Modified Particle Swarm Optimization Research Articles

Enhancing Smart Home Efficiency with Heuristic-Based Energy Optimization

In smart homes, heavy reliance on appliance automation has increased, along with the energy demand in developing urban areas, making efficient energy management an important factor. To address the scheduling of appliances under Demand-Side Management, this article explores the use of heuristic-based optimization techniques (HOTs) in smart homes (SHs) equipped with renewable and sustainable energy resources (RSERs) and energy storage systems (ESSs). The optimal model for minimization of the peak-to-average ratio (PAR), considering user comfort constraints, is validated by using different techniques, such as the Genetic Algorithm (GA), Binary Particle Swarm Optimization (BPSO), Wind-Driven Optimization (WDO), Bacterial Foraging Optimization (BFO) and the Genetic Modified Particle Swarm Optimization (GmPSO) algorithm, to minimize electricity costs, the PAR, carbon emissions and delay discomfort. This research investigates the energy optimization results of three real-world scenarios. The three scenarios demonstrate the benefits of gradually assembling RSERs and ESSs and integrating them into SHs employing HOTs. The simulation results show substantial outcomes, as in the scenario of Condition 1, GmPSO decreased carbon emissions from 300 kg to 69.23 kg, reducing emissions by 76.9%; bill prices were also cut from an unplanned value of 400.00 cents to 150 cents, a 62.5% reduction. The PAR was decreased from an unscheduled value of 4.5 to 2.2 with the GmPSO algorithm, which reduced the value by 51.1%. The scenario of Condition 2 showed that GmPSO reduced the PAR from 0.5 (unscheduled) to 0.2, a 60% reduction; the costs were reduced from 500.00 cents to 200.00 cents, a 60% reduction; and carbon emissions were reduced from 250.00 kg to 150 kg, a 60% reduction by GmPSO. In the scenario of Condition 3, where batteries and RSERs were integrated, the GmPSO algorithm reduced the carbon emission value to 158.3 kg from an unscheduled value of 208.3 kg, a reduction of 24%. The energy cost was decreased from an unplanned value of 500 cents to 300 cents with GmPSO, decreasing the overall cost by 40%. The GmPSO algorithm achieved a 57.1% reduction in the PAR value from an unscheduled value of 2.8 to 1.2.

IMPROVING THE RESPONSE OF PERMANENT MAGNET SYNCHRONOUS MOTORS TO DISTURBANCES USING PSO TECHNIQUES

Permanent Magnet Synchronous Motors (PMSMs) are widely recognized in industrial applications and have become increasingly important in the renewable energy sector, supporting the global shift toward sustainable energy solutions. This study presents a comprehensive analysis of PMSM speed and torque behavior, aiming to reduce ripple effects and enhance overall motor stability. A modified Particle Swarm Optimization (PSO) algorithm is employed to optimize the parameters of a Proportional-Integral (PI) controller under various disturbance scenarios, including sudden changes in load and speed. The proposed control strategy is evaluated using MATLAB/Simulink simulations. Results demonstrate that the optimized PI controller significantly reduces speed and torque fluctuations compared to traditional tuning methods. The improved dynamic performance is evident through reduced current oscillations and enhanced accuracy, with the system showing strong resilience against load disturbances. Furthermore, a comparative analysis highlights the superior performance of the proposed method over conventional approaches, confirming its effectiveness in achieving more stable and efficient motor operation.

Modified particle swarm optimization (MPSO) optimized CNN’s hyperparameters for classification

This paper proposes a convolutional neural network architectural design approach using the modified particle swarm optimization (MPSO) algorithm. Adjusting hyper-parameters and searching for optimal network architecture from convolutional neural networks (CNN) is an interesting challenge. Network performance and increasing the efficiency of learning models on certain problems depend on setting hyperparameter values, resulting in large and complex search spaces in their exploration. The use of heuristic-based searches allows for this type of problem, where the main contribution in this research is to apply the MPSO algorithm to find the optimal parameters of CNN, including the number of convolution layers, the filters used in the convolution process, the number of convolution filters and the batch size. The parameters obtained using MPSO are kept in the same condition in each convolution layer, and the objective function is evaluated by MPSO, which is given by classification rate. The optimized architecture is implemented in the Batik motif database. The research found that the proposed model produced the best results, with a classification rate higher than 94%, showing good results compared to other state-of-the-art approaches. This research demonstrates the performance of the MPSO algorithm in optimizing CNN architectures, highlighting its potential for improving image recognition tasks.

Application Research Of Improved Particle Swarm Computing Intelligent Algorithm In Track And Field Training Target Optimization

This paper studies the influence of the athlete's state and wind direction on the Javelin flight trajectory. The process of plane throwing is analyzed theoretically. An accurate javelin flight mathematical model is established. Using the modified particle swarm optimization method, the paper finds a way to make the Javelin longer distance. This algorithm makes it portable and adaptive. Then, the entropy weight method is used to evaluate the weight of each factor. The experiment shows that this method can reflect the actual Javelin flying condition well, and its prediction effect is in accord with the demand of engineering applications.

Multithreaded and GPU-Based Implementations of a Modified Particle Swarm Optimization Algorithm with Application to Solving Large-Scale Systems of Nonlinear Equations

This paper presents a novel Graphics Processing Unit (GPU) accelerated implementation of a modified Particle Swarm Optimization (PSO) algorithm specifically designed to solve large-scale Systems of Nonlinear Equations (SNEs). The proposed GPU-based parallel version of the PSO algorithm uses the inherent parallelism of modern hardware architectures. Its performance is compared against both sequential and multithreaded Central Processing Unit (CPU) implementations. The primary objective is to evaluate the efficiency and scalability of PSO across different hardware platforms with a focus on solving large-scale SNEs involving thousands of equations and variables. The GPU-parallelized and multithreaded versions of the algorithm were implemented in the Julia programming language. Performance analyses were conducted on an NVIDIA A100 GPU and an AMD EPYC 7643 CPU. The tests utilized a set of challenging, scalable SNEs with dimensions ranging from 1000 to 5000. Results demonstrate that the GPU accelerated modified PSO substantially outperforms its CPU counterparts, achieving substantial speedups and consistently surpassing the highly optimized multithreaded CPU implementation in terms of computation time and scalability as the problem size increases. Therefore, this work evaluates the trade-offs between different hardware platforms and underscores the potential of GPU-based parallelism for accelerating SNE solvers.

Collaborative Control and Intelligent Optimization of a Lead–Bismuth Cooled Reactor Based on a Modified PSO Method

Accelerator-driven subcritical (ADS) reactors with lead–bismuth eutectic (LBE) coolants are some of the Gen-IV nuclear energy systems that can generate clean electricity and potentially transmute spent fuel. The dynamic characteristics and control strategy of an ADS reactor are substantially different from those of traditional nuclear reactors. In this paper, a new collaborative control strategy is proposed using an accelerator beam and a control rod, and the control system’s parameters are optimized using a modified particle swarm optimization (PSO) method. To test the control performance, a simulation platform is developed with a nonlinear reactor dynamic model, a power compensation control system and a coolant temperature control system. Four typical control transients are used, including a ±10% full-power (FP) step change load and a ±5% FP/min linear variable load. The simulation results show that the collaborative control strategy has a better load tracking capability and a higher power control accuracy than the beam single-control strategy and the rod single-control strategy. The results also show that the performance of the collaborative control system in terms of the reactor’s power and coolant temperature is significantly improved based on the modified PSO parameter optimization.

Research on design and control method of active vibration isolation system based on piezoelectric Stewart platform.

Space payloads in orbit are vulnerable to small vibrations from satellite platforms, which can degrade their performance. Traditional methods typically involve installing a passive vibration isolation system between the platform and the payload. However, such systems are usually effective only for high-frequency, large-amplitude vibrations and perform poorly in isolating low-frequency vibrations and resonances below 10Hz. To address this limitation, this paper proposes an active vibration isolation system using a 6-degrees-of-freedom Stewart platform driven by piezoelectric actuators. First, the characteristics of the Stewart platform are analyzed and modeled, with the deformation displacement of each leg calculated through decoupling, allowing for high-precision servo control. Next, given the inherent hysteretic nonlinearity of piezoelectric ceramics, which significantly affects positioning accuracy, the hysteresis mechanism of the actuators is analyzed, and a phenomenological mathematical model based on Bouc-Wen operators is established. A Modified particle swarm optimization (MPSO) method is proposed for identifying the model's nonlinear parameters, significantly enhancing the optimization efficiency. Finally, feedforward inverse compensation and feedback linearization methods are introduced. Experimental results verify that the designed active-passive vibration isolation system greatly improves both the positioning accuracy of the piezoelectric actuators and the active vibration isolation performance of the platform.

Interconnected Microgrids Load‐Frequency Control Using Stage‐by‐Stage Optimized TIDA+1 Error Signal Regulator

ABSTRACTLoad‐frequency control (LFC) is essential for maintaining system stability and ensuring high power quality in microgrids (MGs), particularly those heavily reliant on renewable energy sources (RES) and operating independently of the main grid. This paper introduces a novel control strategy aimed at improving LFC performance in interconnected MGs by correcting the error signal. The proposed controller, denoted as TIDA+1, combines tilt, integrator, derivative, and acceleration operators in a parallel configuration to refine the incoming error signal. The controller parameters are optimized using a modified particle swarm optimization (PSO) algorithm with nonlinear time‐varying acceleration coefficients (NTVAC). The controller's effectiveness is validated through four distinct scenarios, including sudden load variations, system modeling uncertainties, fluctuations in RES outputs, and the impact of nonlinearities. Additionally, a lab‐scale evaluation of the controller has been conducted to further assess its practical applicability. Comparative results demonstrate that the TIDA+1 controller outperforms traditional controllers such as PID and FOPID, especially under complex operational conditions. The study highlights the TIDA+1 controller as a robust and viable solution for LFC in MGs, with potential for future scalability and application in larger systems.

Modified particle swarm optimization for engineering optimization problems and UAV path planning

Modified particle swarm optimization for engineering optimization problems and UAV path planning

Modified Particle Swarm Optimization on Feature Selection for Palm Leaf Disease Classification

Palm oil plantations in Indonesia face challenges in enhancing productivity and profitability, notably due to pest attacks that reduce production. Early identification and classification of plant conditions, particularly palm oil leaves, are crucial for mitigating losses. This study explores the application of artificial intelligence, specifically computer vision and machine learning, for disease detection. Various machine learning techniques, including Local Binary Pattern (LBP), K-Nearest Neighbors (KNN), and Support Vector Machine (SVM), have been used in different studies with varying accuracy. This research focuses on modifying Particle Swarm Optimization (PSO) for feature selection in identifying diseases in palm oil leaves. The PSO modification combined with logistic regression and Bayesian Information Criterion (BIC) significantly enhances KNN performance. Accuracy improved from 95.75% to 97.85%, while precision, recall, and F1-score reached approximately 98.80%. Additionally, the modified KNN+PSO achieved the shortest computation time of 0.0872 seconds, indicating high computational efficiency. These results demonstrate that the PSO modification not only improves accuracy but also computational efficiency, making it an effective method for enhancing KNN performance in detecting palm oil leaf diseases.

Particle swarm optimization-based load balance-aware energy-efficient scheduling for mobile crowd computing

Due to their increasing computational power and energy-efficient hardware, today’s smart mobile devices (SMDs) are replacing desktops and laptops as casual computing devices. Moreover, a cluster of such powerful SMDs can garner substantial high-performance computing (HPC). Such an HPC is achieved by utilizing publicly owned SMDs in mobile crowd computing (MCC). Here, a large computing-intensive task is divided and scheduled for the available SMDs for execution, and the results are recollected. This approach provides an economical and sustainable HPC. However, battery-powered constrained energy is a great hindrance to achieving this goal. Therefore, in the MCC, it is crucial to minimize the overall energy consumption to complete the task. This can be achieved to some extent by optimizing task scheduling to the appropriate SMDs. However, considering only energy efficiency might lead to an enormous load imbalance among SMDs, i.e., the most energy-efficient SMDs would be overloaded most of the time. Considering this, in this paper, we present a modified particle swarm optimization (PSO)-based scheduling algorithm to minimize the overall energy consumption among a set of SMDs designated to execute a set of MCC tasks while maintaining a satisfactory load balance level. Extensive simulations with both synthetic and real data sets are carried out to analyze and validate the proposed method. The work was compared with popular heuristic (minimum completion time (MCT), MinMin, MaxMin, and preconditioned progressive iterative approximation (PPIA)) and metaheuristic (genetic algorithm (GA)) optimization algorithms, which yields significant improvements over others in terms of the considered objectives. In addition, an analysis of variance (ANOVA) test is conducted to provide further evidence regarding the distinctiveness of the proposed algorithm.

Smart steaming for just-in-time-arrival, using modified particle swarm optimization (MPSO) algorithm

Smart steaming for just-in-time-arrival, using modified particle swarm optimization (MPSO) algorithm

Stability study of hybrid MOS memristor memory using modified particle swarm optimization method

Stability study of hybrid MOS memristor memory using modified particle swarm optimization method

Optimised Deep Learning for Time-Critical Load Forecasting Using LSTM and Modified Particle Swarm Optimisation

Short-term electric load forecasting is critical for power system planning and operations due to demand fluctuations driven by variable energy resources. While deep learning-based forecasting models have shown strong performance, time-sensitive applications require improvements in both accuracy and convergence speed. To address this, we propose a hybrid model that combines long short-term memory (LSTM) with a modified particle swarm optimisation (mPSO) algorithm. Although LSTM is effective for nonlinear time-series predictions, its computational complexity increases with parameter variations. To overcome this, mPSO is used for parameter tuning, ensuring accurate forecasting while avoiding local optima. Additionally, XGBoost and decision tree filtering algorithms are incorporated to reduce dimensionality and prevent overfitting. Unlike existing models that focus mainly on accuracy, our framework optimises accuracy, stability, and convergence rate simultaneously. The model was tested on real hourly load data from New South Wales and Victoria, significantly outperforming benchmark models such as ENN, LSTM, GA-LSTM, and PSO-LSTM. For NSW, the proposed model reduced MSE by 91.91%, RMSE by 94.89%, and MAPE by 74.29%. In VIC, MSE decreased by 91.33%, RMSE by 95.73%, and MAPE by 72.06%, showcasing superior performance across all metrics.

Design of Area-Optimized Low-power Voltage Reference Circuits using a Modified Particle Swarm Optimization

Design of Area-Optimized Low-power Voltage Reference Circuits using a Modified Particle Swarm Optimization

A modified PSO based hybrid deep learning approach to predict AQI of urban metropolis

A modified PSO based hybrid deep learning approach to predict AQI of urban metropolis

Optimization of the joint startup of the boom and load hoisting mechanisms of a jib crane

During the joint motion of the boom and load hoisting mechanisms, dynamic loads increase, leading to additional energy consumption, which subsequently contributes to the deterioration of the crane’s structure. The aim of the research was to enhance the efficiency of the jib crane by optimising the joint startup modes of the boom and load hoisting mechanisms, which will minimise energy consumption. To achieve this aim, methods of analytical mechanics, variational calculus, and a modified particle swarm optimisation metaheuristic method were used. As a result of using these methods, the joint startup of the boom and load hoisting mechanisms was optimised. The joint motion of the crane mechanisms is represented by a dynamic model with four degrees of freedom, which accounts for the main motion of the mechanisms, as well as the elastic oscillations of the load hoisting mechanism’s drive and low-frequency oscillations of the load on a flexible suspension. Based on the dynamic model, a mathematical model in the form of a system of second-order differential equations was constructed, which was then reduced to a system of two fourth-order equations. The synthesis of the optimal startup mode of the mechanisms was carried out according to the criterion of the root mean square value of the total power of the drives, taking into account the constraints on the driving torques of the drives. The constrained optimisation problem was reduced to an unconstrained optimisation problem by developing a generalised criterion. The nonlinear problem of optimising the joint startup mode for the boom and load hoisting mechanisms of the crane was solved using a modified particle swarm optimisation metaheuristic method. As a result of the optimisation, startup modes for the boom and load hoisting mechanisms were obtained that minimise the total power of the drives and eliminate low- and high-frequency oscillations of the crane’s structural elements, which leads to increased reliability and reduced energy consumption. This mode is recommended for use in the control systems of crane boom and load hoisting mechanisms

Optimizing energy hub systems: A comprehensive analysis of integration, efficiency, and sustainability

Optimizing energy hub systems: A comprehensive analysis of integration, efficiency, and sustainability

Enhancement of Active Distribution Network Performance with Multiple Distributed Generators and DSTATCOMs using Reptile Search Algorithm

The integration of multiple DGs can introduce power quality issues and instability in the (DN) due to their intermittent and fluctuating nature. Distributed Static Compensators (DSTATCOMs) are devices that effectively manage and regulate both active and reactive powers, thereby maintaining the desired levels of reactive power in the DNs. This paper investigates the problem of determining the optimal number and placement of multiple DSTATCOMs within active DNs with multiple Distributed Generators (DGs) connected to them. To achieve the optimal sizing and allocation of multiple DSTATCOMs, a novel heuristic method called the reptile search algorithm (RSA) is introduced in this study. A combination of the RSA method and the loss sensitivity factor (LSF) are utilized to identify the optimal number, sizes, and locations of DSTATCOMs to enhance the performance of active DNs with different types of DGs and loads. The desired improvements include mitigating voltage deviation at nodes, minimizing system power losses, and alleviating overloading in feeders. The effectiveness of the algorithm is evaluated using an IEEE-33 bus DN, which is modified to incorporate different DGs and load types. To assess the efficiency of the proposed method, a modified particle swarm optimization (MPSO) algorithm is used for comparison. The results demonstrate that the RSA approach presented in this paper is robust in obtaining optimal solutions, offers fast and easy implementation, can be applied to large DNs, and outperforms the MPSO algorithm.

A Multi-Objective Approach with Modified Particle Swarm Optimization and Hybrid Energy Systems

A Multi-Objective Approach with Modified Particle Swarm Optimization and Hybrid Energy Systems