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Showing papers on "Particle swarm optimization published in 2022"


Journal ArticleDOI
TL;DR: The experimental results, along with statistical analysis, reveal the effectiveness of HBA for solving optimization problems with complex search-space, as well as, its superiority in terms of convergence speed and exploration–exploitation balance, as compared to other methods used in this study.

341 citations


Journal ArticleDOI
TL;DR: In this paper , a new metaheuristic optimization algorithm called Honey Badger Algorithm (HBA) is proposed, which is inspired from the intelligent foraging behavior of honey badger, to mathematically develop an efficient search strategy for solving optimization problems.

281 citations


Journal ArticleDOI
TL;DR: Oy et al. as discussed by the authors proposed a new bio-inspired and population-based optimization algorithm named Ebola Optimization Search Algorithm (EOSA) based on the propagation mechanism of the Ebola virus disease.
Abstract: Nature computing has evolved with exciting performance to solve complex real-world combinatorial optimization problems. These problems span across engineering, medical sciences, and sciences generally. The Ebola virus has a propagation strategy that allows individuals in a population to move among susceptible, infected, quarantined, hospitalized, recovered, and dead sub-population groups. Motivated by the effectiveness of this strategy of propagation of the disease, a new bio-inspired and population-based optimization algorithm is proposed. This study presents a novel metaheuristic algorithm named Ebola Optimization Search Algorithm (EOSA) based on the propagation mechanism of the Ebola virus disease. First, we designed an improved SIR model of the disease, namely SEIR-HVQD: Susceptible (S), Exposed (E), Infected (I), Recovered (R), Hospitalized (H), Vaccinated (V), Quarantine (Q), and Death or Dead (D). Secondly, we represented the new model using a mathematical model based on a system of first-order differential equations. A combination of the propagation and mathematical models was adapted for developing the new metaheuristic algorithm. To evaluate the performance and capability of the proposed method in comparison with other optimization methods, two sets of benchmark functions consisting of forty-seven (47) classical and thirty (30) constrained IEEE-CEC benchmark functions were investigated. The results indicate that the performance of the proposed algorithm is competitive with other state-of-the-art optimization methods based on scalability, convergence, and sensitivity analyses. Extensive simulation results show that the EOSA outperforms popular metaheuristic algorithms such as the Particle Swarm Optimization Algorithm (PSO), Genetic Algorithm (GA), and Artificial Bee Colony Algorithm (ABC). Also, the algorithm was applied to address the complex problem of selecting the best combination of convolutional neural network (CNN) hyperparameters in the image classification of digital mammography. Results obtained showed the optimized CNN architecture successfully detected breast cancer from digital images at an accuracy of 96.0%. The source code of EOSA is publicly available at https://github.com/NathanielOy/EOSA_Metaheuristic .

186 citations



Journal ArticleDOI
TL;DR: In this article , a 3D medical watermarking algorithm based on wavelet transform is proposed, which employs the principal component analysis (PCA) transform to reduce the data dimension, which can minimize the error between extracted components and the original data in the mean square sense.
Abstract: In a telemedicine diagnosis system, the emergence of 3D imaging enables doctors to make clearer judgments, and its accuracy also directly affects doctors’ diagnosis of the disease. In order to ensure the safe transmission and storage of medical data, a 3D medical watermarking algorithm based on wavelet transform is proposed in this paper. The proposed algorithm employs the principal component analysis (PCA) transform to reduce the data dimension, which can minimize the error between the extracted components and the original data in the mean square sense. Especially, this algorithm helps to create a bacterial foraging model based on particle swarm optimization (BF-PSO), by which the optimal wavelet coefficient is found for embedding and is used as the absolute feature of watermark embedding, thereby achieving the optimal balance between embedding capacity and imperceptibility. A series of experimental results from MATLAB software based on the standard MRI brain volume dataset demonstrate that the proposed algorithm has strong robustness and make the 3D model have small deformation after embedding the watermark.

167 citations


Journal ArticleDOI
TL;DR: In this paper , a parameter adaptation-based ant colony optimization algorithm based on particle swarm optimization (PSO) algorithm with the global optimization ability, fuzzy system with the fuzzy reasoning ability and 3-Opt algorithm with local search ability, namely PF3SACO is proposed to improve the optimization ability and convergence, avoid to fall into local optimum.

103 citations


Journal ArticleDOI
TL;DR: Particle swarm optimization (PSO) is one of the most well-regarded swarm-based algorithms in the literature as mentioned in this paper , and many researchers have been modifying it resulting in a large number of PSO variants with either slightly or significantly better performance.
Abstract: Particle swarm optimization (PSO) is one of the most well-regarded swarm-based algorithms in the literature. Although the original PSO has shown good optimization performance, it still severely suffers from premature convergence. As a result, many researchers have been modifying it resulting in a large number of PSO variants with either slightly or significantly better performance. Mainly, the standard PSO has been modified by four main strategies: modification of the PSO controlling parameters, hybridizing PSO with other well-known meta-heuristic algorithms such as genetic algorithm (GA) and differential evolution (DE), cooperation and multi-swarm techniques. This paper attempts to provide a comprehensive review of PSO, including the basic concepts of PSO, binary PSO, neighborhood topologies in PSO, recent and historical PSO variants, remarkable engineering applications of PSO, and its drawbacks. Moreover, this paper reviews recent studies that utilize PSO to solve feature selection problems. Finally, eight potential research directions that can help researchers further enhance the performance of PSO are provided.

99 citations


Journal ArticleDOI
TL;DR: Simulation results show that APSO-GA can easily find feasible solutions particularly when the number of switching angles is high; however, the rest of all stuck at local minima due to less exploration capability.
Abstract: In this article, a hybrid asynchronous particle swarm optimization-genetic algorithm (APSO-GA) is proposed for the removal of unwanted lower order harmonics in the cascaded H-bridge multilevel inverter (MLI). The APSO-GA is applicable to all levels of MLI. In the proposed method, ring topology based APSO is hybrid with GA. APSO is applied for exploration and GA is used for the exploitation of the best solutions. In this article, optimized switching angles are calculated using APSO-GA for seven-level and nine-level inverter, and results are compared with GA, PSO, APSO, bee algorithm (BA), differential evolution (DE), synchronous PSO, and teaching–learning-based optimization (TLBO). Simulation results show that APSO-GA can easily find feasible solutions particularly when the number of switching angles is high; however, the rest of all stuck at local minima due to less exploration capability. Also, the APSO-GA is less computational complex than GA, BA, TLBO, and DE algorithms. Experimentally, the performance of APSO-GA is validated on a single-phase seven-level inverter.

86 citations


Journal ArticleDOI
TL;DR: In this article , a new reinforcement learning (RL)-based control approach that uses the Policy Iteration (PI) and a metaheuristic Grey Wolf Optimizer (GWO) algorithm to train the Neural Networks (NNs) is presented.

84 citations



Journal ArticleDOI
TL;DR: Zhang et al. as mentioned in this paper investigated the evolution process of a particle swarm optimization algorithm with care, and then proposed to incorporate more dynamic information into it for avoiding accuracy loss caused by premature convergence without extra computation burden.
Abstract: High-dimensional and sparse (HiDS) matrices are frequently found in various industrial applications. A latent factor analysis (LFA) model is commonly adopted to extract useful knowledge from an HiDS matrix, whose parameter training mostly relies on a stochastic gradient descent (SGD) algorithm. However, an SGD-based LFA model's learning rate is hard to tune in real applications, making it vital to implement its self-adaptation. To address this critical issue, this study firstly investigates the evolution process of a particle swarm optimization algorithm with care, and then proposes to incorporate more dynamic information into it for avoiding accuracy loss caused by premature convergence without extra computation burden, thereby innovatively achieving a novel position-transitional particle swarm optimization (P 2 SO) algorithm. It is subsequently adopted to implement a P 2 SO-based LFA (PLFA) model that builds a learning rate swarm applied to the same group of LFs. Thus, a PLFA model implements highly efficient learning rate adaptation as well as represents an HiDS matrix precisely. Experimental results on four HiDS matrices emerging from real applications demonstrate that compared with an SGD-based LFA model, a PLFA model no longer suffers from a tedious and expensive tuning process of its learning rate, and it can achieve even higher prediction accuracy for missing data of an HiDS matrix. On the other hand, compared with state-of-the-art adaptive LFA models, a PLFA model's prediction accuracy and computational efficiency are highly competitive. Hence, it has high potential in addressing real industrial issues.

Journal ArticleDOI
TL;DR: In this paper , an improved quantum evolutionary algorithm (QEA) based on the niche co-evolution strategy and enhanced particle swarm optimization (PSO) was proposed to allocate the flights to suitable gates within different periods.
Abstract: With the continuous and rapid growth of air traffic demand, gate resource becomes a major bottleneck restricting airport development. Rational gate allocation is regarded as one of the most important means to solve this bottleneck. In this paper, in order to comprehensively considere different stakeholders, a three-objective gate allocation model is to consider a wider scope, in which the minimizing passenger walking distances, the most balanced idle time of each gate and the best full use of large gate are optimized simultaneously to improve the practical efficiency. To efficiently solve this model, an improved quantum evolutionary algorithm (QEA) based on the niche co-evolution strategy and enhanced particle swarm optimization (PSO), namely IPOQEA is designed. An IPOQEA-based gate allocation method is proposed to allocate the flights to suitable gates within different periods. Finally, the actual operation data of Baiyun Airport is used to validate the effectiveness of the proposed method. Comparison results show that the constructed model can address the passenger walking distances, robustness and costs in airport management. Moreover, the IPOQEA has better optimization ability in solving gate allocation problem. Therefore, the proposed gate allocation method has great potential for practical engineering since it can easily make decisions for airport managers.

Journal ArticleDOI
TL;DR: In this article , a hybrid metaheuristic optimization algorithm that combines particle filter (PF) and particle swarm optimization (PSO) algorithms is presented. But this algorithm is not suitable for the optimal tuning of integral-type servo controllers.
Abstract: This article presents a hybrid metaheuristic optimization algorithm that combines particle filter (PF) and particle swarm optimization (PSO) algorithms. The new PF–PSO algorithm consists of two steps: the first generates randomly the particle population;and the second zooms the search domain. An application of this algorithm to the optimal tuning of proportional-integral-fuzzy controllers for the position control of a family of integral-type servo systems is then presented as a second contribution. The reduction in PF–PSO algorithm's cost function allows for reduced energy consumption of the fuzzy control system. A comparison with other metaheuristic algorithms on canonical test functions and experimental results are presented at the end of this article.

Journal ArticleDOI
13 Sep 2022-Energies
TL;DR: The improved model (IPSO-CNN-BiLSTM) proposes a convolution neural network (CNN) to process EIS data which can not only extract the key points but also simplifies the complexity of manual feature extraction.
Abstract: The state of health (SOH) is critical to the efficient and reliable use of lithium-ion batteries (LIBs). Recently, the SOH estimation method based on electrochemical impedance spectroscopy (EIS) has been proven effective. In response to different practical applications, two models for SOH estimation are proposed in this paper. Aiming at based on the equivalent circuit model (ECM) method, a variety of ECMs are proposed. Used EIS to predict the ECM, the results show that the improved method ensures the correctness of the ECM and improves the estimation results of SOH. Aiming at a data-driven algorithm, proposes a convolution neural network (CNN) to process EIS data which can not only extract the key points but also simplifies the complexity of manual feature extraction. The bidirectional long short-term memory (BiLSTM) model was used for serial regression prediction. Moreover, the improved Particle Swarm Optimization (IPSO) algorithm is proposed to optimize the model. Comparing the improved model (IPSO-CNN-BiLSTM) with the traditional PSO-CNN-BiLSTM, CNN-BiLSTM and LSTM models, the prediction results are improved by 13.6%, 93.75% and 94.8%, respectively. Besides that, the two proposed methods are 27% and 35% better than the existing gaussion process regression (GPR) model, which indicates that the proposed improved methods are more flexible for SOH estimation with higher precision.

Journal ArticleDOI
TL;DR: In this article , a Mixed-integer Linear Programming (MILP) model is proposed to find the best sequence of routes for each ambulance and minimize the latest service completion time (SCT) as well as the number of patients whose condition gets worse because of receiving untimely medical services.
Abstract: <p style='text-indent:20px;'>The shortage of relief vehicles capacity is a common issue throughout disastrous situations due to the abundance of injured people who need urgent medical aid. Hence, ambulances fleet management is highly important to save as many injured individuals as possible. In this regard, the present paper defines different patient groups based on their needs and characteristics. In order to provide the affected people with proper and timely medical aid, changes in their health status are also considered. A Mixed-integer Linear Programming (MILP) model is proposed to find the best sequence of routes for each ambulance and minimize the latest service completion time (SCT) as well as the number of patients whose condition gets worse because of receiving untimely medical services. Non-dominated Sorting Genetic Algorithm II (NSGA-II) and Multi-Objective Particle Swarm Optimization (MOPSO) are used to find high-quality solutions over a short time. In the end, Lorestan province, Iran, is considered as a case study to assess the model's performance and analyze the sensitivity of solutions with respect to the major parameters, which results in insightful managerial suggestions.</p>

Journal ArticleDOI
TL;DR: A novel particle swarm optimization (PSO) algorithm for dynamic adjustment of the FO PIλDµ controller parameters, which has the advantages of a small overshoot, short adjustment time, precise control, and strong anti-disturbance control.
Abstract: In this paper, a new fractional-order (FO) PIλDµ controller is designed with the desired gain and phase margin for the automatic rudder of underactuated surface vessels (USVs). The integral order λ and the differential order μ are introduced in the controller, and the two additional adjustable factors make the FO PIλDµ controller have better accuracy and robustness. Simulations are carried out for comparison with a ship’s digital PID autopilot. The results show that the FO PIλDµ controller has the advantages of a small overshoot, short adjustment time, and precise control. Due to the uncertainty of the model parameters of USVs and two extra parameters, it is difficult to compute the parameters of an FO PIλDµ controller. Secondly, this paper proposes a novel particle swarm optimization (PSO) algorithm for dynamic adjustment of the FO PIλDµ controller parameters. By dynamically changing the learning factor, the particles carefully search in their own neighborhoods at the early stage of the algorithm to prevent them from missing the global optimum and converging on the local optimum, while at the later stage of evolution, the particles converge on the global optimal solution quickly and accurately to speed up PSO convergence. Finally, comparative experiments of four different controllers under different sailing conditions are carried out, and the results show that the FO PIλDµ controller based on the IPSO algorithm has the advantages of a small overshoot, short adjustment time, precise control, and strong anti-disturbance control.

Journal ArticleDOI
TL;DR: In this paper , a multi-stage grey wolf optimizer (MGWO) was proposed to improve the performance of the basic GWO by dividing the search process into three stages and using different population updating strategies.

Journal ArticleDOI
TL;DR: In this paper , a deep learning neural network model based on LSTM networks and particle swarm optimization (PSO) is proposed to improve the forecast accuracy and lead time of flooding.
Abstract: Flood forecasting is an essential non-engineering measure for flood prevention and disaster reduction. Many models have been developed to study the complex and highly random rainfall-runoff process. In recent years, artificial intelligence methods, such as the artificial neural network (ANN), have attempted to construct rainfall-runoff models. The more advanced deep learning methods of long short-term memory (LSTM) network have been proved to better predict hydrological time series. However, the selection of LSTM hyperparameters in the past mostly relied on the experience of the staff, which often led to failure to achieve the best performance. The aim of this study is to develop a method to improve flood forecast accuracy and lead time. A deep learning neural network model based on LSTM networks and particle swarm optimization (PSO) is proposed in this paper. The PSO algorithm was used to optimize the LSTM hyperparameter to improve the ability to learn data sequence features. The model focuses on the Jingle Watershed in the Fenhe River and the Lushi Watershed in the Luohe River and was used to predict flood processes using rainfall and runoff observation data from stations in the watersheds. We evaluated the performance of the model with the Nash Sutcliffe efficiency coefficient, root mean square error, and bias. The results show that the PSO-LSTM model outperforms the M-EIES, ANN, PSO-ANN, and LSTM at all stations in the watersheds. The PSO-LSTM model improves the flood forecasting accuracy at different lead times, especially for those exceeding 6 h, and has higher prediction accuracy and stability. The PSO-LSTM model could be used to improve accuracy in short-term flood forecast applications.

Journal ArticleDOI
TL;DR: In this paper, a modified structure of the tilted integral derivative (TID) controller is developed for the load frequency control issue of a multi-area interconnected multi-source power system and a new optimization algorithm known as Archimedes optimization algorithm (AOA) is used to fine-tune the proposed ID-T controller parameters.

Journal ArticleDOI
15 Jan 2022-Energy
TL;DR: In this article , a joint planning model of distributed generations and energy storage is proposed for an active distribution network by using a bi-level programming approach, where the upper level aims to seek the optimal location and capacity of DGs and the lower level optimizes the operation of energy storage devices.

Journal ArticleDOI
TL;DR: In this paper , a dynamic-neighborhood-based switching PSO (DNSPSO) algorithm is proposed, where a new velocity updating mechanism is designed to adjust the personal best position and the global best position according to a distance-based dynamic neighborhood to make full use of the population evolution information among the entire swarm.
Abstract: In this article, a dynamic-neighborhood-based switching PSO (DNSPSO) algorithm is proposed, where a new velocity updating mechanism is designed to adjust the personal best position and the global best position according to a distance-based dynamic neighborhood to make full use of the population evolution information among the entire swarm. In addition, a novel switching learning strategy is introduced to adaptively select the acceleration coefficients and update the velocity model according to the searching state at each iteration, thereby contributing to a thorough search of the problem space. Furthermore, the differential evolution algorithm is successfully hybridized with the particle swarm optimization (PSO) algorithm to alleviate premature convergence. A series of commonly used benchmark functions (including unimodal, multimodal, and rotated multimodal cases) is utilized to comprehensively evaluate the performance of the DNSPSO algorithm. The experimental results demonstrate that the developed DNSPSO algorithm outperforms a number of existing PSO algorithms in terms of the solution accuracy and convergence performance, especially for complicated multimodal optimization problems.

Journal ArticleDOI
01 Mar 2022-Energy
TL;DR: In this paper , the authors proposed an optimized forecasting model-an extreme learning machine (ELM) model coupled with the heuristic Kalman filter (HKF) algorithm to forecast the capacity of supercapacitors.

Journal ArticleDOI
TL;DR: In this article, a hybrid forecasting model is developed by using the decomposition strategy, nonlinear weighted combination, and two deep learning models to overcome the drawbacks of the linear weighted combination and further enhance wind power forecasting accuracy and stability.

Journal ArticleDOI
TL;DR: Zhang et al. as discussed by the authors compared the performance of five popular machine learning methods, namely, particle swarm optimization-extreme learning machine (PSO-ELM), PSO-KELM, PSO, SVM and LSTM, in the prediction of reservoir landslide displacement.

Journal ArticleDOI
TL;DR: This paper proposes a method based on Hybrid Particle Swarm Optimization in order to design a WADC that ensures robustness to power system operating uncertainties, time delays variations on the WadC channels and the permanent failure of the W ADC communication channels.
Abstract: The presence of low-frequency and low-dampened oscillation modes can compromise the operating stability of power systems. Recent research has shown that the use of phasor measurement units data to compose a wide-area damping controller (WADC) has been shown to be effective in mitigating such oscillation modes but the possibility of loss of communication channels due to cyber-attacks or failures can compromise the proper operation of this controller. Besides, traditional control design methods present difficulties for the WADC control design. This article proposes a method based on hybrid particle swarm optimization in order to design a WADC that ensures robustness to power system-operating uncertainties, time delays variations on the WADC channels, and the permanent failure of the WADC communication channels. Modal analysis and nonlinear time-domain simulations were conducted in the IEEE 68-bus power system considering a set of scenarios.

Journal ArticleDOI
TL;DR: In this paper , a hybrid asynchronous particle swarm optimization-genetic algorithm (APSO-GA) is proposed for the removal of unwanted lower order harmonics in the cascaded H-bridge multilevel inverter (MLI).
Abstract: In this article, a hybrid asynchronous particle swarm optimization-genetic algorithm (APSO-GA) is proposed for the removal of unwanted lower order harmonics in the cascaded H-bridge multilevel inverter (MLI). The APSO-GA is applicable to all levels of MLI. In the proposed method, ring topology based APSO is hybrid with GA. APSO is applied for exploration and GA is used for the exploitation of the best solutions. In this article, optimized switching angles are calculated using APSO-GA for seven-level and nine-level inverter, and results are compared with GA, PSO, APSO, bee algorithm (BA), differential evolution (DE), synchronous PSO, and teaching–learning-based optimization (TLBO). Simulation results show that APSO-GA can easily find feasible solutions particularly when the number of switching angles is high; however, the rest of all stuck at local minima due to less exploration capability. Also, the APSO-GA is less computational complex than GA, BA, TLBO, and DE algorithms. Experimentally, the performance of APSO-GA is validated on a single-phase seven-level inverter.

Journal ArticleDOI
TL;DR: Wang et al. as mentioned in this paper proposed a new three-phase hybrid feature selection algorithm based on correlation-guided clustering and particle swarm optimization (HFS-C-P) to tackle the above two problems at the same time.
Abstract: The “curse of dimensionality” and the high computational cost have still limited the application of the evolutionary algorithm in high-dimensional feature selection (FS) problems. This article proposes a new three-phase hybrid FS algorithm based on correlation-guided clustering and particle swarm optimization (PSO) (HFS-C-P) to tackle the above two problems at the same time. To this end, three kinds of FS methods are effectively integrated into the proposed algorithm based on their respective advantages. In the first and second phases, a filter FS method and a feature clustering-based method with low computational cost are designed to reduce the search space used by the third phase. After that, the third phase applies oneself to finding an optimal feature subset by using an evolutionary algorithm with the global searchability. Moreover, a symmetric uncertainty-based feature deletion method, a fast correlation-guided feature clustering strategy, and an improved integer PSO are developed to improve the performance of the three phases, respectively. Finally, the proposed algorithm is validated on 18 publicly available real-world datasets in comparison with nine FS algorithms. Experimental results show that the proposed algorithm can obtain a good feature subset with the lowest computational cost.

Journal ArticleDOI
TL;DR: In this article , a new variant of the marine predator algorithm (MPA), called MPAmu, using additional mutation operators to augment the MPA to prevent its premature convergence on local optima.

Journal ArticleDOI
TL;DR: In this article , a new Symmetric Solar Fed Inverter (SSFI) was proposed with a reduced number of components compared to the classical, modified, conventional type of multilevel Inverters (MLI).
Abstract: A new Symmetric Solar Fed Inverter (SSFI) proposed with a reduced number of components compared to the classical, modified, conventional type of Multilevel Inverter (MLI). The objective of this architecture is to design fifteen-level SSFI, this circuit uses a single switch with minimizing harmonics, and Modulation Index (MI) values. Power Quality (PQ) is developed by using the optimization algorithms like as Particle Swarm Optimization (PSO), Genetic algorithm (GA), Modified Firefly Algorithm (MFA). It’s determined to generate the gating pulse and finding optimum firing angle values calculate as per the input of MPP intelligent controller schemes. The proposed circuit is solar fed inverter used for optimization techniques governed by switching controller approach delivers a major task. The comparison is made for different optimization algorithm has significantly reduced the harmonic content by varying the modulation index and switching angle values. SSFI generates low distortion output uses through without any additional filter component through utilizing MATLAB Simulink software (2020a). The SSFI circuit assist Xilinx Spartan 3-AN Filed Program Gate Array (FPGA) tuned by optimization techniques are presented for the effectiveness of the proposed model.

Journal ArticleDOI
TL;DR: In this paper , a pyramid PSO (PPSO) with novel competitive and cooperative strategies to update particles' information is proposed, which has superior performance in terms of accuracy, Wilcoxon signed-rank test and convergence speed, yet achieves comparable running time in most cases.