Krill Herd Optimization Algorithm Research Articles

Design of a computational intelligence system for detection of multiple sclerosis with visual evoked potentials

In this study, a new approach for modification of membership functions of a fuzzy inference system (FIS) is demonstrated, in order to serve as a pattern recognition tool for classification of patients diagnosed with multiple sclerosis (MS) from healthy controls (HC) using their visually evoked potential (VEP) recordings. The new approach utilizes Krill Herd (KH) optimization algorithm to modify parameters associated with membership functions of both inputs and outputs of an initial Sugeno-type FIS, while making sure that the error corresponding to training of the network is minimized.This novel pattern recognition system is applied for classification of VEP signals in 11 MS patients and 11 HC's. A feature extraction routine was performed on the VEP signals, and later substantial features were selected in an optimized feature subset selection scheme employing Ant Colony Optimization (ACO) and Simulated Annealing (SA) algorithms. This alone provided further information regarding clinical value of many previously unused VEP features as an aide for making the diagnosis. The newly designed computational intelligence system is shown to outperform popular classifiers (e.g., multilayer perceptron, support-vector machine, etc.) and was able to distinguish MS patients from HC's with an overall accuracy of 90%.

Quantum-Inspired Heuristic Algorithm for Secure Healthcare Prediction Using Blockchain Technology.

People's health is adversely affected by environmental changes and poor nutritional habits, emphasizing the importance of health awareness. The healthcare system encounters significant challenges, including data insufficiency, threats, errors, and delays. To address these issues and advance medical care, we propose a secure healthcare prediction method, prioritizing patient privacy and data transmission efficiency. The Quantum-inspired heuristic algorithm combined with Kril Herd Optimization (QKHO) is introduced for healthcare prediction, along with a comparison to the Deep Forward Neural Network (DFNN) optimized using Krill Herd Optimization (KHO) and Quantum-inspired heuristic algorithm combined with Kril Herd Optimization. The proposed QKHO model outperforms conventional models and exhibits higher accuracy, precision, recall, and F1-score. Blockchain technology ensures secure data transmission to the server, surpassing the security level of existing RSA and Diffie-Hellman algorithms.

A Comparative Study of a Hybrid Approach Combining Caesar Cipher with Triple Pass Protocol and Krill Herd Optimization Algorithm (KHO)-Based Hybridization

A Comparative Study of a Hybrid Approach Combining Caesar Cipher with Triple Pass Protocol and Krill Herd Optimization Algorithm (KHO)-Based Hybridization

Krill herd optimization algorithm with deep convolutional neural network fostered breast cancer classification using mammogram images

SummaryIn this paper proposes a Krill Herd Optimization algorithm with Deep Convolutional neural network fostered Breast Cancer Classification using Mammogram Images (BC‐APPDRC‐DCNN‐KHO). Here, the input images are taken from Real time and MAMMOSET datasets. These images are pre‐processed using Altered Phase Preserving Dynamic Range Compression (APPDRC) technique. This APPDRC is applied for preserving local features, compressing dynamic range of images, and enhancing the speckle noise filtering, these are all necessary for better boundary detection. Then, the Pre‐processed images are classified using Deep Convolutional neural network (DCNN). The DCNN weight parameters are optimized based on Krill Herd Optimization algorithm. The Proposed BCC‐DCNN‐KHO‐MI method classifies the input breast cancer imageries into 3 categories: benign, malignant, and normal. The proposed BCC‐DCNN‐KHO‐MI method in Real time dataset attains 18.505%, 19.45%, 16.19%, 17.56% and 16.19% higher accuracy; 15.38%, 12.06%, 12.71%, 26.62% and 18.902% higher Precision; 3.12%, 10.52%, 13.57%, 22.75% and 14.93% higher F‐score, 59.56%, 41.25%, 56.47%, 42.36% and 37.27% lower computation time; 23.87%, 21.87%, 32.87%, 42.76% and 21.05% higher AUC compared with the existing methods, like BCC‐Google Net‐MI, BCC‐Visual Geometry Group Network‐MI, BCC‐Residual Networks‐MI, BC‐RERNN‐LOA‐MI and BC‐CNN‐MI respectively.

Performance analysis and comparison of gray wolf optimization and Krill herd optimization algorithm

Performance analysis and comparison of gray wolf optimization and Krill herd optimization algorithm

A multi-objective optimization of resource management and minimum batch VM migration for prioritized task allocation in fog-edge-cloud computing

Network resources and traffic priorities can be utilized to distribute requested tasks across edge nodes at the edge layer. However, due to the variety of tasks, the edge nodes have an impact on data accessibility. Resource management approaches based on Virtual Machine (VM) migration, job prioritization, and other methods were used to overcome this problem. A Minimized Upgrading Batch VM Scheduling (MSBP) has recently been developed, which reduces the number of batches required to complete a system-scale upgrade and assigns bandwidth to VM migration matrices. However, due to poor resource sharing caused by suboptimal VM utilization, the MSBP was unable to effectively ensure the global best solutions. In order to distribute resources and schedule tasks optimally during VM migration, this paper proposes the MSBP with Multi-objective Optimization of Resource Allocation (MORA) method. The major goal of this proposed methodology is to take into account different objectives and solve the Pareto-front problem to enhance lifetime of the fog-edge network. First, it formulates an NP-hard challenge for MSBP by taking into account a variety of factors such as network sustainability, path contention, network delay, and cost-efficiency. The Multi-objective Krill Herd optimization (MoKH) algorithm is then used to address the NP-hard issue using the Pareto optimality rule and produce the best solution. First, it introduces an NP-hard challenge for MSBP by accounting in network sustainability, path contention, network latency, and cost-efficiency. The Pareto optimality rule is then implemented to overcome the NP-hard problem and provide the optimum solution employing the Multi-objective Krill Herd optimization (MoKH) algorithm. This increases network lifetime and improves resource allocation cost efficiency. Finally, the simulation results show that the MSBP-MORA distributes resources more efficiently and hence increases network lifetime when compared to other traditional algorithms.

Trajectory planning and control of multiple mobile robot using hybrid MKH-fuzzy logic controller

AbstractRobotics with artificial intelligence techniques have been the center of attraction among researchers as it is well equipped in the area of human intervention. Here, the krill herd (KH) optimization algorithm is modified and hybridized with a fuzzy logic controller to frame an intelligent controller for optimal trajectory planning and control of mobile robots in obscure environments. The controller is demonstrated for single and multiple robot’s trajectory planning. A Petri-net controller has also been added to avoid conflict situations in multi-robot navigation. MATLAB and V-REP software are used to simulate the work, backed with real-time experiments under laboratory conditions. The robots efficiently achieved the goals by tracing an optimal path without any collision. Trajectory length and time spent during navigation are recorded, and a good agreement between the results is observed. The proposed technique is compared against existing research techniques, and an improvement of 14.26% is noted in terms of path length.

Multiclass cyber-attack classification approach based on the Krill Herd Optimized Deep Neural Network (KH-DNN) model for WSN

The Intrusion Detection System (IDS) is a useful infiltration detection tool. With IDS, it may mechanically categorize intrusions, attacks, or breaches of security stratagems across host-level within the network and within the organization. The Krill Herd Optimized Deep Neural Network (KH-DNN) is proposed in this document as a multiclass cyberattack methodology. This model is suggested by uniting the network-based intrusion detection system (NIDS) and the host-based intrusion detection system (HIDS) to advance flexible and actual IDS to categorize and detect unexpected and unpredictable cyber attacks. In this method, two types of IDS are classified, that is, NIDS and HIDS. Classification of the system call is exploited to discriminate behavior into normal and attack categories. In this manuscript accuracy, the parameter of the deep neural network classifier is optimized with the Krill Herd optimization algorithm. Finally, the DNN model is performed in the NIDS dataset and the HIDS dataset. For the HIDS dataset, the KDD Cup 99 dataset was used, which employed two datasets: ADFA-LD and ADFA-WD, and several types of attacks were detected and classified, including Adduser, Hydra-FTP, Java-Meterpreter, Hydra-SSH, and Web-Shell. The suggested IDS-KH-DNN algorithm attains higher accuracy 42.56% and 23.4%, high precision 84.74% and 52.43%, high F-score 53.5% 64.455, high sensitivity 33.4% and 45.23%, and high specificity 31.45% and 44.23% for the NIDS dataset. The proposed system is compared to two current processes: intrusion detection using Gray Wolf optimization-based support vector machine (IDS-GWO-SVM) and intrusion detection using artificial bee colony optimization algorithm based support vector machine (IDS-ABCO-SVM). Finally, the simulation results show that the suggested approach is capable of quickly and accurately locating optimal solutions.

Categorization of breast masses based on deep belief network parameters optimized using chaotic krill herd optimization algorithm for frequent diagnosis of breast abnormalities

AbstractSeveral deep learning techniques are utilized for classification of the masses in mammogram images. But the existing method doesnot provide sufficient accuracy . for breast masses classification in mammogram images. In this manuscript, an efficient breast cancer image classification framework utilizing the deep belief network (DBN) through chaotic krill herd optimization (CKHO) algorithm for classification of masses in mammogram images is proposed. Initially the input mammogram images are pre‐processed by altered phase preserving dynamic range compression (APPDRC) method for removing the unwanted noise and artifacts. Then these pre‐processed images are assumed with DBN for classifying that mass in the mammogram images into normal, benign, and malignant lesions. Generally, DBN does not reveal any acceptance of optimization techniques for computing optimal parameters that guarantee an accurate classification. Therefore in this work, proposed CKHO algorithm is employed for optimizing weight parameters of the self‐attention convolutional neural network (SACNN). The simulation process is performed under MATLAB platform. Finally, the proposed DBN with chaotic krill herd optimization algorithm (DBN‐CKHO) attains high accuracy 44.5%, 33.42%, 55.23%, 62.35%, and 43.42% when compared with the existing method such as extreme learning machine classifier with moth flame optimization (ELMC‐MFO), Kernel extreme learning machine classifier with Chaotic salp swarm algorithm (KELMC‐CSSA), Extreme learning machine classifier with fruit fly optimization algorithm (ELMC‐FOA), Kernel extreme learning machine classifier with grasshopper optimization algorithm (KELMC‐GOA) and deep neural network with non‐dominated sorting genetic algorithm‐based classification approach.

Multi-exposure optimized contrast and brightness balance color image enhancement

Color image enhancement has been very challenging for researchers in imaging systems. In this paper, optimal weighted multi-exposure histogram equalization model is proposed for image contrast enhancement. The proposed method divides the input image into two sub-images, specifically low-exposed and high-exposed regions. Then, exposure information obtained from these sub-images is used to divide the input histogram into sub-histograms, and the level of excessive enhancement is controlled by clipping and the optimal weighting process before applying histogram equalization. Next, each sub-histogram is equalized individually and integrated to preserve the naturalness and fine details of the input image. Finally, dual gamma correction is adopted to improve the contrast in dark areas. In the proposed model, a metaheuristic stud krill herd optimization algorithm is employed to determine the optimal constraints to optimize the degree of enhancement based on the objective function defined as discrete entropy or mean square error. Experimental results prove that the proposed technique produces a better visual quality image with an attractive color tone and improved natural appearance without introducing artifacts.

Intrusion Detection System for Energy Efficient Cluster Based Vehicular Adhoc Networks

A vehicular ad hoc network (VANET), a subfield of mobile adhoc network (MANET) is defined by its high mobility by demonstrating the dissimilar mobility patterns. So, VANET clustering techniques are needed with the consideration of the mobility parameters amongst the nearby nodes for constructing the stable clustering techniques. At the same time, security is also a major design issue in VANET, this can be resolved by the intrusion detection systems (IDS). In contrast to the conventional IDS, VANET based IDS are required to be designed in such a way that the functioning of the system does not affect the real-time efficiency of the performance of VANET applications. With this motivation, this paper presents an efficient Fuzzy Logic based Clustering with optimal fuzzy support vector machine (FSVM), called FLC-OFSVM based on the Intrusion Detection System for VANET. The proposed FLC-OFSVM model involves two stages of operations namely clustering and intrusion detections. Primarily, FLC technique is employed for selecting an appropriate set of cluster heads (CHs) and for constructing the clusters. Besides, a lightweight anomaly IDS model named FSVM optimized with krill herd (KH) optimization algorithm is developed for detecting the existence of malevolent attacks in VANET. The KH algorithm based on the herding behavior of krills is used for optimally tuning the parameters of the FSVM model. In order to investigate the performance of the FLC-OFSVM model, an extensive set of simulations have been carried out and the results thus showcased that the OFSVM model has gained maximum outcome with an accuracy of 99.98%.

Social Networks Fake Account and Fake News Identification with Reliable Deep Learning

Recent developments of the World Wide Web (WWW) and social networking (Twitter, Instagram, etc.) paves way for data sharing which has never been observed in the human history before. A major security issue in this network is the creation of fake accounts. In addition, the automatic classification of the text article as true or fake is also a crucial process. The ineffectiveness of humans in distinguishing the true and false information exposes the fake news as a risk to credibility, democracy, logical truth, and journalism in government sectors. Besides, the automatic fake news or rumors from the social networking sites is a major research area in the field of social media analytics. With this motivation, this paper develops a new reliable deep learning (DL) based fake account and fake news detection (RDL-FAFND) model for the social networking sites. The goal of the RDL-FAFND model is to resolve the major problems involved in the social media platforms namely fake accounts, fake news/rumor identification. The presented RDL-FAFND model detects the fake account by the use of a parameter tuned deep stacked Auto encoder (DSAE) using the krill herd (KH) optimization algorithm for detecting the fake social networking accounts. Besides, the presented RDL-FAFND model involves an ensemble of the machine learning (ML) models with different linguistic features (EML-LF) for categorizing the text as true or fake. An extensive set of experiments have been carried out for highlighting the superior performance of the RDL-FAFND model. A detailed comparative results analysis has stated that the presented RDL-FAFND model is considerably better than the existing methods.

Rubber bushing optimization by using a novel chaotic krill herd optimization algorithm

Rubber bushing optimization by using a novel chaotic krill herd optimization algorithm

Probabilistic planning of the active and reactive power sources constrained to securable-reliable operation in reconfigurable smart distribution networks

One of the main goals of SDN is to achieve the desirable operation of a highly secure and reliable network in terms of energy distribution. Hence, this paper presents the probabilistic planning of distributed generations (DGs) and switched capacitive bank (SCB) constrained to the securable-reliable operation (SRO) strategy in reconfigurable SDN. The scheme is formulated as a four-objective optimization problem to simultaneously model the economic, operation, reliability, and security indices of SDN. So that the first to fourth objective minimize the total planning cost and expected operation cost, voltage deviation function (VDF), expected energy not-supplied (EENS), and voltage security index (VSI). It is limited to the AC optimal power flow (AC-OPF) equations, planning and operation model of DG and SCB, formulation of demand response program (DRP), SDN reconfiguration constraints, reliability and voltage security limits. Then, the single-objective model is obtained by using the ε constraint-based Pareto optimization technique. Additionally, the non-parametric probabilistic method models uncertainties of load, energy price, renewable DGs (RDGs) power, and network equipment availability. The strategy is as mixed-integer nonlinear programming (MINLP), where hybrid Krill Herd Optimization (KHO) and Crow Search Algorithm (CSA) achieves a reliable optimal solution. The following provides the planning of different sources based on the SRO strategy with simultaneous formulation of various economic and technical indices considering non-parametric modeling of uncertainties and solving the problem using the mentioned hybrid algorithm presented the contribution in this paper. By implementing this scheme on a 69-bus SDN, the numerical results confirm capability of this scheme in improving the economic, operation, reliability, and security situation of SDN compared to power flow studies. Where it can enhance the mentioned indices up to 16%, 36%, 99%, and 9% taking into account optimal planning and operation of different sources and the DRP compared to the corresponding values obtained by power flow studies.

Adaptive robust optimization for the energy management of the grid-connected energy hubs based on hybrid meta-heuristic algorithm

This paper describes the energy management of energy hubs connected to electricity, gas, and heating networks in which the hub is incorporated as a coordination framework between distributed generations and energy storage systems. The deterministic model of the proposed scheme minimizes the total operating cost of these energy networks in the presence of energy hubs constrained to the optimal power flow equations of different networks and the formulation of hubs with sources and storages. The problem is subject to uncertainties of load, energy prices, renewable sources, and consumption energy of mobile storages. Additionally, the scheme inherently is a non-convex mixed-integer nonlinear programming framework. Adaptive robust optimization is used to model these uncertainties, which is based on a hybrid metaheuristic algorithm due to the nonlinear and non-convex nature of the proposed problem. Hence, a combination of Ant-lion Optimizer and Krill herd Optimization algorithm has been employed, which provides a robust optimal solution with approximate unique response conditions in the worst-case scenario. Eventually, the numerical results obtained by implementing the proposed scheme on a sample test system confirm the capability of the mentioned scheme in improving the operation condition of different energy networks in the worst-case scenario. Consequently, the total energy loss in all mentioned networks and maximum voltage and temperature drop decrease by roughly 8%, 44%, and 74% with respect to power flow analysis in this scenario.

Energy Aware Enhanced Krill Herd Algorithm Enabled Clustering for Unmanned Aerial Vehicles

Recently, unmanned aerial vehicles (UAV) have gained maximum interest in diverse applications ranging from military to civilian areas. The presence of numerous energy-constrained UAVs in an adhoc manner poses several design issues. At the same time, the limited battery, high mobility, and adaptive characteristics of the UAVs need effective design of clustering techniques for UAVs. In this manner, this paper presents a levy flight with a krill herd optimization algorithm (LF-KHOA) for energy-efficient clustering in UAVs. The proposed LF-KHOA technique integrates the concepts of LF to the KHOA to enhance efficiency and search space exploration. In addition, the LF-KHOA technique derives a fitness function involving three input parameters to elect cluster heads (CHs) and organize clusters. The energy consumed by the UAVs depends on the distance from UAVs to nearby nodes. Therefore, the fitness function aims to decrease communication distance, which mitigates energy utilization when transmitting the information. To ensure the better performance of the LF-KHOA technique, an extensive set of simulations takes place, and the results are inspected in terms of different measures. The experimental results highlighted the betterment of the LF-KHOA technique over the current state of art techniques.

Intrusion Detection System for Energy Efficient Cluster based Vehicular Adhoc Networks

A vehicular Adhoc Network, a subfield of Mobile Adhoc Network is defined by its high mobility and by demonstrating dissimilar mobility patterns. So, VANET clustering techniques are needed with the consideration of the mobility parameters amongst nearby nodes to construct stable clustering techniques. At the same time, security is also a major design issue in VANET which can be resolved by the Intrusion Detection Systems. In contrast to the conventional IDS, VANET based IDS are required to be designed in such a way that the functioning of the system does not affect the real-time efficiency of the performance of VANET applications. With this motivation, this paper presents an efficient Fuzzy Logic based Clustering with optimal Fuzzy Support Vector Machine, called FLC-OFSVM based Intrusion Detection System for VANET. The proposed FLC-OFSVM model involves two stages of operations namely clustering and intrusion detection. Primarily, FLC technique is employed to select an appropriate set of cluster heads and construct clusters. Besides, a lightweight anomaly IDS model named FSVM optimized with krill herd optimization algorithm is developed to detect the existence of malevolent attacks in VANET. The KH algorithm which is based on the herding behavior of krillsis used to optimally tune the parameters of the FSVM model. In order to investigate the performance of the FLC-OFSVM model, an extensive set of simulations are carried out and the results are investigated in terms of several performance measures.

Standard Fractal Medical Image Compression with Krill Herd Optimization Algorithm

Standard Fractal Medical Image Compression with Krill Herd Optimization Algorithm

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.

Multi-objective Virtual Machine Selection in Cloud Data Centers Using Optimized Scheduling

In cloud computing, more often times cloud assets are underutilized because of poor allocation of task in virtual machine (VM). There exist inconsistent factors affecting the scheduling tasks to VMs. In this paper, an effective scheduling with multi-objective VM selection in cloud data centers is proposed. The proposed multi-objective VM selection and optimized scheduling is described as follows. Initially the input tasks are gathered in a task queue and tasks computational time and trust parameters are measured in the task manager. Then the tasks are prioritized based on the computed measures. Finally, the tasks are scheduled to the VMs in host manager. Here, multi-objectives are considered for VM selection. The objectives such as power usage, load volume, and resource wastage are evaluated for the VMs and the entropy is calculated for the measured objectives and based on the entropy value krill herd optimization algorithm prioritized tasks are scheduled to the VMs. The experimental results prove that the proposed entropy based krill herd optimization scheduling outperforms the existing general krill herd optimization, cuckoo search optimization, cloud list scheduling, minimum completion cloud, cloud task partitioning scheduling and round robin techniques.