Optimization PSO Research Articles

A Multi-Objective Optimization for enhancing the efficiency of Service in Flying Ad-Hoc Network Environment

Flying Ad-hoc Network (FANET) is one among the emerging technology and it is used in the huge application of the intelligent communication system. FANETs are combined with multiple Unmanned Aerial Vehicles (UAVs) to control the complex environment. Due to high mobility in FANETs the computation overhead and computation delay of the network is greatly increased that reflects in the reduction of the performance of FANETs. So it becomes very essential to provide effective routing and optimization in FANETs to maintain the stable communication. For that purpose, in this paper Multi-Objective Hybrid Optimization for Quality of Service (QoS) Assisted Flying Ad-Hoc Network (MOHOQ-FANET) approach is proposed with the combination of Ant colony optimization (ACO) and particle swarm optimization (PSO). To achieve effective routing in FANETs, reliability of ad-hoc that depend on demand vector routing (RAODV). In order to perform initial shortest path selection in FANETs, ACO algorithm is utilized. The PSO optimization is applied in FANETs to achieve the best optimal solution between the flying nodes during the time of communication between them. The MOHOQ-FANET technique is implemented using NS2 as the platform. As well as being compared to earlier studies like CSPO-FANET and OSNP-FANET, the performance of the FANETs is assessed using metrics like ratio of packet delivery, host-to-host delay, routing overhead, and network throughput. The outcomes have illustrated, as compared to earlier systems, the proposed MOHOQ-FANET approach delivers high packet delivery ratio and throughput as well as reduced host-to-host delay and routing overhead.

A Novel Swarm Approach for Regulating Load Frequency in Two-Area Energy Systems

One of the most important strategies for running and controlling an electric power system is the load frequency controller. LFC can be used to solve a variety of issues, such as when a generating unit is rapidly turned off by protection equipment or when a heavy load is quickly connected or disconnected. When disturbances disrupt the natural power balance, the frequency deviates from what it should be. LFC is in charge of balancing the load and restoring the natural frequency to its proper level. In this case, load frequency control optimization techniques are used in the Multiple Connect Area System to provide reliable and quality operation on frequency and tie line power flow. The purpose of this paper is to demonstrate how optimising LFC in a two-area interconnected energy system with hydro, thermal plants, and a particle swarm optimization (PSO) method may improve power system stability and save revenue on power generation. A standard (PID) controller is used to control the system. The PSO optimization approach is utilised to determine the optimal gain values of the controllers kp, ki, and kd.

Reduction of Electricity Cost of Residential Home Using PSO and WOA Optimization Method

Recently, the reduction of electricity costs in residential areas has become one of the major research fields. A comprehensive power management system is required to lower the cost of both power generation and consumption. Moreover, the world's energy consumption is continuously increasing. This rise is the consequence of perpetual birth rates and the expansion of factories, which have both significantly increased carbon dioxide emissions and global warming. In order to address these difficulties, hybrid renewable energy systems have evolved in an important way since the development of renewable energy sources. Two meta-heuristic approaches are applied in this paper: the first one is the particle swarm optimization (PSO), which is inspired by the social behavior of bird swarms, and the second one is the whale optimization algorithm (WOA) which is inspired by humpback whale hunting behavior, to tackle the main issue in this work which is decreasing the overall electricity cost of a residential home. The residential home considered in this work consists of two renewable energy sources: a solar panel and the wind turbine, and a power storage system based on battery. This residential home is connected to the main grid through a bidirectional inverter. Furthermore, a comparative optimization study was suggested where we propose two different residential load demands. The results showed the best decrease in the total electricity cost and the best optimal solution by employing the whale optimization approach in both proposed cases.

Improving Raisin Grains Classification with a Hybrid PSO-NN Approach

– Raisin grain classification is crucial in the food industry for maintaining product quality.Traditional classification techniques can be labor-intensive and time-consuming, presenting significantchallenges. To address these issues, this study proposes a hybrid approach for raisin classification thatcombines Particle Swarm Optimization (PSO) and Artificial Neural Networks (ANN). The PSO algorithmis utilized to optimize ANN models with the aim of enhancing the accuracy of raisin grain classification.Our research, conducted on a dataset consisting of 900 raisin grains from two distinct categories, evaluatesthe performance of the proposed hybrid PSO-ANN method in comparison to k-Nearest Neighbor (KNN)and Random Tree (RT). The hybrid PSO-NN approach achieved a remarkable classification performance,demonstrating an accuracy rate of 100%, outperforming other methods under evaluation. The respectiveaccuracies of KNN and RT were 87.39% and 94.91%. This outstanding performance underscores theefficacy of integrating PSO optimization with ANN in the field of raisin grain classification. The resultssuggest that the hybrid PSO-ANN approach surpasses other methods in classification accuracy, indicatingits potential to advance raisin grain classification within the food industry.

Indoor occupancy detection and counting system based on boosting algorithm using different sensor data

ABSTRACT This research examined how to estimate a room's occupancy utilizing datasets using data collected from various sensors with boosting methods. Different sensors were used to extract features that indicate the occupancy level and the relative changes (PIR motion detectors, CO2 sensors, plug loads, lighting loads, electricity use and Wi-Fi access points). The proposed two-layer method increases the reliability and accuracy of occupancy detection. Five machine learning models were trained and tested using data on occupancy (Logistic Regression, Multi-Layer Perceptron Classifier, Support Vector Machine, Light Gradient Boosting Machine and Gradient Boosting). Accuracy, Precision, F1-score, Recall and ROC were the performance parameters used. Thus, using the Python tool, the proposed optimized model gives better detection and estimated accuracy of occupancy in the indoor environment. As a result, data fusion in conjunction with the Light GBM algorithm's gradient boosting-based categorical features and PSO optimization techniques may be used to predict occupancy data with high accuracy and F1-score (both 99%).

Investigation of the impact of PSO, ABC, BFO and Cuckoo search optimization techniques on UPFC device for sustainable voltage stability margin improvement

This paper investigates the enhancement of voltage stability margin using Unified Power Flow Control (UPFC) device tuned with Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), Bacterial Foraging Optimization (BFO) and Cuckoo Search techniques (CS) on Nigerian 330 kV 56-bus practical network. In order to have a voltage stable power system, it is good to keep voltages within the acceptable limits. This is achieved using continuation power flow embedded in Power System Analysis Toolbox (PSAT). The optimal location and tuning of the UPFC device is determined using line stability index and the met-heuristics techniques. The effects of UPFC and the proposed optimizing techniques on voltage stability margin are examined. The results so obtained for tuning PSO, ABC, BFO and Cuckoo Search with UPFC device are compared to know the technique that yields the best loading parameter for Nigerian 56- bus power system for voltage stability margin enhancement. It is found that UPFC tuned with Cuckoo Search outperforms the other techniques in terms of the increased loading parameter of the Nigerian power system with margin improvement of 86.6%, 90% and 88.6% for Adiabor (Bus 45), Jalingo (Bus 34) and Jos (Bus 13). The tuned UPFC device has proved beyond reasonable doubt that it can improve voltage stability margin of the entire Nigerian 330 kV Network as envisaged from results before and after optimization.

Power System Stabilizer Design using Genetic Algorithms and Particle Swarm Optimization

in this paper, Meta-heuristic techniques using genetic algorithms GA and particle swarm optimization PSO to tuning optimal design of power system stabilizer PSS proposed. This latter have been used for many years to add damping to electromechanical oscillations of power system, Based on this idea we have proposed multiobjective function composed with tow function, first maximize stability margin by increasing the damping factors while minimizing the real parts of the eigenvalues. Simulation results to comparative study between genetic algorithms and particle swarm optimization obtained by our realized graphical user interface (GUI) proved the efficiency of PSS optimized by genetic algorithms in comparison with Particle Swarm Optimization, showing stable system responses almost insensitive to large parameter variations and under different operating regime (under-excited, nominal and over excited regime).

Contribution to Improve Magnetic Performance and Torque Ripple Reduction of the Low-Speed DSPM Machine

This article deals with the performance improvement of a toothed pole variable reluctance machine excited by permanent magnets housed in the stator yoke. The objective was to reduce the electromagnetic torque ripples caused by the structure geometry and by the supply technique. The machine was designed to meet the specifications of a small wind energy conversion system. The proposed solution improved the electromagnetic design of the new structure in order to minimize the variation of the reluctance. This improvement was obtained by action on the geometry of the structure (the location of the permanent magnets), by action on the stator and rotor tooth pitch, and by the application of an indirect control strategy called torque sharing function. The PSO optimization algorithm was applied in the first part for the optimization of the machine’s global parameters to maximize torque density and then, in the second part, for the research of the optimum tooth pitch parameters to minimize torque ripple. Static and dynamic performances were obtained using 2D-FEM and MATLAB/Simulink software. The results reveal that by action on the stator/rotor tooth pitch, the ripple torque was reduced by about 53%, and by approximately 76% with the used command technique.

Hybrid forecasting models for wind-PV systems in diverse geographical locations: Performance and power potential analysis

In order to combat the global warming, much stress is put on integrating non-conventional energy resources, such as wind power plants and solar energy systems, into standard electricity generation systems. The stochastic nature of wind energy at a large scale requires precise forecasting in short and medium terms to manage electricity demands for regional grids because the safety and control of electricity systems are highly sensitive to voltage drop. Unfortunately, the existing forecasting models achieve less desirable performance in short term power forecasting. To resolve this issue, based on hybrid stochastic algorithm based intelligent models, which are proven to be a highly effective predictive tool, this paper proposes two new hybrid models using modified white shark optimization (MWSO) algorithm based GRNN and RBFN. To enhance the forecasting performance, the parameter mv is projected as a function of time to enforce the convergence by accelerating computation as well for effective exploration and exploitation to reinforce features of data. The supervised control and data acquisition (SCADA) data provide the input metrics to the system. A hybrid decomposition model is used to prep data for feature enhancement. Six case studies are formulated from Turkey and Malaysia to validate the forecasting performance of our technically advanced models. Recommended modifications allow MWSO for enhanced training. The results are compared with four hybrid ANN topologies in combinations with PSO and BMO stochastic optimization algorithms. Seasonal results are compared graphically and statistically through 15 min ahead forecasting. The results show that the proposed MWSO-RBFNN model outperforms the classic models in all cases for point forecasting and interval forecasting with higher convergence rate and lower stochastic error. The point and interval forecasting accuracy is extremely important to ensure power systems’ stability, efficiency, and safety. Our model achieves Nash-Sutcliffe constant score of 0.99 and exhibits superior performance with the least RMSE, RE, R2, and MAE.

Optimized deep learning based single-phase broken fault type identification for active distribution networks

Single-phase broken faults occur frequently, affecting the reliability of distribution network. In order to effectively identify the fault type of single-phase broken fault, this paper proposes a new identification method, which is based on the combination of variational mode decomposition and stacked auto encoder with double optimization (AO-VMD-PSO-SAE). Firstly, the zero sequence voltage, which collected in line, is decomposed into a set of variational modal components. Nextly, the stack automatic encoder is used to conduct unsupervised training on the denoised data to establish a depth learning model, and the AO optimization algorithm and the PSO optimization algorithm are used to determine the super parameters in the model.​ Finally, simulation results supported and the validity of the method was verified. What the results show is that the proposed model named AO-VMD-PSO-SAE can accurately predict the types of single-phase broken fault under noise interference.

Implementation of BLDC Motor in a Solar Water Pumping System

A sun water pumping system is a greater efficient water pumping system with the use of a renewable electricity supply this is Sun, and it can be used with greater effectiveness in houses, and fields. Until now sun water pumping devices is administered by the use of a submersible motor pump best however the submersible motor pump has its risks in terms its velocity, and torque. So one can reduce those risks and run the solar water pumping device extra correctly and efficaciously and the submersible motor pump is replaced by way of the BLDC motor and development inside the velocity, torque is located, then the submersible water motor pump. To obtain this, a solar photovoltaic (PV) array converts sun power into electrical energy, and the BLDC is rotated using the electricity that was obtained. A pump consists of the water pumping, that's connected to the BLDC is used. A PSO set of rules is used to grow the electricity output using an intermediate-degree DC-DC converter, to provide faster performance; a PV feed-forward period time is added. This topology is simulated and MATLAB/Simulink simulation assessments under numerous air situations are used to illustrate its reaction. A modified grey PSO optimization for a sun device is based on most strength point tracking. A quick seek method to the global most factor in a shorter quantity of time is furnished via the changed PSO, which gets rid of the lower-rank and lowest-rank wolves. The MPPT is primarily based on PSO. The Simulink version is constructed and simulated with the use of MATLAB/Simulink; the required effects are acquired and tabulated.

Performance Evaluation of Energy Efficient Optimized Routing Protocol for WBANs Using PSO Protocol

A Wireless Body Area Network (WBAN) is a network that may be worn on the human body or implanted in the human body to transmit data, audio, and video in real-time to assess how vital organs are performing. A WBAN may be either an inter-WBAN or an intra-WBAN network. Intra-WBAN communication occurs when the various body sensors can share information. This is known as inter-WBAN communication, which occurs when two or more WBANs can exchange data with one another. One difficulty involves getting data traffic from wireless sensor nodes to the gateway with as little wasted energy, dropped packets, and downtime as possible. In this paper, the WBAN protocols have been compared with WBAN under Particle Swarm Optimization (PSO), and the performance of various parameters has been analysed for different simulation areas. The WBAN under the PSO protocol reduces the energy consumption by 43.2% against the SIMPLE protocoldue to the effective selection of forwarding nodes based on PSO optimization. In addition to that the experimental WBAN testbed is conducted in indoor environment to study the performance of the routing metrics towards energy and packet reception..

An improved ensemble particle swarm optimizer using niching behavior and covariance matrix adapted retreat phase

Over the past two decades, to overcome the limitations of certain algorithms, ensemble strategies or self-adaptive mechanisms for evolutionary computation algorithms have been proposed. Regardless of how these strategies or mechanisms were designed, their objective was to control the balance between the global and local search capabilities during the evolutionary process. Inspired by this, a novel ensemble strategy with three groups to improve the performance of the ensemble particle swarm optimizer (EPSO) is proposed. The first group uses a covariance matrix adapted retreat phase (CMAR), the second group induces the niching behavior for inertia weight particle swarm optimization (PSO), and the third group maintains the characteristics of a large subpopulation of EPSO. Furthermore, a sample pool and replacement mechanism are proposed to perturb the three groups. This strategy also recommends a group of empirical allocation rates for subpopulations based on various proportion combination tests. The performance of the proposed strategy is evaluated using CEC2005 benchmark functions with 10, 30, and 50-dimensional tests and compared with those of the state-of-the-art PSO variants: EPSO, a modified PSO using adaptive strategy (MPSO), PSO variant for single-objective numerical optimization (PSO-sono), terminal crossover and steering-based PSO (TCSPSO), self-adapting hybrid strategy PSO (SaHSPS), and pyramid PSO (PPSO). Experimental results demonstrate that the improved EPSO, using CMAR, niching behavior, and sample pool, performs best.

Design of a novel robust adaptive neural network‐based fractional‐order proportional‐integrated‐derivative controller on DC/DC Boost converter

AbstractDC/ DC Boost converter has a right half‐plane zero structure called a non‐minimum phase system, which presents several challenging constraints for designing well‐behaved control techniques. The Fractional‐Order concept as a beneficial scheme provides several advantages, such as lower sensitivity to noise and parametric variation. For this purpose, a Fractional‐order Proportional‐Integrated‐Derivative (FOPID) controller is designed for the Boost converter. On the other hand, for wider ranges of disturbances, including parametric variations, load uncertainty, supply voltage variation, and noise, this approach shows an unsuitable practical application based on its fixed gain values; therefore, the control parameters need to be optimized again to provide ideal operations. An Artificial Neural Network structure (ANNs) is adopted here to optimize the gains of the FOPID in challenging conditions. This method suffers from higher complexity and slower dynamics in practical applications. A single‐layer ANNs is designed to reduce the complexity; also, particle swarm optimization (PSO) algorithm is utilized in this decision‐making structure to provide better results with its online mechanism. Furthermore, the Black‐box technique is applied for the proposed system, which does not require an accurate mathematical model resulting in a lower computational burden, easy implementation, and lower dependency on the states of the model. The Artificial Neural Network structure can optimize the FOPID gains real‐time, even in severe challenging conditions, efficiently. To better examine the superiority of the proposed method, conventional Proportional‐Integrated‐Derivative (PID) and the FOPID controllers are proposed to drive a comparison with this work, which are tuned by the PSO optimization algorithm. The evaluation of simulation results demonstrates that the proposed control scheme is suitable not only for preserving stability but also for compensating for the disturbances and uncertainties in the Boost converter, properly.

Back analysis of geomechanical parameters for rock mass under complex geological conditions using a novel algorithm

Geomechanical properties of rock mass are of vital importance for design optimization and stability evaluation in geotechnical engineering. Traditionally, they are estimated by laboratory experiments, in situ tests, and empirical classification, which ignore the scale effect, and may be not representative, leading to uncertainties of parameters. Back analysis method based on the monitoring displacement data provides the state-of-the-art tool for the estimation of geomechanical parameters. This paper proposed a novel back analysis method, which combined the optimized particle swarm optimization (OPSO) algorithm with the support vector machine (SVM) algorithm. And then, we cooperate the method with finite element method (FEM) forming the comprehensive method named OPSO-SVM-ABAQUS. This method has developed the standard PSO algorithm from two aspects for more powerful optimization capability and higher optimization precision. The OPSO algorithm is then used for the estimation of the SVM hyperparameters and the optimal values of geomechanical parameters. Furthermore, the performance of the proposed algorithm is compared with other six algorithms. Finally, the geomechanical parameters of the interlayered soft and hard rocks are calculated accurately, with the average error between the back-analyzed and assumed actual parameters to be 0.91 %. And the parametric investigations are implemented with the trained model. It dominates that the proposed algorithm is feasible for reasonable parameters in geotechnical engineering under complex geological conditions.

Hybrid IACO-A*-PSO optimization algorithm for solving multiobjective path planning problem of mobile robot in radioactive environment

Mobile robots are receiving significant interest in the nuclear energy industry because of their potential intelligence features and efficient operation. Mobile robot path planning (PP) in a radioactive environment can be considered as finding a collision-free path constrained by the path length, cumulative radiation dose rate and energy consumption. To solve this multiobjective path planning (MOPP) problem, we propose a hybrid algorithm based on an improved ant colony optimization (IACO), the A* algorithm and particle swarm optimization (IACO-A*-PSO). First, a modified A* algorithm is presented to find a suboptimal path, which is used to improve the initial ACO pheromone. Next, an improvement of the adaptive heuristic function and pheromone update rule based on an elitist system is proposed to balance the global search ability and convergence speed of the ant colony algorithm. Finally, PSO is used to obtain the optimal ACO control parameters and multiobjective weight coefficients. To evaluate the efficiency of the proposed algorithm, a comparative study has been performed between the proposed IACO-A*-PSO, the A*, ACO, and IACO algorithms and a genetic algorithm (GA) in four radioactive indoor environments with different sizes, radioactive sources and obstacles. The simulation results show that the proposed algorithm has the best comprehensive performance and stronger adaptability to different environments than the A*, ACO, IACO and GA algorithms in terms of the path length, cumulative radiation dose, energy consumption, runtime and success rate simultaneously. This demonstrates that the proposed algorithm is an effective method for solving the MOPP problem of mobile robot in radioactive environment, which is beneficial for improving the safety and reliability of robots in nuclear energy.

Performance enhancement of SVD based dual image watermarking in wavelet domain using PSO and JAYA optimization and their comparison under hybrid attacks

Performance enhancement of SVD based dual image watermarking in wavelet domain using PSO and JAYA optimization and their comparison under hybrid attacks

Estimation of the Equivalent Circuit Parameters in Transformers Using Evolutionary Algorithms

The conventional methods of parameter estimation in transformers, such as the open-circuit and short-circuit tests, are not always available, especially when the transformer is already in operation and its disconnection is impossible. Therefore, alternative (non-interruptive) methods of parameter estimation have become of great importance. In this work, no-interruption, transformer equivalent circuit parameter estimation is presented using the following metaheuristic optimization methods: the genetic algorithm (GA), particle swarm optimization (PSO) and the gravitational search algorithm (GSA). These algorithms provide a maximum average error of 12%, which is twice as better as results found in the literature for estimation of the equivalent circuit parameters in transformers at a frequency of 50 Hz. This demonstrates that the proposed GA, PSO and GSA metaheuristic optimization methods can be applied to estimate the equivalent circuit parameters of single-phase distribution and power transformers with a reasonable degree of accuracy.

Optimal sizing of standalone for hybrid renewable energy system by using PSO optimization technique

Providing electricity to rural regions is difficult for developing countries, such as Iraq, particularly in remote parts without grid connections. The electrical demands of Zerbattiya, a community in southern Iraq near the Iranian border, are discussed in this paper. The proposed system includes wind turbines, solar panels, diesel engines, batteries, etc. This study suggests a techno-economic viable and optimal size for each component to generate electricity for this area. This research uses particle swarm optimization techniques (PSO). The best hybrid renewable energy system (HRES) design is achieved by balancing the lowest possible cost of energy (COE) with the lowest possible loss of power supply probability (LPSP) and the greatest possible reliability factor value. As a result of the findings, the respective ideal values of number of photovoltaics (NPV), number of wind turbines (NWT), number of diesel generator (NDG), number of batteries (NBT), COE, LPSP, and reliability are 138, 43, 2, 324, US$/KWh 0.129, 0.0508%, and 99.9492%, respectively. Finally, it was discovered that implementing a HRES is an effective way to address the electrical demands of remote rural regions in Iraq and other developing countries with similar climates.

Artificial Intelligence and Internet of Things-Based Leak Detection Method for the Water Supply Network

The good management and safe operation of the urban water supply network are of great significance to residents’ lives and industrial production. In view of the difficulties in supervision and leakage location of the urban water supply network, based on the technology of Internet of things and artificial intelligence algorithm, a leakage detection method of the urban water supply network is proposed. First of all, low-power, low-cost terminal detection equipment and gateway monitoring equipment are developed for remote data transmission through WiFi or cellular data networks. The data organization, storage, release and control are realized by using the data center software platform. Second, the leakage location model of the water supply network is established by using remote pressure monitoring data, and the accurate location of pipe network leakage is realized. Based on ALO and PSO optimization algorithms, the water supply network in an industrial area of a city in China is solved. Finally, the performance of the two optimization algorithms is compared and analyzed. The results show that the designed intelligent monitoring system of the water supply network can monitor the pipe network well. In addition, on the problem of leakage detection, the ALO algorithm is superior to the PSO algorithm in terms of optimization ability and search efficiency. The leakage monitoring method of water supply networks proposed in this study can provide a reference for the design and management of urban water supply networks.