Particle Swarm Optimization Technique Research Articles

Prediction of anemia with a particle swarm optimization-based approach

Healthcare enables the maintenance of health through some physical and mental care for the prevention, diagnosis and treatment of disease. Diagnosis of anemia, one of the most common health problems of the age, is also very ambitious. Whereas, pathological individuals could be predicted through various biomedical variables using some appropriate methods. In order to estimate these individuals just by taking into account biological data, particle swarm optimization (PSO) and support vector machine (SVM) clustering techniques have been merged (PSO-SVM). In this respect, the dataset provided has been divided into five clusters based on anemia types consisting of 539 subjects in total, and the anemia values of each subject have been recorded according to corresponding biomedical variables taken as independent parameters. The findings of the PSO-SVM method have been compared to the results of the SVM algorithm. The hybrid PSO-SVM method has proven to be quite effective, particularly in terms of the high predictability of clustered disease types. it is possible to lead the successful creation of appropriate treatment programs for diagnosed patients without overlooking or wasting time during treatment.

Enhancing System Reliability and Transient Voltage Stability through Optimized Power Sources and Network Planning

Renewable energy is an important means of addressing climate change and achieving carbon peaking and carbon neutrality goals. However, the uncertainty and randomness of renewable energy also have a certain impact on the flexibility, reliability, and transient voltage stability of the power system. These effects also pose great challenges to power system planning. In order to address the impact of renewable energy on power system planning, this paper proposes a two-layer optimization model for power sources and network planning which takes into account both reliability and transient voltage stability requirements. The upper-layer grid planning problem is formulated with consideration of the system reliability index, and the transient stability requirements and construction and operation costs are included in the lower-layer problem to determine a construction scheme for power generation and energy storage units. To solve the complex nonlinear problem efficiently, a two-layer iterative algorithm utilizing the adaptive particle swarm optimization (PSO) technique is proposed. The effectiveness of the proposed method is demonstrated via its application to the IEEE 33 test system. The results show that the proposed optimization approach effectively addresses the power system transmission and generation planning problem while improving the efficiency and reliability of the system’s operation. The findings can guide the design and implementation of future power system planning and operation strategies.

A Deep-Learning-Based Graph Neural Network-Long-Short-Term Memory Model for Reservoir Simulation and Optimization With Varying Well Controls

Summary A new deep-learning-based surrogate model is developed and applied for predicting dynamic oil rate and water rate with different well controls. The surrogate model is based on the graph neural networks (GNNs) and long-short-term memory (LSTM) techniques. The GNN models are used to characterize the connections of injector-producer pairs and producer-producer pairs, while an LSTM structure is developed to simulate the evolution of the constructed GNN models over time. In this way, we use geological attributes at wells and well controls with different times as input data. The oil rates and water rates at different times are generated. In this study, the GNN-LSTM surrogate model is applied to a high dimensional oil-gas-water field case with flow driven by 189 wells (i.e., 96 producers and 93 injectors) operating under time-varying control specifications. A total of 500 high-fidelity training simulations are performed in the offline stage, out of which 450 simulations are used for training the GNN-LSTM surrogate model, which takes about 150 minutes on an RTX2060 GPU. The trained model is then used to provide production forecasts under various well control scenarios, which are shown to be consistent with those obtained from the high-fidelity simulations (e.g., around 4.8% and 4.3% average relative errors for water production rates and oil production rates, respectively). The online computations from our GNN-LSTM model take about 0.3 seconds per run, achieving a speedup of over a factor of 1,000 relative to the high-fidelity simulations, which takes about 363 seconds per run. Overall, this model is shown to provide reliable and fast predictions of oil rates and water rates with a large level of perturbations in the well controls. Finally, the proposed GNN-LSTM model, in conjunction with the particle swarm optimization (PSO) technique, is applied to optimize the field oil production by varying the well control schedule of all injectors. Due to the significant speedup and high accuracy of the proposed surrogate model, the improved well-control strategies can be efficiently obtained.

A New PSO Technique Used for the Optimization of Multiobjective Economic Emission Dispatch

Most power is generated using fossil fuels like coal, natural gas, and diesel. The contribution of coal to power generation is very high compared to other sources. Almost all thermal power plants use coal as a fuel for power generation. Such sources of fossil fuels are limited and thus the cost of power generation increases. At the same time, the induced toxic gases due to these fossil fuels pollute the environment. The objective of this work is to solve the economic emission dispatch problem. Economic emission dispatch helps to find out how to operate power plants at the minimum cost and induce the minimum emissions at a thermal power plant. Economic emission dispatch with constraints is a nonlinear optimization problem. For the solution of such nonlinear economic emission load dispatch problems, this work considers a new particle swarm optimization technique. The proposed new PSO gives the best solution for economic emission load dispatch and handles the constraints. For the testing of the proposed new PSO algorithm, this work considered a case study of a system of six generating units, and it was tested for load demands of 700 MW, 800 MW, and 1000 MW. The results of the new PSO for the three load demands considered give the minimum generation cost, minimum emission, and minimum total cost compared to other optimization algorithms. The proposed techniques are effective, and they can help obtain the minimum generation cost and minimize emissions.

Novel Heuristic Optimization Technique to Solve Economic Load Dispatch and Economic Emission Load Dispatch Problems

The fundamental objective of economic load dispatch is to operate the available generating units such that the needed load demand satisfies the lowest generation cost and also complies with the various constraints. With proper power system operation planning using optimized generation limits, it is possible to reduce the cost of power generation. To fulfill the needs of such objectives, proper planning and economic load dispatch can help to plan the operation of the electrical power system. To optimize the economic load dispatch problems, various classical and new evolutionary optimization approaches have been used in research articles. Classical optimization techniques are outdated due to many limitations and are also unable to provide a global solution to the ELD problem. This work uses a new variant of particle swarm optimization techniques called modified particle swarm optimization, which is effective and efficient at finding optimum solutions for single as well as multi-objective economic load dispatch problems. The proposed MPSO is used to solve single and multi-objective problems. This work considers constraints like power balance and power generation limits. The proposed techniques are tested for three different case studies of ELD and EELD problems. (1) The first case is tested using the data of 13 generating unit systems along with the valve point loading effect; (2) the second case is tested using 15 generating unit systems along with the ramp rate limits; and (3) the third case is tested using the economic emission dispatch (EELD) as a multi-objective problem for 6 generating unit systems. The outcomes of the suggested procedures are contrasted with those of alternative optimization methods. The results show that the suggested strategy is efficient and produces superior optimization outcomes than existing optimization techniques.

Composite disc optimization using hunger games search optimization algorithm

Abstract It has been interesting in industrial applications due to the advantages provided by laminated composite applications. Composite materials are of great importance in design optimization due to the advantages they provide, such as lightweight, high strength value, and design flexibility. Finite element programs and artificial intelligence optimization techniques developed in recent years are effectively used in the design of composite materials. In order to optimize the angular orientations of the composite disc, analyses are made using the finite element program. Particle swarm optimization algorithm (PSO), genetic algorithm (GA), and hunger games search optimization algorithm(HGS) are used to determine the best stacking angle value on the disc plate using the analysis data obtained after the completion of the analyses. When the results obtained were examined, it was determined that the results obtained from particle swarm optimization, genetic algorithm, and hunger games search optimization techniques showed that hunger games search optimization algorithm gives better results as other and HGS could be used for the stacking of composite materials.

Parametric analysis and optimization of 660 MW supercritical power plant

The newly set up power plant has been committed to fulfilling the power supply demand of the world. Therefore, optimizing operating variables within constraints of varying power demand becomes necessary. The research aims to identify and optimize several parameters influencing the performance and efficiency of a 660 MW supercritical power plant at different operating conditions, such as steam temperature, pressure, feedwater flow rate, and fuel consumption. Ultimately, the research aims to contribute to developing sustainable and environmentally friendly power generation technologies. The present study covers the multi-objective optimization of a 660 MW capacity fossil fuel-fired SUPP. The overall plant efficiency, cost of electricity, and exergetic efficiency are taken as objective functions. The Particle Swarm Optimization (PSO) technique and a semi-empirical model of energy, economic, and exergy analysis of fossil fuel-fired SUPP have been employed. The varying power outputs, coal calorific value, amount of coal consumption, inlet temperature, and pressure conditions of turbines set are decision variables taken for the study. The parametric study was carried out with the variation in plant load and mass of coal consumption concerning the variation of the objective function. The lower temperature at the inlet of the low-pressure turbine is preferred for lowing the cost of electricity. The maximum value of plant efficiency of 41.643% and exergy efficiency of 39.834% with a minimum cost of electricity of 3.1456 INR/Unit have been evaluated using multi-objective PSO. The outcome of the present study is that the optimized value of decision variables will reduce the dependency on high-grade coal from an energy, exergy, and economic point of view. The outcome of the present study will explore the scope for future researchers and engineers.

Volume Optimisation of Multi-stable Origami Bellows for Deployable Space Habitats

AbstractOrigami bellows are formed by folding flat sheets into closed cylindrical structures along predefined creases. As the bellows unfold, the volume of the origami structure will change significantly, offering potential for use as inflatable deployable structures. This paper presents a geometric study of the volume of multi-stable Miura-ori and Kresling bellows, focusing on their application as deployable space habitats. Such habitats would be compactly stowed during launch, before expanding once in orbit. The internal volume ratio between different deployed states is investigated across the geometric design space. As a case study, the SpaceX Falcon 9 payload fairing is chosen for the transportation of space habitats. The stowed volume and effective deployed volume of the origami space habitats are calculated to enable comparison with conventional habitat designs. Optimal designs for the deployment of Miura-ori and Kresling patterned tubular space habitats are obtained using particle swarm optimisation (PSO) techniques. Configurations with significant volume expansion can be found in both patterns, with the Miura-ori patterns achieving higher volume expansion due to their additional radial deployment. A multi-objective PSO (MOPSO) is adopted to identify trade-offs between volumetric deployment and radial expansion ratios for the Miura-ori pattern.

Spectral Estimation of Electroencephalogram Signal using ARMAX Model and Particle Swarm Optimization

Electroencephalogram (EEG) signal is short-length time series data. Parametric and non-parametric methods are used to estimate the power spectral density (PSD) of the time series data. While the non-parametric approach of spectrum estimate of short-length data is unreliable, the parametric approach of spectrum estimate is widely suggested. In the presented work, a parametric autoregressive moving average with exogenous input (ARMAX) model was implemented to find the PSD of the EEG signal. The performance of the implemented ARMAX model was contrasted with other parametric methods like autoregressive AR) and autoregressive moving average (ARMA) and nonparametric methods like periodogram. Coefficients of ARMAX and (ARMA) were estimated using the particle swarm optimization (PSO) technique based on swarm intelligence. The PSO algorithm adapted with the system identification technique and statistics yielded highly satisfactory results in finding the coefficients of the ARMAX model. All the programming and visualization were performed in a MATLAB environment.

Modeling and Performance Analysis of a Grid-Connected Photovoltaic System with Advanced Controller considering Varying Environmental Conditions

This paper presents a mathematical model of 255 kW grid-connected solar photovoltaic (SPV) system. To study the performance characteristics of the grid-connected SPV system, a new hybrid adaptive grasshopper optimization algorithm with the recurrent neural network (AGO-RNN) control technique was implemented. Furthermore, the power quality at the point of common coupling (PCC) has been studied using the conventional (PSO) and proposed AGO-RNN controllers. The characteristics of the PV system were analyzed under varying environmental (variable irradiance and temperature) conditions considering 3 different cases such as (i) standard test conditions (STC), (ii) variable radiation with constant temperature, and (iii) variable radiation with variable temperature. For each case, the total harmonic distortion (THD) has been calculated using the proposed AGO-RNN control technique, and the results were compared with particle swarm optimization (PSO) technique. The 255 kW PV model is initially developed and connected to a three-level NPC inverter, an MPPT-based perturbation and observation algorithm. Later, the PV model is controlled by an AGO-RNN pulse width modulation (PWM) controller and is then integrated to the main grid at PCC. The main advantage of this technique is exploiting the separate DC-DC converter between the SPV module and the inverter. Finally, the proposed grid-connected SPV system was simulated on MATLAB for analyzing the performance of the system based on its I-V and P-V characteristics, inverter voltage, grid power, gird voltage, grid current, power factor, and THD under different environmental conditions. The simulation results demonstrate that the current magnitude and THD of the SPVGC system are improved with the cutting-edge AGO-RNN controller compared to PSO in all three different scenarios, and this value is less than 1.6%, which is within the permitted limits of IEC 61727 standards.

Handover Network Selection for Vehicle in LTE-A Communication Networks

One of the challenges to achieving smooth communications in heterogeneous networks is to find the right balance between various handover parameters for network selection. The Receive Signal Strength (RSS) from a network's Point of Attachment (PoA) is the primary factor used in traditional approaches to network selection. Therefore, network selection is vital for a successful handover in the vehicle communications network. However, vehicles moving through such diverse networks faced difficulties like frequent unwanted handovers, selecting the wrong candidate networks for handovers, and taking too long to finish the drawn-out handover procedure, which results in failed handovers. All of these factors will impair the efficiency of the handover process, which will affect the smooth operation of the vehicles. Hence, the main idea of this research is to improve the handover network performance selection performance. The proposed work is for vehicles to self-evaluate a candidate list of Access Points (AP) that are located in the vehicle movement direction and select the best underlying candidate network. In this project, the network selection framework of the vehicle communications network with the distance between the target candidate and the trajectory of the vehicle movement was developed as well as the vehicle mobility information. The important parameters which are the relative direction index, proximity index and residence time index are chosen to identify the best candidate network. Once the target of the base station has been identified, then the optimum value of the distance is calculated using the Particle Swarm Optimization (PSO) technique to initiate the handover process. The performance of the proposed handover network selection is simulated and evaluated using MATLAB software. The results show that the proposed handover network selection method improved the successful handover rate as compared to the conventional method.

Particle Swarm Optimisation for Emotion Recognition Systems: A Decade Review of the Literature

Particle Swarm Optimisation (PSO) is a popular technique in the field of Swarm Intelligence (SI) that focuses on optimisation. Researchers have explored multiple algorithms and applications of PSO, including exciting new technologies, such as Emotion Recognition Systems (ERS), which enable computers or machines to understand human emotions. This paper aims to review previous studies related to PSO findings for ERS and identify modalities that can be used to achieve better results through PSO. To achieve a comprehensive understanding of previous studies, this paper will adopt a Systematic Literature Review (SLR) process to filter related studies and examine papers that contribute to the field of PSO in ERS. The paper’s primary objective is to provide better insights into previous studies on PSO algorithms and techniques, which can help future researchers develop more accurate and sustainable ERS technologies. By analysing previous studies over the past decade, the paper aims to identify gaps and limitations in the current research and suggest potential areas for future research. Overall, this paper’s contribution is twofold: first, it provides an overview of the use of PSO in ERS and its potential applications. Second, it offers insights into the contributions and limitations of previous studies and suggests avenues for future research. This can lead to the development of more effective and sustainable ERS technologies, with potential applications in a wide range of fields, including healthcare, gaming, and customer service.

Reliability Enhancement on Distribution System Using Modified Multi-Objective Particle Swarm Optimization Technique

This research paper introduces an innovative approach for optimizing the placement of fault indicators (FIs) within electric distribution systems. The primary focus is on addressing how the presence of existing protection and control devices affects the time it takes to restore service to customers in the event of a fault. Unlike conventional FI placement methods, this extended approach considers the uncertainties associated with automatic switching and introduces a novel technical objective function known as the Customers' Average Restoration Time Index (CARTI). To tackle the complex task of multi-objective optimization, a solution approach has been devised, with the goal of simultaneously minimizing both economic and technical objectives. This methodology harnesses a Multi-Objective Particle Swarm Optimization (MOPSO) algorithm and incorporates a Modified Particle Swarm Optimization technique to select the most suitable solution from the set of Pareto optimal solutions generated. The proposed method's effectiveness is demonstrated through its application in two scenarios: firstly, in the context of bus number four within the Roy Billinton test system (RBTS4), and secondly, in a practical real-life distribution network. The study assesses technical objectives, specifically the System Average Interruption Duration Index (SAIDI) and CARTI, providing insights into the method's practical utility and its capacity to enhance FI placement for improved distribution system performance.

Coverage Optimization of Eureka Digital Sound Broadcasting Single Frequency Network Using Simulated Annealing and Particle Swarm Optimization

Due to scarcity of bandwidth available for sound broadcasting, Digital sound broadcasting Technology is emerging to be the Technology of resort in sound broadcasting industry towards replacing the analogue sound broadcasting currently dominated by FM Radio. There are many digital sound broadcasting systems being proposed with different performance and bandwidth efficiency. Static delays are artificial delays intentionally introduced at each Transmitter in order to minimize interference in a Single Frequency Network (SFN). In this paper, we have looked at the Terrestrial digital audio broadcasting (T-DAB) system specifically to optimize its final SFN coverage by finding an optimal set of static delays for transmitters. For the sake of simulation, hexagonal model of transmitters operating under Single Frequency Network (SFN) was used. The aim of this study is to maximize SFN coverage by using optimal set of artificial static delays, Particle Swarm Optimization (PSO) have strong ability in finding the global optimistic result while Stimulated Annealing (SA) algorithm has a strong ability to find the local Optimistic result and therefore based on their unique strength, these methods were selected so that our study can have a good comparison in terms of coverage by using both global and local optimistic results. We report the increase of coverage by 1.12% and 2.38% using Simulated Annealing and Particle Swarm Optimization technique respectively.

Deep learning based object detection and surrounding environment description for visually impaired people

Object detection, one of the most significant contributions of computer vision and machine learning, plays an immense role in identifying and locating objects in an image or a video. We recognize distinct objects and precisely get their information through object detection, such as their size, shape, and location. This paper developed a low-cost assistive system of obstacle detection and the surrounding environment depiction to help blind people using deep learning techniques. TensorFlow object detection API and SSDLite MobileNetV2 have been used to create the proposed object detection model. The pre-trained SSDLite MobileNetV2 model is trained on the COCO dataset, with almost 328,000 images of 90 different objects. The gradient particle swarm optimization (PSO) technique has been used in this work to optimize the final layers and their corresponding hyperparameters of the MobileNetV2 model. Next, we used the Google text-to-speech module, PyAudio, playsound, and speech recognition to generate the audio feedback of the detected objects. A Raspberry Pi camera captures real-time video where real-time object detection is done frame by frame with Raspberry Pi 4B microcontroller. The proposed device is integrated into a head cap, which will help visually impaired people to detect obstacles in their path, as it is more efficient than a traditional white cane. Apart from this detection model, we trained a secondary computer vision model and named it the “ambiance mode.” In this mode, the last three convolutional layers of SSDLite MobileNetV2 are trained through transfer learning on a weather dataset. The dataset comprises around 500 images from four classes: cloudy, rainy, foggy, and sunrise. In this mode, the proposed system will narrate the surrounding scene elaborately, almost like a human describing a landscape or a beautiful sunset to a visually impaired person. The performance of the object detection and ambiance description modes are tested and evaluated in a desktop computer and Raspberry Pi embedded system. Detection accuracy and mean average precision, frame rate, confusion matrix, and ROC curve measure the model's accuracy on both setups. This low-cost proposed system is believed to help visually impaired people in their day-to-day life.

Robustness enhancement study of augmented positive identification controller by a sigmoid function

The dissolved oxygen concentration in the wastewater treatment process (WWTP) must remain in a specific range while the factory operates. The augmented positive identification (PID) controller with a nonlinear element (sigmoid function) is proposed to assure stability and reduce uncertainties in the wastewater direct reuse/recycling model. The nonlinear controller gains (PID controller with sigmoid function) for uncertain wastewater treatment processes are tuned using the particle swarm optimization (PSO) technique. The proposed robust method for controlling wastewater treatment processes has good robustness during model mismatching, reduces treatment time compared to traditional positive identification (PID) controllers tuned by PSO, is easy to apply, and has good performance, according to simulation results.

PSO tuned interval type-2 fuzzy logic for load frequency control of two-area multi-source interconnected power system

Nowadays, most of modern power systems integrate concentrated renewable energy resources power plants like solar and wind parks in addition to central conventional plants. The output power from these concentrated renewable energy resources varies continuously according to weather conditions like solar irradiance value or wind speed and direction, the variation for their output power may be in mega watts. In this work, Robust secondary load frequency controller (LFC) based on one of artificial intelligent technique which called interval type-2 fuzzy logic controller (IT2FLC) has been proposed for two-area multi-source interconnected power system with central solar park power plants in each area while considering non-linearities in the power system. IT2FLC has accommodated vagueness, distortions and imprecision for the power system input signals which caused by weather fluctuations and system non-linearities. In addition to LFC, another controller based also on IT2FLC has been proposed to control the output power from the central solar parks in each area of generation during cloudy periods instead of maximum power point tracking method (MPPT) in order to enhance the stability for the power system during disturbance periods. In order to enhance the performance of the proposed LFC, particle swarm optimization technique (PSO) has been utilized to optimize the proposed LFC gains to minimize the steady state error, over/under shooting value, settling time and system oscillation for the investigated power system frequency. The performance and the superiority of the proposed PSO tuned IT2FLC is evaluated and compared with another LFC based on PSO tuned cascaded PID controller while applying severe demand load and solar irradiance changes. the simulation has been carried out using matlab/simulink program.

Dynamic Inertia Weight Particle Swarm Optimization for Anomaly Detection: A Case of Precision Irrigation

Anomaly-based Intrusion Detection System (IDS) is a type of IDS that detects abnormal behaviors by analyzing system activity and network traffic. Anomaly-based IDS works by establishing a baseline of normal behavior for a system or a network. However, these types of systems are less used compared to signature-based IDS for one primary challenge: How to define this normal behavior baseline? The answer to this question is complicated, since it involves not only analyzing or learning from historical data, but requires and understanding of the business domain the system is implemented in. The present study proposes a novel approach to constructing an unsupervised data classifier that combines both Particle Swarm Optimization (PSO) and clustering techniques for anomaly detection. The primary objective of this methodology is to surmount the limitations that conventional clustering algorithms suffer from, such as their inability to identify non-linear patterns within the data, susceptibility to initial conditions, and difficulty in overcoming the problem of local optima. The concept of particle systems is discussed by examining their origins, search strategies, and convergence mechanisms. We use a variant of the Particle Swarm Optimization called Dynamic Inertia Weight-Particle Swarm optimization (DIW-PSO) for our clustering process, and we elaborate on the reasoning behind this decision. Subsequently, we describe the labeling algorithm used for the resulting clusters and we explain the process for identifying anomalous clusters. We have demonstrated the effectiveness of our method by applying it to an intelligent irrigation control system for cotton plants. The results show that our classifier was able to accurately detect abnormal patterns that deviated from the optimal water requirements and growth conditions of the plants.

Two-phase heat transfer microchannel system identification with Particle Swarm Optimization (PSO) approach

The complex behavior of two-phase flow particularly in microchannels can be unpredictable. Experimental measurements are near impossible because of the unavailable compatible assessment equipment. Meanwhile, repeated experiments for reliability of outcomes are costly and involved much time and effort. Environmentally friendly propane is currently being considered as a replacement for hazardous coolants in available refrigeration and air-conditioning systems. This paper reports a system identification (SI) analysis of the collected experimental data of two-phase flow of refrigerant R290 in a microchannel test rig. An ARX model was chosen as the dynamic model, and the modeling of the input–output data was done using a new methodology based on particle swarm optimization (PSO) technique. Measured temperature difference across the microchannel test section and the mass flow rate were the input and output, respectively. The performance of the particle swarm optimization with discoverer (PSOd) was investigated and compared to the original PSO technique. The model was then validated by mean-squared error (MSE). Results demonstrate the advantages of discoverer in PSOd over its standard counterpart with a smaller MSE of 6.2629 × 10−11 and a faster convergence. The SI allows a better prediction of the mass flow rate before any further experiments to obtain the heat transfer coefficient are done. The model provides better management of design of experiments that involve the complex two-phase flow in a microchannel, consequently saving experimental time and cost.

Enhancing Efficiency of Grid-Connected Solar Photovoltaic System with Particle Swarm Optimization & Long Short-Term Memory Hybrid Technique

Maximum Power Point Tracking (MPPT) is a technique used in photovoltaic (PV) systems to maximize the power output from the solar panel by constantly tracking and adjusting the optimal operating point. To achieve this, various algorithms have been developed, with Particle Swarm Optimization (PSO) being a widely used method. By adjusting the control system’s parameters, PSO can determine the optimal operating point of the solar panel and improve its overall performance. PSO employs swarm intelligence by simulating the behavior of particles to find the best solution for a given problem. Long Short-Term Memory (LSTM) belongs to the family of Recurrent Neural Networks (RNN) in machine learning and is designed to address the limitations of traditional RNNs in capturing long-term dependencies that exist in sequential data. The combination of PSO and LSTM techniques can result in an efficient MPPT algorithm that leverages the benefits of both. PSO is utilized to optimize the control parameters of the MPPT algorithm, while LSTM is used to predict the solar panel’s power output based on historical data. Consequently, this integration can lead to an accurate and efficient MPPT algorithm that can effectively track the solar panel’s maximum power point. In this research article, an effort has been made to control the duty cycle of the converter by suitably controlling the system gain. A Matlab-based Simulink model in conjunction with Python programming has been used to make the system more robust.