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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
    Priority hybrid and EEF uplink scheduling algorithm for IEEE 802.16E

    Priority hybrid and EEF uplink scheduling algorithm for IEEE 802.16E by Oad, Aneel

    Published 2013
    “…The simulation results indicate that legacy algorithms are not suitable for the multi-class traffic systems of WiMAX. …”
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  4. 4
    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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  5. 5
    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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  6. 6
    Max-average: an extended max-min scheduling algorithm for Grid computing environtment

    Max-average: an extended max-min scheduling algorithm for Grid computing environtment by Maipan-uku, J. Y., Muhammed, Abdullah, Abdullah, Azizol, Hussin, Masnida

    Published 2016
    “…This paper presents a new proposed grid based scheduling algorithm called Max-Average, inspired from Max-Min algorithm. …”
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  7. 7
    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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  8. 8
    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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  9. 9
    Adaptive beamforming with 16 element linear array using MaxSIR and MMSE algorithms

    Adaptive beamforming with 16 element linear array using MaxSIR and MMSE algorithms by Islam, Md. Rafiqul, Hafriz, Fahmy, Norfauzi, Muhammad

    Published 2007
    “…Both algorithms were calculated and simulated using Matlab as well as Zeland’s IE3D software. …”
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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
    Quality of service management algorithms in WiMAX networks

    Quality of service management algorithms in WiMAX networks by Saidu, Ibrahim

    Published 2015
    “…Simulation have been extensively used to evaluate the proposed algorithm. …”
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  12. 12
    Simulation model to improve QoS performance over fixed WiMAX using OPNET

    Simulation model to improve QoS performance over fixed WiMAX using OPNET by Lawal, I.A., Md Said, A., Mu'azu, A.A.

    Published 2013
    “…A Model was developed based on the proposed new distributed model (Master-Slave), the first scenario comprised of 3 Base Stations (BS) and thirty Subscriber Station (SS) with one Master BS selected by the designed algorithm (Nearest Neighborhood Algorithms). Simulations run while increasing the number of BSs, SSs and master BSs using point-to-multipoint connection with multicast transmission based on Orthogonal Frequency Division Multiplexing (OFDM). …”
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  13. 13
    Analysis of WiMAX Positioning Using Received- Signal-Strength Method

    Analysis of WiMAX Positioning Using Received- Signal-Strength Method by Awang Md Isa, Azmi, Mohamad Isa, Mohd Sa'ari, Mohd Zin, Mohd Shahril Izuan, Haron, Nor Zaidi, Ja'afar, Abd Shukur

    Published 2013
    “…A simulator was developed using MATLAB to demonstrate the ability of RSS method for WiMAX positioning. …”
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  14. 14
    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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  15. 15
    Fuzzy Logic Based Self-Adaptive Handover Algorithm for MobileWiMAX.

    Fuzzy Logic Based Self-Adaptive Handover Algorithm for MobileWiMAX. by Mohd Ali, Borhanuddin, Ismail, Alyani, Noordin, Nor Kamariah, Ben-Mubarak, Mohammed A., Ng , Ben Chee Kyun

    Published 2013
    “…The conventional handover decision algorithm in mobile WiMAX is based on a single criterion, which usually uses the received signal strength indicator (RSSI) as an indicator, with the other fixed handover parameters such as handover threshold and handover margin. …”
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  16. 16
    Maximum 2-satisfiability in radial basis function neural network

    Maximum 2-satisfiability in radial basis function neural network by Shehab Abdulhabib Alzaeemi, Saratha Sathasivam, Mohd Shareduwan Mohd Kasihmuddin, Mohd. Asyraf Mansor

    Published 2020
    “…The simulated results suggest that the proposed algorithm is effective in doing MAX2SAT logic programming by analysing the performance by obtaining lower Root Mean Square Error, high ratio of satisfied clauses and lesser CPU time.…”
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  17. 17
    Fair uplink bandwidth allocation and latency guarantee for mobile WiMAX using fuzzy adaptive deficit round robin

    Fair uplink bandwidth allocation and latency guarantee for mobile WiMAX using fuzzy adaptive deficit round robin by Alsahag, Ali Mohammed, Mohd Ali, Borhanuddin, Noordin, Nor Kamariah, Mohamad, Hafizal

    Published 2014
    “…In this paper, an efficient bandwidth allocation algorithm for the uplink traffic in mobile WiMAX is proposed. …”
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  18. 18
    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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  19. 19
    Prediction and multi-criteria-based schemes for seamless handover mechanism in mobile WiMAX networks

    Prediction and multi-criteria-based schemes for seamless handover mechanism in mobile WiMAX networks by Mubarak, Mohammed Awadh Ahmed Ben

    Published 2013
    “…For real-time applications, simulation results show that RFuzSAHO decision algorithm enhances the VoIP quality (Mean Opinion Score, MOS) up to 5.4% compared to FuzSAHO algorithm when the MS velocity is 20m/s.…”
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  20. 20
    A new WDM Application Response Time in WLAN Network and Fixed WiMAX using Distributed

    A new WDM Application Response Time in WLAN Network and Fixed WiMAX using Distributed by Nisar, K., Lawal, I.A., Abualsaud, K., El-Fouly, T.M.

    Published 2014
    “…The new distributed Client-Server model was simulated in OPNET modeler 16.0 with multiple Base Stations (BSs), Subscribers Stations (SSs) and some Server BSs selected by the Nearest Neighborhood Algorithms using Orthogonal Frequency Division Multiplexing (OFDM) techniques and compared with the existing Centralized model using Frequency Division Multiplexing (FDM) techniques. …”
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