Krill Herd Optimization 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%.

Partition Clustering in Complex Weighted Networks Using K-Cut Ranking and Krill-Herd Optimization

Partition Clustering in Complex Weighted Networks Using K-Cut Ranking and Krill-Herd Optimization

C-KHCS: Multi-Objective Workflow Scheduling using Chaotic Krill Herd Optimization and Improved Cuckoo Search in Fog Computing

C-KHCS: Multi-Objective Workflow Scheduling using Chaotic Krill Herd Optimization and Improved Cuckoo Search in Fog Computing

Meta-algorithmic optimized power allocation in cybertwin-based sixth generation cooperative communication system

The sixth-generation (6G) communication paradigm, powered by an Internet of Everything (IoE), promises unprecedented data density and transformative applications across various sectors like smart cities, healthcare, transportation, and agriculture. This study explores an emerging hybrid cybertwin-enabled 6G cooperative network architecture, focusing on secrecy capacity analysis. The approach involves Base Stations (BS) employing Non-Orthogonal Multiple Access (NOMA) techniques to transmit data to cybertwin hosts and servers via direct wireless links with Nakagami-m fading. Additionally, a Power Line Communication (PLC) link, subjected to Rayleigh fading, supplements the wireless link for communication between cybertwin hosts and servers. Bio-meta-heuristic optimization algorithms are introduced to optimize NOMA power allocation, specifically Grey Wolf Optimization (GWO) and a hybrid of Grey Wolf Optimization with Krill Herd Optimization (KHO). The system's capability to ensure secure data transmission while mitigating eavesdropping risks is assessed through thorough secrecy capacity and throughput analyses. Simulation results highlight the efficacy of the hybrid GWO-KHO-based NOMA power allocation scheme, showcasing superior efficiency over traditional methods. This study contributes novel insights into optimized power allocation and efficiency in cybertwin-enabled 6G networks, underscoring the significance of hybrid optimization approaches in future communication systems.

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.

SSKHOA: Hybrid Metaheuristic Algorithm for Resource Aware Task Scheduling in Cloud-fog Computing

Cloud fog computing is a new paradigm that combines cloud computing and fog computing to boost resource efficiency and distributed system performance. Task scheduling is crucial in cloud fog computing because it decides the way computer resources are divided up across tasks. Our study suggests that the Shark Search Krill Herd Optimization (SSKHOA) method be incorporated into cloud fog computing's task scheduling. To enhance both the global and local search capabilities of the optimization process, the SSKHOA algorithm combines the shark search algorithm and the krill herd algorithm. It quickly explores the solution space and finds near-optimal work schedules by modelling the swarm intelligence of krill herds and the predator-prey behavior of sharks. In order to test the efficacy of the SSKHOA algorithm, we created a synthetic cloud fog environment and performed some tests. Traditional task scheduling techniques like LTRA, DRL, and DAPSO were used to evaluate the findings. The experimental results demonstrate that the SSKHOA outperformed the baseline algorithms in terms of task success rate increased 34%, reduced the execution time by 36%, and reduced makespan time by 54% respectively.

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

HDQNN-Net: An Optimal Asthma Disease Detection Technique for Voice Signal Using Hybrid Deep Q-Neural Networks

Recently, asthma patients are severely suffering COVID-19 disease, thus the asthma has become one of the dangerous diseases in the world. Further, asthma is occurring in all age groups, which causing huge loss to patient’s health. The primary way to detect the asthma in humans is done by their speech signals, as the asthma severity is increases, which manipulates the properties of speech signal. The conventional methods are failed to extract the maximum features from the speech signals, which resulted in low classification performance. Thus, this article is focused on implementation of real time asthma disease detection and identification technique from speech signals using Hybrid Deep Q Neural Networks (HDQNN). Initially, the features from the speech signals are extracted by using Krill herd optimization (KHO) approach, which extracts the detailed disease specific features. Further, the optimal features are extracted by using chaotic opposition krill herd optimization (COKHO) algorithm. Then, HDQNN is used to classify the type of asthma such as normal, and stridor classes. Further, COKHO is also used to optimize the losses generated in the HDQNN model. The simulation results shows that the proposed HDQNN method resulted in superior performance as compared to state of art approaches.

A Hierarchical Optimized Resource Utilization based Content Placement (HORCP) model for cloud Content Delivery Networks (CDNs)

Content Delivery Networks (CDNs) have grown in popularity as a result of the ongoing development of the Internet and its applications. The workload on streaming media service systems can be significantly decreased with the help of the cooperative edge-cloud computing architecture. In the traditional works, a different types of content placement and routing algorithms are developed for improving the content delivery of cloud systems with reduced delay and cost. But, the majority of existing algorithms facing complexities in terms of increased resource usage, ineffective delivery, and high system designing complexity. Therefore, the proposed work aims to develop a new framework, named as, Hierarchical Optimized Resource Utilization based Content Placement (HORCP) model for cloud CDNs. Here, the Chaotic Krill Herd Optimization (CKHO) method is used to optimize the resource usage for content placement. Then, a Hierarchical Probability Routing (HPR) model is employed to enable a dependable end-to-end data transmission with an optimized routing path. The performance of the proposed HORCP model is validated and compared by using several performance metrics. The obtained results are also compared with current state-of-the-art methodologies in order to show the superiority of the proposed HORCP model. By using the HORCP mechanism, the overall memory usage of the network is reduced to 80%, CPU usage is reduced to 20%, response is minimized to 2 s, and total congestion cost with respect to the network load level is reduced to 100.

Diagnose crop disease using Krill Herd optimization and convolutional neural scheme

Diagnose crop disease using Krill Herd optimization and convolutional neural scheme

Design and Optimization of PI Controller for Multiple-output Wireless Electric Vehicle Charger

Abstract: This paper focuses on designing and modeling multiple-output wireless chargers for electric vehicles (EVs). This paper uses a closed loop buck converter to obtain variable output dc voltage of a wireless electric vehicle charger (WEVC). For designing a closed-loop buck converter, a PI controller is used. The tuning of a PI controller for a nonlinear system is a typical task; therefore, in the paper, three optimization techniques such as Krill Herd Optimization (KHO), Harris Hawks Optimization (HHO), and Sparrow Search Optimization (SSO) utilized to find the optimum value of a PI gain parameters (Kp and Ki). Background: Wired charging system is famous but contains problems like unkempt wires and protection concerns in a damp environment. Then a solution to this problem is a wireless charger because it does not contain any exposed wires. Hence it is convenient for charging and intrepid transmission of power in antagonistic environmental conditions. A wireless charger can be used in unmanned electric vehicles. Objective: The objective of this study is to develop a wireless electric vehicle charger at the variable output voltage. The performance analysis by different optimization techniques such as SSO, KHO, and HHO for variable reference output voltage. Methods: Three optimization techniques are used, such as SSO, KHO, and HHO, for obtaining smooth output dc voltage, reducing harmonics, and improving power transfer capability. Results: SSO gives better results for the variable output voltage of the wireless charger. Conclusion: Wireless charger is designed at a variable reference output voltage and SSO-optimized PI-gain parameters of the charger and provides better performance than other optimization techniques, such as HHO and KHO.

Enhanced Dual-Selection Krill Herd Strategy for Optimizing Network Lifetime and Stability in Wireless Sensor Networks.

Wireless sensor networks (WSNs) enable communication among sensor nodes and require efficient energy management for optimal operation under various conditions. Key challenges include maximizing network lifetime, coverage area, and effective data aggregation and planning. A longer network lifetime contributes to improved data transfer durability, sensor conservation, and scalability. In this paper, an enhanced dual-selection krill herd (KH) optimization clustering scheme for resource-efficient WSNs with minimal overhead is introduced. The proposed approach increases overall energy utilization and reduces inter-node communication, addressing energy conservation challenges in node deployment and clustering for WSNs as optimization problems. A dynamic layering mechanism is employed to prevent repetitive selection of the same cluster head nodes, ensuring effective dual selection. Our algorithm is designed to identify the optimal solution through enhanced exploitation and exploration processes, leveraging a modified krill-based clustering method. Comparative analysis with benchmark approaches demonstrates that the proposed model enhances network lifetime by 23.21%, increases stable energy by 19.84%, and reduces network latency by 22.88%, offering a more efficient and reliable solution for WSN energy management.

Design performance optimization of laser beam welded joints made for vehicle chassis application using deep neural network-based Krill Herd method

The welding to be performed must be defect free, offer lower residual stress and strain, be compact in size and withstand different load conditions. However, the existing investigations in this scenario are still not modernized. Therefore, in this study, a specific welding method called laser beam welding (LBW) is performed and different weld parameters have been inspected and analysed. Advanced instruments based on the non-destructive (ND) are implemented to find the variable LBW responses such as weld bead defects, residuals and strain. The experimentation has been designed using Design Expert software, response surface methodology (RSM) and Box Behnken design (BBD) and verified by analysis of variance (ANOVA) analysis and FIT statistics. Moreover, a hybrid deep neural network-based Krill Herd optimization (DNN-KHO) is implemented to predict the output parameters like, undercut (µm), overlap (µm), total strain (mm/mm) and residual stress (MPa) during welding. The proposed DNN-KHO was also used to optimize LBW input parameters such as, peak power (W), weld speed (mm/s), gas flow rate (l/min) and beam diameter (µm) simultaneously. Predictions show that the proposed DNN-KHO algorithm outperformed by 21.53%, 45.428% and 41.31% higher in accuracy compared to respective hybrid random forest based grey wolf method (RF-GWO), RF and DNN predictions.

TEA-EKHO-IDS: An intrusion detection system for industrial CPS with trustworthy explainable AI and enhanced krill herd optimization

TEA-EKHO-IDS: An intrusion detection system for industrial CPS with trustworthy explainable AI and enhanced krill herd optimization

Virtual power plants planning in the distribution network constrained to system resiliency under extreme weather events

The present study finds the proper location and size of virtual power plants (VPPs) in distribution networks, while taking into account network resilience to severe weather conditions. This is formulated as an optimization problem with two objective functions to minimize both the planning cost of the VPPs in a year and expected energy not-supplied of the network during natural events like flood and earthquake. The problem is subject to some boundaries, including AC power flow relationships, limits on operating and resilience situation, and planning terms of the VPP. There are also uncertain quantities present in the problem, such as those related to the amount of load, renewable power generation, price of energy, and accessibility of equipment and VPP devices. The uncertainties are modeled using the stochastic programming. The two objectives are merged to be a single objective by adopting Pareto optimization based on ɛ-constraint method. Krill herd optimization (KHO) and grey wolf optimization (GWO) are also combined to introduce a new hybrid algorithm so that a dependable optimal solution that shows low dispersion. Finally, a 33-bus distribution network is used to test the validity of the proposed method. According to the result, the optimal size and site of VPPs are found and the network achieves more suitable economic, operating, and resilience indices.

An optimized model for network intrusion detection systems in industry 4.0 using XAI based Bi-LSTM framework.

Industry 4.0 enable novel business cases, such as client-specific production, real-time monitoring of process condition and progress, independent decision making and remote maintenance, to name a few. However, they are more susceptible to a broad range of cyber threats because of limited resources and heterogeneous nature. Such risks cause financial and reputational damages for businesses, well as the theft of sensitive information. The higher level of diversity in industrial network prevents the attackers from such attacks. Therefore, to efficiently detect the intrusions, a novel intrusion detection system known as Bidirectional Long Short-Term Memory based Explainable Artificial Intelligence framework (BiLSTM-XAI) is developed. Initially, the preprocessing task using data cleaning and normalization is performed to enhance the data quality for detecting network intrusions. Subsequently, the significant features are selected from the databases using the Krill herd optimization (KHO) algorithm. The proposed BiLSTM-XAI approach provides better security and privacy inside the industry networking system by detecting intrusions very precisely. In this, we utilized SHAP and LIME explainable AI algorithms to improve interpretation of prediction results. The experimental setup is made by MATLAB 2016 software using Honeypot and NSL-KDD datasets as input. The analysis result reveals that the proposed method achieves superior performance in detecting intrusions with a classification accuracy of 98.2%.

Optimized scheduling with prioritization to enhance network sustainability in edge-cloud environment

Due to decentralized infrastructure in modern Internet-of-Things (IoT), the tasks should be shared around the edge devices via network resources and traffic prioritizations, which weaken the information interoperability. To solve this issue, a Minimized upgrading batch Virtual Machine (VM) Scheduling and Bandwidth Planning (MSBP) was adopted to reduce the amount of batches needed to complete the system-scale upgrade and allocate the bandwidth for VM migration matrices. But, the suboptimal use of VM and possible loss of tasks may provide inadequate Resource Allocation (RA). Hence, this article proposes an MSBP with the Priority-based Task Scheduling (MSBP-PTS) algorithm to allocate the tasks in a prioritized way and maximize the profit by deciding which request must handle by the edge itself or send to the cloud server. At first, every incoming request in its nearest fog server is allocated and processed by the priority scheduling unit. Few requests are reallocated to other fog servers when there is an inadequate resource accessible for providing the request within its time limit. Then, the request is sent to the cloud if the fog node doesn’t have adequate resources, which reduces the response time. However, the MSBP is the heuristics solution which is complex and does not ensure the global best solutions. Therefore, the MSBP-PTS is improved by adopting an Optimization of RA (MSBP-PTS-ORA) algorithm, which utilizes the Krill Herd (KH) optimization instead of heuristic solutions for RA. The simulation outcomes also demonstrate that the MSBP-PTS-ORA achieve a sustainable network more effectively than other traditional algorithms.

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.

Co‐planning of generation and transmission expansion planning for network resiliency improvement against extreme weather conditions and uncertainty of resiliency sources

Generation and transmission expansion increase the flexibility of power systems and hence their ability to deal with contingency. This paper presents a resilient-constrained generation and transmission expansion planning (RCGTEP) model considering the occurrence of earthquakes and floods. The proposed model minimizes the investment and operation costs of resiliency sources (RSs) and resiliency (blackout) costs arising from the outage of the network against the occurrence of extreme conditions. For further consideration, uncertainties of load and RSs availability are included as a Stochastic programming model. A hybrid solver of teaching-learning-based optimization (TLBO) and krill herd optimization (KHO) is used to solve the proposed problem and achieve the optimal solution, including a low standard deviation in the final optimal response. The model is tested using a modified version of the IEEE 6-Bus and IEEE 89-Bus transmission networks. Numerical results show the potential of the mentioned approach to improve indices of operation, economics, and resiliency in the transmission network.

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.