Relevant bibliographies by topics / Particle swarm optimization cloud computing

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Particle swarm optimization cloud computing'

Author: Grafiati
Published: 4 June 2025
Last updated: 1 August 2025

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Journal articles on the topic "Particle swarm optimization cloud computing"

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Zhao, Hong Wei, and Li Wei Tian. "Resource Schedule Algorithm Based on Artificial Fish Swarm in Cloud Computing Environment." Applied Mechanics and Materials 635-637 (September 2014): 1614–17. http://dx.doi.org/10.4028/www.scientific.net/amm.635-637.1614.

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Cloud computing needs to manage a large number of computing resources, while resources scheduling strategy plays a key role in determining the efficiency of cloud computing. evolutionary algorithms (EA) as appropriate tools to optimize multi-objective problems have been applied to optimize Resources Scheduling of cloud computing ,However, studies on improving the convergence ratio and processing time in the most applied algorithms such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) in Resources Scheduling domains remain poorly understood. the res
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Reddy, B. Ramana, M. Indiramma, and N. Nagarathna. "Intelligent Particle Swarm Optimization Based Resource Provisioning Technique in Cloud Computing." Indian Journal Of Science And Technology 16, no. 16 (2023): 1241–49. http://dx.doi.org/10.17485/ijst/v16i16.308.

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Shi, Weihang. "QoE Guarantee Scheme Based on Cooperative Cognitive Cloud and Opportunistic Weight Particle Swarm." Journal of Electrical and Computer Engineering 2015 (2015): 1–9. http://dx.doi.org/10.1155/2015/398709.

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It is well known that the Internet application of cloud services may be affected by the inefficiency of cloud computing and inaccurate evaluation of quality of experience (QoE) seriously. In our paper, based on construction algorithms of cooperative cognitive cloud platform and optimization algorithm of opportunities weight particle swarm clustering, the QoE guarantee mechanism was proposed. The mechanism, through the sending users of requests and the cognitive neighbor users’ cooperation, combined the cooperation of subcloud platforms and constructed the optimal cloud platform with the differ
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Jia, Jia, and Dejun Mu. "Low-Energy-Orientated Resource Scheduling in Cloud Computing by Particle Swarm Optimization." Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University 36, no. 2 (2018): 339–44. http://dx.doi.org/10.1051/jnwpu/20183620339.

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In order to reduce the energy cost in cloud computing, this paper represents a novel energy-orientated resource scheduling method based on particle swarm optimization. The energy cost model in cloud computing environment is studied first. The optimization of energy cost is then considered as a multiobjective optimization problem, which generates the Pareto optimization set. To solve this multiobjective optimization problem, the particle swarm optimization is involved. The states of one particle consist of both the allocation plan for servers and the frequency plans on servers. Each particle in
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Huang, Mei Geng, and Zhi Qi Ou. "Review of Task Scheduling Algorithm Research in Cloud Computing." Advanced Materials Research 926-930 (May 2014): 3236–39. http://dx.doi.org/10.4028/www.scientific.net/amr.926-930.3236.

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The cloud computing task scheduling field representative algorithms was introduced and analyzed : genetic algorithm, particle swarm optimization, ant colony algorithm. Parallelism and global search solution space is the characteristic of genetic algorithm, genetic iterations difficult to proceed when genetic individuals are very similar; Particle swarm optimization in the initial stage is fast, slow convergence speed in the later stage ; Ant colony algorithm optimization ability is good, slow convergence speed in its first stage; Finally, the summary and prospect the future research direction.
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Ramasree, T. "Resource Allocation Using Particle Swarm Optimization Algorithm in Cloud Computing." International Journal for Research in Applied Science and Engineering Technology 9, no. 8 (2021): 2679–82. http://dx.doi.org/10.22214/ijraset.2021.37856.

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Abstract: Cloud computing is now widely used in organisations and bussiness firms because of its on-demand accessibility of framework assets, web innovation, and pay-as-you-go principle. Despite of numerous advantages of cloud computing, such as availability and accessibility, it also has few significant drawbacks. The most fundamental issue is the resource management, where Cloud computing provides IT assets such as memory, network, storage, and so on based on a virtualization concept and a pay-as-you-go model. Much research has gone into the administration of these assets. The suggested fram
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Liu, Jing, Xing Guo Luo, Xing Ming Zhang, and Fan Zhang. "Job Scheduling Algorithm for Cloud Computing Based on Particle Swarm Optimization." Advanced Materials Research 662 (February 2013): 957–60. http://dx.doi.org/10.4028/www.scientific.net/amr.662.957.

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Cloud computing is an emerging high performance computing environment with a large scale, heterogeneous collection of autonomous systems and flexible computational architecture. The performance of the scheduling system influences the cost benefit of this computing paradigm. To reduce the energy consumption and improve the profit, a job scheduling model based on the particle swarm optimization(PSO) algorithm is established for cloud computing. Based on open source cloud computing simulation platform CloudSim, compared to GA and random scheduling algorithms, the results show that the proposed al
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Prasanna, Mrs G., Mr R. Siva Sankar Reddy, Mrs B. Sree Harini, and Tadepalli Sreenija. "FUZZY BASED ANT COLONY OPTIMIZATION SCHEDULING IN CLOUD COMPUTING." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 11, no. 2 (2020): 1258–66. http://dx.doi.org/10.61841/turcomat.v11i2.14535.

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Cloud computing is an Information Technology deployment model established on virtualization. Task scheduling states the set of rules for task allocations to an exact virtual machine in the cloud computing environment. However, task scheduling challenges such as optimal task scheduling performance solutions, are addressed in cloud computing. First, the cloud computing performance due to task scheduling is improved by proposing a Dynamic Weighted Round-Robin algorithm. This recommended DWRR algorithm improves the task scheduling performance by considering resource competencies, task priorities,
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Poltronieri, Filippo, Cesare Stefanelli, Mauro Tortonesi, and Mattia Zaccarini. "Reinforcement Learning vs. Computational Intelligence: Comparing Service Management Approaches for the Cloud Continuum." Future Internet 15, no. 11 (2023): 359. http://dx.doi.org/10.3390/fi15110359.

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Modern computing environments, thanks to the advent of enabling technologies such as Multi-access Edge Computing (MEC), effectively represent a Cloud Continuum, a capillary network of computing resources that extend from the Edge of the network to the Cloud, which enables a dynamic and adaptive service fabric. Efficiently coordinating resource allocation, exploitation, and management in the Cloud Continuum represents quite a challenge, which has stimulated researchers to investigate innovative solutions based on smart techniques such as Reinforcement Learning and Computational Intelligence. In
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Wu, Zhou, and Jun Xiong. "A Novel Task-Scheduling Algorithm of Cloud Computing Based on Particle Swarm Optimization." International Journal of Gaming and Computer-Mediated Simulations 13, no. 2 (2021): 1–15. http://dx.doi.org/10.4018/ijgcms.2021040101.

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With the characteristics of low cost, high availability, and scalability, cloud computing has become a high demand platform in the field of information technology. Due to the dynamic and diversity of cloud computing system, the task and resource scheduling has become a challenging issue. This paper proposes a novel task scheduling algorithm of cloud computing based on particle swarm optimization. Firstly, the resource scheduling problem in cloud computing system is modeled, and the objective function of the task execution time is formulated. Then, the modified particle swarm optimization algor
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Dissertations / Theses on the topic "Particle swarm optimization cloud computing"

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Alkayal, Entisar. "Optimizing resource allocation using multi-objective particle swarm optimization in cloud computing systems." Thesis, University of Southampton, 2018. https://eprints.soton.ac.uk/418464/.

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Allocating resources in data centers is a complex task due to their increase in size, complexity, and consumption of power. At the same time, consumers' requirements regarding execution time and cost have become more sophisticated and demanding. These requirements often conflict with the objectives of cloud providers. Set against this background, this thesis presents a model of resource allocation in cloud computing environments that focuses on developing the allocation process in three phases: (i) negotiation between consumers and providers to select the data center, (ii) scheduling tasks ins
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Ференс, Дмитро Андрійович. "Дослідження методів розподілу ресурсів критичної ІТ інфраструктури за допомогою технологій штучного інтелекту". Master's thesis, Київ, 2018. https://ela.kpi.ua/handle/123456789/23028.

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Проведено аналіз існуючих проблем в управлінні ресурсами ІТ інфраструктури. Запропоновані способи застосування технологій штучного інтелекту для вирішення задачі розподілу ресурсів. Запропоновано модифікований варіант фітнес-функції, що дозволяє здійснювати оптимальний розподіл елементів інфраструктури, враховуючи їх поточне розміщення. Проведено порівняння розроблених алгоритмів з існуючими та підтвердженно високу якість отриманих результатів.<br>The analysis of existing problems in IT infrastructure resources management is carried out. Methods of application of artificial intelligence techno
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Al-Olimat, Hussein S. "Optimizing Cloudlet Scheduling and Wireless Sensor Localization using Computational Intelligence Techniques." University of Toledo / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1403922600.

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Maripi, Jagadish Kumar. "AN EFFECTIVE PARALLEL PARTICLE SWARM OPTIMIZATION ALGORITHM AND ITS PERFORMANCE EVALUATION." OpenSIUC, 2010. https://opensiuc.lib.siu.edu/theses/275.

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Population-based global optimization algorithms including Particle Swarm Optimization (PSO) have become popular for solving multi-optima problems much more efficiently than the traditional mathematical techniques. In this research, we present and evaluate a new parallel PSO algorithm that provides a significant performance improvement as compared to the serial PSO algorithm. Instead of merely assigning parts of the task of serial version to several processors, the new algorithm places multiple swarms on the available nodes in which operate independently, while collaborating on the same task. W
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Hadavi, Hamid. "Isometry Registration Among Deformable Objects, A Quantum Optimization with Genetic Operator." Thèse, Université d'Ottawa / University of Ottawa, 2013. http://hdl.handle.net/10393/24286.

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Non-rigid shapes are generally known as objects whose three dimensional geometry may deform by internal and/or external forces. Deformable shapes are all around us, ranging from protein molecules, to natural objects such as the trees in the forest or the fruits in our gardens, and even human bodies. Two deformable shapes may be related by isometry, which means their intrinsic geometries are preserved, even though their extrinsic geometries are dissimilar. An important problem in the analysis of the deformable shapes is to identify the three-dimensional correspondence between two isometric shap
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Green, Robert C. II. "Novel Computational Methods for the Reliability Evaluation of Composite Power Systems using Computational Intelligence and High Performance Computing Techniques." University of Toledo / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1338894641.

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NOBILE, MARCO SALVATORE. "Evolutionary Inference of Biological Systems Accelerated on Graphics Processing Units." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2015. http://hdl.handle.net/10281/75434.

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In silico analysis of biological systems represents a valuable alternative and complementary approach to experimental research. Computational methodologies, indeed, allow to mimic some conditions of cellular processes that might be difficult to dissect by exploiting traditional laboratory techniques, therefore potentially achieving a thorough comprehension of the molecular mechanisms that rule the functioning of cells and organisms. In spite of the benefits that it can bring about in biology, the computational approach still has two main limitations: first, there is often a lack of adequate kn
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Rodríguez, García Javier. "Metodología para la optimización del beneficio de la respuesta de la demanda en consumidores industriales: caracterización por procesos y aplicación." Doctoral thesis, Universitat Politècnica de València, 2021. http://hdl.handle.net/10251/165574.

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[ES] En la actualidad, existe una creciente necesidad de cambiar el modelo energético global basado en combustibles fósiles por un modelo cien por cien renovable, proceso conocido como "transición energética". Sin embargo, la mayoría de los recursos de generación renovables no son gestionables y presentan una fuerte variabilidad en su producción de energía difícilmente predecible, lo que requiere de un sistema eléctrico más flexible para poder operarse de forma segura. Por otro lado, las tecnologías de la información y la comunicación han evolucionado rápidamente como resultado del proceso de
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Souza, Jackson Gomes de. "T?cnicas de computa??o natural para segmenta??o de imagens m?dicas." Universidade Federal do Rio Grande do Norte, 2009. http://repositorio.ufrn.br:8080/jspui/handle/123456789/15282.

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Made available in DSpace on 2014-12-17T14:55:35Z (GMT). No. of bitstreams: 1 JacksonGS.pdf: 1963039 bytes, checksum: ed3464892d7bb73b5dcab563e42f0e01 (MD5) Previous issue date: 2009-09-28<br>Image segmentation is one of the image processing problems that deserves special attention from the scientific community. This work studies unsupervised methods to clustering and pattern recognition applicable to medical image segmentation. Natural Computing based methods have shown very attractive in such tasks and are studied here as a way to verify it's ap
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Chung, Hui-chun, and 鍾惠君. "Particle Swarm Optimization for Workflow Scheduling in Cloud Computing Environments." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/88755113164942329200.

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碩士<br>國立臺灣科技大學<br>電子工程系<br>101<br>With the progress of Internet and improvement of hardware, there are more and more users rent virtual machines that are provided by a cloud provider to executing tasks, which usually are represented as a workflow. Cloud providers, such as Amazon and Google, offer several virtual machines of various types, allowing users to quickly scale compute capacity as computing requirements change. Therefore, in Cloud computing, efficient task allocation for optimizing the tradeoff of time and cost constraints has become an important and challenging issue. In this thesis,
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Book chapters on the topic "Particle swarm optimization cloud computing"

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Fu, Zheng, Haidong Hu, Chuangye Wang, and Hao Gao. "A Particle Swarm Optimization with an Improved Updating Strategy." In Cloud Computing and Security. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-48674-1_47.

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Wang, Jiaju, and Baochuan Fu. "Chaotic Particle Swarm Algorithm for QoS Optimization in Smart Communities." In Green, Pervasive, and Cloud Computing. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-99-9896-8_17.

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C, Devaraj Verma, Harshvardhan Tiwari, and Madhumala RB. "A Review of Particle Swarm Optimization in Cloud Computing." In Smart IoT for Research and Industry. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-71485-7_5.

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Ramezani, Fahimeh, Jie Lu, and Farookh Hussain. "Task Scheduling Optimization in Cloud Computing Applying Multi-Objective Particle Swarm Optimization." In Service-Oriented Computing. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-45005-1_17.

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LI, Zhe, Ruilian TAN, and Baoxiang REN. "Research on particle swarm optimization of variable parameter." In Advances on P2P, Parallel, Grid, Cloud and Internet Computing. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-49109-7_3.

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Jiali, Wang, Liu Hongshen, and Ruan Yue. "Multi-threshold Image Segmentation Through an Improved Quantum-Behaved Particle Swarm Optimization Algorithm." In Cloud Computing and Security. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-27051-7_10.

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Wang, Jin, Chunwei Ju, Huan Ji, Geumran Youn, and Jeong-Uk Kim. "A Particle Swarm Optimization and Mutation Operator Based Node Deployment Strategy for WSNs." In Cloud Computing and Security. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-68505-2_37.

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Chen, Huangning, and Wenzhong Guo. "Real-Time Task Scheduling Algorithm for Cloud Computing Based on Particle Swarm Optimization." In Cloud Computing and Big Data. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-28430-9_11.

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Zhao, Shuang, Xianli Lu, and Xuejun Li. "Quality of Service-Based Particle Swarm Optimization Scheduling in Cloud Computing." In Proceedings of the 4th International Conference on Computer Engineering and Networks. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-11104-9_28.

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Guo, Lizheng, Shuguang Zhao, Shigen Shen, and Changyuan Jiang. "A Particle Swarm Optimization for Data Placement Strategy in Cloud Computing." In Lecture Notes in Electrical Engineering. Springer London, 2012. http://dx.doi.org/10.1007/978-1-4471-2386-6_123.

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Conference papers on the topic "Particle swarm optimization cloud computing"

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Sun, Xutao, Tong Liu, and Yong Fu. "A Solution for Projection Pursuit Problem Based on Particle Swarm Optimization Simulated Annealing." In 2024 6th International Communication Engineering and Cloud Computing Conference (CECCC). IEEE, 2024. https://doi.org/10.1109/ceccc62598.2024.11063629.

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Senthilselvi, A., V. S. Varshini, E. Reena Sharan, T. Shruthi Shree, Balika J. Chelliah, and S. Senthil Pandi. "Load-balancing in Cloud Computing Environment using Hybrid Particle Swarm Optimization and Ant Colony Optimization Algorithm." In 2024 2nd International Conference on Advances in Computation, Communication and Information Technology (ICAICCIT). IEEE, 2024. https://doi.org/10.1109/icaiccit64383.2024.10912094.

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Kumar, Ravi, and Manu Vardhan. "Multi-Objective Task Scheduling in Cloud Environments using Dynamic Velocity-Adaptive Particle Swarm Optimization." In 2024 OPJU International Technology Conference (OTCON) on Smart Computing for Innovation and Advancement in Industry 4.0. IEEE, 2024. http://dx.doi.org/10.1109/otcon60325.2024.10687713.

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Bandyopadhyay, Biswadip, Pratyay Kuila, and Mahesh Chandra Govil. "Particle Swarm Optimization for Latency-aware Multi-user Task Offloading in Resource-constrained Fog-cloud Framework." In 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT). IEEE, 2024. http://dx.doi.org/10.1109/icccnt61001.2024.10724815.

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Sridhar, M., and G. Rama Mohan Babu. "Hybrid Particle Swarm Optimization scheduling for cloud computing." In 2015 IEEE International Advance Computing Conference (IACC). IEEE, 2015. http://dx.doi.org/10.1109/iadcc.2015.7154892.

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Busetti, Riccardo, Nabil El Ioini, Hamid R. Barzegar, and Claus Pahl. "Distributed Synchronous Particle Swarm Optimization for Edge Computing." In 2022 9th International Conference on Future Internet of Things and Cloud (FiCloud). IEEE, 2022. http://dx.doi.org/10.1109/ficloud57274.2022.00027.

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Acharya, Jigna, Manisha Mehta, and Baljit Saini. "Particle swarm optimization based load balancing in cloud computing." In 2016 International Conference on Communication and Electronics Systems (ICCES). IEEE, 2016. http://dx.doi.org/10.1109/cesys.2016.7889943.

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Paul, Ankita, and Avtar Singh. "Fuzzy based Particle Swarm Optimization Scheduling in Cloud Computing." In 2024 3rd International Conference for Innovation in Technology (INOCON). IEEE, 2024. http://dx.doi.org/10.1109/inocon60754.2024.10512134.

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Xu, Shengsheng, Yulin He, and Joshua Zhexue Huang. "Observation Points-Based Particle Swarm Optimization Algorithm." In 2020 7th IEEE International Conference on Cyber Security and Cloud Computing (CSCloud)/2020 6th IEEE International Conference on Edge Computing and Scalable Cloud (EdgeCom). IEEE, 2020. http://dx.doi.org/10.1109/cscloud-edgecom49738.2020.00031.

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Yu, Meng'ao, Zhong Chen, Linzhi Ding, and Haoyu Cheng. "Particle swarm optimization based on simulated annealing rules." In International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2023), edited by Kannimuthu Subramaniam and Sandeep Saxena. SPIE, 2023. http://dx.doi.org/10.1117/12.2678840.

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