Fuzzy Particle Swarm Optimization Algorithm Research Articles

Hierarchical optimal allocation of BESS using APT-FPSO based on stochastic programming model considering voltage sensitivity and eigenvalues analyses

Integration of energy storage systems (ESSs) to microgrids (MGs) due to the growing penetration of intermittent energy resources is a great motivation to develop studies in the context of optimal sizing and placement of these systems. In this paper, a multi-objective bi-level optimization problem is addressed based on the stochastic nature and dynamic model of power system elements.As a preliminary step, the optimal size and capacity of the battery energy storage system (BESS) are determined to minimize frequency fluctuations. This level has been performed based on three approaches considering the daily intra-hour stochastic data. In the secondary level, the dynamic behaviour of all MG elements has been considered and correspondingly a dynamic objective function based on system eigenvalues and voltage sensitivity index (VSI) has been developed. According to the proposed objective function, the secondary step for sizing and placement of the battery units has been performed by adaptive particularly tuneable fuzzy particle swarm optimization (APT-FPSO) algorithm to reduce power losses, improve the voltage profile/ stability, and mitigate low-frequency fluctuations. Finally, the effectiveness of the proposed approach is evaluated using time-domain simulations, voltage sensitivity studies, and eigenvalue analysis.

Simulation of Medical Data Cloud Information Storage Encryption Based on Fuzzy Particle Swarm Optimization

Abstract In order to reduce the information storage load of network library and improve the security of medical data cloud information storage, information encryption design is needed. Therefore, a cloud information storage encryption simulation based on fuzzy particle swarm optimization algorithm is proposed. Firstly, a complex database access model based on fuzzy particle swarm optimization algorithm is constructed, the encrypted statistical features of cloud storage information are extracted, and the random scrambling matching method is used for coding correction. Fuzzy particle swarm clustering realizes the fusion of cloud information output results and realizes the optimization of cloud information storage encryption.

Research on the Education and Design policy of bio economic and management facts of the intelligent test based on fuzzy particle swarm optimization algorithm

with the passage of time, the era of technology is coming, and the huge demand of this modern world is the best education of the people in order to survive in the fast-developing era. Education system of any country holds great importance in the development of the country economy. Therefore, many developing and under developing countries has been focusing on the development of the education system in their country to improve their country’s economy as well as meet the requirements of the modern world. Apart from this, this allows organizations exceeds several universal standards, particularly in terms of educational fulfilment and higher education system exposition. moreover, there seems significant problems that policies makers must address, such as a low retention rate in blog academic achievement, universities and school improvement, a significant achievement gap between poor class, education funding, social classes, etc., this research focus on the management of the educational policies and intelligent test scheduling system based on fuzzy swarm particle optimized algorithm involving the business sector and educational program changes. Moreover, for the progress of the education system there is essential need to reduce errors rate in the system. this study explains the research on the design and management of the intelligent test scheduling system. Aiming at the problem of high error rate in the traditional intelligent test scheduling system of higher education institutions, an intelligent test scheduling system of higher education institutions based on fuzzy particle swarm optimization algorithm is designed. In terms of hardware, x86 server is designed. In terms of software, standardize the business process of intelligent arrangement in colleges. The fitness function is established based on fuzzy particle swarm optimization algorithm, and the intelligent test scheduling time selection operator is dynamically planned. This paper analyzes the data flow of intelligent test scheduling in colleges, constructs a database, and comprehensively considers the conflict factors of test scheduling. In the case of no missing candidates, the intelligent test scheduling of higher education institutions is realized, and the system design is completed. For this purpose, we collected sample data from various educational institutions of China and investigated collected data by utilizing optimized particle swarm algorithm. The experimental results show that the test scheduling error rate of the designed system is significantly lower than that of the traditional test scheduling systems 1, 2 and 3, which can solve the problem of high-test scheduling error rate of the traditional intelligent test scheduling system in colleges.

Logistics Transportation Time Optimization Based on Fuzzy Particle Swarm Optimization

The fuzzy particle swarm optimization (FPSO-RRB- algorithm is used to solve the logistics transportation time optimization problem, a three-dimensional particle coding method based on receiving point, particle position sequence and particle position rounding operation is proposed, the results are compared with those of fuzzy particle swarm optimization algorithm. The experimental results show that the fuzzy particle swarm optimization algorithm can effectively optimize the logistics transportation time.

Quadrotor Attitude Control by Fractional-Order Fuzzy Particle Swarm Optimization-Based Active Disturbance Rejection Control

In this paper, Active Disturbance Rejection Control (ADRC) is utilized in the attitude control of a quadrotor aircraft to address the problem of attitude destabilization in flight control caused by parameter uncertainties and external disturbances. Considering the difficulty of optimizing the parameter of ADRC, a fractional-order fuzzy particle swarm optimization (FOFPSO) algorithm is proposed to optimize the parameters of ADRC for quadrotor aircraft. Simultaneously, the simulation experiment is designed, which compares with the optimized performance of traditional particle swarm optimization (PSO), fuzzy article swarm optimization (FPSO) and adaptive genetic algorithm-particle swarm optimization (AGA-PSO). In addition, the turbulent wind field model is established to verify the disturbance rejection performance of the controller. Finally, the designed controller is deployed to the actual hardware platform by using the model-based design method. The results show that the controller has a small overshoot and stronger disturbance rejection ability after the parameters are optimized by the proposed algorithm.

Braking intensity recognition with optimal K-means clustering algorithm

Recognizing a driver’s braking intensity plays a pivotal role in the development of modern driver assistance and energy management systems; and therefore, it is especially important to autonomous and electric vehicles. This paper aims at developing a system for recognizing a driver’s braking intensity based on the pressure produced in the brake master cylinder. In this regard, a model-based, synthetic data generation concept is used to generate the training dataset. This technique involves two closed-loop controlled models: an upper-level longitudinal vehicle dynamics model, and a lower-level brake hydraulic, dynamic model. The adaptive particularly tunable fuzzy particle swarm optimization algorithm is recruited to solve the optimal K-means clustering problem and to determine the best number of clusters and centroids into which the brake pressure data are to be divided. The obtained results reveal that the brake pressure data for a vehicle traveling the new European driving cycle can be best partitioned into two clusters. A driver’s braking intensity may, therefore, be clustered as moderate or intensive. With the ability to automatically recognize a driver’s pedal feel, the system developed in this research could be implemented in intelligent driver assistance systems as well as in electric vehicles equipped with intelligent, electromechanical brake boosters.

Deployment of On-Orbit Service Vehicles Using a Fuzzy Adaptive Particle Swarm Optimization Algorithm

On the basis that satellites given fixed count and orbit elements can be served in bounded time when an on-orbit serving mission order is set at any uncertain time in a given time interval, the deployment of on-orbit service vehicle (OSV) serving satellites becomes a complex multiple nested optimization problem, and the essence of deployment is to determine the count and orbit elements of OSVs. In consideration of the characteristics of this deployment problem, we propose a fuzzy adaptive particle swarm optimization (FAPSO) algorithm to solve this problem. First, on the basis of double pulse rendezvous hypothesis, a transfer optimization model of a single OSV serving multiple satellites is established based on genetic algorithm (GA), and this is used to compute the indexes of the subsequent two optimization models. Second, an assignment optimization model of OSVs is established based on the discrete particle swarm optimization (DPSO) algorithm, laying the foundation of the next optimization model. Finally, the FAPSO algorithm, which improves the performance of PSO algorithm by adjusting the inertia weight, is proposed to solve the deployment problem of multiple OSVs. The simulation results demonstrate that all optimization models in this study are feasible, and the FAPSO algorithm, which has a better convergence result than that obtained using the other optimization algorithms, can effectively solve the deployment problem of OSVs.

DEEPORCD: Detection of Oral Cancer using Deep Learning

Oral cancer is a widespread and complex cancer with a high severity. Oral cancer is the eighth most common cancer in the world in India, with 130,000 deaths in each year. The tumor occurs in the salivary glands, tonsils, as well as in the neck, face and mouth. There are various diagnostic methods for oral cancer, such as a biopsy, in which a small tissue sample is taken from a part of the body and tested under a microscope also some screening methods. But the downside is that cannot clearly identify cancer cells and cannot classify the number of cells affected by cancer, so in this work cancer cells will find and classify that affected in the oral area through digital processing technology. The use of advanced technologies and an in-depth learning algorithm are possible for early detection and classification. This work uses three characteristics-extracting techniques such as the bag histogram of oriented gradients, wavelet features and the Zernike Moment. Once retrieving the texture characteristics, the fuzzy particle swarm optimization algorithm (FPSO) is applied to choose the best characteristic. Finally, these characteristics were classified using the Convolution Neural Network (CNN) classifier. For comparison of the efficiency of the proposed method, Recall Rate, Classification Accuracy, Precision Rate, and Error Rate. Evaluation outcomes demonstrated that the combination of ABC, FPSO and CNN performs better in the detection of oral cancer.

Unified Control with fuzzy MPPT and PSO based Parameter Optimization for Single Stage PV System based on Energy Saving

This paper presents a unified control for single stage grid-connected system with non-linear load. The proposed control not only ensures power injection into the grid, but also shows its advantage of controlling both active and reactive powers as well as harmonics restraints. This paper explains also the advantage of single stage using quasi- z source inverter (QZSI) which plays the buck/boost operation using space vector pulse width modulation technique (SVPWM) for shoot through insertion during the modulation period. Instantaneous power theory is used to simplify the implementation of the control design for both the inner and outer layer as well as for the reference current generation. To take off the challenge of tracking the maximum power point (MPPT) in any condition, including change in solar irradiance, fuzzy technique is used. Thereby, exhibiting its robustness and adaptability. Besides, to ameliorate the efficiency of the system, the well repute particle swarm optimization algorithm (PSO) is employed to improve the PI controllers’ parameters. Finally matlab/Simulink environment is used to implement and to confirm the effectiveness and the feasibility of the proposed control strategy as well as the optimization method.

Design of reactive power optimization control for electromechanical system based on fuzzy particle swarm optimization algorithm

Due to low precision and premature tendency of traditional particle swarm optimization, the reactive power optimization control of electromechanical system based on fuzzy particle swarm optimization algorithm was designed. The premise is to meet the constraints of operation conditions. The active network loss was reduced and the static reactive power optimization mathematical model of electromechanical system was constructed by changing the voltage and reactive power distribution of system. Meanwhile, the voltage did not exceed the limit, and the discrete control variables were limited by the maximum allowable action times, so that the dynamic reactive power optimization mathematical model of electromechanical system was built by minimizing the sum of network loss in twenty-four hours of a day. The particle swarm algorithm was optimized by adaptive adjustment strategy, and then the particle position of particle swarm optimization algorithm was updated. Moreover, the static and dynamic reactive power optimization mathematical model of electromechanical system was solved. Finally, the reactive power optimization control of the electromechanical system is realized. Experimental results show that the proposed method has high convergence performance, so it is able to realize the precise control of reactive power optimization for electromechanical system and eliminate the voltage exceeding specified limits of electromechanical system. In this way, the node voltage can always be within the specified range.

Moving object tracking in video by using fuzzy particle swarm optimization algorithm

Moving object tracking in video by using fuzzy particle swarm optimization algorithm

T-S fuzzy systems optimization identification based on FCM and PSO

The division of fuzzy space is very important in the identification of premise parameters, and the Gaussian membership function is applied to the premise fuzzy set. However, the two parameters of Gaussian membership function, center and width, are not easy to be determined. In this paper, based on Fuzzy c-means (FCM) and particle swarm optimization (PSO) algorithm, a novel T-S fuzzy model optimal identification method of optimizing two parameters of Gaussian function is presented. Firstly, we use FCM algorithm to determine the Gaussian center for rough adjustment. Then, under the condition that the center of Gaussian function is fixed, the PSO algorithm is used to optimize another adjustable parameter, the width of the Gaussian membership function, to achieve fine-tuning, so as to complete the identification of prerequisite parameters of fuzzy model. In addition, the recursive least squares (RLS) algorithm is used to identify the conclusion parameters. Finally, the effectiveness of this method for T-S fuzzy model identification is verified by simulation examples, and the higher identification accuracy can be obtained by using the novel identification method described compared with other identification methods.

Associating earthquakes with faults using cluster analysis optimized by a fuzzy particle swarm optimization algorithm for Iranian provinces

Fault zones play an important role in seismic hazard analysis and are often selected in accordance with the judgments of experts, making them controversial and diverse in a specific region. The delineation of seismic sources has a great impact on the uncertainty in seismic hazard analysis. Applications of seismic hazard analysis demonstrate that a 3D approach, compared to a 2D one, may lead to more realistic hazard scenarios. Another problem with seismic hazard analysis is how to associate earthquakes with faults. This paper attempted to connect seismic events to faults by a fuzzy particle swarm optimization algorithm, an optimized version of the fuzzy clustering approach. In this algorithm, two objective functions were minimized: the distance of the events from the faults, and the distance to the centers of events’ clusters. The study areas included two provinces in southwestern and southeast Iran, namely Fars and Kerman, respectively. Ten main faults were recognized in the Fars Province and 13 main faults in the other study area, Kerman Province. Moreover, 1100 and 665 seismic events with the Mw ≥ 4 and a depth of 4 km–100 km (obtained by teleseismic depth assessment) were recorded from 1900 to 2011 in these two areas, respectively. Events were clustered and associated with the faults using the algorithm. A comparison of the results of the applied algorithm and those of the known and documented earthquakes revealed an accuracy of 85.3% and 75% for Fars and Kerman Provinces, respectively, when determining the associated fault.

Blood glucose level prediction for diabetes based on modified fuzzy time series and particle swarm optimization

AbstractBlood glucose control is an essential goal for the patients who have Type‐1 diabetes (T1D). The prediction of the blood glucose levels for the next 30‐minute is crucial. If the predicted blood glucose level is in the critical ranges, and these predictions can be known in advance, then the patients can take the necessary cautions to prevent from it. In this article, we propose a modified fuzzy particle swarm optimization algorithm for the prediction of blood glucose levels of 30‐minute after the last measurement. We form the average and patient‐specific models to predict the blood glucose level of the patients. Both models are tested on two different datasets which contain patients with T1D. The experimental results are evaluated in terms of root mean squared error and Clarke error grid analysis metrics. The results indicate that our proposed modified algorithm is feasible to be applied to the prediction of blood glucose levels. In addition, this approach can assist patients with T1D for their blood glucose control.

Adaptive particularly tunable fuzzy particle swarm optimization algorithm

Particle Swarm Optimization (PSO) is a metaheuristic optimization algorithm that owes much of its allure to its simplicity and its high effectiveness in solving sophisticated optimization problems. However, since the performance of the standard PSO is prone to being trapped in local extrema, abundant variants of PSO have been proposed by far. For instance, Fuzzy Adaptive PSO (FAPSO) algorithms have been being studied extensively in recent years. In this study, a modified version of PSO algorithms is presented and is named as Adaptive Particularly Tunable Fuzzy Particle Swarm Optimization (APT-FPSO). In it, the global and personal learning coefficients of every single particle are tuned adaptively and particularly, at an individual extent, within each iteration with the aid of fuzzy logic concepts. Ample statistical evidence is provided indicating that the proposed algorithm further improves the potentialities and capabilities of the standard PSO.

Deep learning for lung Cancer detection and classification

Lung cancer is one of the main reasons for death in the world among both men and women, with an impressive rate of about five million deadly cases per year. Computed Tomography (CT) scan can provide valuable information in the diagnosis of lung diseases. The main objective of this work is to detect the cancerous lung nodules from the given input lung image and to classify the lung cancer and its severity. To detect the location of the cancerous lung nodules, this work uses novel Deep learning methods. This work uses best feature extraction techniques such as Histogram of oriented Gradients (HoG), wavelet transform-based features, Local Binary Pattern (LBP), Scale Invariant Feature Transform (SIFT) and Zernike Moment. After extracting texture, geometric, volumetric and intensity features, Fuzzy Particle Swarm Optimization (FPSO) algorithm is applied for selecting the best feature. Finally, these features are classified using Deep learning. A novel FPSOCNN reduces computational complexity of CNN. An additional valuation is performed on another dataset coming from Arthi Scan Hospital which is a real-time data set. From the experimental results, it is shown that novel FPSOCNN performs better than other techniques.

Design and implementation of battery charging system on solar tracker based stand alone PV using fuzzy modified particle swarm optimization

Design of battery charging system on solar tracker based PV system and its application has been presented in this paper. To improve the system performance, a solar tracking system as an innovative device of PV has been developed with an intelligent controller. PV equipped by solar tracker can significantly enhace its performance up to 40% of conventional system. In this research solar tracker designed has active tracking mode with double axis. In order to keep the PV performance optimum, a smart battery charging system has been developed and provided to store the electricity generated by PV system. A novel algorithm was implemented to the system which allows the battery charging process to operate quickly and safely. Besides, the components involved in the system are DC-DC converter, sensor, actuator and battery. DC-DC Converter used is Single Ended Primary Inductance Mode (SEPIM) with MOSFET as its actuator. Battery charging system has used intelligent control based on fuzzy-PSO algorithm. In this case, PSO functions to optimize and modify fuzzy parameters to obtain the best model. Optimized fuzzy controller has then been implemented and programmed in an Arduino microcontroller module to generate control signal which commands actuator element to control the voltage of battery through duty cycle manipulation variable. This algorithm has been able to improve the solar charging controller significantly and more convincingly increase PV performance.

Forecasting of software reliability using neighborhood fuzzy particle swarm optimization based novel neural network

This paper proposes an artificial neural network &#x0028 ANN &#x0029 based software reliability model trained by novel particle swarm optimization &#x0028 PSO &#x0029 algorithm for enhanced forecasting of the reliability of software. The proposed ANN is developed considering the fault generation phenomenon during software testing with the fault complexity of different levels. We demonstrate the proposed model considering three types of faults residing in the software. We propose a neighborhood based fuzzy PSO algorithm for competent learning of the proposed ANN using software failure data. Fitting and prediction performances of the neighborhood fuzzy PSO based proposed neural network model are compared with the standard PSO based proposed neural network model and existing ANN based software reliability models in the literature through three real software failure data sets. We also compare the performance of the proposed PSO algorithm with the standard PSO algorithm through learning of the proposed ANN. Statistical analysis shows that the neighborhood fuzzy PSO based proposed neural network model has comparatively better fitting and predictive ability than the standard PSO based proposed neural network model and other ANN based software reliability models. Faster release of software is achievable by applying the proposed PSO based neural network model during the testing period.

Accurate Parameter Estimation of a Hydro-Turbine Regulation System Using Adaptive Fuzzy Particle Swarm Optimization

Parameter estimation is an important part in the modeling of a hydro-turbine regulation system (HTRS), and the results determine the final accuracy of a model. A hydro-turbine is normally a non-minimum phase system with strong nonlinearity and time-varying parameters. For the parameter estimation of such a nonlinear system, heuristic algorithms are more advantageous than traditional mathematical methods. However, most heuristics based algorithms and their improved versions are not adaptive, which means that the appropriate parameters of an algorithm need to be manually found to keep the algorithm performing optimally in solving similar problems. To solve this problem, an adaptive fuzzy particle swarm optimization (AFPSO) algorithm that dynamically tunes the parameters according to model error is proposed and applied to the parameter estimation of the HTRS. The simulation studies show that the proposed AFPSO contributes to lower model error and higher identification accuracy compared with some traditional heuristic algorithms. Importantly, it avoids a possible deterioration in the performance of an algorithm caused by inappropriate parameter selection.

The fuzzy particle swarm optimization algorithm design for dynamic positioning system under unexpected impacts

The vessel motion is a nonlinear and complicated in practical applications. The factors which affect vessel motion, mainly come from environmental influences. In this paper, we develop a fuzzy particle swarm optimization algorithm that applies to dynamic positioning system for stabilizing a vessel motion under unexpected impacts. The structure parameter of fuzzy system is calibrated by particle swarm optimization method. The coverage domain width and the overlap degree influence of membership function are adjusted dynamically from system errors. Thereby optimizing the control signal and enhancing the dynamic positioning system quality. Simulation studies with comparisons on a supply vessel are carried out. The proposed in a better response compared to other method such as fuzzy that proved effective of the proposed controller.