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  1. 1

    Quantum Particle Swarm Optimization Technique for Load Balancing in Cloud Computing by Elrasheed Ismail, Sultan

    Published 2013
    “…The choice of a scheduling strategy has significant impact on the runtime Central Processing Unit, memory consumption as well as the storage systems. Load balancing optimization techniques such as Ant Colony Optimization (ACO), First Come First Served (FCFS), Round Robin (RR) and Particle Swarm Optimization (PSO) are popular techniques being used for scheduling and load balancing. …”
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    Thesis
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    A novel peak load shaving algorithm for isolated microgrid using hybrid PV-BESS system by Rana, M.M., Romlie, M.F., Abdullah, M.F., Uddin, M., Sarkar, M.R.

    Published 2021
    “…To evaluate the effectiveness of the algorithm, simulation case studies have been conducted with actual load data and actual PV generation data. …”
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    Article
  4. 4

    One day ahead daily peak hour load forecasting by using invasive weed optimization learning algorithm based Artificial Neural Network by Rahim, Muhammad Fitri

    Published 2012
    “…In this project, an Artificial Neural Network (ANN) trained by the Invasive Weed Optimization (IWO) learning algorithm is proposed for short term load forecasting (STLF) model. …”
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    Student Project
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    Resource scheduling algorithm with load balancing for cloud service provisioning by Priya, V., Sathiya Kumar, C., Kannan, R.

    Published 2019
    “…Increasing utilization of Virtual Machines through effective and fair load balancing is then achieved by dynamically selecting a request from a class using Multidimensional Queuing Load Optimization algorithm. …”
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    Article
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    An Optimized Binary Scheduling Controller for Microgrid Energy Management Considering Real Load Conditions by Mannan M., Roslan M.F., Reza M.S., Mansor M., Jern K.P., Hossain M.J., Hannan M.A.

    Published 2024
    “…This study presents an optimal schedule controller for microgrid energy management, utilizing the Binary Particle Swarm algorithm (BPSO) to minimize costs and ensure optimal power delivery to loads. …”
    Conference Paper
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    Hybrid ant colony optimization algorithm for container loading problem by Yap, Ching Nei

    Published 2012
    “…The proposed algorithm is tested on two standard benchmark data sets to evaluate the performance and to determine the effectiveness of the algorithm. …”
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    Thesis
  8. 8

    Load-Balancing Models for Scheduling Divisible Load on Large Scale Data Grids by Abduh Kaid, Monir Abdullah

    Published 2009
    “…There are some scheduling models, which have been studied, such as Constraint DLT (CDLT), Task Data Present (TDP) and Genetic Algorithm (GA). However, there has been no optimal solution reached. …”
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    Thesis
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    CAT CHAOTIC GENETIC ALGORITHM BASED TECHNIQUE AND HARDWARE PROTOTYPE FOR SHORT TERM ELECTRICAL LOAD FORECASTING by ISLAM, BADAR UL ISLAM

    Published 2017
    “…In the hybrid scheme, the initial parameters of the modified BP neural network are optimized by using the global search ability of genetic algorithm, improved by cat chaotic mapping to enrich its optimization capability. …”
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    Thesis
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    Simulated annealing algorithm for scheduling divisible load in large scale data grids. by Abdullah, Monir, Othman, Mohamad, Ibrahim, Hamidah, Subramaniam, Shamala

    Published 2009
    “…Many Scheduling approaches have been studied but there is no optimal solution. This paper proposes a novel Simulated Annealing (SA) algorithm for scheduling divisible load in large scale data grids. …”
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    Article
  12. 12

    Multi-objective optimization for integrated task scheduling and load balancing in fog computing for IoT applications by Rafique, Majid

    Published 2025
    “…By using a task scheduling and offloading method, the FOCS algorithm arranges data according to size and sends it to the appropriate fog nodes. …”
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    Thesis
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    Optimized Load Balancing based Task Scheduling in Cloud Environment by Noraziah, Ahmad, Sultan, Elrasheed Ismail, Faisal, Alamri, Abdalla, Ahmed N.

    Published 2014
    “…The fitness function based minimizing the makespan and data transmission cost. In addition, the salient feature of this algorithm is to optimize node available throughput dynamically using MatLab10A software. …”
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    Article
  14. 14

    Simulated annealing algorithm for scheduling divisible load in large scale data grids by Abdullah, Monir, Othman, Mohamed, Ibrahim, Hamidah, K. Subramaniam, Shamala

    Published 2008
    “…This paper proposes a novel simulated annealing (SA) algorithm for scheduling divisible load in large scale data grids. …”
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    Conference or Workshop Item
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    Optimal demand response of solar energy generation using Genetic Algorithm / Muhammad Asyraaf Adlan by Adlan, Muhammad Asyraaf

    Published 2025
    “…The aim of this study is to optimize the demand response of solar energy generation using Genetic Algorithm (GA) to minimize the daily yield loss caused by load shedding. …”
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    Thesis
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    A novel hybrid metaheuristic algorithm for short term load forecasting by Zuriani, Mustaffa, Mohd Herwan, Sulaiman, Yuhanis, Yusof, Syafiq Fauzi, Kamarulzaman

    Published 2017
    “…Later, the efficiency of GWO-LSSVM is compared against three comparable hybrid algorithms namely LSSVM optimized by Artificial Bee Colony (ABC), Differential Evolution (DE) and Firefly Algorithms (FA). …”
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    Article
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    Energy Management in Integrated Microgrids: An Optimal Schedule Controller Utilizing Gradient Descent Algorithm by Abdolrasol M.G.M., Ker P.J., Hannan M.A., Ayob A., Tiong S.K.

    Published 2024
    “…Weather data, including wind, solar, fuel, and battery status, is integrated into the BGD algorithm for optimizing ON and OFF schedules. …”
    Conference Paper
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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
    “…Additionally, if a VM cannot meet a cloudlet's deadline, the algorithm redirects the cloudlet to a secondary data centre and reconfigures CPU resources among VMs to ensure optimal allocation. …”
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    Article