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  1. 1
    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
    “…This research proposes an enhancement of the ant colony optimization algorithm that caters for dynamic scheduling and load balancing in the grid computing system. …”
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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
    “…This research proposes an Enhanced Ant Colony Optimization (EACO) algorithm that caters dynamic scheduling and load balancing in the grid computing system. …”
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  3. 3
    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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  4. 4
    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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  5. 5
    An ensemble of neural network and modified grey wolf optimizer for stock prediction

    An ensemble of neural network and modified grey wolf optimizer for stock prediction by Das, Debashish

    Published 2019
    “…This update process may cause the algorithm to fall to local optima especially in the cases where there are many competing local optima. …”
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  6. 6
    Parametric and Semiparametric Competing Risks Models for Statistical Process Control with Reliability Analysis

    Parametric and Semiparametric Competing Risks Models for Statistical Process Control with Reliability Analysis by Mohamed Elfaki, Faiz Ahmed

    Published 2004
    “…A simulation data using subdistribution function for the two types of failure are carried out to compare the performance of the modified model. …”
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  7. 7
    Field data-based mathematical modeling by Bode equations and vector fitting algorithm for renewable energy applications

    Field data-based mathematical modeling by Bode equations and vector fitting algorithm for renewable energy applications by Sabry, Ahmad H., Wan Hasan, Wan Zuha, Ab Kadir, M. Zainal A., Mohd Radzi, Mohd Amran, Shafie, Suhaidi

    Published 2018
    “…Several numerical techniques compete to fit the curves of empirical data such as wind, solar, and demand power rates. …”
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  8. 8
    Selective chaotic maps Tiki-Taka algorithm for the S-box generation and optimization

    Selective chaotic maps Tiki-Taka algorithm for the S-box generation and optimization by Kamal Z., Zamli, Abdul Kader, ., Fakhrud Din, ., Alhadawi, Hussam S.

    Published 2021
    “…Unlike competing works (which typically integrates a single chaotic map into a particular metaheuristic algorithm), SCMTTA assembles five chaotic maps (i.e., tent map, logistic map, Chebyshev map, singer map and sine map) as part of the algorithm itself in order to further enhance ergodicity and unpredictability of the generated solution. …”
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  9. 9
    Development of a systematic method in lean tool selection for automotive industry

    Development of a systematic method in lean tool selection for automotive industry by Anvari, Alireza

    Published 2012
    “…Based on the systematic features of this algorithm, the efficiency and effectiveness of the model in presenting optimizing techniques for lean tool selection with the automotive industry seems reasonable and useful. …”
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