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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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    Thesis
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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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    Monograph
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  4. 4
    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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  5. 5
    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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  6. 6
    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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  7. 7
    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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  8. 8
    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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  9. 9
    A genetic algorithm for capital budgeting problem with fuzzy parameters

    A genetic algorithm for capital budgeting problem with fuzzy parameters by Rashidi-Bajgan, Hannaneh, Rezaeian, Javad, Nehzati, Taravatsadat, Ismail, Napsiah

    Published 2010
    “…Thereupon, a fuzzy simulation-based genetic algorithm is provided for solving chance constrained integer programming model with fuzzy parameters.…”
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  10. 10
    Explicit solution of parameter estimate using multiparametric programming for boost converter

    Explicit solution of parameter estimate using multiparametric programming for boost converter by Mid, E.C., Mukhtar, N.M., Syed Yunus, S.H., Abdul Hadi, Dayanasari, Ruslan, Eliyana

    Published 2023
    “…A multiparametric programming (MPP) algorithm is fundamental to the suggested methodology, which aims to develop parameters as explicit functions of measurements. …”
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  11. 11
    A power flow solvability identification and calculation algorithm using evolutionary programming (EP) / Kahardi Muhammad Talib

    A power flow solvability identification and calculation algorithm using evolutionary programming (EP) / Kahardi Muhammad Talib by Muhammad Talib, Kahardi

    Published 2016
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  12. 12
    System program management environment in cloud computing using hybrid Genetic Algorithm and Moth Flame Optimization (GA-MFO)

    System program management environment in cloud computing using hybrid Genetic Algorithm and Moth Flame Optimization (GA-MFO) by Mohd Erwan Mazalan

    Published 2022
    “…This project present a system program algorithm based on Moth Flame Optimization (MFO) algorithm to assign an optimal set of system program to meet the satisfaction of quality of service requirements of cloud computing in such a way that the total execution time of tasks is minimized. …”
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    Academic Exercise
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  13. 13
    A new islanding detection technique based on passive parameter using a combination of artificial neural network and evolutionary programming algorithm / Hasmaini Mohamad …[et al.]

    A new islanding detection technique based on passive parameter using a combination of artificial neural network and evolutionary programming algorithm / Hasmaini Mohamad …[et al.] by Mohamad, Hasmaini, Ab Salam, Aimie Nadia, Md Razali, Norhasniza, Salim, Nur Ashida, Mat Yasin, Zuhaila

    Published 2021
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  14. 14
    Control algorithm for two-tank system using multiparametric programming

    Control algorithm for two-tank system using multiparametric programming by Zakaria, A., Mid, E.C., Mohamed, M.F., Hussin, M.H.M., Shaari, A.S., Ruslan, Eliyana, Hadi, Dayanasari, Masri, M.

    Published 2023
    “…The two-tank system consists of two connected tanks, where the goal is to control the liquid levels in both tanks by manipulating the inflow rates. This study proposes a control algorithm for a two-tank system based on multiparametric programming. …”
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  15. 15
    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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  16. 16
    Adaptive differential evolution algorithm with fitness based selection of parameters and mutation strategies / Rawaa Dawoud Hassan Al-Dabbagh

    Adaptive differential evolution algorithm with fitness based selection of parameters and mutation strategies / Rawaa Dawoud Hassan Al-Dabbagh by Rawaa Dawoud Hassan, Al-Dabbagh

    Published 2015
    “…The second taxonomy is a new taxonomy proposed to classify the adaptive DE algorithms in particular into two categories (DE with adaptive parameters and DE with adaptive parameters and strategies) considering the adaptive components used in this algorithm. …”
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  17. 17
    Different mutation and crossover set of genetic programming in an automated machine learning

    Different mutation and crossover set of genetic programming in an automated machine learning by Masrom, S., Mohamad, M., Hatim, S.M., Baharun, N., Omar, N., Abd. Rahman, A.S.

    Published 2020
    “…As a family of evolutionary based algorithm, the effectiveness of Genetic Programming in providing the best machine learning pipelines for a given problem or dataset is substantially depending on the algorithm parameterizations including the mutation and crossover rates. …”
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  18. 18
    Different mutation and crossover set of genetic programming in an automated machine learning

    Different mutation and crossover set of genetic programming in an automated machine learning by Masrom, S., Mohamad, M., Hatim, S.M., Baharun, N., Omar, N., Abd. Rahman, A.S.

    Published 2020
    “…As a family of evolutionary based algorithm, the effectiveness of Genetic Programming in providing the best machine learning pipelines for a given problem or dataset is substantially depending on the algorithm parameterizations including the mutation and crossover rates. …”
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  19. 19
    Evolution Performance of Symbolic Radial Basis Function Neural Network by Using Evolutionary Algorithms

    Evolution Performance of Symbolic Radial Basis Function Neural Network by Using Evolutionary Algorithms by Alzaeemi, Shehab Abdulhabib, Tay, Kim Gaik, Huong, Audrey, Sathasivam, Saratha, Majahar Ali, Majid Khan

    Published 2023
    “…The SRBFNN’s objective function that corresponds to Satisfiability logic programming can be minimized by different algorithms, including Genetic Algorithm (GA), Evolution Strategy Algorithm (ES), Differential Evolution Algorithm (DE), and Evolutionary Programming Algorithm (EP). …”
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  20. 20
    OPTIMAL DESIGN OF A BLDC MOTOR BY GENETIC ALGORITHM

    OPTIMAL DESIGN OF A BLDC MOTOR BY GENETIC ALGORITHM by OTHMAN, AZRUL HISHAM

    Published 2007
    “…The project report describes an optimal design of Brushless DC (BLDC) motor using Genetic Algorithm (GA) and Simulated Annealing (SA). A constrained optimization on the objective function is performed and optimal parameters are derived. …”
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