Search Results - (( evolution optimization based algorithm ) OR ( using computational cloud algorithm ))

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  1. 1

    Task scheduling in cloud computing using hybrid genetic algorithm and bald eagle search (GA-BES) by Kamal Khairi Supaprhman

    Published 2022
    “…Task scheduling and resource allocation are essential aspects of cloud computing. This Study proposes task scheduling in cloud computing using a hybrid genetic algorithm, and bald eagle search proposed to solve the task scheduling problem. …”
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    Academic Exercise
  2. 2

    Cloudlet deployment and task offloading in mobile edge computing using variable-length whale and differential evolution optimization and analytical hierarchical process for decisio... by Daba, Layth Muwafaq Abdulhussein

    Published 2023
    “…Comparing this developed algorithm with other algorithms shows its superiority in multi-objective optimization (MOO) evaluation metrics. …”
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    Thesis
  3. 3

    The exploration of hybrid metaheuristics-based approaches: A bibliometric analysis by Nur Hidayah, Azmidi, Noryanti, Muhammad, Rozieana, Khairuddin

    Published 2025
    “…The rapid evolution of computational intelligence has driven significant interest in hybrid metaheuristics, which combine multiple optimization algorithms to solve complicated problems more efficiently. …”
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    Article
  4. 4

    Task scheduling in cloud computing environment using hybrid genetic algorithm and bat algorithm by Muhammad Syahril Mohamad Sainal

    Published 2022
    “…Furthermore, CloudSim simulator will be used to evaluate the performance of this algorithm. …”
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    Academic Exercise
  5. 5

    System program management environment in cloud computing using hybrid Genetic Algorithm and Moth Flame Optimization (GA-MFO) by Mohd Erwan Mazalan

    Published 2022
    “…In this project, Genetic Algorithms (GA) is combine Moth Flame Optimization (MFO) to improve the cloud computing environment. …”
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    Academic Exercise
  6. 6

    Task scheduling in cloud computing environment using hybrid of genetic algorithm and naked mole rat algorithm (GA-NMRA) by Mohammad Ozaniezie Onasis

    Published 2022
    “…Task scheduling and resource allocation are essential aspects of cloud computing. This research proposes task scheduling in cloud computing using a hybrid genetic algorithm and naked mole rat algorithm to solve the task scheduling problem. …”
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    Academic Exercise
  7. 7

    Performance-aware cost-effective brokering and load balancing algorithms for data center in large scale cloud computing by Naha, Ranesh Kumar

    Published 2015
    “…It helps organization to reduce computing infrastructure cost. Incloud computing concept, cloud users can use computing resources according to their needs and requirements. …”
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    Thesis
  8. 8

    Modified workflow scheduling using hybrid PSO-GA algorithm in cloud computing by Oke, Omotayo Patrick

    Published 2019
    “…The goal of the Hybrid GA-PSO algorithm is to reduce the makespan and cost and balance the load of dependent tasks in cloud computing environments over the heterogonous resources. …”
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    Thesis
  9. 9

    Locust- inspired meta-heuristic algorithm for optimising cloud computing performance by Fadhil, Mohammed Alaa

    Published 2023
    “…Cloud computing offers high computational resources at a reasonable pricelevel. …”
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    Thesis
  10. 10

    Performance evaluation of load balancing algorithm for virtual machine in data centre in cloud computing by Parmesivan, Yuganes, Hasan, Sazlinah, Muhammed, Abdullah

    Published 2018
    “…Cloud computing has become biggest buzz in the computer era these days.It runs entire operating systems on the cloud and doeverything on cloud to store data off-site.Cloud computing is primarily based on grid computing, but it’s a new computational model.Cloud computing has emerged into a new opportunity to further enhance way of hosting data centre and provide services.The primary substance of cloud computing is to deal the computing power,storage,different sort of stages and services which assigned tothe external users on demand through the internet.Task scheduling in cloud computing is vital role optimisation and effective dynamic resource allocation for load balancing.In cloud, the issue focused is under utilisation and over utilisation of the resources to distribute workload of multiple network links for example,when cloud clients try to access and send request tothe same cloud server while the other cloud server remain idle at that moment, leads to the unbalanced of workload on cloud data centers.Thus, load balancing is to assign tasks to the individual cloud data centers of the shared system so that no single cloud data centers is overloaded or under loaded.A Hybrid approach of Honey Bee (HB) and Particle Swarm Optimisation (PSO) load balancing algorithm is combined in order to get effective response time.The proposed hybrid algorithm has been experimented by using CloudSim simulator.The result shows that the hybrid load balancing algorithm improves the cloud system performance by reducing the response time compared to the Honey Bee (HB) and Particle Swarm Optimisation (PSO) load balancing algorithm.…”
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    Article
  11. 11

    Deadline guarantee for scientific workflow using dynamic scheduling algorithms on IaaS clouds by Alqaisy, Sarah Abdulrahman Shukur

    Published 2018
    “…Nabrzyski ,2015). Normally, commercial Cloud computing is rapidly becoming the target platform on which to preform scientific computation. …”
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    Thesis
  12. 12

    Reliably optimal PMU placement using disparity evolution-based genetic algorithm by Matsukawa, Yoshiaki, Othman, Mohammad Lutfi, Watanabe, Masayuki, Mitani, Yasunori

    Published 2017
    “…In this paper, Disparity Evolution-type Genetic Algorithm (DEGA) based on disparity theory of evolution is applied. …”
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    Article
  13. 13

    Two objectives big data task scheduling using swarm intelligence in cloud computing by Diallo, Laouratou, Hassan Abdalla Hashim, Aisha, Olanrewaju, Rashidah Funke, Islam, Shayla, Zarir, Abdullah Ahmad

    Published 2016
    “…On the other hand, scheduling algorithms; starting from traditional to Hyper-heuristic; are widely used in computing systems such as cloud computing to monitor the use of resources. …”
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    A refined differential evolution algorithm for improving the performance of optimization process by A. R., Yusoff, Nafrizuan, Mat Yahya

    Published 2011
    “…DE is developed based on an improved Genetic Algorithm and come with different strategies for faster optimization. …”
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    Conference or Workshop Item
  16. 16

    A new modified differential evolution algorithm scheme-based linear frequency modulation radar signal de-noising by Al-Dabbagh, Mohanad Dawood, Al-Dabbagh, Rawaa Dawoud, Raja Abdullah, Raja Syamsul Azmir, Hashim, Fazirulhisyam

    Published 2015
    “…The main intention of this study was to investigate the development of a new optimization technique based on the differential evolution (DE) algorithm, for the purpose of linear frequency modulation radar signal de-noising. …”
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    Article
  17. 17

    Application of swarm intelligence optimization on bio-process problems / Mohamad Zihin Mohd Zain by Mohamad Zihin , Mohd Zain

    Published 2018
    “…BSA gave the best overall performance by showing improved solutions and more robust convergence in comparison with various metaheuristics used in this work. Multi-objective optimization problems are also addressed by proposing a modified multi-criterion optimization algorithm based on a Pareto-based Particle Swarm Optimization (PSO) algorithm called Multi-Objective Particle Swarm Optimization (MOPSO). …”
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    Thesis
  18. 18

    Hybrid cryptography algorithm to improve security cloud storage by Abd Almohsen, Inam Razzaq

    Published 2017
    “…Security concerns are also very high due to increase in use of cloud computing by the general public. The weakness in user’s authentication process and lack of effective security policy in cloud storage leads to many challenges in cloud computing. …”
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    Thesis
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    Broadening selection competitive constraint handling algorithm for faster convergence by Shaikh, T.A., Hussain, S.S., Tanweer, M.R., Hashmani, M.A.

    Published 2020
    “…In this study, the BSCCH algorithm has been coupled with Differential Evolution algorithm as a proof of concept because it is found to be an efficient algorithm in the literature for constrained optimization problems. …”
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    Article