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Investigation of Task Scheduling in Cloud Computing by using Imperialist Competitive and crow search algorithms

Procedia Computer Science 2023年 230卷 879-889页

作者： Jayavadivel Ravi Rajkumar N Viji C Loganathan D K S N Sushma Stalin M Department of Computer Science & Engineering Alliance College of Engineering and Design Alliance University Bangalore Karnataka India Department of Information Science & Engineering Cambridge Institute of Technology Bangalore Karnataka India Research Scholar School of Computer Science and Engineering REVA University Bangalore Karnataka India

Cloud Storage is a complex method that is a method of processing and data of a cloud built by duplication of thousands of related devices in a complex manner.... 详细信息

Cloud Storage is a complex method that is a method of processing and data of a cloud built by duplication of thousands of related devices in a complex manner. The main function of the data processing server is to show how many users are being investigated and provide accurate, efficient and efficient information. Important algorithm Editing Players in the Cloud Defines the virtual machine (VM) required for this purpose. The role of editing the algorithm reduces the effect of the schedule. Naturally affected algorithms have recently been used to quickly comply with the recent traditional algorithms. Given many consumers of many cloud computing services, many researchers may have a serious explanation that many researchers take and discuss the complex themes of NP. Some sites use imperialist algorithms (ICA) and birds. The purpose of the proposed project is to develop intelligent scientific algorithms that focus on the integration of ICA and CSA to obtain data. CSA is concentrated on the corner of food habits. The crow is looking for his friends to get enough food for today's food. This will help the CSA find suitable VMs for these machines and complete the equipment. Cloud Sim is used to calculate CSA output with minmin and ant algorithms. The simulation results show that the CSA is extra powerful than the MinMin and Ant procedures .

关键词： crow search algorithm Imperialist competitive algorithm. Task scheduling Nature-inspired and Cloud computing

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Enhanced Automatic Generation Control in Multiarea Power Systems: crow search Optimized Cascade FOPI-TIDDN Controller With Integrated Renewable Solar Thermal Models and HVDC Lines

Engineering Reports

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Engineering Reports 2025年第5期7卷

作者： Naladi Ram Babu Pamarthi Sunitha Ganesh Pardhu B. S. S. Sanjeev Kumar Bhagat Adireddy Ramesh Arindita Saha Wulfran Fendzi Mbasso Pradeep Jangir Ahmed Hossam-Eldin Department of Electrical and Electronics Engineering Aditya University Kakinada India Department of Electronics and Communication Engineering Aditya University Kakinada India Department of Electrical Engineering Sandip University Madhubani India Department of Electrical Engineering Regent Education and Research Foundation Group of Institutions Kolkata India Technology and Applied Sciences Laboratory U.I.T. of Douala University of Douala Douala Cameroon Department of Biosciences Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Applied Science Research Center Applied Science Private University Amman Jordan University Centre for Research and Development Chandigarh University Gharuan Mohali India Department of CSE Graphic Era Hill University Dehradun India Department of CSE Graphic Era Deemed to be University Dehradun India Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Rajpura Punjab India Department of Electrical and Electronics Engineering J.J. Technology Tiruchirappalli Tamilnadu India Department of Electrical Power and Machines Faculty of Engineering Alexandria University Alexandria Egypt

As renewable energy sources (RES) are increasingly unified into multiarea power systems, automatic generation control (AGC) faces challenges such as frequency instability, longer settling times, and higher overshoot. While existing optimization techniques like Firefly (FF) and gray wolf (GW) suffer from slow convergence and local optima trapping, conventional controllers like FOPI, PIDN, TIDN, and TIDDN struggle to maintain stability under fluctuating load conditions. Fractional-Order Proportional-Integral with Tilt Integral Double Derivative and Filter (FOPI-TIDDN), a novel cascade controller optimized using the crow search (CS) algorithm, is proposed in this paper to overcome these issues. Furthermore, a two-area AGC framework incorporates realistic dish-Stirling solar thermal systems (RDSTS) and parabolic trough solar thermal plants (PTSTP), and the effects of these systems are examined under different patterns of solar insolation. Additionally, the study assesses how high voltage direct current (HVDC) tie-lines contribute to increased system stability. According to simulation data, the FOPI-TIDDN controller works noticeably better than others in terms of improved frequency regulation, faster settling time, and less overshoot. Compared to FF and GW approaches, the CS algorithm guarantees faster convergence. An ideal fixed-random solar insolation method and HVDC integration also improve system performance. The suggested method enhances renewable-integrated power systems' resilience, efficiency, and stability.

关键词： controller crow search algorithm PTSTP RDSTS solar insolations

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Fractional crow search-based support vector neural network for patient classification and severity analysis of tuberculosis

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IET IMAGE PROCESSING 2019年第1期13卷 108-117页

作者： Chithra, R. S. Jagatheeswari, P. Ponjesly Coll Engn Nagercoil 629003 Tamil Nadu India

The world is chasing towards the automation in the severity analysis and classification of the patients based on the severity of tuberculosis (TB). The automatic classification is very much useful for developing countries that are struggling to reduce the fatality rate of the persons suffering from TB as it is a top standing infectious disease. Thus, the automatic classification of the TB patients using the sputum images with the proposed fractional crow search-based support vector neural network is presented. The proposed classification method is the integration of the fractional theory in the crow search algorithm that increases the computational speed and reduces the cost and time spent on analysing the test samples. The importance of the proposed method is that it requires minimum manual power and hence, the inaccuracies are reduced. The experimentation performed using the Ziehl-Neelsen sputum smear microscopy image database proves that the proposed classifier is highly accurate and offered an improved performance in terms of accuracy rate, true positive rate, and false-positive rate.

关键词： medical image processing support vector machines image segmentation neural nets diseases image classification biomedical optical imaging image colour analysis optical microscopy fractional crow search-based support vector neural network patient classification tuberculosis severity analysis automatic classification TB patients classification method crow search algorithm false-positive rate computational speed sputum images fractional theory

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A Hybrid crow search and Grey Wolf Optimization Technique for Enhanced Medical Data Classification in Diabetes Diagnosis System

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INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS 2021年第1期14卷 1页

作者： Mallika, C. Selvamuthukumaran, S. EGS Pillay Engn Coll Dept Master Comp Applicat Nagapattinam Tamil Nadu India AVC Coll Engn Dept Comp Applicat Mayiladuthurai Tamil Nadu India

Diabetes is an extremely serious hazard to global health and its incidence is increasing vividly. In this paper, we develop an effective system to diagnose diabetes disease using a hybrid optimization-based Support Vector Machine (SVM).The proposed hybrid optimization technique integrates a crow search algorithm (CSA) and Binary Grey Wolf Optimizer (BGWO) for exploiting the full potential of SVM in the diabetes diagnosis system. The effectiveness of our proposed hybrid optimizationbased SVM (hereafter called CS-BGWO-SVM) approach is carefully studied on the real-world databases such as UCIPima Indian standard dataset and the diabetes type dataset from the Data World repository. To evaluate the CS-BGWO-SVM technique, its performance is related to several state-of-the-arts approaches using SVM with respect to predictive accuracy, Intersection Over-Union (IoU), specificity, sensitivity, and the area under receiver operator characteristic curve (AUC). The outcomes of empirical analysis illustrate that CS-BGWO-SVM can be considered as a more efficient approach with outstanding classification accuracy. Furthermore, we perform the Wilcoxon statistical test to decide whether the proposed cohesive CS-BGWO-SVM approach offers a substantial enhancement in terms of performance measures or not. Consequently, we can conclude that CS-BGWO-SVM is the better diabetes diagnostic model as compared to modern diagnosis methods previously reported in the literature.

关键词： Classification crow search algorithm Diabetes Grey wolf optimizer Hybrid optimization Support vector machine

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A novel search scheme based on the social behavior of crow flock for feed-forward learning improvement in predicting the soil compression coefficient

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ENGINEERING WITH COMPUTERS 2022年第2期38卷 1645-1658页

作者： Xu, Fang Foong, Loke Kok Lyu, Zongjie Cent South Univ Sch Civil Engn Changsha 410075 Peoples R China Ton Duc Thang Univ Dept Management Sci & Technol Dev Ho Chi Minh City Vietnam Ton Duc Thang Univ Fac Civil Engn Ho Chi Minh City Vietnam Duy Tan Univ Inst Res & Dev Da Nang 550000 Vietnam Duy Tan Univ Fac Civil Engn Da Nang 550000 Vietnam

Recent improvements achieved using nature-inspired optimizers encouraged the authors to employ a novel type of metaheuristic algorithms, namely crow search algorithm (CSA) in this study. The CSA is employed for optimizing a feed-forward artificial neural network (ANN) in predicting the soil compression coefficient (SCC). The SCC is one of the most crucial geotechnical parameters that the early prediction of it can increase the safety and cost-effectiveness of a project. For more reliability, the used data are collected from a real-world project. After developing the CSA-ANN hybrid, the most proper values for the algorithm parameters, including flock size, flight length, and awareness probability are found by sensitivity analysis (to be 400, 2, and 0.1, respectively). A comparison between the results of the typical ANN and the CSA-trained version revealed that the proposed algorithm can effectively reduce the mean absolute error (MAE) in both learning and predicting the SCC pattern (by 8.25 and 7.29%, respectively). Moreover, the increase of the coefficient of determination (R-2) from 70.64 to 74.83% in the training phase, and from 73.74 to 76.19% in the testing phase proves the efficiency of the CSA in enhancing the ANN. The suggested CSA-ANN, therefore, can be an efficient model for the early prediction of the SCC in civil/geotechnical engineering projects.

关键词： Geotechnical engineering Coefficient of compression Neural learning crow search algorithm

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crow search based virtual machine placement strategy in cloud data centers with live migration

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COMPUTERS & ELECTRICAL ENGINEERING 2018年 69卷 334-350页

作者： Satpathy, Anurag Addya, Sourav Kanti Turuk, Ashok Kumar Majhi, Banshidhar Sahoo, Gadadhar Birla Inst Technol Dept Comp Sci & Engn Ranchi Bihar India Natl Inst Technol Rourkela Dept Comp Sci & Engn Rourkela Odisha India

Cloud computing has emerged as the most revolutionary technology in the field of computing. The cloud service providers (CSPs) have high computational facilities called data centers (DCs) at their disposal. CSPs provide services to the users through virtual machines (VMs). VM placement is the mapping of VMs onto physical machine called hosts. In this paper, we propose a two-tier virtual machine placement algorithm. Firstly, we propose a queueing structure to manage and schedule a large set of VMs. Secondly, a multi-objective VM placement algorithm called crow search based VM placement (CSAVMP) is proposed to reduce the resources wastage and power consumption at the data centers. VM migration is an indispensable part of any cloud platform for activities like maintenance, load balancing, fault tolerance etc. Three different migration strategies namely serial, parallel, improved serial have been tested and a comparative result has been produced. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Cloud computing Data center Virtual machine crow search algorithm Migration

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Improved crow search-Based Feature Selection and Ensemble Learning for IoT Intrusion Detection

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IEEE ACCESS 2024年 12卷 33218-33235页

作者： Jayalatchumy, D. Ramalingam, Rajakumar Balakrishnan, Aravind Safran, Mejdl Alfarhood, Sultan Perunthalaivar Kamarajar Inst Engn & Technol Dept Comp Sci & Engn Karaikal 609603 India Vellore Inst Technol Ctr Automat Sch Comp Sci & Engn Chennai 600127 Tamil Nadu India Madanapalle Inst Technol & Sci Dept Comp Sci & Technol Madanapalle 517325 Andhra Pradesh India King Saud Univ Coll Comp & Informat Sci Dept Comp Sci Riyadh 11543 Saudi Arabia

Network intrusion detection in the Internet of Things (IoT) framework has posed considerable challenges in recent decades. A wide variety of machine-learning approaches are introduced in network intrusion detection. The existing methodologies commonly lack consistency in achieving optimal performance across various multi-class categorization tasks. The present study elucidates implementing a unique intrusion system with the primary objective of enriching the efficacy of network intrusion detection. In the initial phase, it is imperative to employ data-denoising methodologies to effectively tackle the issue of data imbalance. In the next step, the enhanced crow search algorithm is used to determine the most significant features that aid in better classifying intrusion attacks. In the final phase, the ensemble classifier takes the selected features as input to categorize the standard and invader labels. The present work introduces an ensemble mechanism that comprises four distinct classifiers. The assessment of the proposed approach is validated on two denoised datasets, specifically NSL-KDD and UNSW-NB15. The experimental outcomes demonstrate that the formulated approach achieves exceptional accuracy of 99.4% and 99.2% for the NSL-KDD and UNSW-NB15 datasets, respectively.

关键词： Network intrusion detection crow search algorithm machine learning ensemble learning Internet of Things

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Memory-Efficient, Accurate and Early Diagnosis of Diabetes Through a Machine Learning Pipeline Employing crow search-Based Feature Engineering and a Stacking Ensemble

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IEEE ACCESS 2021年 9卷 134335-134354页

作者： Samreen, Shirina Majmaah Univ Coll Comp & Informat Sci Dept Comp Sci Al Majmaah 11952 Saudi Arabia

The early diagnosis of diabetes helps in avoiding the major risks associated with the disorder. The proposed research involves the design of a machine learning pipeline which generates the most representative feature subset of minimal size that predicts the onset of Diabetes with highest accuracy. It employs a novel diabetes dataset which is gender-neutral and representative enough unlike the well-known PID dataset. The machine learning pipelines involve multiple feature engineering pipelines to generate a reduced feature subset which is fed into multiple heterogeneous classifiers. The feature engineering involves feature selection as well as feature extraction. The former uses the ANOVA filter and crow search Optimization algorithm. The latter employs the Singular Value Decomposition. The classification is performed on the preprocessed dataset using a wide range of heterogeneous classifiers like Naive Bayes', Logistic Regression, K-Nearest Neighbor, Decision Trees, Support Vector Machine, Random Forest, AdaBoost, and GradientBoost as base learners followed by their stacking ensemble. The performance evaluation of each machine learning pipeline is done through Repeated Stratified K-fold Cross Validation using the metrics of accuracy, precision, recall, F1 Score and area under Receiver Operating Characteristic curve. For each pipeline, the number of features in the preprocessed dataset varies and the highest accuracy of 98.4% is achieved with crow search algorithm through a stacking ensemble of multiple heterogeneous classifiers. A comparative analysis with a recent related work on the same dataset shows that the proposed feature engineering pipelines with the same set of classifiers outperform with improved accuracy using a feature set of reduced size.

关键词： Diabetes Feature extraction Radio frequency Pipelines Predictive models Classification algorithms Bagging Early diabetes diagnosis machine learning stacking ensemble feature engineering dimensionality reduction crow search algorithm ANOVA filter singular value decomposition

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Perturbation Based Efficient crow search Optimized FLANN for System Identification: A Novel Approach 17th

Perturbation Based Efficient Crow Search Optimized FLANN for...

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17th International Conference on Hybrid Intelligent Systems (HIS)

作者： Naik, Bighnaraj Mishra, Debasmita Nayak, Janmenjoy Pelusi, Danilo Abraham, Ajith Veer Surendra Sai Univ Technol Dept Comp Applicat Sambalpur 768018 Odisha India Sri Sivani Coll Engn Dept Comp Sci & Engn Srikakulam 535402 Andhra Pradesh India Univ Teramo Commun Sci Coste St Agostino Campus Teramo Italy Machine Intelligence Res Labs MIR Labs Washington DC USA

ISBN: (纸本)9783319763514;9783319763507

Designing an efficient classifier is a tough task as it must be suitable for solving maximum real life problems with high accuracy and less error rate. In this paper, a novel functional link neural network based system identification model is developed to solve the classification problem of data mining. To increase the accuracy of the model and for an optimized performance, an enhanced crow search algorithm (CSA) with perturbation has been introduced. This enhanced version of CSA based model avoids premature convergence and stagnation in classical CSA, by introducing the new neighbourhood searching operation through perturbation. Experimental results reveal that the proposed model outperforms several other standard models in terms of accuracy and error rate.

关键词： crow search algorithm Perturbation FLANN Classification Neural network

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Transformer fault diagnosis based on BP neural network optimized by the strongest crow search algorith 16

Transformer fault diagnosis based on BP neural network optim...

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16th IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Zhao Lingling Chen Quan Zhang Huaying Xie Fang Anhui Univ Sch Elect Engn & Automat Hefei Peoples R China Shenzhen Power Supply Co Ltd New Smart City High Qual Power Supply Joint Lab China Southern Power Grid Shenzhen Peoples R China

ISBN: (纸本)9781665422482

In order to improve the accuracy of transformer fault diagnosis, a transformer fault diagnosis model based on the Strongest crow search algorithm(SCSA) to optimize BP neural network is proposed. In the SCSA-BP fault diagnosis model, adaptive weights are introduced to coordinate the global and local search capabilities of the crow search algorithm(CSA). The crazy factor is introduced to increase the diversity of the crow population and prevent individual crows from falling into the local optimum. Bidirectional Random Optimization(BRO) is added to reduce the amount of nonessential exploration by the crow and to improve the search efficiency of the crow. SCSA is used to optimize the weights and thresholds of the BP neural network to avoid the premature problem of the BP neural network Simulation calculations show that the accuracy of the transformer fault diagnosis of the SCSA-BP model is 8.955% and 4.478% higher than that of the PSO-BP and CSA-BP diagnosis models, respectively.

关键词： transformer fault diagnosis crow search algorithm adaptive weight crazy factor bidirectional random search

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