Multi-objective league championship algorithm for real-time task scheduling

Subbaraj, Saroja; Thiagarajan, Revathi; Rengaraj, Madavan

doi:10.1007/s00521-018-3950-y

Cited by 12 publications

(5 citation statements)

References 41 publications

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“…Also, this category includes artificial bee colony optimization, which is based on the social action of bees [26,27]. Other algorithms that fall under this category and have been suggested to tackle the load balancing problem include Honey bee behavior algorithms, firefly, particle swarm optimization algorithm (PSO), genetic, ant colony, water wave, league championship, bat, etc [20,28,29].…”

Section: Meta-heuristic Approachmentioning

confidence: 99%

Cost-effective clonal selection and AIS-based load balancing in cloud computing environment

Mosayebi¹,

Azmi²

2023

Preprint

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One of the challenges in cloud computing is increasing efficiency, which calls for the application of task scheduling and load balancing. Numerous load-balancing methods that have been suggested for the cloud environment can increase efficiency. The Throttled policy is one of them that aids in evenly distributing user requests among virtual machines. In the first part of this article, we examined the cost of the Clonal selection algorithm with Throttled load balancing Policy and the Clonal selection algorithm with Distributed Service Broker Policy and contrasted them to the clonal selection and other immune systems mechanisms. Additionally, we suggest two additional mechanisms based on the artificial immune system. In the two proposed mechanisms, algorithms are introduced so that by benefiting from load balancing policies when receiving requests from users, in the shortest possible time and the fastest processing of data and with the lowest cost, they respond to users. In the second part, we introduce two other new algorithms that were created as a result of combining an evolutionary algorithm with the artificial immune system mechanisms. Algorithms have been evaluated and compared with other algorithms using the Cloud Analyst simulator in terms of average response time, average data center processing time, total data transfer cost, and total cost. The results attained show how much better the suggested algorithms are.

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Section: Meta-heuristic Approachmentioning

confidence: 99%

Cost-effective clonal selection and AIS-based load balancing in cloud computing environment

Mosayebi¹,

Azmi²

2023

Preprint

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“…The proposed scheduling technique is divided into two sections: task ordering and processor assignment [36]. All of the tasks in the supplied application task graph are ranked in the task-ordering section.…”

Section: Scheduling Model and Problem Formulationmentioning

confidence: 99%

Real-Time Task Scheduling Using Hybrid Multi-objective Crow Search Algorithm

Subbaraj

Rengaraj²,

Sikkandar³

2023

Preprint

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This paper proposes a Multi-objective Crow Search Algorithm (MOCSA) based task scheduling approach for determining optimal solutions with simultaneous optimization of finish time (F), tardiness (T) and energy consumption (E). The proposed algorithm solves real-time task scheduling problem in heterogeneous multi-processor systems by adopting novel crow search algorithm to solve the discrete optimization problem. The three different multiple objectives with different magnitudes are fused using a weighted product method. Our methodology enables the user to choose the solution that best suits their needs. Experimental results demonstrate the efficiency of the proposed algorithm in achieving all the three objectives: minimizing finish time, tardiness and energy consumption.

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“…To reduce the number of features, Sewak et al [ 24 ] employed a binary classifier that correctly identified heart disease with a 100% improvement in accuracy. Various techniques are available for feature selection [ 25 , 26 ]. PCA [ 27 , 28 ] has been employed as a feature retrieval strategy for categorization in healthcare services in recent research.…”

Section: Literature Workmentioning

confidence: 99%

Moth-Flame Optimization for Early Prediction of Heart Diseases

Haseena¹,

Priya²,

Subbaraj³

et al. 2022

Computational and Mathematical Methods in Medicine

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Heart disease is among the leading causes of mortality globally. Predicting cardiovascular disease is a major difficulty in clinical data analysis. AI has been demonstrated to be powerful in deciding and anticipating an enormous measure of information created by the health domain. We provide a unique method for finding essential traits employing machine learning approaches in this paper, which enhances the effectiveness of identifying heart diseases. Decision tree (DT), support vector machine (SVM), artificial neural network (ANN), and K-nearest neighbor (KNN) are the classification techniques used to create the proposed system. Ensemble stacking integrates the four classification models to create a single best-fit predictive model using logistic regression. Many explorations have been directed at the identification of cardiac infection; however, the exactness of the outcomes is poor. Accordingly, to further enhance the efficiency, Moth-Flame Optimization (MFO) algorithm is proposed. The feature selection strategies are used to improve the classification accuracy while shortening the execution time of the classification system. Medical data are used to assess the probability of heart disease based on BP, age, gender, chest ache, cholesterol, blood sugar, and other variables. Results revealed that the proposed system excelled other existing models, obtaining 99% accuracy in the Cleveland dataset.

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Multi-objective league championship algorithm for real-time task scheduling

Cited by 12 publications

References 41 publications

Cost-effective clonal selection and AIS-based load balancing in cloud computing environment

Cost-effective clonal selection and AIS-based load balancing in cloud computing environment

Real-Time Task Scheduling Using Hybrid Multi-objective Crow Search Algorithm

Moth-Flame Optimization for Early Prediction of Heart Diseases

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