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  1. 1
    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
    “…The simulation is implemented with iFogSim and java programming language. …”
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  2. 2
    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
    “…Global pheromone update is performed after the completion of processing the jobs in order to reduce the pheromone value of resources. A simulation environment was developed using Java programming to test the performance of the proposed EACO algorithm against existing grid resource management algorithms such as Antz algorithm, Particle Swarm Optimization algorithm, Space Shared algorithm and Time Shared algorithm, in terms of processing time and resource utilization. …”
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  3. 3
    Ant colony optimization algorithm for load balancing in grid computing

    Ant colony optimization algorithm for load balancing in grid computing by Ku-Mahamud, Ku Ruhana, Mohamed Din, Aniza

    Published 2012
    “…The proposed algorithm is known as the enhance ant colony optimization (EACO). …”
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  4. 4
    Development of an islanding detection scheme based on combination of slantlet transform and ridgelet probabilistic neural network in distributed generation

    Development of an islanding detection scheme based on combination of slantlet transform and ridgelet probabilistic neural network in distributed generation by Ahmadipour, Masoud

    Published 2019
    “…In order to train Ridgelet probabilistic neural network, a modified differential evolution algorithm with new mutation phase, crossover process, and selection mechanism is introduced. …”
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  5. 5
    Fair scheduling algorithm in LTE-advanced networks

    Fair scheduling algorithm in LTE-advanced networks by Al-Shibly, Mohammed Abuljawad M, Habaebi, Mohamed Hadi, Islam, Md Rafiqul, Chebil, Jalel, Zyoud, Alhareth, Alfaqawi, Mohammed I M

    Published 2015
    “…The results demonstrate that the effectiveness of enhanced Modified Largest Weighted Delay First (M-LWDF) algorithm in improving throughput.…”
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  6. 6
    OPTIMIZED MIN-MIN TASK SCHEDULING ALGORITHM FOR SCIENTIFIC WORKFLOWS IN A CLOUD ENVIRONMENT

    OPTIMIZED MIN-MIN TASK SCHEDULING ALGORITHM FOR SCIENTIFIC WORKFLOWS IN A CLOUD ENVIRONMENT by Murad S.S., Badeel R., Alsandi N.S.A., Alshaaya R.F., Ahmed R.A., Muhammed A., Derahman M.

    Published 2023
    “…To achieve this, we propose a new noble mechanism called Optimized Min-Min (OMin-Min) algorithm, inspired by the Min-Min algorithm. …”
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  7. 7
    An FPGA implementation of exp-bet scheduling algorithm in LTE networks / Yusmardiah Yusuf

    An FPGA implementation of exp-bet scheduling algorithm in LTE networks / Yusmardiah Yusuf by Yusuf, Yusmardiah

    Published 2017
    “…It is performed by the scheduling algorithm implemented in the Long Term Evolution (LTE) base station, Evolved Node B. …”
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  8. 8
    Self-configured link adaptation using channel quality indicator-modulation and coding scheme mapping with partial feedback for green long-term evolution cellular systems

    Self-configured link adaptation using channel quality indicator-modulation and coding scheme mapping with partial feedback for green long-term evolution cellular systems by Salman, Mustafa Ismael

    Published 2015
    “…In this thesis, an analytical model that represents the tradeoff between energy efficiency (EE), spectral efficiency (SE), and transmission delay in both green and conventional Long-Term Evolution (LTE) eNodeB is formulated. Consequently, a modified downlink scheduler based on a Packet Prediction Mechanism (PPM) is conducted at the eNodeB. …”
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  9. 9
    Embedded system for indoor guidance parking with Dijkstra’s algorithm and ant colony optimization

    Embedded system for indoor guidance parking with Dijkstra’s algorithm and ant colony optimization by Mohammad Ata, Karimeh Ibrahim

    Published 2019
    “…This study proposes a car parking management system which applies Dijkstra’s algorithm, Ant Colony Optimization (ACO) and Binary Search Tree (BST) in structuring a guidance system for indoor parking. …”
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  10. 10
    Features selection for intrusion detection system using hybridize PSO-SVM

    Features selection for intrusion detection system using hybridize PSO-SVM by Tabaan, Alaa Abdulrahman

    Published 2016
    “…Hybridize Particle Swarm Optimization (PSO) as a searching algorithm and support vector machine (SVM) as a classifier had been implemented to cope with this problem. …”
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  11. 11
    Resource management in grid computing using ant colony optimization

    Resource management in grid computing using ant colony optimization by Ku-Mahamud, Ku Ruhana, Mohamed Din, Aniza

    Published 2011
    “…Resources with high pheromone value are selected to process the submitted jobs.Global pheromone update is performed after completion processing the jobs in order to reduce the pheromone value of resources.A simulation environment was developed using Java programming to test the performance of the proposed EACO algorithm against other ant based algorithm, in terms of resource utilization.Experimental results show that EACO produced better grid resource management solution.…”
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  12. 12
    Implementation of locust inspired scheduling algorithm with huge number of servers for energy efficiency in a cloud datacenter

    Implementation of locust inspired scheduling algorithm with huge number of servers for energy efficiency in a cloud datacenter by Azhar, Nur Huwaina

    Published 2019
    “…Cloudsim is used as Discrete Event Simulation tool and Java as coding language to evaluate LACE algorithm. …”
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  13. 13
    A multi-objective particle swarm optimization algorithm based on dynamic boundary search for constrained optimization

    A multi-objective particle swarm optimization algorithm based on dynamic boundary search for constrained optimization by Mohd Zain, Mohamad Zihin, Kanesan, Jeevan, Chuah, Joon Huang, Dhanapal, Saroja, Kendall, Graham

    Published 2018
    “…M-MOPSO is compared with four other algorithms namely, MOPSO, Multi-Objective Grey Wolf Optimizer (MOGWO), Multi-Objective Evolutionary Algorithm based on Decompositions (MOEA/D) and Multi-Objective Differential Evolution (MODE). …”
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  14. 14
    A novel scheduling algorithm based on game theory and multicriteria decision making in LTE network

    A novel scheduling algorithm based on game theory and multicriteria decision making in LTE network by Hindia, M.N., Reza, A.W., Noordin, K.A.

    Published 2015
    “…However, 3GPP is not a standardized scheduling algorithm to utilize LTE properties in smart grid applications. …”
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  15. 15
    Design And Development Of Modified-Proportional Fair Scheduler For LTE/LTE-Advanced

    Design And Development Of Modified-Proportional Fair Scheduler For LTE/LTE-Advanced by Mohd Khairy, Ismail, Azmi, Awang Md Isa, Mohd Nor, Husain, Muhammad Syahrir, Johal, Mohd Riduan, Ahmad

    Published 2016
    “…The main contribution of this paper is the design of a new scheduling scheme and its performance is compared with the Proportional Fair (PF) and Round Robin (RR) downlink schedulers for LTE by utilizing LTE Downlink System Level Simulator. The proposed new scheduling algorithm, namely the Modified-PF scheduler divides a single subframe into multiple time slots and allocates the resource block (RB) to the targeted User Equipment (UE) in all time slots for each subframe based on the instantaneous Channel Quality Indicator (CQI) feedback received from UEs. …”
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  16. 16
    Enhancing the QoS performance for mobile station over LTE and WiMAX networks / Mhd Nour Hindia

    Enhancing the QoS performance for mobile station over LTE and WiMAX networks / Mhd Nour Hindia by Hindia, Mhd Nour

    Published 2015
    “…The results obtained show that in terms of handover issues, a modified multi criteria with two threshold algorithm increases the efficiency of target network selection. …”
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  17. 17
    Islanding detection method using ridgelet probabilistic neural network in distributed generation

    Islanding detection method using ridgelet probabilistic neural network in distributed generation by Ahmadipour, Masoud, Hizam, Hashim, Othman, Mohammad Lutfi, Mohd Radzi, Mohd Amran

    Published 2019
    “…A modified differential evolution (MDF) algorithm with a new mutation phase, crossover process, and selection mechanism is proposed to train the RPNN. …”
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  18. 18
    SG-PBFS : Shortest Gap-Priority Based Fair Scheduling technique for job scheduling in cloud environment

    SG-PBFS : Shortest Gap-Priority Based Fair Scheduling technique for job scheduling in cloud environment by Murad, Saydul Akbar, Zafril Rizal, M. Azmi, Muzahid, Abu Jafar Md, Bhuiyan, Md Khairul Bashar, Saib, Md, Rahimi, Nick, Prottasha, Nusrat Jahan, Bairagi, Anupam Kumar

    Published 2024
    “…To conduct this experiment, we employed the CloudSim simulator, which is implemented using the Java programming language.…”
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  19. 19
    Optimization of chemotherapy using metaheuristic optimization algorithms / Prakas Gopal Samy

    Optimization of chemotherapy using metaheuristic optimization algorithms / Prakas Gopal Samy by Prakas Gopal , Samy

    Published 2024
    “…The HM emerges as a dominant strategy, driven by the Multi-Objective Differential Evolution (MODE) algorithm under literature-based control parameter settings for the mathematical model. …”
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  20. 20
    Design Of Feature Selection Methods For Hand Movement Classification Based On Electromyography Signals

    Design Of Feature Selection Methods For Hand Movement Classification Based On Electromyography Signals by Too, Jing Wei

    Published 2020
    “…The second method is called the Modified Binary Tree Growth Algorithm (MBTGA) that applies swap, crossover, and mutation operators. …”
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