Fractional Order Darwinian Particle Swarm Research Articles

Analysis of the Corneal Geometry of the Human Eye with an Artificial Neural Network

In this paper, a hybrid cuckoo search technique is combined with a single-layer neural network (BHCS-ANN) to approximate the solution to a differential equation describing the curvature shape of the cornea of the human eye. The proposed problem is transformed into an optimization problem such that the L2–error remains minimal. A single hidden layer is chosen to reduce the sink of the local minimum values. The weights in the neural network are trained with a hybrid cuckoo search algorithm to refine them so that we obtain a better approximate solution for the given problem. To show the efficacy of our method, we considered six different corneal models. For validation, the solution with Adam’s method is taken as a reference solution. The results are presented in the form of figures and tables. The obtained results are compared with the fractional order Darwinian particle swarm optimization (FO-DPSO). We determined that results obtained with BHCS-ANN outperformed the ones acquired with other numerical routines. Our findings suggest that BHCS-ANN is a better methodology for solving real-world problems.

Multilevel Colonoscopy Histopathology Image Segmentation Using Particle Swarm Optimization Techniques.

Histopathology image segmentation is a challenging task in medical image processing. This work aims to segment lesion regions from colonoscopy histopathology images. Initially, the images are preprocessed and then segmented using the multilevel image thresholding technique. Multilevel thresholding is considered an optimization problem. Particle swarm optimization (PSO) and its variants, darwinian particle swarm optimization (DPSO), and fractional order darwinian particle swarm optimization (FODPSO) are used to solve the optimization problem and they generate the threshold values. The threshold values obtained are used to segment the lesion regions from the images of the colonoscopy tissue data set. Segmented images containing the lesion regions are then postprocessed to remove unnecessary regions. Experimental results reveal that the FODPSO algorithm with Otsu's discriminant criterion as the objective function achieves the best accuracy, Dice and Jaccard values of 0.89, 0.68 and 0.52, respectively, for the colonoscopy data set. The FODPSO algorithm also outperforms other optimization methods such as artificial bee colony and the firefly algorithms in terms of the accuracy, Dice and Jaccard values.

Multilevel Multiobjective Particle Swarm Optimization Guided Superpixel Algorithm for Histopathology Image Detection and Segmentation.

Histopathology image analysis is considered as a gold standard for the early diagnosis of serious diseases such as cancer. The advancements in the field of computer-aided diagnosis (CAD) have led to the development of several algorithms for accurately segmenting histopathology images. However, the application of swarm intelligence for segmenting histopathology images is less explored. In this study, we introduce a Multilevel Multiobjective Particle Swarm Optimization guided Superpixel algorithm (MMPSO-S) for the effective detection and segmentation of various regions of interest (ROIs) from Hematoxylin and Eosin (H&E)-stained histopathology images. Several experiments are conducted on four different datasets such as TNBC, MoNuSeg, MoNuSAC, and LD to ascertain the performance of the proposed algorithm. For the TNBC dataset, the algorithm achieves a Jaccard coefficient of 0.49, a Dice coefficient of 0.65, and an F-measure of 0.65. For the MoNuSeg dataset, the algorithm achieves a Jaccard coefficient of 0.56, a Dice coefficient of 0.72, and an F-measure of 0.72. Finally, for the LD dataset, the algorithm achieves a precision of 0.96, a recall of 0.99, and an F-measure of 0.98. The comparative results demonstrate the superiority of the proposed method over the simple Particle Swarm Optimization (PSO) algorithm, its variants (Darwinian particle swarm optimization (DPSO), fractional order Darwinian particle swarm optimization (FODPSO)), Multiobjective Evolutionary Algorithm based on Decomposition (MOEA/D), non-dominated sorting genetic algorithm 2 (NSGA2), and other state-of-the-art traditional image processing methods.

Performance Analysis of Optimization Based FOC and DTC Methods for Three Phase Induction Motor

Three-phase induction motors are becoming increasingly utilized in industrial field due to their better efficiency and simple manufacture. The speed control of an induction motor is essential in a variety of applications, but it is difficult to control. This research analyses the three-phase induction motor’s performance using field-oriented control (FOC) and direct torque control (DTC) techniques. The major aim of this work is to provide a critical evaluation of developing a simple speed controller for induction motors with improving the performance of Induction Motor (IM). For controlling a motor, different optimization approaches are accessible; in this research, a Fuzzy Logic Controller (FLC) with Fractional Order Darwinian Particle Swarm Optimization (FODPSO) algorithm is presented to control the induction motor. The FOC and DTC are controlled using FODPSO, and their performance is compared to the traditional FOC and DTC technique. Each scheme had its own simulation model, and the results were compared using hardware experimental and MATLAB-Simulink. In terms of time domain specifications and torque improvement, the proposed technique surpasses the existing method.

Automated brain tumour segmentation from multi-modality magnetic resonance imaging data based on new particle swarm optimisation segmentation method.

Segmentation of brain tumours is a complex problem in medical image processing and analysis. It is a time-consuming and error-prone task. Therefore, computer-aided detection systems need to be developed to decrease physicians' workload and improve the accuracy of segmentation. This paper proposes a level set method constrained by an intuitive artificial intelligence-based approach to perform brain tumour segmentation. By studying 3D brain tumour images, a new segmentation technique based on the Modified Particle Swarm Optimisation (MPSO), Darwin Particle Swarm Optimisation (DPSO), and Fractional Order Darwinian Particle Swarm Optimisation (FODPSO) algorithms were developed. The introduced technique was verified according to the MICCAI RASTS 2013 database for high-grade glioma patients. The three algorithms were evaluated using different performance measures: accuracy, sensitivity, specificity, and Dice similarity coefficient to prove the performance and robustness of our 3D segmentation technique. The result is that the MPSO algorithm consistently outperforms the DPSO and FO DPSO.

Hybrid Optimization Approach for Energy Control in Electric Vehicle Controller for Regulation of Three-Phase Induction Motors

Three-phase induction motors are becoming increasingly popular for electric cars and industrial uses because of their improved efficiency and simplicity of production, among other things. Many enterprises and industries use induction motors in several rotating applications. However, it is a difficult talent to master when it comes to controlling the speed of an induction motor for various purposes. This study examines the performance of a three-phase induction motor using approaches such as field-oriented control and direct torque control. This work utilized the fractional order Darwinian particle swarm optimization (FODPSO) method in fuzzy methodology to optimize a motor’s performance. Field-oriented control (FOC) and Direct torque control (DTC) methods are regulated by FODPSO, which is compared to standard FOC and DTC methods. MATLAB-Simulink was used to compare the outcomes of each system’s simulation model to determine which one performed the best. The support vector machine-direct torque control (SVM-DTC) technology is famous for its rapid dynamic response and decreased torque ripples. Using torque and settling time and rising time reduction, the suggested technique is proved to be superior to the present way.

Quantum fractional order Darwinian particle swarm optimization for hyperspectral multi-level image thresholding

A Hyperspectral Image (HSI) is a data cube consisting of hundreds of spatial images. Each captured spatial band is an image at a particular wavelength. Thresholding of these images is itself a tedious task. Two procedures, viz., Qubit Fractional Order Particle Swarm Optimization and Qutrit Fractional Order Particle Swarm Optimization are proposed in this paper for HSI thresholding. The Improved Subspace Decomposition Algorithm, Principal Component Analysis, and a Band Selection Convolutional Neural Network are used in the preprocessing stage for band reduction or informative band selection. For optimal segmentation of the HSI, modified Otsu’s criterion, Masi entropy and Tsallis entropy are used. A new method for quantum disaster operation is implemented to prevent the algorithm from getting stuck into local optima. The implementations are carried out on three well known datasets viz., the Indian Pines, the Pavia University and the Xuzhou HYSPEX. The proposed methods are compared with state-of-the-art methods viz., Particle Swarm Optimization (PSO), Ant Colony Optimization, Darwinian Particle Swarm Optimization, Fractional Order Particle Swarm Optimization, Exponential Decay Weight PSO and Heterogeneous Comprehensive Learning PSO concerning the optimal thresholds, best fitness value, computational time, mean and standard deviation of fitness values. Furthermore, the performance of each method is validated with Peak signal-to-noise ratio and Sørensen–Dice Similarity Index. The Kruskal–Wallis test, a statistical significance test, is conducted to establish the superiority in favor of the proposed methods. The proposed algorithms are also implemented on some benchmark functions and real life images to establish their universality.

Optimizing channel selection using multi-objective FODPSO for BCI applications

ABSTRACT Brain-computer interfacing (BCI) requires multichannel electroencephalogram (EEG) or electrocorticogram (ECoG) signal acquisition for better performance. The selection of optimum channels that provides the best accuracy is itself a problem to solve because noisy/irrelevant channels can complexify the system and degrade its performance. This paper presents a novel automated model for optimum channel selection for BCI applications using the Fractional Order Darwinian Particle Swarm Optimization (FODPSO). To assess the information content of selected channels, a Support Vector Machine (SVM) classifier is used. The weighted sum of the number of channels and the classification accuracy on validation samples is taken as the fitness value for the FODPSO based binary optimization method (where all the variables are binary). The FODPSO and SVM based algorithm is evaluated on the electrocorticography (ECoG) recordings that have been used in BCI competition III. Dual tree complex wavelet transform (DTCWT) is used for preprocessing of the data and sample entropy is calculated for obtaining the most informative features from the preprocessed data. Eight channels are selected using this algorithm, yielding a classification accuracy of 0.81 for the testing dataset (classifying the ECoG signals of imagined movement of little finger and tongue) that compares favorably with the already reported methods. Experimental results successfully demonstrate that this channel selection algorithm works better both in terms of classification accuracy and in the reduction of the number of required channels.

Fractional order Darwinian particle swarm optimization for parameters identification of solar PV cells and modules

Improved Fractional Order Darwinian Particle Swarm Optimization (FODPSO) has been proposed to identify the parameters of solar PV cells and modules. For this purpose, the traditional PSO algorithm has been adapted and an improved one using the fractional order calculus has been especially designed as well. The proposed FODPSO together with the PSO has been applied in both the single and double diode models to describe the specified PV units for purpose of comparison. To examine the advantages of the proposed FODPSO over conventional PSO, two approaches have been performed; the first approach is implementing the FODPSO and PSO algorithms using input data obtained from previous international publications for different types of PV cells/modules, then comparing the FODPSO algorithm's results to the results of PSO and the other previous optimization techniques that were using the same input data and identical PV cells/modules. The second validation approach is achieved by performing real measurements in outdoor conditions for another two types of PV modules (mono and poly crystalline Silicon) and inserting these measurements' readings as input data to the PSO and FODPSO algorithms. A comparative study has also been done for both algorithms' results. Enhanced results have been achieved using the FODPSO technique.It is worth mentioning that during the development of this work, other efforts have been made with different techniques that may result in similar or even better results, yet this paper is still introducing for the first time the FODPSO technique (as a modified technique of the PSO) as applied on the PV modeling problem, in addition the paper presents the benefits of the proposed FODPSO over the conventional PSO and presents a comparison that shows the superiority of the FODPSO over the conventional PSO.

Design of fractional evolutionary processing for reactive power planning with FACTS devices

Reactive power dispatch is a vital problem in the operation, planning and control of power system for obtaining a fixed economic load expedition. An optimal dispatch reduces the grid congestion through the minimization of the active power loss. This strategy involves adjusting the transformer tap settings, generator voltages and reactive power sources, such as flexible alternating current transmission systems (FACTS). The optimal dispatch improves the system security, voltage profile, power transfer capability and overall network efficiency. In the present work, a fractional evolutionary approach achieves the desired objectives of reactive power planning by incorporating FACTS devices. Two compensation arrangements are possible: the shunt type compensation, through Static Var compensator (SVC) and the series compensation through the Thyristor controlled series compensator (TCSC). The fractional order Darwinian Particle Swarm Optimization (FO-DPSO) is implemented on the standard IEEE 30, IEEE 57 and IEEE 118 bus test systems. The power flow analysis is used for determining the location of TCSC, while the voltage collapse proximity indication (VCPI) method identifies the location of the SVC. The superiority of the FO-DPSO is demonstrated by comparing the results with those obtained by other techniques in terms of measure of central tendency, variation indices and time complexity.

Validation of an Improved Optimization Technique for Photovoltaic Modeling

Particle Swarm Optimization technique has been improved by fractional order calculus to be used for photovoltaic (PV) modeling. The modified technique which is called Fractional Order Darwinian Particle Swarm Optimization (FODPSO) has been constructed to estimate the optimal electrical parameters of PV modules. Single and double diode models have been used to designate the PV modules. FODPSO and PSO algorithms have been designed and applied on two different PV modules at different irradiances and temperatures. In order to validate the proposed modeling technique, Root Mean Square Error (RMSE) of the current, RMSE of power and Summation of the Individual Absolute Error (SIAE) results obtained using FODPSO and traditional Particle Swarm Optimization (PSO) algorithms have been compared. Minimum RMSE and SIAE have been achieved using the FODPSO technique. To verify the FODPSO results accuracy, accurate measurements of short circuit current, open circuit voltage, and maximum power, voltage at maximum power and current at maximum power have been performed for both PV modules. FODPSO-estimated results show excellent agreement with the experimental ones at different irradiances and temperatures.

Iot based sustainable wind green energy for smart cites using fuzzy logic based fractional order darwinian particle swarm optimization

In view of the fast growing countries, most of the people prefer to live in smart cities. Green and sustainable energy is an important factor for creating a smart system because only an integrated smart grid system can be an alternative solution from all the traditional methods. Wind energy is one of the renewable energy sources growing in popularity because of its many advantages such as lower cost of production, sustainability, and being environmentally friendly. There is a specific ratio for each wind turbine, at which the extracted power is maximum. To work at this optimal ratio, Wind Energy Conversion System (WECS) is essential to adjust the power converter duty cycle and rapidly maximize the energy of wind in the system. In this paper, a Maximum Power Point Tracking (MPPT) controller is proposed to achieve utmost available wind power and the controller parameters are optimized by Fractional Order Darwinian Particle Swarm Optimization (FODPSO) based on Fuzzy Logic Controller (FLC) techniques. A wind turbine has a variety of sensors for monitoring and measuring the state of the turbine which are connected to the internet using Internet of Things (IoT). This paper proposed an IoT based communication framework for the purpose of reliable communication between wind turbines and control centre. The proposed FODPSO tunes the FLC continuously for generate duty cycle and adjust the converter switches propositional to the variations in wind speed. This FODPSO technique used in the controller gives high speed and reliability as well as excellent accuracy to track maximum wind power. Proposed FODPSO-FLC extracts 20% and 13% more power than existing FLC and PSO-FLC respectively. Finally Proposed FODPSO-FLC reduces 20% and 9% THD than existing FLC and PSO-FLC respectively. The obtained results show that the new IOT solution can accurately predict and control the wind turbine state.

Early Diabetes Discovery From Tongue Images

Abstract Medical imaging shows the internal structures hidden in the skin and bone to diagnose the disease. Diabetes mellitus (DM) is a metabolic disorder that causes high blood sugar levels due to the inadequate secretion of insulin or the body does not respond properly to the secreted insulin. This paper proposes a non-invasive method to detect DM at an early stage based on the physiognomy extracted from tongue images. The tongue extends to identify the disease of a human body. However, unpredictable response of the human body parts such as the stomach, pancreas, liver and intestines revert in the tongue. The changes in the tongue ensure the dereliction of the internal organs of the human being. The changes are difference in the color and surface of the tongue. Processing of tongue image is done by fractional order Darwinian particle swarm optimization (FODPSO) algorithm. The system framework involves obtaining the image, alluring of the image, identifying the texture and color feature and finally classified as normal or diabetic. In this paper, the authors propose to diagnose DM at an early stage from tongue digital image. The tongue image is acquired and processed with FODPSO to extract edge and texture features. Tongue reflects and diagnoses diabetes in a person.

Automatic body segmentation for accelerated rendering of digitally reconstructed radiograph images

Abstract The rendering of digitally reconstructed radiograph (DRR) images involves creating a digital reconstruction of an image made by a three-dimensional (3D) imaging system, such as a computed tomography (CT) scan, to produce a new two-dimensional (2D) image that emulates a medical X-ray scan. Acceleration of DRR generation has been the focus of much research in recent years, especially for medical applications that are designed to work on different imaging systems such as 2D/3D medical image registration. In this study, we succeeded in reducing the DRR generation time by using an automatic body segmentation approach on the CT volume. The proposed automatic body segmentation methodology consists of several steps that are carried out sequentially, starting with the use of fractional order Darwinian particle swarm optimization to generate a binary mask for all CT volume slices and ending with the use of a watershed transform to obtain the parameters required to identify the human body or part thereof as a confirmed region of interest (ROI). In our experiment, nearly half of the CT volume was identified as a non-ROI by the proposed approach. The approach also achieved an acceleration in the DRR generation process because the time complexity rate is reduced from O ( N 3 ) to O ( M 3 ) , where M ≈ 1 2 N . That is to say, the DRR generation process was accelerated from 9.3 s when using the original (512 × 512 × 350) CT volume to 4.9 s when using the automated segmented CT volume, which involved the applications of the traditional ray-casting projection technique. The proposed methodology was applied by using a computer with an Intel® Core™ i7-7500 U processor, 8 GB RAM and 2.70 GHz processing speed. We used the two most commonly employed measurements, i.e., the dice similarity coefficient (DSC) and the Tanimoto coefficient (TC) to evaluate the segmentation results for the tested datasets. The results were also compared with ground truth images drawn by a specialist at the King Hussein Cancer Center in Jordan. The comparison of the automated segmented volume and its ground truth showed that the proposed methodology obtained 99.50% accuracy based on the DSC and 99.03% based on TC.

An Online Video Segmentation using Improved Particle Swarm Optimization Technique

Video segmentation is the original needed stage in the video method wherever the whole video is splits into volumetric sections, each segment consists of spatial substantive with temporal domain information. We proposed a video segmentation algorithm using Short Term Hierarchical Fast Watershed Algorithm (STHFWA )and Fractional Order Darwinian Particle Swarm Optimization(FODPSO) technique. In STHFWA ,the k-means clustering is used to find out the initial segments based on the color intensity present in the frames and Fast watershed algorithm is applied to construct the rigid lines based on the catchment basin coordinates to avoid over segmentation. Finally, FODPSO optimization is used to reduce the computational complexity. Also, the experimental results are analyzed on a VSB100 dataset, shows that the proposed algorithm outperforms modern online video segmentation techniques significantly.

Fractional Order Darwinian Particle Swarm Optimization based K Best Detection Algorithm for Mimo Communication

This paper describes the hardware implementation prototype for the recent massive Multiple In and Out communication. In the entire MIMO communication system, decoder plays an important role in reducing the complexity of system. The objective is to recast the operating K-best detection methodology by forwarding the backwash wrapped up in organizing the path metrics to enhance aptness for the implementation of hardware with very durable BER performance. A 8x8 MIMO and 64 QAM FODPSO K-Best decoder using Schnorr-Euchner (SE) enumeration and new parameter Rlimit is proposed for reducing the complexity, thereby provides a better performance. The architecture for reducing the BER to 0.3 dB with list size K and Rlimit to 4 is designed for a word length of 16 bits. The proposed architecture is synthesized using XST in 45nm CMOS technology and coded in Verilog for a Maximum frequency of 181.8 MHz, 1090.8 Mbps through and a power consumption of 782mW and latency of 0.044𝝁s.

Multilevel thresholding hyperspectral image segmentation based on independent component analysis and swarm optimization methods

High dimensional problems are often encountered in studies related to hyperspectral data. One of the challenges that arise is how to find representations that are accurate so that important structures can be clearly easily. This study aims to process segmentation of hyperspectral image by using swarm optimization techniques. This experiments use Aviris Indian Pines hyperspectral image dataset that consist of 103 bands. The method used for segmentation image is particle swarm optimization (PSO), Darwinian particle swarm optimization (DPSO) and fractional order Darwinian particle swarm optimization (FODPSO). Before process segmentation image, the dimension of the hyperspectral image data set are first reduced by using independent component analysis (ICA) technique to get first independent component. The experimental show that FODPSO method is better than PSO and DPSO, in terms of the average CPU processing time and best fitness value. The PSNR and SSIM values when using FODPSO are better than the other two swarm optimization method. It can be concluded that FODPSO method is better in order to obtain better segmentation results compared to the previous method.

Segmentation by Fractional Order Darwinian Particle Swarm Optimization Based Multilevel Thresholding and Improved Lossless Prediction Based Compression Algorithm for Medical Images

The image segmentation refers to the extraction of region of interest and it plays a vital role in medical image processing. This work proposes multilevel thresholding based on optimization technique for the extraction of region of interest and compression of DICOM images by an improved prediction lossless algorithm for telemedicine applications. The role of compression algorithm is inevitable in data storage and transfer. Compared to the conventional thresholding, multilevel thresholding technique plays an efficient role in image analysis. In this paper, the Particle Swarm Optimization (PSO), Darwinian Particle Swarm Optimization (DPSO), and Fractional Order Darwinian Particle Swarm Optimization (FODPSO) are employed in the estimation of the threshold value. The simulation results reveal that the FODPSO-based multilevel level thresholding generate superior results. The fractional coefficient in FODPSO algorithm makes it effective optimization with fast convergence rate. The classification and blending prediction-based lossless compression algorithm generates efficient results when compared with the JPEG lossy and JPEG lossless approaches. The algorithms are tested for various threshold values and higher value of PSNR indicates the proficiency of the proposed segmentation approach. The performance of the compression algorithms was validated by metrics and was found to be appropriate for data transfer in telemedicine. The algorithms are developed in Matlab2010a and tested on DICOM CT images.

Automated approach for detection of ischemic stroke using Delaunay Triangulation in brain MRI images

It is difficult to develop an accurate algorithm to detect the stroke lesions using magnetic resonance imaging (MRI) images due to variation in different lesion sizes, variation in morphological structure, and similarity in intensity of lesion with normal brain in three types of stroke, namely partial anterior circulation syndrome (PACS), lacunar syndrome (LACS) and total anterior circulation stroke (TACS). In this paper, we have integrated the advantages of Delaunay triangulation (DT) and fractional order Darwinian particle swarm optimization (FODPSO), called DT-FODPSO technique for automatic segmentation of the structure of the stroke lesion. The approach was validated on 192 MRI images obtained from different stroke subjects. Statistical and morphological features were extracted and classified according to the Oxfordshire community stroke project (OCSP) using support vector machine (SVM) and random forest (RF) classifiers. The method effectively detected the stroke lesions and achieved promising results with an average sensitivity of 0.93, accuracy of 0.95, JI of 0.89 and Dice similarity index of 0.93 using RF classifier. These promising results indicates the DT based optimized approach is efficient in detecting ischemic stroke and it can aid the neuro-radiologists to validate their routine screening.

Novel application of FO-DPSO for 2-D parameter estimation of electromagnetic plane waves

Parameter estimation of plane waves emitted by sources lying in Fraunhofer zone is one of the active areas of research for last few decades. In this study, Fractional Order Darwinian Particle Swarm Optimization (FO-DPSO) algorithm is designed for direction of arrival and amplitude estimation of plane waves impinging on uniform linear array representing the scenario of monostatic Multiple Input and Multiple Output (MIMO) radar. Approximation theory in the mean square error sense is exploited to develop a fitness function of the problem. Design parameters of the system model are optimized by utilizing the strength of the FO-DPSO algorithm in case of various numbers of non-coherent sources, and analysis is performed in terms of fitness, mean square error, Nash–Sutcliffe efficiency, and computational complexity operators. Worth of the proposed FO-DPSO based optimization mechanism is established by consistently achieving the near to optimal values of performance metrics in all three scenarios for monostatic MIMO radar systems.