Search Results - (( _ optimisation system algorithm ) OR ( data virtualization based algorithm ))

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    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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    A hyper-heuristic cost optimisation approach for Scientific Workflow Scheduling in cloud computing by Alkhanak, Ehab Nabiel, Lee, Sai Peck

    Published 2018
    “…The CTDHH approach employs four well-known population-based meta-heuristic algorithms, which act as Low Level Heuristic (LLH) algorithms. …”
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    Article
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    Optimising cloud computing performance with an enhanced dynamic load balancing algorithm for superior task allocation by Zhanuzak, Raiymbek, Ala'anzy, Mohammed Alaa, Othman, Mohamed, Algarni, Abdulmohsen

    Published 2024
    “…This paper presents an Enhanced Dynamic Load Balancing (EDLB) algorithm designed to optimise task scheduling and resource allocation in cloud environments. …”
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    Locust- inspired meta-heuristic algorithm for optimising cloud computing performance by Fadhil, Mohammed Alaa

    Published 2023
    “…The third part addresses the limitations of LACE algorithms, leading to the proposition of a novel meta-heuristic algorithm called the Locust-Inspired Algorithm (LIA) that can effectively map virtual machines (VMs) for efficient server consolidation. …”
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    Thesis
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    Modelling of optimal placement and sizing of battery energy storage system using hybrid whale optimization algorithm and artificial immune system for total system losses reduct... by Wong Ling Ai

    Published 2023
    “…Having those said, this study proposes BESS optimisation to reduce the total system losses using modified Whale Optimisation Algorithm (WOA) with high exploration and exploitation features. …”
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    Optimization Of Pid Controller Using Grey Wolf Optimzer And Dragonfly Algorithm by Nik Mohamed Hazli, Nik Muhammad Aiman

    Published 2018
    “…The expected result is the algorithms are able to optimise the PID controller. …”
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    Monograph
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    Improved TLBO-JAYA Algorithm for Subset Feature Selection and Parameter Optimisation in Intrusion Detection System by Aljanabi, Mohammad, Mohd Arfian, Ismail, Mezhuyev, Vitaliy

    Published 2020
    “…The proposed method combined the improved teaching-learning-based optimisation (ITLBO) algorithm, improved parallel JAYA (IPJAYA) algorithm, and support vector machine. …”
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    Optimisation of automatic generation control performance in two-area power system with pid controllers using mepso / Lu Li by Lu , Li

    Published 2018
    “…From the simulation results, it was found that with the same number of PID controllers, the performance of AGC optimised by using MEPSO-TVAC algorithm is better in terms of overshoot and fitness value than using EPSO and PSO algorithms. …”
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    Thesis
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    An improved leader particle swarm optimisation algorithm for solving flexible ac transmission systems optimisation problem in power system by Jordehi, Ahmad Rezaee

    Published 2014
    “…The results of applying improved leader PSO to IEEE 14 bus power system shows its significant outperformance over six other optimisation algorithms including conventional PSO, mutated PSO, enhanced PSO, harmony search,genetic algorithm and gravitational search algorithm. …”
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    Hybrid evolutionarybarnacles mating optimisation-artificial neural network based technique for solving economic power dispatch planning and operation / Nor Laili Ismail by Ismail, Nor Laili

    Published 2024
    “…In this study, a new optimisation algorithm termed Hybrid Evolutionary-Barnacles Mating Optimisation (HEBMO) was initially formulated to solve optimisation problems. …”
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    Thesis
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    Novel Metaheuristic Hybrid Spiral-Dynamic Bacteria-Chemotaxis Algorithms for Global Optimisation by Ahmad Nor Kasruddin, Nasir, Tokhi, M. O.

    Published 2015
    “…This paper presents hybrid spiral-dynamic bacteria-chemotaxis algorithms for global optimisation and their application to control of a flexible manipulator system. …”
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    Optimal placement and sizing of battery energy storage system considering the duck curve phenomenon by Wong L.A., Ramachandaramurthy V.K., Walker S.L., Ekanayake J.B.

    Published 2023
    “…Location; Particle swarm optimization (PSO); Secondary batteries; Battery energy storage systems; Distribution systems; Energy storage systems; Exploration and exploitation; Location and sizings; Meta heuristic algorithm; Optimal placement and sizings; Particle swarm optimisation; Energy storage…”
    Article