Particle Swarm Optimization Technique Research Articles

Optimizing capacitor size and placement in radial distribution networks for maximum efficiency

As distribution systems continue to expand, they face challenges such as increased system losses and inadequate voltage regulation. To address these issues, shunt capacitors are being deployed in distribution networks. These capacitors offer reactive power compensation, enhance power factor, improve voltage profiles, promote system stability, and significantly reduce losses. However, determining the appropriate capacitor sizes and their optimal placements requires careful consideration of both technical and economic factors. The nonlinear nature of optimal capacitor placement and sizing, leveraging optimization techniques becomes crucial in identifying the best locations and values for capacitors. This paper demonstrates the effective utilization of Particle Swarm Optimization (PSO) and Real Coded Genetic Algorithm (RCGA) optimization techniques for capacitor placement and selection. The optimization techniques are applied to a 33-bus IEEE standard radial distribution system, to reduce the real power loss and to improve the voltage profile considering both constant and variable loads. Both PSO and RCGA algorithms identify suitable locations for the placement of capacitors for reactive power compensation within the distribution system. By optimizing the objective function associated with capacitor placement costs and maximizing annual cost savings, the PSO and RCGA techniques yield promising results. After implementing the optimal capacitor placements at the identified candidate nodes, a significant reduction in losses within the radial distribution system is observed. Moreover, the cost savings achieved through optimal placement and sizing are substantial.

Optimizing Track Contact Length for Enhanced Dynamics and Performance of Tracked Vehicles: A Particle Swarm Optimization Approach

Abstract Through an in-depth investigation of tracked vehicle dynamics, this paper explores the crucial role that the track contact length (L) plays in affecting the dynamic performance of tracked vehicles. The study delves into the basic equations that control straight-line motion and turning as functions of track contact length, with an emphasis on these concepts. To improve the tracked vehicle’s overall performance, an optimization procedure is carried out using the Particle Swarm Optimization (PSO) technique that is implemented in MATLAB. The aim of this study is to clarify the complex relationship between track contact length and tracked vehicle dynamics, offering useful information for enhancing vehicles’ mobility. The study includes a pioneering validation, aligning computational results with real-world data, affirming the accuracy and practical utility of the designed code. This research provides valuable insights for designing tracked vehicles through offering a reliable guide for optimizing track contact length.

Optimal production plan in a production inventory model under the coexistence of both green and regular products: A study on Indian textile industry

The necessity to restrict environmental pollution worldwide has led to the development and implementation of green products. Various sectors within the industrial domain are now placing emphasis on the creation of environmentally friendly products in order to minimize their impact on the environment. The textile industry, both in India and globally, significantly contributes to environmental pollution through the release of untreated effluents into water and the emission of carbon into the atmosphere. Consequently, the popularity of green apparel is steadily increasing as a means to alleviate this crisis. It has been reported that, despite customer preference for green apparel, factors such as the cost of these products and a lack of consumer awareness regarding their importance deter a significant portion of potential buyers from making purchases. In order to stimulate demand for green apparel, the government could implement a subsidy scheme and initiate advertising campaigns highlighting the benefits of using such products. In the market, green apparel and regular apparel are viewed as substitutes for one another and coexist. As a result, there is a constant price competition between these two types of products. This research examines the optimal inventory decisions of manufacturers in a production inventory model where both green and regular products are present and product substitution is allowed. The demand for both types of products is influenced by their respective selling prices. Furthermore, the level of greenness and the efforts put into advertising green products have positive effects on generating demand for these products. The resulting objective function in this model is non-concave, and the quantum behaved particle swarm optimization technique is utilized to solve the corresponding optimization problem. Additionally, Mathematica software has also been employed to verify the efficiency of the proposed solution algorithm. To provide economic validation of the proposed model, a numerical example is presented. Finally, a sensitivity analysis of the key parameters is conducted to gain insights into their impact on the optimal solution. The results indicate that strategic and well-executed advertising campaigns, along with government subsidy schemes, can boost the market for green products and generate greater profits for manufacturers.

Oral Cancer Detection Using Deep Learning

Oral cancer is a very serious, complex, and common type of cancer. Oral cancer ranks eighth globally in terms of cancer incidence in India, with 130,000 deaths reported annually. The tumor affects the tonsils, salivary glands, neck, face, and mouth. Numerous methods, including screening procedures and biopsies—which entail taking a small sample of body tissue and analyzing it under a microscope—can be used to identify oral cancer. The disadvantage of cancer cells is that they are hard to identify and quantify. For this reason, digital processing technology will be employed in this study to identify and classify cancer cells that have affected the oral cavity. State-of-the-art technology and an in-depth learning algorithm can be employed for early detection and categorization. This work employs the Zernike Moment, wavelet features, and the bag histogram of directed gradients as three techniques for character extraction. After the characteristics are obtained, the best texture characteristic is chosen using the fuzzy particle swarm optimization technique (FPSO). In the end, these features were classified using the Faster Region-based Convolution Neural Network (faster RCNN) classifier. to evaluate the efficiency, error, recall rate, precision rate, and classification accuracy of the recommended approach.

Design and comparison of particle swarm optimization tuned Kalman filter based linear quadratic Gaussian controller and linear quadratic regulator for surface to air missile guidance system

AbstractThe study of missile guidance systems is a well‐known nonlinear control engineering area of research. To enhance the control performance of a missle guidance system, several technologies have been proposed in existing works. To resolve the weighting matrix selection issue of a linear quadratic Gaussian (LQG) controller for the surface‐to‐air missile guidance control system, this study utilizes the particle swarm optimization (PSO) technique. Selecting the best state (Q) and input (R) weighting matrices is a significant difficulty in the design of the LQG controller for real‐time applications since it affects the controller's performance and optimality. The weighting matrices are often chosen by a trial‐and‐error method that not only complicates the design but also does not yield optimal outcomes. Therefore, in this paper, a PSO method is developed and used in the design of the linear quadratic regulator (LQR) and LQG controllers for the surface‐to‐air missile control system to choose the elements of the Q and R matrices in the best possible way. Finally, a comparative analysis between the designed controllers was presented. The results shows that a good performance was achieved by using the proposed PSO‐tuned design process. The LQG and LQR are designed by manually adjusting the weighting matrices and utilizing an intelligent procedure, PSO algorithm which achieved optimal results. Further results indicate that the designed controllers, the PSO tuned LQR and LQG achieved a better performance over the manually adjusted LQR and LQG controllers.

Removal of N-Nitrosodiphenylamine from contaminated water: A novel modeling framework using metaheuristic-based ensemble models

Investigating the complex interactions among physicochemical variables that influence the adsorptive removal of pollutants is a challenge for conventional one-variable-at-a-time (OVAT) batch methods. The adoption of machine learning-based chemometric prediction models is expected to be more accurate than the conventional method. This study proposed a novel modeling framework for predicting and optimizing the adsorptive removal of N-Nitrosodiphenylamine (NDPhA). Initially, models were trained by using OVAT data, with their hyperparameters subsequently fine-tuned through Bayesian optimization. In the second phase, the particle swarm optimization (PSO) technique was adopted to identify optimal parameters, specifically time, concentration, temperature, pH, and dose, to ensure the highest removal. The adopted analytical method enhances both prediction accuracy and removal efficiency. Utilizing OVAT data for NDPhA removal, the XGBoost regressor significantly outperformed other models. With a correlation coefficient of 0.9667 in the testing dataset, the XGBoost model exhibited its accuracy, emphasized by its low mean squared errors of 28.45 and mean absolute errors of 0.0982. Feature importance analysis consistently identified time and concentration as the most critical factors across all models.

A novel approach for techno-economic reliability oriented planning and assessment of droop-control technique for DG allocation in islanded power distribution systems

An effective planning strategy for deploying distributed generators (DGs) provides economic advantages through optimal net present cost (NPC), improved technical performance, enhanced voltage profile, and minimized line losses. Nevertheless, technical performance varies based on the control techniques implemented for power regulation in DGs. Therefore, it is crucial to incorporate DG control strategies for efficient and robust power distribution system planning. This study discusses a novel approach for techno-economic reliability-oriented planning in an IEEE-33 bus droop-controlled islanded power distribution system. The presented study evaluates the influence of incorporating control techniques on the sizing and placement of DGs along with the technical, economic and reliability parameters. The problem formulated for optimal DG allocation aims to achieve the optimum NPC, minimize line losses, improve system reliability, and ensure voltage and frequency agreement with IEEE std 1547™-2018. The problem formulation is structured as a multi-objective problem and is solved using the particle swarm optimization (PSO) technique to achieve optimal bus location and size for DG installation. The study reveals that considering the control strategy helps in achieving efficient and reliable planning structure.

Cost–Performance Optimization of a Renewable Energy Resources Based Multimicrogrid System

The growing energy demand, the increase in its price, and the increasing pollution worldwide are considered large problems that need unusual solutions. To overcome these problems, it is necessary to search for innovative solutions using renewable resources and energy management of the energy systems. Energy management means reducing the operating, maintenance, and generation costs of the system and enhancing system performance with methods such as power loss reduction and stability enhancement and alleviating the harmful emissions to the environment. Thus, the energy management of a micro-grid has become one of the most vital aspects of the power or energy system all over the world.The objective of this paper is to optimize a multiobjective problem of a renewable energy resources (RER) based multimicrogid (MMG) system considering the output variation of the photovoltaic (PV) and wind Turbine (WT) as renewable energy resources and variation of the load demand and electricity prices. In this paper three microgrids (MGs) are connected with IEEE 33-bus distribution system each MG consists of a PV and WT. A three objective functions are modeled for this system to optimize the total annual cost, the voltage deviation and the voltage stability index (cost –performance multiobjective function). The optimization problem is solved with particle swarm optimization (PSO) technique for the system with and without RER.A comparison is carried out with two other optimization techniques, mountain gazelle optimization (MGO) and gorilla troop optimization (GTO). The results of the simulation show that the system cost is considerably reduced and the performance optimized.

An Approach to Deepfake Video Detection Based on ACO-PSO Features and Deep Learning

The rapid advancement of deepfake technology presents significant challenges in detecting highly convincing fake videos, posing risks such as misinformation, identity theft, and privacy violations. In response, this paper proposes an innovative approach to deepfake video detection by integrating features derived from ant colony optimization–particle swarm optimization (ACO-PSO) and deep learning techniques. The proposed methodology leverages ACO-PSO features and deep learning models to enhance detection accuracy and robustness. Features from ACO-PSO are extracted from the spatial and temporal characteristics of video frames, capturing subtle patterns indicative of deepfake manipulation. These features are then used to train a deep learning classifier to automatically distinguish between authentic and deepfake videos. Extensive experiments using comparative datasets demonstrate the superiority of the proposed method in terms of detection accuracy, robustness to manipulation techniques, and generalization to unseen data. The computational efficiency of the approach is also analyzed, highlighting its practical feasibility for real-time applications. The findings revealed that the proposed method achieved an accuracy of 98.91% and an F1 score of 99.12%, indicating remarkable success in deepfake detection. The integration of ACO-PSO features and deep learning enables comprehensive analysis, bolstering precision and resilience in detecting deepfake content. This approach addresses the challenges involved in facial forgery detection and contributes to safeguarding digital media integrity amid misinformation and manipulation.

PARTICLE SWARM OPTIMIZATION AND CFD ANALYSIS OF A HEAT SINK FOR FORCED CONVECTIVE COOLING OF A DC/AC INVERTER

Power inverters play a crucial role in converting DC voltage and current to AC voltage and current. Its efficient operation relies on effective thermal management of the semiconductor devices, such as the IRF 3205 MOSFET chip which facilitate the switching operations to prevent thermo-mechanical stress that could to failure. This research developed and optimize a heat sink for an IRF 3205 MOSFET chip in a 3.5 KVA power inverter, using the Particle Swarm Optimization technique and experimented with a 3.5 KVA 24 V power inverter operating a 1.5HP air-conditioning unit. The heat sink geometry has 26 fins, 4 mm fin thickness, 40 mm fin height, 3 mm fin spacing, 2 mm fin base thickness, 179 mm length, 80 mm width and a thermal resistance of 60.75 °C/W. The thermal performance was evaluated using ANSYS Computational Fluid Dynamics (CFD) and results showed maximum base temperature of 65.85°C and experimental temperature of 64°C after two hours of steady operation dissipating heat of 45W. The results from both numerical and experimental data demonstrate the effectiveness of the optimized heat sink in maintaining the chip's temperature below its maximum working threshold of 170°C.

Optimisation of collapse in functionally graded thickness honeycomb under axial impact loading

This research investigates the collapse behaviour of a honeycomb energy absorber featuring variable wall thickness. Initially, numerical simulations were conducted to analyse the effect of wall thickness in upper and lower sections of the absorber, as well as the parameter 'n‘ that characterises the type of thickness variations within the wall, as design variables. A comprehensive database was compiled, encompassing various combinations of these parameters. Subsequently, a mathematical model was developed by fitting it to the initial data using the response surface methodology. The specific absorption energy and initial crush force were computed and utilised as the objective function for optimisation. Ultimately, by employing the Particle Swarm Optimisation (PSO) technique and applying problem constraints, an optimal configuration for the honeycomb absorber was determined, with a focus on maximising the specific absorption energy. The optimised outcome obtained from the optimisation algorithm exhibited an enhancement of approximately 11% compared to the maximum values of the input samples. Furthermore, multi-objective functions were employed during the optimisation process to identify absorber parameters that would achieve both the maximum specific absorption energy and the minimum initial peak force.

Techno-economic analysis and dynamic power simulation of a hybrid solar-wind-battery system for power supply in rural areas in Pakistan

This study presents the optimal design and operation of a proposed hybrid renewable energy system (HRES) for the electrification of a residential building in rural areas in Pakistan. The main contributions of this study are twofold. Firstly, it develops a size optimization model based on the particle swarm optimization (PSO) technique to determine the optimal configuration for two hybrid renewable energy systems (HRES), including both grid-tied and off-grid modes, integrating wind and photovoltaic (PV) systems with battery storage. The optimal configuration is determined by minimizing the levelized cost of electricity, using local meteorological and electricity load data, along with technical specifications of the main HRES components. Secondly, dynamic simulations of two HRES configurations are conducted, using MATLAB Simulink, ensuring the optimal energy balance between multiple energy sources and the load at each operation hour. To meet an annual electrical demand of 131.035 MWh, the grid-tied HRES yields 146.081 MWh annually, with solar contributing 68.85 MWh and wind 77.272 MWh. Conversely, the off-grid system generates 133.533 MWh annually, with solar and wind output power at 43.932 MWh and 89.601 MWh, respectively. The grid-tied system achieves an LCOE of approximately 0.29 $/kWh, with optimal wind turbine and PV capacities of 11 kW and 29 kW, respectively. While in off-grid configuration, the off-grid scenario exhibits an LCOE of 0.91 $/kWh, with optimal capacities of 10 kW for wind turbine, 20 kW for PV, and 2437.5 AH for batteries. The findings provide insights relevant to diverse locations, emphasizing the importance of local meteorological and geographical data. Multiple case studies ensure the robustness and applicability of the proposed system under varying conditions.

Low salinity water flooding: estimating relative permeability and capillary pressure using coupling of particle swarm optimization and machine learning technique

The reservoir’s properties are required for proper reservoir simulation, which also involves uncertainties. Experimental methods to estimate the relative permeability and capillary pressure data are expensive and time-consuming. This study aims to determine the relative permeability and capillary pressure functions of a sandstone core in the presence and absence of clay during low-salinity water floods. The data were provided by automatic history matching the results from previously lab-reported studies through coupling a simulator with the particle swarm optimization algorithm. Correlations were proposed using multiple-linear regression for relative permeability and capillary pressure parameters at low-salinity conditions. They were validated against experimental results of no clay and clayey formation with regression of 95% and 97%. To assign one curve of relative permeability and capillary pressure to the grid cells of the simulator, averaging techniques were implemented. The effect of salinity and clay content on the obtained curves was investigated. Changing salinity from 42000 to 4000 ppm, the reduction in water relative permeability appeared to be higher than the oil relative permeability increment. Moreover, a noticeable shift in the relative permeability curves toward the highest saturations related to the clay content was observed. The proposed hybrid method could be a suitable tool to estimate the relative permeability and capillary pressure functions of the water-based EOR methods.

Accurate Surge Arrester Modeling for Optimal Risk-Aware Lightning Protection Utilizing a Hybrid Monte Carlo–Particle Swarm Optimization Algorithm

The application of arresters is critical for the safe operation of electric grids against lightning. Arresters limit the consequences of lightning-induced over-voltages. However, surge arrester protection in electric grids is challenging due to the intrinsic complexities of distribution grids, including overhead lines and power components such as transformers. In this paper, an optimal arrester placement technique is developed by proposing a multi-objective function that includes technical, safety and risk, and economic indices. However, an effective placement model demands a comprehensive and accurate modeling of an electric grid’s components. In this light, appropriate models of a grid’s components including an arrester, the earth, an oil-immersed transformer, overhead lines, and lightning-induced voltage are developed. To achieve accurate models, high-frequency transient mathematical models are developed for the grid’s components. Notably, to have an accurate model of the arrester, which critically impacts the performance of the arrester placement technique, a new arrester model is developed and evaluated based on real technical data from manufacturers such as Pars, Tridelta, and Siemens. Then, the proposed model is compared with the IEEE, Fernandez, and Pinceti models. The arrester model is incorporated in an optimization problem considering the performance of the over-voltage protection and the risk, technical, and economic indices, and it is solved using the particle swarm optimization (PSO) and Monte Carlo (MC) techniques. To validate the proposed arrester model and the placement technique, real data from the Chopoghloo feeder in Bahar, Hamedan, Iran, are simulated. The feeder is expanded over three different geographical areas, including rural, agricultural plain, and mountainous areas.

Stochastic configuration networks with particle swarm optimisation search

While building stochastic configuration networks (SCNs), there is no guarantee that the randomly generated weights will satisfy the supervisory mechanism and that the adopted weights will significantly reduce the training error. This paper extends SCN by applying the particle swarm optimisation (PSO) technique for one-step optimising of the set of random weights generated. These optimised weights provide a higher likelihood of satisfying the supervisory mechanism and improving the learning rate. Simulations are carried out over five regression and three classification datasets. Results demonstrate that an improved training rate and generalisation can be achieved.

DC Bus Voltage Control of Islanded Microgrid using Renewable Energy Based Distributed Generation

DC bus voltage control in DC islanded microgrids is essential for the power quality and reliability. There are various algorithms being used in the literature review to control the bus voltages of DC microgrids. However, most of the research works controlled the generation side that requires high performance devices such as super capacitors etc., On the other hand, battery storage systems are also being used to regulate the bus voltage. In this research work, a separate strategy was developed in which battery storage was implemented at the load side to maintain stability of DC bus voltage. The load management scheme is introduced in which during the voltage instability, the loads are cutoff from the main supply and local installed storage system feed the loads. A particle swarm optimization (PSO) technique was used to optimally decide for the load in watts to be operated. A DC microgrid hardware was designed to test the control algorithm. The loads are not connected to the main system until the power supply is stable again. The load management scheme not only reduces the cost of the system but also stabilizes the overall system with 60% charge available in the batteries using the proposed load management scheme. The results were achieved while performing experiments on developed DC Microgrid hardware.

Grey wolf algorithm for cost optimization of cloud computing repairable system with [formula omitted]-policy, discouragement and two-level Bernoulli feedback

With the increasing reliance on cloud computing as the foundational infrastructure for technological operations, the urgency and efficacy with which failed virtual machines can be successfully repaired earns significant weight. In this study, we have presented a queueing model that analyzes the repair process of failed virtual machines in cloud computing system. This model includes various important components, such as control N-policy, server unreliability, discouragement behavior of failed virtual machines (like balking and reneging), retention of reneged machines, redundancy, and two-level Bernoulli feedback. A steady-state performance analysis has been performed. The proposed model provides practical suggestions for cloud service providers to improve the reliability and availability of their services, which insights more than theoretical frameworks. This study is unique in the field of queueing models of cloud computing machining environment, as it incorporates parallel consideration of control N policies, balking, reneging, retention, server vacation, two-level Bernoulli feedback and unreliable server behavior. A thorough examination of the behavior of key performance indicators has been conducted and presented using detailed graphical representations. In addition, a cost function is formulated for the proposed model. Further, the Grey Wolf Optimization (GWO) and Particle Swarm Optimization (PSO) techniques are utilized to optimize the total cost function, thereby gaining valuable insights into the system’s behavior across various conditions as well as to compare the outcomes of GWO and PSO.

Development of IoT based intelligent irrigation system using particle swarm optimization and XGBoost techniques

A crop needs regular watering throughout its life to grow well. Irrigation improves food growth. Machines irrigate plants. The dry Sahel, which gets a lot of rain during the summer season but is dry in winter, needs irrigation. When it doesn't rain enough, crops need watering. By constantly monitoring soil moisture, humidity, temperature, and pH, precision agriculture reduces water use and increases crop output. Precision gardening uses less water. In many wealthy nations, efficient farming requires the internet of things (IoT). Particle swarm optimization (PSO) and XGBoost are used in this IoT-based intelligent watering system. Humidity and moisture sensors gather soil data at grass roots. Sensors constantly gather this data. These data are useless for smart watering. PSOselects smart watering data. This reduces central cloud info storage. Then, machine learning methods are trained using soil humidity, moisture, crop, and weather data. These programs can calculate a crop's water requirements. IoT devices control irrigation system water flow and results in saving fresh water. XGBoost algorithm is saving water from 23% to 27% for different crops.

OPTIMAL PLACEMENT OF UNIFIED POWER FLOW CONTROLLER USING PARTICLE SWARM OPTIMIZATION TECHNIQUE

The load growth in recent times due to advancements in technology and population increase has led to shortage of reactive power in the power system resulting in key issues such as voltage instability, large line losses, power outages among others. In this work, Particle Swarm Optimization (PSO) technique was used for optimal placement of Unified Power Flow Controller (UPFC) for power loss minimization using the IEEE 6-bus system as a test network. The load flow analysis was performed using Gauss-Seidel iterative method with and without inclusion of PSO-based UPFC. The PSO algorithm was used to find the optimum point for UPFC location and determined using the line with the least active power loss among other lines randomly selected by the PSO algorithm, with p.u defined as the voltage statutory limit. The simulation results revealed that the optimum point obtained was line 4-5, and the voltage profile were within the statutory limit with and without optimally placed UPFC. The total active power loss reduced from 495.5 to 273.9 MW, giving a reduction of 44.72 with optimally placed UPFC. The total reactive power loss equally reduced from 1439 to 936.2 MVAr, producing a reduction of 34.94 with optimally placed UPFC. These results obtained showed that PSO-based UPFC placement is suitable for power loss minimization and enhancement of the system voltage profile.

Adaptive Particle Swarm Optimization based improved modeling of Solar Photovoltaic module for parameter determination

Solar Photovoltaic (SPV) panel manufacturers provide a datasheet, which contains key electrical specifications of the SPV module for the user's reference. However, these data alone prove inadequate in anticipating the performance of PV systems under fluctuating atmospheric conditions. The approach put forth in this paper ascertains five parameters of a PV module using information supplied by the manufacturer. This approach considers the impact of solar radiation and cell temperature. The paper outlines the process for determining the parameters of the five-parameter model and establishes a comparison between projected current-voltage curves and manufacturer-provided data. To extract the unknown parameters of a single diode model Adaptive Particle Swarm Optimization (APSO) techniques with barrier constraints was developed. The study investigates both multi-crystalline and mono-crystalline PV module technologies to validate the efficacy of the proposed method. Results demonstrate that the proposed technique, besides being straightforward, proves adept at accurately simulating the dependable performances of PV modules. Comparative analysis reveals that the proposed methods extract parameters with minimal modeling errors and high precision, regardless of temperature fluctuations, outperforming conventional PSO methods.