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  1. 1
    Batch mode heuristic approaches for efficient task scheduling in grid computing system

    Batch mode heuristic approaches for efficient task scheduling in grid computing system by Maipan-Uku, Jamilu Yahaya

    Published 2016
    “…We simulate our proposed algorithms using a Java based simulator that is purposedly built for Grid computing simulations. …”
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  2. 2
    Performance evaluation of real-time multiprocessor scheduling algorithms

    Performance evaluation of real-time multiprocessor scheduling algorithms by Alhussian, H., Zakaria, N., Abdulkadir, S.J., Fageeri, S.O.

    Published 2016
    “…The CPU profiler of JavaTM VisualVM measures the number of invocations of scheduling event handlers (procedures) in each algorithm as well as the total time spent in all invocations of this handler. …”
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  3. 3
    A Toolkit for Simulation of Desktop Grid Environment

    A Toolkit for Simulation of Desktop Grid Environment by FOROUSHAN, PAYAM CHINI

    Published 2014
    “…In this type of environment it is nearly impossible to prove the effectiveness of a scheduling algorithm. Hence the main objective of this study is to develop a desktop grid simulator toolkit for measuring and modeling scheduler algorithm performance. …”
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  4. 4
    Computer Lab Timetabling Using Genetic Algorithm Case Study - Unit ICT

    Computer Lab Timetabling Using Genetic Algorithm Case Study - Unit ICT by Abdullah, Amran

    Published 2006
    “…Genetic Algorithm is one of the most popular optimization solutions used in various applications such as scheduling. …”
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  5. 5
    Simulated annealing algorithm for scheduling divisible load in large scale data grids.

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

    Published 2009
    “…This paper proposes a novel Simulated Annealing (SA) algorithm for scheduling divisible load in large scale data grids. …”
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  6. 6
    Improving Class Timetabling using Genetic Algorithm

    Improving Class Timetabling using Genetic Algorithm by Qutishat, Ahmed Mohammed Ali

    Published 2006
    “…This paper reports the power fill techniques using GA in scheduling. Class timetabling problem is one of the applications in scheduling. …”
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  7. 7
    Examination timetabling using genetic algorithm case study: KUiTTHO

    Examination timetabling using genetic algorithm case study: KUiTTHO by Mohd Salikon, Mohd Zaki

    Published 2005
    “…This paper reports the powerful techniques using GA in scheduling. Examination timetabling problem is one of the applications in scheduling. …”
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  8. 8
    Simulated annealing algorithm for scheduling divisible load in large scale data grids

    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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  9. 9
    An estimation-based dynamic load balancing algorithm for efficient load distribution and balancing in heterogeneous Grid computing environment

    An estimation-based dynamic load balancing algorithm for efficient load distribution and balancing in heterogeneous Grid computing environment by Eng, Kailun, Muhammed, Abdullah, Abdullah, Azizol, Hussin, Masnida, Hasan, Sazlinah, Mohamed, Mohamad Afendee

    Published 2022
    “…The comparative results of simulation experiments show that the proposed load balancing approach gives superior performance and outperforms contemporary load balancing approaches in the literature. …”
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  10. 10
    Examination Timetabling Using Genetic Algorithm Case Study : KUiTTHO

    Examination Timetabling Using Genetic Algorithm Case Study : KUiTTHO by Mohd. Zaki, Mohd. Salikon

    Published 2005
    “…This paper reports the powerful techniques using GA in scheduling. Examination timetabling problem is one of the applications in scheduling. …”
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  11. 11
    Attribute reduction based scheduling algorithm with enhanced hybrid genetic algorithm and particle swarm optimization for optimal device selection

    Attribute reduction based scheduling algorithm with enhanced hybrid genetic algorithm and particle swarm optimization for optimal device selection by Nwogbaga, Nweso Emmanuel, Latip, Rohaya, Affendey, Lilly Suriani, Abdul Rahiman, Amir Rizaan

    Published 2022
    “…Therefore, in this paper, we proposed Dynamic tasks scheduling algorithm based on attribute reduction with an enhanced hybrid Genetic Algorithm and Particle Swarm Optimization for optimal device selection. …”
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  12. 12
    Enhancement of Ant Colony Optimization for Grid Job Scheduling and Load Balancing

    Enhancement of Ant Colony Optimization for Grid Job Scheduling and Load Balancing by Husna, Jamal Abdul Nasir

    Published 2011
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  13. 13
    Bacterial foraging optimization algorithm for optimal load shedding in power systems / Wan Nur Eliana Afif Wan Afandie

    Bacterial foraging optimization algorithm for optimal load shedding in power systems / Wan Nur Eliana Afif Wan Afandie by Wan Afandie, Wan Nur Eliana Afif

    Published 2016
    “…By using Bacterial Foraging Optimization Algorithm (BFOA) technique, for each types of load shedding, the simulations are tested on IEEE 30-bus system for 3 and 5 load shedding locations. 3 different load conditions and one line outage cases are tested for 3 objective function and 3 multi-objective functions. …”
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  14. 14
    Optimising cloud computing performance with an enhanced dynamic load balancing algorithm for superior task allocation

    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
    “…Furthermore, the EDLB algorithm enhances load balancing by 46.46%. These results highlight the effectiveness of the EDLB algorithm in addressing critical load balancing issues and surpassing existing methods. …”
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  15. 15
    Load balancing using enhanced ant algorithm in grid computing

    Load balancing using enhanced ant algorithm in grid computing by Abdul Nasir, Husna Jamal, Ku-Mahamud, Ku Ruhana, Mohamed Din, Aniza

    Published 2010
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  16. 16
    Adaptive linear neural network approach for three-phase four-wire active power filtering under non-ideal grid and unbalanced load scenarios

    Adaptive linear neural network approach for three-phase four-wire active power filtering under non-ideal grid and unbalanced load scenarios by Hoon, Yap, Mohd Radzi, Mohd Amran, Al-Ogaili, Ali Saadon

    Published 2019
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  17. 17
    Computational simulation for analysis and synthesis of impact resilient structure

    Computational simulation for analysis and synthesis of impact resilient structure by Djojodihardjo, Harijono

    Published 2013
    “…The first objective of the work is to develop a computational algorithm to analyze flat plate as a generic structure subjected to impact loading for numerical simulation and parametric study. …”
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  18. 18
    Classroom finder system with student availability, space and time constraint

    Classroom finder system with student availability, space and time constraint by Loh, Yong Bin

    Published 2024
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  19. 19
    An Intelligent Voltage Controller for a PV Inverter System Using Simulated Annealing Algorithm-Based PI Tuning Approach

    An Intelligent Voltage Controller for a PV Inverter System Using Simulated Annealing Algorithm-Based PI Tuning Approach by Najeeb, Mushtaq, Muhamad, Mansor, Ramdan, Razali, Hamdan, Daniyal, Ali, Mahmood

    Published 2017
    “…This study associates an intelligent voltage controller based PI approach for PV electrical inverter by employing a meta-heuristic optimization algorithmic called a Simulated Annealing (SA) algorithm. …”
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  20. 20
    A meta-heuristics based input variable selection technique for hybrid electrical energy demand prediction models

    A meta-heuristics based input variable selection technique for hybrid electrical energy demand prediction models by ul Islam, B., Baharudin, Z.

    Published 2017
    “…The results show that the neural network optimized with genetic algorithm and trained with an optimally and intelligently selected input vector containing historical load and meteorological variables produced the best prediction accuracy. © 2017, UK Simulation Society. …”
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