Capuchin search algorithm based task scheduling in cloud computing environment

Ramu, Sivakumar; Ranganathan, Rameshkumar; Ramamoorthy, Ramakrishnan

doi:10.53370/001c.33767

Cited by 5 publications

(3 citation statements)

References 32 publications

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“…A similar study was conducted by Zakaria et all [20]. Ramu et all used the modified CapSA algorithm (MCS) to solve the cloud performance scheduling problem, which minimizes the completion time and improves resource utilisation [21]. Broumandnia et all hybridized the CapSA algorithm with the inverted ant colony optimization (IACO) algorithm for the optimization of some processes related to the cloud system and obtained better performance [22].…”

Section: Introductionmentioning

confidence: 94%

Performance Evaluation of Capuchin Search Algorithm Through Non-linear Problems, and Optimization of Gear Train Design Problem

EKER

2023

EJT

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The purpose of this paper is to demonstrate the superiority of the Capuchin Search Algorithm (CapSA), a metaheuristic, in competitive environments and its advantages in optimizing engineering design problems. To achieve this, the CEC 2019 function set was used. Due to the challenging characteristics of the CEC 2019 function set in reaching a global solution, it effectively showcases the algorithm's quality. For this comparison, sea-horse optimizer (SHO), grey wolf optimizer (GWO), sine-cosine algorithm (SCA), and smell agent optimization (SAO) were chosen as current and effective alternatives to the CapSA algorithm. Furthermore, the gear train design problem (GTD) was selected as an engineering design problem. In addition to the CapSA algorithm, a hybrid of SCA and GWO algorithm (SC-GWO) and genetic algorithm (GA) were chosen as alternatives for optimizing this problem. The performance superiority and optimization power of the CapSA algorithm were assessed using statistical metrics and convergence curves, then compared with alternative algorithms. Experimental results conclusively demonstrate the significant effectiveness and advantages of the CapSA algorithm.

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Section: Introductionmentioning

confidence: 94%

Performance Evaluation of Capuchin Search Algorithm Through Non-linear Problems, and Optimization of Gear Train Design Problem

EKER

2023

EJT

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“…P. Akki in [20] used the powerful performance of neural networks to reduce the energy consumption of mobile devices, and simulation experiments illustrated a 30.3% reduction in energy consumption. In terms of completion time, S. Ramu in [21] used the Capuchin search algorithm for task scheduling under mobile cloud computing, and simulation experiments showed significant advantages over existing state-of-the-art methods in terms of completion time, execution time, and resource utilization. M. G. Chen in [22] presented a robust computation offloading strategy with failure recovery in an intermittently connected cloudlet system, and simulation experiments illustrated the great advantage of this strategy in terms of task time reduction.…”

Section: Related Workmentioning

confidence: 99%

Research on Task Scheduling Strategy under Mobile Cloud Computing Based on ICSO

Chen¹,

Chen²

2022

Journal of Internet Technology

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<p>With the gradual application of mobile terminals such as cell phones in production and life, mobile cloud computing has become an important part of the internet. Different from traditional cloud computing task scheduling methods, mobile cloud computing task scheduling needs to consider not only task time minimization but also the lowest possible mobile device energy consumption. We propose an improved chicken swarm optimization (ICSO) algorithm applied to the task scheduling strategy under mobile cloud computing. First, we establish a multiobjective optimization strategy with minimum completion time and minimum energy consumption. Second, for the shortcomings of the chicken swarm optimization algorithm that easily fall into local optimums leading to algorithm stagnation, we use reverse learning initialization for the chicken flock population to expand the space of understanding and an adaptive strategy for learning factors and following coefficients. To illustrate the effectiveness of our algorithm in scheduling, we chose the number of mobile devices as 50, 100, and 150 and compared the improved chicken swarm optimization algorithm, ant colony algorithm, particle swarm algorithm, and chicken swarm optimization algorithm. The results illustrate that our proposed algorithm can reduce the task completion time, control the energy consumption of mobile devices well, and save energy.</p> <p> </p>

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“…In this study, a newly developed meta-heuristic, named capuchin search algorithm (CSA) [ 27 ], was adopted to solve broadly available feature selection problems in the field of medical diagnosis. Although CSA has the ability to get the optimal solution in solving diverse problems in the optimization field [ 28 , 29 ], it is customarily confined to the local optima especially when it encounters complex problems with many local optimums. This may be ascribed to its narrow search ability and modest convergence property.…”

Section: Introductionmentioning

confidence: 99%

Cognitively Enhanced Versions of Capuchin Search Algorithm for Feature Selection in Medical Diagnosis: a COVID-19 Case Study

et al. 2023

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Feature selection (FS) is a crucial area of cognitive computation that demands further studies. It has recently received a lot of attention from researchers working in machine learning and data mining. It is broadly employed in many different applications. Many enhanced strategies have been created for FS methods in cognitive computation to boost the performance of the methods. The goal of this paper is to present three adaptive versions of the capuchin search algorithm (CSA) that each features a better search ability than the parent CSA. These versions are used to select optimal feature subset based on a binary version of each adapted one and the k-Nearest Neighbor (k-NN) classifier. These versions were matured by applying several strategies, including automated control of inertia weight, acceleration coefficients, and other computational factors, to ameliorate search potency and convergence speed of CSA. In the velocity model of CSA, some growth computational functions, known as exponential, power, and S-shaped functions, were adopted to evolve three versions of CSA, referred to as exponential CSA (ECSA), power CSA (PCSA), and S-shaped CSA (SCSA), respectively. The results of the proposed FS methods on 24 benchmark datasets with different dimensions from various repositories were compared with other k-NN based FS methods from the literature. The results revealed that the proposed methods significantly outperformed the performance of CSA and other well-established FS methods in several relevant criteria. In particular, among the 24 datasets considered, the proposed binary ECSA, which yielded the best overall results among all other proposed versions, is able to excel the others in 18 datasets in terms of classification accuracy, 13 datasets in terms of specificity, 10 datasets in terms of sensitivity, and 14 datasets in terms of fitness values. Simply put, the results on 15, 9, and 5 datasets out of the 24 datasets studied showed that the performance levels of the binary ECSA, PCSA, and SCSA are over 90% in respect of specificity, sensitivity, and accuracy measures, respectively. The thorough results via different comparisons divulge the efficiency of the proposed methods in widening the classification accuracy compared to other methods, ensuring the ability of the proposed methods in exploring the feature space and selecting the most useful features for classification studies.

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Capuchin search algorithm based task scheduling in cloud computing environment

Cited by 5 publications

References 32 publications

Performance Evaluation of Capuchin Search Algorithm Through Non-linear Problems, and Optimization of Gear Train Design Problem

Performance Evaluation of Capuchin Search Algorithm Through Non-linear Problems, and Optimization of Gear Train Design Problem

Research on Task Scheduling Strategy under Mobile Cloud Computing Based on ICSO

Cognitively Enhanced Versions of Capuchin Search Algorithm for Feature Selection in Medical Diagnosis: a COVID-19 Case Study

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