamometer tests. Compared with the LSTM (Long Short-Term Memory) method, the GRU algorithm optimized by PSO avoids the accuracy reduction problem caused by gradient descent in the training process and hence reduces the prediction error and computational cost. The combined PSO–GRU algorithm increases the coefficient of determination (R2) of the prediction by 4.7%, reduces the MAE (mean absolute error) by 14.3%, and increases the prediction speed by 40.1% compared with the standalone GRU method. The prediction method based on machine learning proposed in this study can not only be applied to the prediction of automobile braking COFs but also for other frictional system problems, such as the prediction of braking noise and the friction of various bearing transmission components.

RNA pseudouridine modification exists in different RNA types of many species, and it has a significant role in regulating the expression of biological processes. To understand the functional mechanisms for RNA pseudouridine sites, the accurate identification of pseudouridine sites in RNA sequences is essential. Although several fast and inexpensive computational methods have been proposed, the challenge of improving recognition accuracy and generalization still exists. This study proposed a novel ensemble predictor called PseUpred-ELPSO for improved RNA pseudouridine site prediction. After analyzing the nucleotide composition preferences between RNA pseudouridine site sequences, two feature representations were determined and fed into the stacking ensemble framework. Then, using five tree-based machine learning classifiers as base classifiers, 30-dimensional RNA profiles are constructed to represent RNA sequences, and using the PSO algorithm, the weights of the RNA profiles were searched to further enhance the representation. A logistic regression classifier was used as a meta-classifier to complete the final predictions. Compared to the most advanced predictors, the performance of PseUpred-ELPSO is superior in both cross-validation and the independent test. Based on the PseUpred-ELPSO predictor, a free and easy-to-operate web server has been established, which will be a powerful tool for pseudouridine site identification.

Permeable concrete is a type of porous concrete with the special function of water permeability, but the permeability of permeable concrete will decrease gradually due to the clogging behavior arising from the surrounding environment. To reliably characterize the clogging behavior of permeable concrete, particle swarm optimization (PSO) and random forest (RF) hybrid artificial intelligence techniques were developed in this study to predict the permeability coefficient of permeable concrete and optimize the aggregate mix ratio of permeable concrete. Firstly, a reliable database was collected and established to characterize the input and output variables for the machine learning. Then, PSO and 10-fold cross-validation were used to optimize the hyperparameters of the RF model using the training and testing datasets. Finally, the accuracy of the developed model was verified by comparing the predicted value with the actual value of the permeability coefficients (R = 0.978 and RMSE = 1.3638 for the training dataset; R = 0.9734 and RMSE = 2.3246 for the testing dataset). The proposed model can provide reliable predictions of the clogging behavior that permeable concrete may face and the trend of its development.

Extreme-mass-ratio inspirals (EMRIs) are significant observational targets for spaceborne gravitational wave detectors, namely, LISA, Taiji, and Tianqin, which involve the inspiral of stellar-mass compact objects into massive black holes (MBHs) with a mass range of approximately 104∼107M⊙. EMRIs are estimated to produce long-lived gravitational wave signals with more than 105 cycles before plunge, making them an ideal laboratory for exploring the strong-gravity properties of the spacetimes around the MBHs, stellar dynamics in galactic nuclei, and properties of the MBHs itself. However, the complexity of the waveform model, which involves the superposition of multiple harmonics, as well as the high-dimensional and large-volume parameter space, make the fully coherent search challenging. In our previous work, we proposed a 10-dimensional search using Particle Swarm Optimization (PSO) with local maximization over the three initial angles. In this study, we extend the search to an 8-dimensional PSO with local maximization over both the three initial angles and the angles of spin direction of the MBH, where the latter contribute a time-independent amplitude to the waveforms. Additionally, we propose a 7-dimensional PSO search by using a fiducial value for the initial orbital frequency and shifting the corresponding 8-dimensional Time Delay Interferometry responses until a certain lag returns the corresponding 8-dimensional log-likelihood ratio’s maximum. The reduced dimensionality likelihoods enable us to successfully search for EMRI signals with a duration of 0.5 years and signal-to-noise ratio of 50 within a wider search range than our previous study. However, the ranges used by both the LISA Data Challenge (LDC) and Mock LISA Data Challenge (MLDC) to generate their simulated signals are still wider than the those we currently employ in our direct searches. Consequently, we discuss further developments, such as using a hierarchical search to narrow down the search ranges of certain parameters and applying Graphics Processing Units to speed up the code. These advances aim to improve the efficiency, accuracy, and generality of the EMRI search algorithm.

In this article, the control of a DC/DC converter was carried out using the proposed methods of conventional PI, PSO-based PI, PSO-based FOPI, GA-based PI, and GA-based FOPI controllers in order to improve the performance of PEMFCs. Simulink models of a PEMFC model with two inputs—hydrogen consumption and oxygen air flow—and with controllers were developed. Then, the outputs of a system based on conventional PI were compared with the proposed methods. IAE, ISTE, and ITAE were employed as fitness functions in optimization algorithms such as PSO and GA. Fitness function value, maximum overshoot, and rising time were utilized as metrics to compare the performance of the methods. PI and FOPI parameters were optimized with the proposed methods and the results were compared with traditional PI in which the optimum parameters were determined by an empirical approach. This research study indicates that the proposed methods perform better than the conventional PI method. However, it becomes apparent that the GA-FOPI approach outperforms the others. The simulation result also shows that the PEMFC model with conventional PI and FOPI controllers in which the controller parameters are tuned using PSO and GA has an acceptable control performance.

Swarm intelligence (SI) methods are nature-inspired metaheuristics for global optimization that exploit a coordinated stochastic search strategy by a group of agents. Particle swarm optimization (PSO) is an established SI method that has been applied successfully to the optimization of rugged high-dimensional likelihood functions, a problem that represents the main bottleneck across a variety of gravitational wave (GW) data analysis challenges. We present results from the first application of PSO to one of the most difficult of these challenges, namely the search for the Extreme Mass Ratio Inspiral (EMRI) in data from future spaceborne GW detectors such as LISA, Taiji, or Tianqin. We use the standard Generalized Likelihood Ratio Test formalism, with the minimal use of restrictive approximations, to search 6 months of simulated LISA data and quantify the search depth, signal-to-noise ratio (SNR), and breadth, within the ranges of the EMRI parameters, that PSO can handle. Our results demonstrate that a PSO-based EMRI search is successful for a search region ranging over ≳10σ for the majority of parameters and ≳200σ for one, with σ being the SNR-dependent Cramer–Rao lower bound on the parameter estimation error and 30≤SNR≤50. This is in the vicinity of the search ranges that the current hierarchical schemes can identify. Directions for future improvement, including computational bottlenecks to be overcome, are identified.

This article addresses challenges in the design of underground high-voltage transmission lines, focusing on thermal management and cable ampacity determination. It introduces an innovative proposal that adjusts the dimensions of the backfill to enhance ampacity, contrasting with the conventional approach of increasing the core cable’s cross-sectional area. The methodology employs a particle swarm optimization (PSO) technique with adaptive penalization and restart strategies, implemented in MATLAB for parameter autoadaptation. The article emphasizes more efficient solutions than traditional PSO, showcasing improved convergence and precise results (success probability of 66.1%). While traditional PSO is 81% faster, the proposed PSO stands out for its accuracy. The inclusion of thermal backfill results in an 18.45% increase in cable ampacity, considering variations in soil thermal resistivity, backfill properties, and ambient temperature. Additionally, a sensitivity analysis was conducted, revealing conservative values that support the proposal’s robustness. This approach emerges as a crucial tool for underground installation, contributing to continuous ampacity improvement and highlighting its impact on decision making in energy systems.

Plant height and biomass are important indicators of rice yield. Here we combined measured plant physiological traits with a crop growth model driven by unmanned aerial vehicle spectral data to quantify the changes in rice plant height and biomass under different irrigation and fertilizer treatments. The study included two treatments: I—water availability factor (i.e., three drought objects, optimal, and excess water); and II—two levels of deep percolation and five nitrogen fertilization doses. The introduced model is extreme learning machine (ELM), back propagation neural network (BPNN), and particle swarm optimization-ELM (PSO-ELM), respectively. The results showed that: (1) Proper water level regulation (3~5 cm) significantly increased the accumulation of spike biomass, which was about 6% higher compared to that under flooded conditions. (2) For plant height inversion, the ELM model was optimal with a mean coefficient of determination of 0.78, a mean root mean square error of 0.26 cm, and a mean performance deviation rate of 2.08. For biomass inversion, the PSO-ELM model was optimal with a mean coefficient of determination of 0.88, a mean root mean square error of 3.8 g, and a mean performance deviation rate of 3.29. This study provided the possible opportunity for large-scale estimations of rice yield under environmental disturbances.

In this paper, we present a general version of polygonal fitting problem called Unconstrained Polygonal Fitting (UPF). Our goal is to represent a given 2D shape S with an N-vertex polygonal curve P with a known number of vertices, so that the Intersection over Union (IoU) metric between S and P is maximized without any assumption or prior knowledge of the object structure and the location of the N-vertices of P that can be placed anywhere in the 2D space. The search space of the UPF problem is a superset of the classical polygonal approximation (PA) problem, where the vertices are constrained to belong in the boundary of the given 2D shape. Therefore, the resulting solutions of the UPF may better approximate the given curve than the solutions of the PA problem. For a given number of vertices N, a Particle Swarm Optimization (PSO) method is used to maximize the IoU metric, which yields almost optimal solutions. Furthermore, the proposed method has also been implemented under the equal area principle so that the total area covered by P is equal to the area of the original 2D shape to measure how this constraint affects IoU metric. The quantitative results obtained on more than 2800 2D shapes included in two standard datasets quantify the performance of the proposed methods and illustrate that their solutions outperform baselines from the literature.

Frequency-guided sequential demodulation (FSD) is a technique used to recover the phase of closed fringe patterns when only a single interferogram is available. FSD is a sequential method that follows simple and intuitive processing steps for interferogram demodulation, guided by the frequency of the fringes. The objective of this work is to apply an optimizer block that feeds from the first stages of the FSD and make feedback in such a manner as to optimize the search for guided frequencies. This approach significantly reduces demodulation errors, especially when dealing with high-frequency fringe patterns. The results of using the FSD with and without optimizers are compared. The optimizers used are the Harmonic Search Optimization (HSO) as well as the Particle Swarm Optimization (PSO). Results show that FSD with PSO delivers better results compared with other methods here analyzed.

Recently, the precise location of sensor nodes has emerged as a significant challenge in the realm of Internet of Things (IoT) applications, including Wireless Sensor Networks (WSNs). The accurate determination of geographical coordinates for detected events holds pivotal importance in these applications. Despite DV-Hop gaining popularity due to its cost-effectiveness, feasibility, and lack of additional hardware requirements, it remains hindered by a relatively notable localization error. To overcome this limitation, our study introduces three new localization approaches that combine DV-Hop with Chicken Swarm Optimization (CSO). The primary objective is to improve the precision of DV-Hop-based approaches. In this paper, we compare the efficiency of the proposed localization algorithms with other existing approaches, including several algorithms based on Particle Swarm Optimization (PSO), while considering random network topologies. The simulation results validate the efficiency of our proposed algorithms. The proposed HW-DV-HopCSO algorithm achieves a considerable improvement in positioning accuracy compared to those of existing models.

Currently, the bionic robotic fish is primarily devoted to the bionic appearance of fish and rarely involves the bionic swimming posture. However, biological studies have proved that fish have extraordinary swimming ability, so it is necessary to imitate the swimming posture of fish. This paper introduces a manta ray robot driven by three fin rays, with its pectoral fins being structurally simulated using silicone rubber. According to the manta ray’s integrated gliding and flapping propulsion characteristics, we established a central-pattern-generator-based (CPG-based) biomimetic-motion-control method to realize the manta ray robot’s integrated gliding and flapping propulsion control. We designed an evaluation method of motion–posture similarity for the manta ray robot and optimized the control parameters to enhance the resemblance to the manta ray robot. The experimental results show that the online similarity-optimization method based on particle swarm optimization enhances the resemblance to 86.93%, and its swimming stability is also improved to some extent, which verifies the effectiveness of the optimization method. This study provides a new idea for optimizing motion control of the manta ray robot and realizing environmental affinity.

Ecological forests are an important part of terrestrial ecosystems, are an important carbon sink and play a pivotal role in the global carbon cycle. At present, the comprehensive utilization of optical and radar data has broad application prospects in forest parameter extraction and biomass estimation. In this study, tree and topographic data of 354 plots in key nature reserves of Liaoning Province were used for biomass analysis. Remote sensing parameters were extracted from Landsat 8 OLI and Sentinel-1A radar data. Based on the strong correlation factors obtained via Pearson correlation analysis, a linear model, BP neural network model and PSO neural network model were used to simulate the biomass of the study area. The advantages of the three models were compared and analyzed, and the optimal model was selected to invert the biomass of Liaoning province. The results showed that 44 factors were correlated with forest biomass (p < 0.05), and 21 factors were significantly correlated with forest biomass (p < 0.01). The comparison between the prediction results of the three models and the real results shows that the PSO-improved neural network simulation results are the best, and the coefficient of determination is 0.7657. Through analysis, it is found that there is a nonlinear relationship between actual biomass and remote sensing data. Particle swarm optimization (PSO) can effectively solve the problem of low accuracy in traditional BP neural network models while maintaining a good training speed. The improved particle swarm model has good accuracy and speed and has broad application prospects in forest biomass inversion.

Photovoltaic (PV) systems play a crucial role in clean energy systems. Effective maximum power point tracking (MPPT) techniques are essential to optimize their performance. However, conventional MPPT methods exhibit limitations and challenges in real-world scenarios characterized by rapidly changing environmental factors and various operating conditions. To address these challenges, this paper presents a performance evaluation of a novel extended grey wolf optimizer (EGWO). The EGWO has been meticulously designed in order to improve the efficiency of PV systems by rapidly tracking and maintaining the maximum power point (MPP). In this study, a comparison is made between the EGWO and other prominent MPPT techniques, including the grey wolf optimizer (GWO), equilibrium optimization algorithm (EOA), particle swarm optimization (PSO) and sin cos algorithm (SCA) techniques. To evaluate these MPPT methods, a model of a PV module integrated with a DC/DC boost converter is employed, and simulations are conducted using Simulink-MATLAB software under standard test conditions (STC) and various environmental conditions. In particular, the results demonstrate that the novel EGWO outperforms the GWO, EOA, PSO and SCA techniques and shows fast tracking speed, superior dynamic response, high robustness and minimal power fluctuations across both STC and variable conditions. Thus, a power fluctuation of 0.09 W could be achieved by using the proposed EGWO technique. Finally, according to these results, the proposed approach can offer an improvement in energy consumption. These findings underscore the potential benefits of employing the novel MPPT EGWO to enhance the efficiency and performance of MPPT in PV systems. Further exploration of this intelligent technique could lead to significant advancements in optimizing PV system performance, making it a promising option for real-world applications.

In rural regions with limited access to the power grid, self-reliance for electricity generation is paramount. This study focuses on enhancing the design of stand-alone photovoltaic installations (SAPV) to replace conventional fuel generators thanks to the decreasing costs of PV modules and batteries. This study presents a particle swarm optimization (PSO) method for the reliable and cost-effective sizing of SAPV systems. The proposed method considers the variability of PV generation and domestic demand and optimizes the system design to minimize the total cost of ownership while ensuring a high level of reliability. The results show that for the PSO method with 500 iterations, the error is around 2%, and the simulation time is approximately 2.25 s. Moreover, the PSO method allows a much lower number of iterations to be used in the Monte Carlo simulation, with a total of 100 iterations used to obtain the averaged results. The optimization results, encompassing installed power, battery capacity, reliability, and annual costs, reveal the effectiveness of our approach. Notably, our discretized PSO algorithm converges, yielding specific parameters like 9900 W of installed power and a battery configuration of five 3550 Wh units for the case study under consideration. In summary, our work presents an efficient SAPV system design methodology supported by concrete numerical outcomes, considering supply reliability and installation and operational costs.

The increasing exploration and development of complex oil and gas fields pose challenges to drilling efficiency and safety due to the presence of formations with varying hardness, abrasiveness, and rigidity. Consequently, there is a growing demand for drilling parameter optimization and speed-up technologies. However, existing models based on expert experience can only achieve single-objective optimization with limited accuracy, making real-time adaptation to changing drilling conditions and formation environments challenging. The emergence of artificial intelligence provides a new approach for optimizing drilling parameters. In this study, we introduce the Bi-directional Long Short-Term Memory (Bi-LSTM) deep learning algorithm with the attention mechanism to predict the rate of penetration (ROP). This algorithm improves the ROP prediction accuracy to 98.33%, ensuring reliable subsequent optimization results. Additionally, we propose a coupling optimization algorithm that combines Bi-LSTM with the particle swarm optimization algorithm (PSO) to enhance drilling efficiency through parameter optimization. Our approach aims to maximize drilling footage while maintaining the highest ROP. The optimal solutions obtained are verified through multi-parameter cloud image analysis, yielding consistent results. The application of our approach demonstrates an 81% increase in drilling speed and a 28% reduction in drill bit energy losses. Moreover, the real-time optimization results effectively guide field operations.

The study focuses on the integration of a fuzzy logic-based Maximum Power Point Tracking (MPPT) system, an optimized proportional Integral-based voltage controller, and the Jellyfish Optimization Algorithm into a solar PV battery setup. This integrated approach aims to enhance energy harvesting efficiency under varying environmental conditions. The study’s innovation lies in effectively addressing challenges posed by diverse environmental factors and loads. The utilization of MATLAB 2022a Simulink for modeling and the Jellyfish Optimization Algorithm for PI-controller tuning further strengthens our findings. Testing scenarios, including constant and variable irradiation, underscore the significant enhancements achieved through the integration of fuzzy MPPT and the Jellyfish Optimization Algorithm with the PI-based voltage controller. These enhancements encompass improved power extraction, optimized voltage regulation, swift settling times, and overall efficiency gains.

Lung cancer is a prevalent malignancy that impacts individuals of all genders and is often diagnosed late due to delayed symptoms. To catch it early, researchers are developing algorithms to study lung cancer images. The primary objective of this work is to propose a novel approach for the detection of lung cancer using histopathological images. In this work, the histopathological images underwent preprocessing, followed by segmentation using a modified approach of KFCM-based segmentation and the segmented image intensity values were dimensionally reduced using Particle Swarm Optimization (PSO) and Grey Wolf Optimization (GWO). Algorithms such as KL Divergence and Invasive Weed Optimization (IWO) are used for feature selection. Seven different classifiers such as SVM, KNN, Random Forest, Decision Tree, Softmax Discriminant, Multilayer Perceptron, and BLDC were used to analyze and classify the images as benign or malignant. Results were compared using standard metrics, and kappa analysis assessed classifier agreement. The Decision Tree Classifier with GWO feature extraction achieved good accuracy of 85.01% without feature selection and hyperparameter tuning approaches. Furthermore, we present a methodology to enhance the accuracy of the classifiers by employing hyperparameter tuning algorithms based on Adam and RAdam. By combining features from GWO and IWO, and using the RAdam algorithm, the Decision Tree classifier achieves the commendable accuracy of 91.57%.

Optimizing large-scale numerical problems is a significant challenge with numerous real-world applications. The optimization process is complex due to the multi-dimensional search spaces and possesses several locally optimal regions. In response to this issue, various metaheuristic algorithms and variations have been developed, including evolutionary and swarm intelligence algorithms and hybrids of different artificial intelligence techniques. Previous studies have shown that swarm intelligence algorithms like PSO perform poorly in high-dimensional spaces, even with algorithms focused on reducing the search space. However, we propose a modified version of the PSO algorithm called Dynamical Sphere Regrouping PSO (DSRegPSO) to avoid stagnation in local optimal regions. DSRegPSO is based on the PSO algorithm and modifies inertial behavior with a regrouping dynamical sphere mechanism and a momentum conservation physics effect. These behaviors maintain the swarm’s diversity and regulate the exploration and exploitation of the search space while avoiding stagnation in optimal local regions. The DSRegPSO mechanisms mimic the behavior of birds, moving particles similar to birds when they look for a new food source. Additionally, the momentum conservation effect mimics how birds react to collisions with the boundaries in their search space or when they are looking for food. We evaluated DSRegPSO by testing 15 optimizing functions with up to 1000 dimensions of the CEC’13 benchmark, a standard for evaluating Large-Scale Global Optimization used in Congress on Evolutionary Computation, and several journals. Our proposal improves the behavior of all variants of PSO registered in the toolkit of comparison for CEC’13 and obtains the best result in the non-separable functions against all the algorithms.

Nowadays, the Internet of Underwater Things (IoUT) provides many marine 5G applications. However, it has some issues with energy efficiency and network lifetime. The network clustering approach is efficient for optimizing energy consumption, especially for underwater acoustic communications. Recently, many algorithms have been developed related to clustering-based underwater communications for energy efficiency. However, these algorithms have drawbacks when considered for heterogeneous IoUT applications. Clustering efficiency in heterogeneous IoUT is influenced by the uniform distribution of cluster heads (CHs). As a result, conventional schemes are inefficient when CHs are arranged in large and dense nodes since they are unable to optimize the right number of CHs. Consequently, the clustering approach cannot improve the IoUT network, and many underwater nodes will rapidly consume their energies and be exhausted because of the large number of clusters. In this paper, we developed an efficient clustering scheme to effectively select the best CHs based on artificial bee colony (ABC) and Q-learning optimization approaches. The proposed scheme enables an effective selection of the CHs based on four factors, the residual energy level, the depth and the distance from the base station, and the signal quality. We first evaluate the most suitable swarm algorithms and their impact on improving the CH selection mechanism. The evaluated algorithms are generic algorithm (GA), particle swarm optimization (PSO), ant colony optimization (ACO), and ABC. Then, the ABC algorithm process is improved by using the Q-learning approach to improve the process of ABC and its fitness function to optimize the CH selection. We observed from the simulation performance result that an improved ABC-QL scheme enables efficient selection of the best CHs to increase the network lifetime and reduce average energy consumption by 40% compared to the conventional ABC.

This study presents an implementation of a proportional–integral–derivative (PID) controller utilizing particle swarm optimization (PSO) to enhance the compromise on road holding and ride comfort of a quarter car semi-active suspension system (SASS) through simulation and experimental study. The proposed controller is verified with a processor-in-the-loop (PIL) approach before real-time suspension tests. Using experimental data, the magnetorheological damper (MR) is modeled by an artificial neural network (ANN). A series of experiments are applied to the system for three distinct bump disturbances. The algorithm performance is evaluated by various key metrics, such as suspension deflection, sprung mass displacement, and sprung mass acceleration for simulation. The phase plane method is used to prove the stability of the system. The experimental results reveal that the proposed controller for the SASS significantly improves road holding and ride comfort simultaneously.

Low-index coal and gas outburst (LI-CGO) is difficult to predict, which seriously threatens the efficient mining of coal. To predict the LI-CGO, the Support Vector Machine (SVM) algorithm was used in this study. The Particle Swarm Optimization (PSO) algorithm was used to optimize the parameters of the SVM algorithm. The results show that based on the training sets and test set in this study, the prediction accuracy of SVM is higher than that of Back Propagation Neural Network and Distance Discriminant Analysis. The prediction accuracy of the SVM model trained by the training set T2 with LI-CGO cases is higher than that of the SVM model trained by the training set T1 without LI-CGO cases. The prediction accuracy gets better when the SVM model is trained by the training set T3, made by adding the data of the other two coal mines (EH and SH) to the training set T2, that only contains the data of XP and PJ. Furthermore, the PSO-SVM model achieves a better predictive effect than the SVM model, with an accuracy rate of 90%. The research results can provide a method reference for the prediction of LI-CGO.