Krill Herd Optimization Research Articles

Cyber Attack Detection Process in Sensor of DC Micro-Grids Under Electric Vehicle Based on Hilbert–Huang Transform and Deep Learning

In this article, a new procedure is proposed on the basis of Hilbert-Huang Transform and deep learning for cyber-attacks detection in direct current (DC) micro-grids (MGs) as well as detection of the attacks in distributed generation (DG) units and its sensors. An advanced elective group deep learning method with Krill Herd Optimization (KHO) algorithm is proposed. At first, Hilbert-Huang Transform is used with the aim of extracting the signals feature and next these features are applied as the multiple deep input basis models are made with the aim of capturing automatically sentient traits from raw fluctuation signals. At third, to make sure the variety of the basis patterns, linear decoder, denoising autoencoder and sparse autoencoder are applied to make various deep autoencoders, respectively. Further, Bootstrap is applied with the aim of designing separate educational data subsets for any base model. Fourth, for implementing selective ensemble learning, a combination strategy of enhanced weighted voting (EWV) with class-particular thresholds is studied. Eventually, KHO algorithm is applied with the aim of adaptive selecting the optimal class-specific thresholds. In the offered tactic, firstly, a DC micro-grid is functioned and controlled with the lack of any false data injection attacks (FDIAs) to collect adequate information within the usual operation needed for the educating of deep learning networks. It is noteworthy that, in the procedure of datum production, load variable is also determined with the aim of having distinctive datasets for cyber-attack scenarios and load variables. Also, to provide more realistic method, the smart plug-in electric vehicle is also considered in the model. Outcomes of Simulation in various scenarios are applied with the aim of verifying the benefit of the offered procedure. The outcomes propose that the offered procedure is able to more accurate and robust know various type of false data injection attack over than 93.76% accuracy detection of true rate.

Deep learning based genome analysis and NGS-RNA LL identification with a novel hybrid model

The conventional image segmentation techniques have a lot of issues with highest computational cost and low level accuracy for medical image diagnosis and genome analysis. The deep learning based optimization models utilize to predict the liver cancer with RNA genome using CT images and the prediction of genome classification with NGS is a higher probable in recent medical disease classification. This paper proposes a hybrid deep learning technique constructs with SegNet, MultiResUNet, and Krill Herd optimization (KHO) algorithm to perform the extraction of the liver lesions and RNA sequencing that the optimization techniques used into the deep learning method. The proposed technique implements the SegNet for segregating the liver with genome from the CT scan; the MultiResUNet is constructed to perform the extractions of liver lesions. The KHO algorithm is combined with the deep learning approaches for tuning the hyper parameters to every Convolutional neural network model and enhances the segmentation process which may elaborately identifies the sequence that causes the liver classification disease. The proposed technique is compared with the related techniques on liver lesion classification (LL) for NGS in genome. The performance results show that the proposed technique is better to other algorithms on various performance metrics.

RETRACTED ARTICLE: Monitoring of air pollution to establish optimal less polluted path by utilizing wireless sensor network

An efficient air pollution monitoring (APM) scheme is proposed to establish an optimal less polluted path using WSN (wireless sensor network), in which the sensor node (SN) senses the temperature and CO gas (carbon monoxide) concentration’s existent in the air. Initially, the sensed information from the SNs is preprocessed. During preprocessing, the value that is missed in the sensed information is imputed. Next, Hadoop's distributed file system (HDFS) MapReduce (MR) is implemented on the preprocessed data and subsequently, the resulting data is saved in the cloud server. The resulting data is analyzed using Improved-Adaptive Neuro-Fuzzy Inference System (I-ANFIS) Algorithm for checking air pollutions severities and its location is then presented in the Google Map. After that, the multi-path routing is established through the less polluted area. Lastly, the optimal path is chosen with the assistance of KHOA (Krill Herd Optimization Algorithm). The outcomes are evaluated by contrasting the proposed and prevailing techniques.

Detection and Classification of Cancer on CT Lung Images using Krill Herd Optimization Algorithm and ANN

Detection and Classification of Cancer on CT Lung Images using Krill Herd Optimization Algorithm and ANN

Segmentation, Classification and Krill Herd Optimization of Breast Cancer

Researcher physicists understand the importance of analogous quality image analysis. These analyses are used to confirm the presence or absence of the disease and to support the evaluation and development of the disease. This quality assessment is used to understand the reality on Earth for a specific diagnosis that has been shown to be a specific type of chromatin in a cancerous core that may indicate an abnormal gene. Additionally, a significant amount of pathology images is important not only for the curriculum but also for science programs. The findings of this combination are important for some of the symptoms. In fact, the number of mitotic cell calculation provides clues to evaluate the proliferation and tumour growth, which is a significant move in the assessment of numerous types of cancer. The method fuzzy clustering and convolutional neural network is proposed here to detect the cell isolation, and abnormal cell growth. By finding the abnormal cell division it’s easy to detect the normal cell from the mitotic cell. Results: Finally, the aim is to reduce the rate of loss, which in turn, increases the accuracy of the detection, in that way we can distinguish abnormal cells from non-mitotic cell.

Optimal design for SCAP/battery power management applied in electric vehicle (EV) applications: a KHO–RDF technique

This dissertation proposes power management of optimal control scheme for hybrid energy storage system (HESS) like super capacitor and (SCAP) battery in electric vehicles. The proposed technique is a parallel performance of both the random decision forest (RDF) and krill herd optimization (KHO), and thus, it is called as KHO–RDF method. The main objective is to minimize the difference between the actual and reference power in the battery and SCAP. Here, the HESS framework comprises of two sections: (1) figuring the SCAP reference voltage dependent on load dynamics. (2) Maximizing the power flow through HESS. The reference voltage of SCAP by evaluating real-time load dynamics is computed at first, i.e., the vehicle dynamic, motor characteristics, regenerative braking systems and driving conditions. Furthermore, at the same time the magnitude variety of battery power was minimized and the power loss will occur. The input parameters of SCAP are load current, battery current and state of charge. In proposed technique, possible control signals dataset of HESS is fused to produce KHO. By utilizing the practiced dataset of KHO, the RDF is trained and predicts the optimal parameters of HESS. Moreover, the proposed technique advances the SCAP voltage, battery current magnitude, battery current varieties and battery power. With the proposed approach, the parameter of HESS is optimized and it provides certain solutions. The proposed technique is executed in Matrix Laboratory (MATLAB)/Simulink working platform. By using the comparison analysis with the existing procedures, the performance of the HESS is surveyed.

Optimised feature selection and entropy-based graph classification of gene expression data

Gene expression (GE) profiles expansively revised to disclose intuition into the multifariousness of cancer furthermore to discover concealed information which provides biological knowledge for the classification of cancer. Precise cancer classification straightly through original GE profiles stays challenging on account of the intrinsic high-dimension feature along with the small magnitude of the data samples. Therefore, choosing high discriminative genes as of the GE data have turn into progressively fascinating in the bioinformatics field. This given paper gives a technique for the GE data classification utilising entropy-based graph classifier. Initially, the proposed technique evaluate the GE data's signal to noise ratio (SNR) values, additionally, selects the relevant features using krill herd (KH) optimization process. The truth is that not all features are helpful for classification, and some redundant together with the irrelevant features might even serve as outlier. To dispose the outliers, feature reduction is done with the assist of Euclidean distance. Classification is made utilising entropy-based graph classifier. The proposed process' effectiveness contrasted with the existing method concerning classifications is established from the experimental outcome.

GAIT based Behavioral Authentication using Hybrid Swarm based Feed Forward Neural Network

Authentication of appropriate users for accessing the liable gadgets exists as one among the prime theme in security models. Illegal access of gadgets such as smart phones, laptops comes with an uninvited consequences, such as data theft, privacy breakage and a lot more. Straight forward approaches like pattern based security, password and pin based security are quite expensive in terms of memory where the user has to keep remembering the passwords and in case of any security issue risen then the password has to be changed and once again keep remembering the recent one. To avoid these issues, in this paper an effective GAIT based model is proposed with the hybridization of Artificial Neural Network model namely Feedforward Neural Network Model with Swarm based algorithm namely Krill Herd optimization algorithm (KH). The task of KH is to optimize the weight factor of FNN which leads to the convergence of optimal solution at the end of the run. The proposed model is examined with 6 different performance measures and compared with four different existing classification model. The performance analysis shows the significance of proposed model when compared with the existing algorithms.

Augmented Powell-Based Krill Herd Optimization for Roadside Unit Deployment in Vehicular Ad Hoc Networks

Abstract This article focuses on roadside unit (RSU) deployment based on the analysis of aggregation delay causes between the accident-occurring location and the nearby RSU. Information aggregation in vehicular ad hoc networks using RSU is one of the phenomenal concepts that look ahead in today’s recent advancements, since the consistency in aggregation and dissemination of data in a volatile network is high when compared with vehicle to vehicle communications. Deploying the RSU as appropriate in rural areas is cost-sensitive since the collection of information is not up to the mark when compared with urban regions. A mathematical model called Powell’s Method and a bioinspired algorithm, namely krill herd algorithm, have been hybridized and proposed in this research article for effective RSU deployment. The proposed algorithm has been tested under three different road maps, and the consistency of the algorithm has been evaluated under appropriate performance measures.

Multilevel thresholding for image segmentation using Krill Herd Optimization algorithm

In this paper a novel multilevel thresholding algorithm using a meta-heuristic Krill Herd Optimization (KHO) algorithm has been proposed for solving the image segmentation problem. The optimum threshold values are determined by the maximization of Kapur’s or Otsu’s objective function using Krill Herd Optimization technique. The proposed method reduces the computational time for computing the optimum thresholds for multilevel thresholding. The applicability and computational efficiency of the Krill Herd Optimization based multilevel thresholding is demonstrated using various benchmark images. A detailed comparative analysis with other existing bio-inspired techniques based multilevel thresholding techniques such as Bacterial Foraging (BF), Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and Moth-Flame Optimization (MFO) has been performed to prove the superior performance of the proposed method.

Single and multi-objective operation management of micro-grid using krill herd optimization and ant lion optimizer algorithms

In this paper, two recent heuristic optimization algorithms are presented to optimally manage the operation of the micro-grid (MG) with installed renewable energy sources (RESs); krill herd (KH) optimization and ant lion optimizer (ALO) algorithms. The first algorithm is used for solving single-objective function represents either total operation cost or total pollutant emission injected from the installed generating units while ALO is applied to solve the multi-objective function of both total operating cost and emission. The problem is formulated as nonlinear constrained objective function with equality and inequality constraints. In this work; the devices installed in MGs are photovoltaic panel (PV), wind turbine (WT), micro-turbine (MT), fuel cell (FC), battery and grid. Two scenarios are studied; the first one is optimizing MG with installing all RESs within specified limits in addition to grid, while the second scenario is operating both PV and WT at their rated powers. The obtained results are compared with different reported algorithms like genetic algorithm (GA), Fuzzy self-adaptive PSO (FSAPSO) and others programmed like particle swarm optimization (PSO), grey-wolf optimizer (GWO) and whale optimization algorithm (WOA). For first scenario; the proposed KH gives the best optimal cost of 105.94 €ct while the best emission is 420.57 kg, the best optimal cost and emission of 592.86 €ct 339.71 kg are obtained via KH in the second scenario.

Krill herd algorithm-based neural network in structural seismic reliability evaluation

ABSTRACTIn this research work, the relative displacement of the stories has been determined by means of a feedforward Artificial Neural Network (ANN) model, which employs one of the novel methods for the optimization of the artificial neural network weights, namely the krill herd algorithm. For the purpose of this work, the area, elasticity, and load parameters were the input parameters and the relative displacement of the stories was the output parameter. To assess the precision of the feedforward (FF) model optimized using the Krill Herd Optimization (FF-KH) algorithm, comparison of results has been performed relative to the results obtained by the linear regression model, the Genetic Algorithm (GA), and the back propagation neural network model. The comparison of results has been carried out in the training and test phases. It has been revealed that the artificial neural network optimized with the krill herd algorithm supersedes the afore-mentioned models in potential, flexibility, and precision.

Mitral Regurgitation Severity Analysis Based on Features and Optimal HE (OHE) with Quantification using PISA Method

Abstract Heart disease is the foremost reason for death and also the main source of incapability in the developed nations. Mitral regurgitation (MR) is a typical heart disease that does not bring about manifestations until its end position. In view of the hidden etiologies of heart distress, functional MR can be partitioned into two subgroups, ischemic and no ischemic MR. A procedure is progressed for jet area separation and quantification in MR evaluation in arithmetical expressions. Thus, a strategy that depends on echocardiography recordings, image processing methods, and artificial intelligence could be useful for clinicians, particularly in marginal cases. In this research paper, MR segmentation is analyzed by the optimal histogram equalization (OHE) system used to segment the jet area. For a better execution of the work, threshold in HE was improved with the help of the krill herd optimization (KHO) strategy. With the MR quantification procedure, this segmented jet area was supported by the proximal isovelocity surface area (PISA); in this procedure, a few parameters in the segmentation were evaluated. From the results, this proposed methodology accomplishes better accuracy in the segmented and quantification method in contrast with the existing examination.

QoE enhancement in cloud virtual machine allocation using Eagle strategy of hybrid krill herd optimization

This paper presents a novel VM allocation policy using the Eagle Strategy of Hybrid Krill Herd (KH) Optimization technique to enhance the cloud service experience to internet users. The Cloud Service provides data centre connectivity to share the resources through Virtual Machines (VM). The performance issue in Virtual Machine (VM) allocation during eccentrics degrades data centres load and clogging degrades the Quality of Experience (QoE) with Cloud Data Centres. Proposed optimized virtual machine allocation policy eradicates eccentrics effect and clogging effect, as it provides uninterrupted access throughput for better QoE. An optimization flag is additionally added in the Service Level Agreement (SLA). During burst load time, VM expansion is done automatically using Hybrid Krill Herd (KH) re-optimization if the optimization flag is set as true. Change and Response Protocol and Agreement Protocol have been proposed to predict the optimal resources for the Virtual Machine to avoid eccentrics and clogging. The prime parameters like delay, latency, packet rate and throughput are monitored continuously to activate the re-optimization and for achieving QoE with minimized load. Experimental analysis proves the capability of Hybrid KH algorithm over Particle Swarm Optimization, Ant Colony Optimization, Genetic Algorithm and Simulated Annealing algorithms.

Optimal Allocation of Measurement Devices for Distribution State Estimation Using Multiobjective Hybrid PSO–Krill Herd Algorithm

This paper proposes a new multiobjective hybrid particle swarm optimization (PSO)–krill herd (KH) Pareto-based optimization algorithm to optimize number and location of the measurement devices for accurate state estimation (SE) in smart distribution networks. Three objectives are considered to be minimized: 1) the total cost; 2) the average relative percentage error (APE) of bus voltage magnitude; and 3) APE of bus voltage angle. As the objective functions are conflicting with respect to each other, a multiobjective Pareto-based nondominated sorting hybrid PSO–KH optimization algorithm is proposed. In this approach, the random variation in loads and the metrological error of the measurement devices are also taken into account. The proposed algorithm minimizes the cost and enhances the accuracy of the distribution state estimator for better monitoring and control of the system. Furthermore, the impacts of distributed generation on SE performance are also investigated. The feasibility of the proposed algorithm is demonstrated on IEEE 69-bus system and practical Indian 85-bus radial distribution network. The results obtained are compared with conventional KH algorithm and PSO, with well-known multiobjective nondominated sorting genetic algorithm and also with an existing technique based on dynamic programming method for validation.

A comprehensive review of krill herd algorithm: variants, hybrids and applications

Krill herd (KH) is a novel swarm-based metaheuristic optimization algorithm inspired by the krill herding behavior. The objective function in the KH optimization process is based on the least distance between the food location and position of a krill. The KH method has been proven to outperform several state-of-the-art metaheuristic algorithms on many benchmarks and engineering cases. This paper presents a comprehensive review of different versions of the KH algorithm and their engineering applications. The study is divided into the following general parts: KH variants, engineering optimization/application, and theoretical analysis. In addition, specific features of KH and future directions are discussed.

Parameter Identification of a Chaotic Circuit with a Hidden Attractor Using Krill Herd Optimization

Parameter estimation plays an important role in modeling and system identification. However, parameter estimation of chaotic systems has some basic differences with other dynamical systems due to butterfly effect. In this paper, we apply a new cost function for parameter estimation in a very interesting chaotic system, a system with a plane of equilibrium which belongs to a newly introduced category of dynamical systems: systems with hidden attractor. The nonlinear dynamics of this system is described in terms of equilibria and its stability, phase portraits, bifurcation diagram and Lyapunov exponents. In order to minimize the proposed cost function and obtain the correct parameters, we use a new efficient optimization method, Krill Herd algorithm. The results show the success of proposed procedures.

Dynamic Layered Dual-Cluster Heads Routing Algorithm Based on Krill Herd Optimization in UWSNs.

Aimed at the limited energy of nodes in underwater wireless sensor networks (UWSNs) and the heavy load of cluster heads in clustering routing algorithms, this paper proposes a dynamic layered dual-cluster routing algorithm based on Krill Herd optimization in UWSNs. Cluster size is first decided by the distance between the cluster head nodes and sink node, and a dynamic layered mechanism is established to avoid the repeated selection of the same cluster head nodes. Using Krill Herd optimization algorithm selects the optimal and second optimal cluster heads, and its Lagrange model directs nodes to a high likelihood area. It ultimately realizes the functions of data collection and data transition. The simulation results show that the proposed algorithm can effectively decrease cluster energy consumption, balance the network energy consumption, and prolong the network lifetime.

Optimized sensor and sensorless control of PMSM modeled in discrete mode: a comparison of KH and PSO algorithms

Purpose The purpose of this paper is to improve dynamic performance of the permanent magnet synchronous motor (PMSM) field oriented control by means of optimizing its PI controller’s coefficients. Design/methodology/approach In this paper, Krill Herd (KH) optimization algorithm is proposed to optimize PI controller coefficients. Moreover, drive control system and steady state estimator are implemented in discrete mode. In order to evaluate the performance of the proposed algorithm, a comparison is carried out between KH and PSO algorithms. It is noteworthy that PSO algorithm has presented the best dynamic response among the studied approaches in literature. Findings By using the proposed optimization algorithm, the system tracks the reference speed with a very low error. In addition, the discrete mode simulations do not require initiating integrators. Moreover, it significantly increases the processing speed. The proposed approach is carried out in both sensor and sensorless drive systems. The simulation results indicate lower torque ripple, shorter settling time, lower overshoot, faster dynamic response and higher robustness against speed, torque and machine parameter changes. Practical implications The PI controller is extensively used in practical drive systems, due to its simple structure and implementation as well as its acceptable performance. On the other hand, PMSM benefits from high power density, high efficiency, low torque ripple, low inertia, and high performance in a wide speed range. This paper presents an efficient approach to optimize PI coefficients of the PMSM drive system. Originality/value This paper proposes a new algorithm (KH algorithm) to determine the coefficients of PI controllers in order to improve dynamic performance of the PMSM drive system.

A Multi-Stage Krill Herd Algorithm for Global Numerical Optimization

A multi-stage krill herd (MSKH) algorithm is presented to fully exploit the global and local search abilities of the standard krill herd (KH) optimization method. The proposed method involves exploration and exploitation stages. The exploration stage uses the basic KH algorithm to select a good candidate solution set. This phase is followed by fine-tuning a good candidate solution in the exploitation stage with a focused local mutation and crossover (LMC) operator in order to enhance the reliability of the method for solving global numerical optimization problems. Moreover, the elitism scheme is introduced into the MSKH method to guarantee the best solution. The performance of MSKH is verified using twenty-five standard and rotated and shifted benchmark problems. The results show the superiority of the proposed algorithm to the standard KH and other well-known optimization methods.