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The inverter module, as a critical device in arc plasma power supplies for converting DC to AC, often encounters issues such as stability errors and system disturbances due to the impact of shock loads when tracking rated voltage and frequency signals. This paper proposes an improved Particle Swarm Optimization (PSO) strategy for Fractional-Order PID (FOPID) control with limited parameter selection, aiming to address the poor performance of inverter modules in arc plasma power supply systems. The proposed strategy incorporates the spiral position updating mode from the Whale Optimization Algorithm into the position updating process of PSO, which improves particle diversity, global search capability and optimization stability. The effectiveness of the proposed method is verified by comparing it with the Ziegler–Nichols tuning method and the standard PSO algorithm in the MATLAB/Simulink simulation environment. A model of an inverter system is established for validation purposes. The results indicate that applying the improved PSO-based FOPID control strategy to the inverter process of the arc plasma power supply system significantly enhances the output waveform quality, reduces harmonic distortion, and achieves excellent control performance. [ABSTRACT FROM AUTHOR]

Traffic engineering is critical for optimizing network performance and resource utilization. Software-Defined Networking (SDN) offers a flexible architecture that allows dynamic traffic management. However, traditional traffic engineering methods can struggle as SDN networks grow in size and complexity, making congestion management a key challenge. This paper presents a novel traffic engineering solution for SDN that utilizes Particle Swarm Optimization (PSO) within a scalable hierarchical framework. The approach addresses the difficulties of dynamic traffic patterns and resource allocation in large-scale SDN deployments by breaking down traffic management tasks into manageable domains. By leveraging PSO's ability to explore large solution spaces, the algorithm optimizes routing paths and bandwidth allocation based on a fitness function designed to minimize congestion, latency, and other key performance metrics. The proposed method, called Particle Swarm Optimization for Congestion Management (PSOCB), focuses on optimizing cluster switches using meta-heuristic techniques. PSOCB achieves a 13% increase in throughput and an 8% improvement in link performance, while reducing delays compared to traditional methods. Although PSOCB occasionally showed higher congestion than baseline approaches, it quickly restored normal network conditions through its congestion removal strategies. Simulations on realistic network topologies demonstrate substantial performance improvements over conventional static routing, highlighting the effectiveness and scalability of our PSO-based hierarchical traffic engineering solution. [ABSTRACT FROM AUTHOR]

Traditional clustering algorithms such as K-means need to input the number of clusters before the start of the algorithm. Affinity propagation (AP) clustering algorithm solves this problem by considering each data point as a prospective cluster head (exemplar) and finding a set of appropriate exemplars by message passing. However, the AP clustering algorithm requires two parameters: preference and damping factor. Providing the parameters in advance poses the same issue faced in the traditional clustering algorithm. Moreover, all data points are not equally relevant for becoming cluster heads. To overcome these problems, we propose two parameter-free particle swarm optimization-based algorithms, PSO-APver1 and PSO-APver2. Furthermore, we introduce a novel version of mutant PSO where two cluster validity indices are used to judge the quality of the clustering solution. In PSO-APver2, we consider the internal data distribution using the square wave function to determine the initial preference value of data points. We conducted experiments on 8 real-world datasets to show the efficacy of our proposed algorithms over classic algorithms and two AP-based algorithms. We conducted the Friedman test followed by post hoc analysis to exhibit the significance of our work. [ABSTRACT FROM AUTHOR]

The motion response of offshore floating wind turbines significantly influences their structural integrity, power generation efficiency, operational complexity, safety, and stability. Therefore, predicting the motion response of offshore floating wind turbines is of paramount importance. In engineering practice, especially in extreme marine environments, the motion of wind turbines becomes more complex, making accurate prediction more challenging. In this era of rapid development in deep learning technology, some solutions have emerged for this problem. In this paper, we propose a hybrid model, namely the OVMD-FE-PSO-LSTM model. We begin by conducting numerical simulations of a 5 MW-OC4 semi-submersible floating wind turbine in extreme sea conditions, obtaining motion data for the turbine's six degrees of freedom. We then decompose the initial motion data using an optimized traditional VMD method, assess the modal complexity with the FE method, combine modal components with similar complexity to reduce computational load, and make predictions using the PSO-LSTM model. Finally, we analyze and compare the predictive results of different models. The results demonstrate that the proposed hybrid model outperforms other comparative models in terms of accuracy, providing new insights into the prediction of the motion response of offshore floating wind turbines. [ABSTRACT FROM AUTHOR]

In this investigation, an optimized heat treatment following rolling was applied to an A390/6Ti₂AlC composite, and its influence on the microstructure and properties was studied. These composites, known for their enhanced mechanical properties, are used in critical applications like face plate assemblies and brake rotors. Analysis using field emission scanning electron microscopy (FESEM), energy-dispersive X-ray spectroscopy (EDX) and X-ray diffraction (XRD) techniques indicated a uniform distribution of particulates. Notably, composites with a 6% Ti₂AlC weight fraction exhibited a substantial improvement, with a 16% increase in tensile strength and a 4% increase in yield strength compared to the base alloy. To further enhance the material's mechanical properties for superior durability and performance, an optimized regimen of rolling followed by heat treatment was applied, refining the microstructure and improving the composite material's balance of strength and flexibility for specific applications. [ABSTRACT FROM AUTHOR]

The cable-driven redundant space manipulator (CDRSM) has a wide range of potential applications in on-orbit servicing due to its light weight, low inertia, and intrinsic compliance. Especially for capturing and detumbling large-momentum tumbling targets in space, it requires agile approaching and synchronizing, compliant contact and dexterous post-capture manipulation. Due to the limited energy resources of the space robot, we propose a coordinated configuration and end-effector (EE) trajectory optimization method to minimize base disturbance, as well as maximize EE stiffness for guaranteeing the accuracy of post-capture manipulation. The dynamics coupling between the free-floating base and the CDRSM is derived, along with the kinematic modeling and stiffness analysis of the cable-driven joints. Considering the equiangular constraint relationship and the kinematic coupling characteristics of the cable-driven joints, the configuration of the CDRSM and the EE trajectory are parametrically represented using the arm angle and the rational Bézier curves, respectively. Further, based on these parameter variables, the particle swarm optimization and multiple-objective particle swarm optimization algorithms are used to minimize the base disturbance and maximize EE stiffness during post-capture manipulation. Finally, several simulation results demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This article presents an innovative asymmetric multilevel inverter (MLI) topology that outperforms conventional counterparts. The introduced topology presents a breakthrough in implementing power electronics control by maximizing specific levels while minimizing switching components. A cutting-edge control scheme for optimal operation of the cascaded half-bridge MLI is presented. The ant lion optimization (ALO) algorithm was implemented to optimize the switching control to reduce the total harmonic distortion (THD) and improve power quality. For verification, the performance and effectiveness of the ALO technique are assessed by comparing its results to those obtained using the simplified sinusoidal pulse width modulation (SSPWM) technique, genetic algorithm (GA), and particle swarm optimization (PSO) in existing literature. Simulation results verified the efficacy of ALO in finding the optimal parameters. The suggested method showcases a remarkable reduction in the THD compared to SSPWM. The quality of the resulting waveform was enhanced, and both filter size and cost were significantly reduced. To meet stringent IEEE standards, an LC filter has been designed with minimal size and proper requirements. Experimental results validation of the suggested scheme, using a dSPACE R&D controller board unequivocally, confirmed its robustness and effectiveness. This groundbreaking study not only introduces a superior asymmetric MLI topology but also validates its exceptional performance through comprehensive analysis and experimentation. The experimental waveforms showed good matching with the simulation outcomes. The findings hold immense promise for advancing the field of power system control and revolutionizing the designing and implementation of efficient and cost-effective inverter systems. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

For civil engineering structures, structural damage usually occurs at limited positions in the preliminary stage of the structural failure. Compared with the numerous elements of the entire structure, the damaged elements are sparsely distributed in space. Based on this important prior information, this paper proposed to utilize a sparse Bayesian learning method to identify the damage to structures while considering the measurement noise and modeling error. The particle Swarm Optimization (PSO) algorithm was first introduced to address the associated computational efficiency issue, and the optimization performances of PSO and Sequential Quadratic Programming (SQP) algorithm in the process of model updating were compared, positive outcomes revealed that the PSO algorithm has the stronger searching ability and better robustness. To investigate the effectiveness and practicality of the sparse Bayesian learning with a PSO algorithm in structural damage detection, an asymmetrical frame in different scenarios (e.g. with single and multiple damages) was constructed in the laboratory. The encouraging results of the experimental case studies compellingly demonstrate that the presented methodology not only can detect the location and extent of structural damage with high precision and efficiency, but also can proficiently assess the posterior uncertainties associated with the damage detection results. [ABSTRACT FROM AUTHOR]

In this study, a hybrid particle swarm optimization (HPSO) approach is presented for enhancing the performance of a three-phase, 12-slot, 19-pole yoke-less axial-field flux-switching permanent magnet (YASA-AFFSPM) motor. The aim of the optimization process is to achieve a motor that demonstrates high efficiency and stable operation. To facilitate this, a finite element analysis model is developed, and a multi-objective optimization function utilizing weight coefficients is created. The variables in this study include the split ratio, stator axial length, sandwiching pole angle, rotor pole angle, permanent magnet arc, and the number of conductors per slot. The optimization objectives encompass efficiency, power factor, cogging torque, and average torque, evaluated under both no-load and load conditions. Findings indicate that the hybrid particle swarm optimization method excels in its search capabilities and convergence speed, resulting in motor designs that align closely with the intended specifications. Finite element simulations further validate the proposed methodology's effectiveness and practicality. Finally, experimental results are presented to confirm the reliability of the suggested algorithm. [ABSTRACT FROM AUTHOR]

In this study, a linked simulation optimization (SO) model is presented for identification of groundwater contaminant sources. The SO model consists of two steps namely, simulation and optimization. The simulation step entails developing a groundwater contaminant transport model in which the advection–dispersion-reaction equation (ADRE) is solved for predicting the concentration of the contaminant. The system parameters (hydraulic conductivity, dispersivity, etc.) and control variables (pumping, recharge, etc.,) are given as model inputs. A meshless technique called the meshless Local Radial Point Interpolation Method (LRPIM) is employed to solve the contaminant transport equation. The simulation model is linked with three different swarm intelligence-based optimization models namely, teaching–learning based optimization (TLBO), grey wolf optimization (GWO) and particle swarm optimization (PSO) to form three SO models namely LRPIM-PSO, LRPIM-GWO and LRPIM-TLBO. The SO model minimizes the difference between the predicted and observed concentrations to determine the unknown source locations and release histories. The applicability of the developed SO models for source identification (SI) is demonstrated with a hypothetical and real aquifer problems to identify the groundwater contaminant sources. All the 3 models are able to locate the sources and release histories satisfactorily. However, the LRPIM-TLBO has been found to be more accurate followed by LRPIM-PSO and LRPIM-GWO. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

As the world endeavors to shift toward sustainable energy solutions, the pivotal role of solar energy, specifically photovoltaics, becomes increasingly evident. This study investigates the critical task of accurately predicting photovoltaics power output, a fundamental aspect of maximizing economic benefits and ensuring stability in modern electric power systems. Three categories of models, including deterministic, statistical, and hybrid, are explored, with a focus on machine learning (ML) models such as AdaBoost and HGBoost. The results indicate that AdaBoost generally outperforms HGBoost in terms of accuracy metrics, such as R2, RMSE, and VAF, demonstrating its effectiveness in photovoltaics power prediction. However, the difference in performance, while notable, may not be substantial across all metrics, suggesting that the choice of the best model could depend on specific use cases and trade-offs between accuracy and computational efficiency. In addition, this study introduces hybrid models incorporating optimization algorithms such as fruit-fly optimizer, satin bowerbird optimizer, and particle swarm optimizer, with the integration of HGBoost and satin bowerbird optimizer emerging as the top-performing hybrid model (R2 = 0.9907), depicting enhanced accuracy and reduced error rates. The study concludes that combining ML with optimization algorithms significantly enhances PV power prediction accuracy, offering valuable insights for integrating renewable energy into modern energy systems. [ABSTRACT FROM AUTHOR]

In recent years, the high cost of searching and processing raw materials and the fact that raw material resources have come to the point of depletion force industry and science to seek different solutions. In addition, due to the necessity of increasing the quality, the tendency to composite production has increased. However, it is necessary to make improvements in parameters such as quality, time, and cost in composite production. The use of artificial intelligence technology in the production of composites is becoming more and more common. On the other hand, studies on the determination of raw material mixture ratios are not common. Such studies are usually carried out with experimental productions, which increases the production cost and prolongs the production process. In this study, it was studied on the proportional optimization of the mixture components of thermoplastic hybrid composite materials by using the particle swarm optimization algorithm. First, a dataset was created with the real values obtained from the experimental productions, and the raw material mixture ratios to be included in the mixture were determined by making simulation studies on this dataset. With these obtained ratios, new productions were made, and tests were applied on these productions. Test results have shown that the resulting products are more successful by over 95%. Thus, it has been proven that especially the use of artificial intelligence technology in the field of composites in pre-production stages such as raw material and mixture processes can both reduce the cost and increase the quality. [ABSTRACT FROM AUTHOR]

The half-adder (HA) is one of the most useful combinational logic functions for data processing. We report on demonstrating an all-optical HA comprising AND and XOR all-optical logic gates (AO-LGs) without nonlinearity and thus with arbitrarily low intensity, ultrafast light-by-light control. The proposed method engages the coherent interaction of optical waves on a metasurface nanostructure, known as coherent perfect absorption (CPA), which is only 50 nm thick, to efficiently control the absorption of near-infrared (700–800 nm) pulses. Particle Swarm Optimization (PSO) determines the most efficient dimensions of metasurface unit cells. Another significant innovation of this article is the utilization of an adaptive neuro-fuzzy inference system (ANFIS) instead of a simple threshold. The flexibility of the designed fuzzy system allows it to be used for any CPA system, applied in the implementation of logic gates. Achieved figures of merit of AO-LGs, including contrast ratio (CR = 89.77 dB) and extinction ratio (ER = 87.53 dB), prove the impressive effects of ANFIS on the gates' efficiency. Smaller dimensions, lower power consumption, and a reduced logic state range compared to other HAs are additional specifications of the designed HA, achieved through optimized metasurface-based CPA and ANFIS. [ABSTRACT FROM AUTHOR]

Considering the increasing accuracy of machine learning in detection and recognition tasks, this paper proposes a classification model, which consists of an improved genetic algorithm (GA), the improved particle swarm optimisation (IPSO) and balanced support vector machine (SVM) (namely GA-IPSO-BSVM), to increase the accuracy and to optimise the convergence of logistics data performance classification. Firstly, this paper embeds the elimination mechanism of GA to my model at the early stage of iterations to delete a lot of particles with low speed. Then, in the middle of the iteration, this paper improves the topology structure of particle relationship in PSO to avoid the algorithm trapping into local optimal solution. At the late stage of the iteration, this paper combines the excellent particles in all regions into the excellent particle population and iterates the population to obtain the global optimal solution. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Purpose: An artificial respiratory system provides support to critically ill patients. Optimizing the control of airway pressure in an artificial respiratory system is difficult due to its non-linear characteristics. A PID controller is most widely used for artificial respiratory systems. This paper presents the optimization of a PID controller for controlling airway pressure in an artificial respiratory system based on a Teaching Learning-Based Optimization Algorithm. Methods: The artificial respiratory system is modeled mathematically, and a transfer function is derived to design the proposed controller. A comparative study of the proposed TLBO-based controller is done with a Particle Swarm Optimization, PSO-based controller, and a conventional Zeigler-Nichols tuned controller in terms of performance indices. The proposed controller was also tested for robustness by varying lung compliance and leakage resistance from - 20% to + 20% of the nominal value. Results: It is observed that the proposed controllers based on the TLBO Algorithm showed better response, in terms of rise time, overshoot, and settling time. The overshoot was reduced to 0% in the case of the TLBO-tuned PID controller, as compared to 10.56% in the PSO-tuned PID controller. Also, in the case of the proposed TLBO-tuned controller rise time and settling time were reduced to 2.30 and 2.98 seconds, from 4.65 and 5.26 seconds in the conventionally tuned Zeigler Nichols-tuned PID controller. Conclusions: In this study, simulation results of Particle swarm optimization tuned and teaching learning optimization-based PID controllers on an artificial respiratory system are presented. The results showed that the poor performance of the conventionally tuned PID controller can be improved significantly by the application of TLBO algorithm-tuned controllers. [ABSTRACT FROM AUTHOR]

With the rapid advancement of the new energy transformation process, the stability of photovoltaic microgrid output is particularly important. However, current photovoltaic microgrids suffer from unstable output and power fluctuations. To improve the stability and system controllability of photovoltaic microgrid output, this study constructs an optimized grey wolf optimization algorithm. Using the idea of small step perturbation, it is applied to the maximum power point tracking solar controller to construct a maximum power point controller algorithm based on the improved algorithm. Secondly, the algorithm is combined with photovoltaic arrays to construct a maximum tracking point control system for photovoltaic arrays based on the algorithm. Finally, the system is combined with low-pass filtering power allocation and secondary power allocation strategies, as well as a hybrid storage system, to construct a photovoltaic microgrid control model. In the performance comparison analysis of the research algorithm, the average accuracy and average loss value of the algorithm were 98.2% and 0.15, respectively, which were significantly better than the compared algorithms. The performance analysis of the photovoltaic microgrid control model showed that the model could effectively regulate and control the output power of the microgrid under two operating conditions, demonstrating its effectiveness. The above results indicate that The proposed algorithm and the improved algorithm of the PV microgrid control model can not only improve the steady-state tracking accuracy, but also have better dynamic performance and improve the tracking speed. The control strategy can maintain the operational stability of the microgrid system and realize the smooth switching control of each mode, meeting the stability and flexibility requirements of the PV microgrid system. The novelty of this study is that the improved Grey Wolf optimization algorithm enhances the global search ability by introducing the random jump mechanism of Levy flight algorithm and the combination of particle swarm optimization algorithm and Grey Wolf optimization algorithm to avoid falling into the local optimal. The randomness and ergodicity of Levy flight algorithm enable the hybrid algorithm to quickly adapt to the changes of light intensity and environmental conditions, and maintain the efficient operation of MPPT. Moreover, particle swarm optimization has strong local search ability, and gray Wolf optimization improves local search accuracy. The combination of the two improves local search accuracy. By combining the characteristics of Levy flight algorithm, the parameters of PSO and GWO algorithm, such as inertia weight and convergence factor, are dynamically adjusted to adapt to different working conditions of MPPT. The optimal solution is output as the optimal strategy of MPPT through collaboration. The potential practical impact is that the improved MPPT control strategy can track the maximum power point more effectively, improve the efficiency and stability of the photovoltaic power generation system, reduce energy waste by improving the tracking accuracy and convergence speed of the photovoltaic system, and improve the robustness of the system. [ABSTRACT FROM AUTHOR]