Particles In Particle Swarm Optimization Research Articles

A Multiobjective Array Beamforming Method for Arrays of Flexible Shape

High-performance beamforming incorporating multiple objectives for large-scale antenna arrays becomes increasingly important to improve the capacity and efficiency of wireless communication systems. The speed of synthesizing a desired beam pattern is critical in wireless communications systems to adapt to highly dynamic wireless channels. A modified particle swarm optimization (PSO) algorithm for synthesizing array beam patterns is proposed in this study. The initial positions of particles in PSO are designated following a Taylor distribution instead of being given uniformly distributed random values as in the classical PSO algorithm. The fitness functions are defined to include multiple objectives represented by producing multiple main lobes with customized deep and broadened nulls. Several scenarios have been established to examine the feasibility of the proposed algorithm. Moreover, the performance of the proposed algorithm is compared with those of the ones based on the classical PSO. A significant performance improvement for obtaining beamforming coefficients has been achieved. The robustness of the proposed algorithm is demonstrated further by applying it to a finite array on a curved surface for beamforming.

Hybrid Energy Microgrids: A Comparative Study of Optimization Techniques

This study examines the relative effectiveness of Genetic Algorithms (GA), Particle Swarm Optimization (PSO), Simulated Annealing (SA), and Linear Programming (LP) in optimizing hybrid energy microgrids. Drawing upon empirical data derived from the study, the research explores many facets, including economic efficacy, environmental viability, and microgrid robustness. The use of GA showcases a significant 10% decrease in overall system expenses, highlighting its efficacy in augmenting economic feasibility. PSO diligently tracks, attaining an 8% decrease, while SA and LP make significant contributions but provide somewhat lesser cost reductions at 7% and 6%, correspondingly. Within the domain of renewable energy integration, GA and PSO have emerged as frontrunners, with remarkable advancements of 12% and 10%, respectively. SA and LP provide commendable contributions, demonstrating their effectiveness in optimizing the usage of renewable energy sources inside the microgrid, as seen by their respective increases of 8% and 7%. The environmental factor, as quantified by the decrease of carbon emissions, highlights the commendable efficacy of GA and PSO, resulting in significant reductions of 15% and 12% respectively. SA and LP provide praiseworthy environmental efforts, achieving reductions of 10% and 8% respectively. The resilience index highlights the strength of GA and PSO in assessing the resilience of the microgrid, with GA showing an increase of 0.05 and PSO showing an increase of 0.04. SA and LP make a significant contribution, with increments of 0.03 and 0.02, underscoring the potential of evolutionary and swarm-based methodologies to bolster the microgrid’s resilience against disturbances. Scenario analysis effectively brings unpredictability into the operational environment of the microgrid, continually showcasing the remarkable flexibility of GA (Genetic Algorithm) and PSO (Particle Swarm Optimization) over a wide range of situations. SA and LP demonstrate consistent efficacy but with somewhat reduced flexibility. Statistical evaluations provide compelling evidence confirming the exceptional efficacy of GA and PSO in improving microgrid metrics. Ultimately, this research provides valuable perspectives on the intricate trade-offs between various optimization techniques, empowering decision-makers to choose strategies that align with specific microgrid objectives. Moreover, it contributes to the wider discussion on resilient, sustainable, and economically feasible energy infrastructures.

PriMPSO: A Privacy-Preserving Multiagent Particle Swarm Optimization Algorithm.

Centralized particle swarm optimization (PSO) does not fully exploit the potential of distributed or parallel computing and suffers from single-point-of-failure. Particularly, each particle in PSO comprises a potential solution (e.g., traveling route and neural network model parameters) which is essentially viewed as private data. Unfortunately, previously neither centralized nor distributed PSO algorithms fail to protect privacy effectively. Inspired by secure multiparty computation and multiagent system, this article proposes a privacy-preserving multiagent PSO algorithm (called PriMPSO) to protect each particle's data and enable private data sharing in a privacy-preserving manner. The goal of PriMPSO is to protect each particle's data in a distributed computing paradigm via existing PSO algorithms with competitive performance. Specifically, each particle is executed by an independent agent with its own data, and all agents jointly perform global optimization without sacrificing any particle's data. Thorough investigations show that selecting an exemplar from all particles and updating particles through the exemplar are critical operations for PSO algorithms. To this end, this article designs a privacy-preserving exemplar selection algorithm and a privacy-preserving triple computation protocol to select exemplars and update particles, respectively. Strict privacy analyses and extensive experiments on a benchmark and a realistic task confirm that PriMPSO not only protects particles' privacy but also has uniform convergence performance with the existing PSO algorithm in approximating an optimal solution.

MLNet: Metaheuristics-Based Lightweight Deep Learning Network for Cervical Cancer Diagnosis.

One of the leading causes of cancer-related deaths among women is cervical cancer. Early diagnosis and treatment can minimize the complications of this cancer. Recently, researchers have designed and implemented many deep learning-based automated cervical cancer diagnosis models. However, the majority of these models suffer from over-fitting, parameter tuning, and gradient vanishing problems. To overcome these problems, in this paper a metaheuristics-based lightweight deep learning network (MLNet) is proposed. Initially, the hyper-parameters tuning problem of convolutional neural network (CNN) is defined as a multi-objective problem. Particle swarm optimization (PSO) is used to optimally define the CNN architecture. Thereafter, Dynamically hybrid niching differential evolution (DHDE) is utilized to optimize the hyper-parameters of CNN layers. Each particle of PSO and solution of DHDE together represent the possible CNN configuration. F-score is used as a fitness function. The proposed MLNet is trained and validated on three benchmark cervical cancer datasets. On the Herlev dataset, MLNet outperforms the existing models in terms of accuracy, f-measure, sensitivity, specificity, and precision by 1.6254%, 1.5178%, 1.5780%, 1.7145%, and 1.4890%, respectively. Also, on the SIPaKMeD dataset, MLNet achieves better performance than the existing models in terms of accuracy, f-measure, sensitivity, specificity, and precision by 2.1250%, 2.2455%, 1.9074%, 1.9258%, and 1.8975%, respectively. Finally, on the Mendeley LBC dataset, MLNet achieves better performance than the competitive models in terms of accuracy, f-measure, sensitivity, specificity, and precision by 1.4680%, 1.5845%, 1.3582%, 1.3926%, and 1.4125%, respectively.

Implementation of Random Forest and Extreme Gradient Boosting in the Classification of Heart Disease using Particle Swarm Optimization Feature Selection

Heart disease is a condition that ranks as the primary cause of death worldwide. Based on available data, over 36 million people have succumbed to non-communicable diseases, and heart disease falls within the category of non-communicable diseases. This research employs a heart disease dataset from the UCI Repository, consisting of 303 instances and 14 categorical features. In this research, the data were analyzed using the classification methods XGBoost (Extreme Gradient Boosting) and Random Forest, which can be applied with PSO (Particle Swarm Optimization) as a feature selection technique to address the issue of irrelevant features. This issue can impact prediction performance on the heart disease dataset. From the results of the conducted research, the obtained values for the XGBoost (Extreme Gradient Boosting) model were 0.877, and for the Random Forest model, it was 0.874. On the other hand, in the model utilizing Particle Swarm Optimization (PSO), the obtained AUC values are 0.913 for XGBoost (Extreme Gradient Boosting) and 0.918 for Random Forest. These research results demonstrate that PSO (Particle Swarm Optimization) can enhance the AUC of heart disease prediction performance. Therefore, this research contributes to enhancing the precision and efficiency of heart disease patient data processing, which benefits heart disease diagnosis in terms of speed and accuracy.

Optimal temperature and humidity control for autonomous control system based on PSO‐BP neural networks

AbstractIn order to solve the problems of difficult control, poor stability, and low control precision in complex autonomous non‐linear systems, and some sensors have non‐linear errors in special environments. Based on the PSO (Particle Swarm Optimization) algorithm, an PSO‐BP‐PID (Particle Swarm Optimization Back Propagation neural network PID) control method and a sensor error compensation algorithm based on BP (Back Propagation) neural network are designed for optimal temperature and humidity control and sensor error compensation in the autonomous greenhouse system. The error between the average temperature value and the target value after steady state is 0.5°C, and the error between the average humidity value and the target value is 1% RH. The results show that the control method can effectively compensate the non‐linear error of the sensor and improve the performance of the control system in a complex environment, which is suitable for the stable and control of actuators in autonomous systems. The error of temperature and humidity sensor is compensated by BP neural network; PSO (Particle Swarm Optimization) was used to optimize the BP‐PID parameters of the automatic greenhouse system.

Elite archives-driven particle swarm optimization for large scale numerical optimization and its engineering applications

Particle swarm optimization (PSO) is a very simple and effective metaheuristic algorithm. Search operators with similar behavior may lead to the loss of diversity in the search space. All particles in PSO have the same and single search strategy. Therefore, PSO may suffer from premature convergence in solving complex multimodal problems. To improve the global search ability of PSO, this paper reports an elite archives-driven particle swarm optimization (EAPSO). Note that, EAPSO only needs population size and terminal condition for performing the search task, which can distinguish EAPSO over the other reported variants of PSO. In addition, EAPSO has a clear structure, which first builds three types of elite archives to save three different hierarchical particles. Then, six learning strategies for updating the positions of particles are designed by reusing these particles of the three elite archives. To verify the performance of EAPSO, EAPSO is employed to solve CEC 2013 test suite with dimensions 30-100 and three constrained engineering problems. Experimental results show that EAPSO outperforms the compared seven powerful variants of PSO on more than half of test functions and offers highly competitive optimal solutions on the considered engineering problems. That is, experimental results support the validity of the improved strategies and prove the superiority of EAPSO in solving complex multimodal problems. The source code of EAPSO can be found by the following website: https://github.com/jsuzyy/EAPSO.

Tomato Leaf Disease Segmentation Using Clustering Method Based on FATPSO with Multi Features

Early blight is one of diseases that infects tomato leaves. This disease causes a decrease in the production of tomato plants. The early detection of this diseases is very important to maintain the tomato production. Monitoring tomato leaves health manually in large area is very time-consuming and inefficient. The drones and computer vision technology give an alternative in solving this problem. One of the important steps in detecting the tomato leaf disease based on computer vision is the segmentation area of the tomato leaf into the healthy and diseased tomato leaf. The K-means clustering offers an image segmentation method that is simple, fast and works unsupervised. However, the solutions of the K-means clustering often be trapped into the local optimum. The Particle Swarm Optimization (PSO) offers a solution of this problem. However, the performance of PSO depends on the particle velocity of the PSO, if the particle velocity is not determined precisely then the PSO will converge prematurely. Fuzzy Adaptive Turbulence Particle Swarm Optimization (FATPSO) is able to control minimum velocity the PSO particles adaptively for overcoming the premature convergence problem in PSO. The good features from image will increase the accuracy of machine learning algorithm. For this reason, these papers the tomato leaf segmentation based on the FATPSO clustering algorithm with multi features. The fitness function of FATPSO uses an objective function of K-means. The experiments use the image taken manually from garden tomatoes. The images have good quality but they have many varieties in size and color. The next research should be considered to use the image taken by drone to guarantee a robust method of image quality produced by drones. The experimental results show that the FATPSO clustering algorithm with multi features has a better performance than the PSO algorithm with multi feature in the tomato leaf disease segmentation

Hybrid Optimization Based PID Controller Design for Unstable System

PID controllers play an important function in determining tuning parameters in any process sector to deliver optimal and resilient performance for nonlinear, stable and unstable processes. The effectiveness of the presented hybrid metaheuristic algorithms for a class of time-delayed unstable systems is described in this study when applicable to the problems of PID controller and Smith PID controller. The Direct Multi Search (DMS) algorithm is utilised in this research to combine the local search ability of global heuristic algorithms to tune a PID controller for a time-delayed unstable process model. A Metaheuristics Algorithm such as, SA (Simulated Annealing), MBBO (Modified Biogeography Based Optimization), BBO (Biogeography Based Optimization), PBIL (Population Based Incremental Learning), ES (Evolution Strategy), StudGA (Stud Genetic Algorithms), PSO (Particle Swarm Optimization), StudGA (Stud Genetic Algorithms), ES (Evolution Strategy), PSO (Particle Swarm Optimization) and ACO (Ant Colony Optimization) are used to tune the PID controller and Smith predictor design. The effectiveness of the suggested algorithms DMS-SA, DMS-BBO, DMS-MBBO, DMS-PBIL, DMS-StudGA, DMS-ES, DMS-ACO, and DMS-PSO for a class of dead-time structures employing PID controller and Smith predictor design controllers is illustrated using unit step set point response. When compared to other optimizations, the suggested hybrid metaheuristics approach improves the time response analysis when extended to the problem of smith predictor and PID controller designed tuning.

Hybrid Particle Swarm Optimization-based Modeling of Wireless Sensor Network Coverage Optimization

To address the problem of insufficient coverage of WSN and poor network coverage in obstacle environments, the study proposes an improved particle swarm optimization (PSO) combined with a hybrid grey wolf algorithm. The speed and position of the PSO particle's search for superiority are enhanced through the guiding nature of the superior wolf in the grey wolf optimization (GWO), thus the convergence speed and search precision are improved. Based on this, the study applies the improved PSO to a wireless sensor networks (WSO) coverage optimization model and uses model comparison to test the effectiveness and superiority of the algorithm. According to the results, the node network coverage of PSO, genetic algorithm (GA), data envelopment analysis (DEA), GWO, and grey wolf particle swarm optimization (GWPSO) reach 85.97%, 87.24%, 88.76%, 89.31%, and 91.05% respectively in the trapezoidal obstacle environment. And the node network coverage of the research-designed GWPSO algorithm reaches the highest value of its kind. This shows that the research-designed GWPSO has superior performance in the optimization control of sensor coverage deployment compared with similar algorithms. The design provides a new path for optimizing wireless sensor node network coverage.

Kinematic draping simulation optimization of a composite B-pillar geometry using particle swarm optimization

The present work presents an algorithmic approach to determine the optimal starting point for any complex geometry draping processes. The time-efficient Kinematic Draping Simulation (KDS) is used to assess the drapability of a geometry depending on many different starting points. The optimization problem is then solved by applying Particle Swarm Optimization (PSO). The proposed methodology is applied to and validated with complex geometry and a common part of the automobile industry: the B-Pillar geometry. The results show that the PSO algorithm may improve random search up to 78 times. After several experiments, PSO particles have discrete coordinates and are located at optimum global and local regions most of the time, leading to solutions for complex objective functions. The global solution is such that the starting point is located near the geometrical centre of the B-Pillar. The novelty of the work is evident: it uses optimization for a real engineering application, and it draws pattern-related conclusions for other geometries. Experimental results are shown to be consistent with simulation results.

Camera calibration based on improved differential evolution particle swarm

Aiming at the problem of camera calibration with multiple parameters, this paper proposes the fusion optimization algorithm of improved differential evolution and particle swarm in the calibration of the camera. Adaptive judgment factors is Introduced to control the improvement of differential evolution during each iteration (IDE) algorithm and particle swarm optimization (PSO) algorithm solve the multiple parameters of traditional camera calibration algorithm. The proposed algorithm can ensure the diversity and effectiveness of individual evolution of the population. Experimental results show that the algorithm has excellent global search capabilities and local optimizations ability. It can accurately calibrate the camera. The convergence precision, speed and robustness performance significantly is superior to PSO (Particle swarm optimization algorithm), DE (differential evolution algorithm), GA (Genetic algorithm) and Zhang’s method. It improves the precision and speed of the proposed calibration method. The root mean square error of the calibration algorithm proposed in this paper is only 0.182, the calibration error result is smaller than other several algorithms. The reprojection error of our method is 0.05938 (Ex/pixel) and 0.02988 (Ey/pixel). It is smaller than GA, PSO, DE, and Zhang’s method. So, the algorithm performance is excellent.

SOLVING MULTICAST ROUTING PROBLEM USING PARTICLE SWARM OPTIMIZATION

In a given network there exist many paths from source to destination, among all selected paths finding the optimal shortest path is a challenging task. In this research paper, we proposed a new concept of particle swarm optimization technique, called swarm intelligence, to solve the multicast routing problem to find out the optimal route associated with a network, here we also used triangular fuzzy number tools to encode particles in PSO (particle swarm optimization), first it breaks the network into small spaces and from the small space it computes the optimal path.

Stochastic stability analysis of composite dynamic system for particle swarm optimization

The stochastic stability analysis for particle swarm optimization (PSO) has met formidable challenges due to the intricate influences on particles’ motion from collaboration capability, cognitive competence, selective randomness and environmental complexity. This paper firstly regards the above-mentioned factors as forces acting on particles of PSO: interaction, damping, randomness and external forces. Based on this mechanical analogy, a novel stochastic composite dynamic model (SCDM) for PSO is proposed which can be classified into two parts: diffusion process and drift process. The diffusion process can not be easily obtained by analytical analysis, but its approximate distribution can be experimentally justified to obey Gaussian model, and the drift process is partly determined by its optimization function.To analyze the influence on PSO from the external environment, linear and quadratic optimization functions are chose to simplify the analysis. And the SCMD’s stability is discussed via constructing the responding Lyapunov functions. We conclude that the gradient of linear function only decides the uniform ultimate bound while the gradient of quadratic function affects the stable conditions besides the uniform ultimate bound. Furthermore, the stable point of PSO is proved to be the optimum of quadratic function. The convergence time is also discussed to measure PSO’s performance.

Comparative performance analysis of Fuzzy Logic and Particle Swarm Optimization (PSO) techniques for image quality improvement: With special emphasis to old and distorted folk paintings

Cultural artwork has always held an important place in the society. These artworks hold within themselves a treasure trove of information about one’s society/community, their way of living and their history and many more things. However such artworks are not properly maintained and need to be carefully preserved. Preserving them digitally can give them a long life and the digital image processing techniques can be used to restore or enhance the quality of degraded artworks. This paper presents a comparison of two such techniques, PSO (Particle Swarm Optimization) and Fuzzy logic technique for enhancement of the distorted images. Image parameters like PSNR (Peak-to-Signal Noise Ratio), SSIM (Structure Similarity Index), AMBE (Absolute Mean Brightness Error), r (Pearson Correlation Coefficient), NMSE (Normalized Mean Square Error), IEF (Image Enhancement Factor) and CPP (Contrast per Pixel) have been tabulated to compare the performance of both the techniques. MATLAB software has been used for implementation of the algorithms and calculations of the parameters.

Research on Combined Forecasting of Cooling Load Based on Advanced Cuckoo Search and Improved Particle Swarm Optimization

This study aims to improve the real-time accuracy of cooling load forecasting for heating, ventilating and air-conditioning systems (HVAC). This article takes the cooling load in a study room in Qingdao, China, which has been put into use for the first time, as the research object, and establishes a TRNSYS simulation platform to obtain sufficient load data. After using the mean influence value (MIV) and Spearman correlation coefficient to screen the characteristic variables, a hybrid algorithm (CS-CPSO) based on cuckoo search (CS) and particle swarm optimization (PSO) is proposed. Firstly, the iterative extremum is introduced to PSO, secondly, mechanism of levy random flight to generate random new nest in CS is used to initialize PSO particles adaptively, Finally, the optimization algorithm is applied to optimize the back propagation (BP) and support vector regression (SVR) load training models (WBP, WSVR, RBP, RSVR) of the working day (W) and rest day (R), respectively. The maximum grey correlation coefficient is utilized to establish the both models (CS-CPSO-CW, CS-CPSO-CR) of the working day (W) and rest day (R) based on CS-CPSO. In this way, the forecasting results are optimized and then compared with the regression prediction method. The analysis shows that the accuracy of the optimized BP model and SVR model are improved and fully considering the differences, the accuracy of the cooling load prediction is effectively promoted by separately, optimal selection between the prediction values of advanced models (CS-CPSO-WBP, CS-CPSO-WSVR and CS-CPSO-RBP, CS-CPSO-RSVR) gives full play to each algorithm’s advantages and makes up for their shortcomings, and it greatly increases reliability and improves accuracy, which in turn provides the basis for the optimal plan, control, and operation of the HVAC.

Event-Based Under-Frequency Load Shedding Scheme in a Standalone Power System

Under-frequency load shedding (UFLS) prevents a power grid from a blackout when a severe contingency occurs. UFLS schemes can be classified into two categories—event-based and response-driven. A response-driven scheme utilizes 81L relays with pre-determined settings while an event-based scheme develops a pre-specified look-up table. In this work, an event-based UFLS scheme is presented for use in an offshore standalone power grid with renewables to avoid cascading outages due to low frequency protection of wind power generators and photovoltaic arrays. Possible “N-1” and “N-2” forced outages for peak and off-peak load scenarios in summer and winter are investigated. For each forced outage event, the total shed load is minimized and the frequency nadir is maximized using particle swarm optimization (PSO). In order to reduce the computation time, initialization and parallel computing are implemented using MATLAB/Simulink because all forced outage events and all particles in PSO are mutually independent. A standalone 38-bus power grid with two wind turbines of 2 × 2 MW and photovoltaics of 7.563 MW was studied. For each event, the proposed method generally obtains a result with a smaller shed load and a smaller overshoot frequency than the utility and existing methods. These simulation results verify that the proposed method is practically applicable in a standalone power system with penetration of renewables.

A new path plan method based on hybrid algorithm of reinforcement learning and particle swarm optimization

PurposeTo solve the path planning problem of the intelligent driving vehicular, this paper designs a hybrid path planning algorithm based on optimized reinforcement learning (RL) and improved particle swarm optimization (PSO).Design/methodology/approachFirst, the authors optimized the hyper-parameters of RL to make it converge quickly and learn more efficiently. Then the authors designed a pre-set operation for PSO to reduce the calculation of invalid particles. Finally, the authors proposed a correction variable that can be obtained from the cumulative reward of RL; this revises the fitness of the individual optimal particle and global optimal position of PSO to achieve an efficient path planning result. The authors also designed a selection parameter system to help to select the optimal path.FindingsSimulation analysis and experimental test results proved that the proposed algorithm has advantages in terms of practicability and efficiency. This research also foreshadows the research prospects of RL in path planning, which is also the authors’ next research direction.Originality/valueThe authors designed a pre-set operation to reduce the participation of invalid particles in the calculation in PSO. And then, the authors designed a method to optimize hyper-parameters to improve learning efficiency of RL. And then they used RL trained PSO to plan path. The authors also proposed an optimal path evaluation system. This research also foreshadows the research prospects of RL in path planning, which is also the authors’ next research direction.

Restoration and quality improvement of distorted tribal artworks using Particle Swarm Optimization (PSO) technique along with nonlinear filtering

Cultural artworks that are not properly cared for, degrades over time. However modern image processing techniques proves to be a boon for such artworks. Various techniques have been developed time to time for different aspects of image processing like noise removal, improving brightness and image quality, etc. One of such techniques is PSO (Particle Swarm Optimization) that can be used for image restoration. In this paper a combination of median filter and PSO has been used for the quality improvement of the distorted tribal artworks and the results are compared with few HE (Histogram Equalization) techniques like the CLAHE, BBHE and DSIHE. The comparison is done on the basis of various image quality assessment parameters, namely PSNR, AMBE, NMSE, CPP and MSE.

Economic Load Dispatch

ELD or Economic load dispatch is an online process of allocating generating among the available generating units to minimize the total generating cost and satisfy the equality and inequality constraint. ELD means the real and reactive power of the generator vary within the certain limits and fulfils theload demand with less fuel cost. There are some traditional methods for = 1; 2; :::;N) isgiven as Vi=[Vi;1; Vi;2; :::; Vi;D]. The index ivaries from solving ELD include lambda irritation method, Newton-Raphson method, Gradient method, etc. All these traditional algorithms need the incremental fuel cost curves of the generators to be increasing monotonically or piece-wise linear. But in practice the input-output characteristics of a generator are highly non-linear leading to a challenging non-convex optimization problem. Methods like artificial intelligence, DP (dynamic programming), GA (genetic algorithms), and PSO (particle swarm optimization), ALO ( ant-lion optimization), solve non convex optimization problems in an efficient manner and obtain a fast and near global and optimum solution. In this project ELD problem has been solved using Lambda-Iterative technique, ALO (ant-lion Optimization) and PSO (Particle Swarm Optimization) and the results have been compared. All the analyses have been made in MATLAB environment