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  1. 1

    Inversed Control Parameter in Whale Optimization Algorithm and Grey Wolf Optimizer for Wrapper-Based Feature Selection: A Comparative Study by Li Yu Yab, Li Yu Yab, Wahid, Noorhaniza, A Hamid, Rahayu

    Published 2023
    “…—Whale Optimization Algorithm (WOA) and Grey Wolf Optimizer (GWO) are well-perform metaheuristic algorithms used by various researchers in solving feature selection problems. …”
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    Article
  2. 2

    Inversed Control Parameter in Whale Optimization Algorithm and Grey Wolf Optimizer for Wrapper-Based Feature Selection: A Comparative Study by Li Yu Yab, Li Yu Yab, Wahid, Noorhaniza, A Hamid, Rahayu

    Published 2023
    “…Whale Optimization Algorithm (WOA) and Grey Wolf Optimizer (GWO) are well-perform metaheuristic algorithms used by various researchers in solving feature selection problems. …”
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    Article
  3. 3

    Modified multi-verse optimizer for nonlinear system identification of a double pendulum overhead crane by Julakha, Jahan Jui, Mohd Ashraf, Ahmad, Muhammad Ikram, Mohd Rashid

    Published 2021
    “…The efficiency of the proposed HMVOSCA algorithm is evaluated using the convergence curve, parameter estimation error, bode plot, function plot, and Wilcoxon's test method. …”
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    Conference or Workshop Item
  4. 4

    Automated-tuned hyper-parameter deep neural network by using arithmetic optimization algorithm for Lorenz chaotic system by Nurnajmin Qasrina Ann, ., Pebrianti, Dwi, Mohammad Fadhil, Abas, Bayuaji, Luhur

    Published 2023
    “…This study proposes an automated-tuned hyper-parameter for DNN using a metaheuristic optimization algorithm, arithmetic optimization algorithm (AOA). …”
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    Article
  5. 5

    Automated-tuned hyper-parameter deep neural network by using arithmetic optimization algorithm for Lorenz chaotic system by Ayop Azmi, Nurnajmin Qasrina Ann, Pebrianti, Dwi, Abas, Mohammad Fadhil, Bayuaji, Luhur

    Published 2023
    “…This study proposes an automated-tuned hyper�parameter for DNN using a metaheuristic optimization algorithm, arithmetic optimization algorithm (AOA). …”
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    Article
  6. 6

    A Method For Solving Mult-Objective Optimization Problem: Vector Evaluated Genetic Algorithm (Vega) by Tan, Tun Tai

    Published 2009
    “…Consequently, some components of GAs had been modified to produce Vector Evaluated Genetic Algorithm (VEGA) in order to adapt the nature of MOOP. …”
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    Final Year Project Report / IMRAD
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    Fuzzy clustering method and evaluation based on multi criteria decision making technique by Sameer, Fadhaa Othman

    Published 2018
    “…This proposed algorithm is developed based on heuristic method named modified binary particle swarm optimization (MBPSO) with kernel fuzzy clustering method as a fitness function. …”
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    Thesis
  9. 9

    Fast and optimal tuning of fractional order PID controller for AVR system based on memorizable-smoothed functional algorithm by Ren Hao, Mok, Ahmad, Mohd Ashraf

    Published 2022
    “…Therefore, this study proposes a modified smoothed function algorithm (MSFA) based method to tune the FOPID controller of AVR system since it requires fewer number of function evaluation per iteration. …”
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    Comparison of mabsa, PSO and GWO of PI-PD controller for dc motor by Nur Naajihah, Ab Rahman

    Published 2024
    “…The swarm intelligence group selected Particle Swarm Optimization (PSO), Grey Wolf Optimization (GWO), and Modified Adaptive Bats Sonar Algorithms (MABSA) to optimize the parameters of the PI-PD controller. …”
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    Thesis
  12. 12

    Optimizing n-1 contingency rankings using a nature-inspired modified sine cosine algorithm by Priyadi, Irnanda, Daratha, Novalio, Gunawan, Teddy Surya, Ramli, Kalamullah, Jalistio, Febrian, Mokhlis, Hazlie

    Published 2025
    “…The MSCA method is validated using the IEEE 30-bus test case, focusing on optimal parameter tuning for population size, iterations, and key variables. …”
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    Article
  13. 13

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

    Published 2024
    “…The Multi-Objective Particle Swarm Optimizer (MOPSO) demonstrates superior performance in the HM, while the Modified Multi-Objective Particle Swarm Optimizer (M-MOPSO) excels within the PMM, highlighting its crucial role in optimizing cancer therapy with enhanced control parameters. …”
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    Thesis
  14. 14

    VLSI floor planning optimization using genetic algorithm and cross entropy method / Angeline Teoh Szu Fern by Angeline Teoh, Szu Fern

    Published 2012
    “…Two methods of optimization are used for CBLL. They are Cross Entropy and also Genetic Algorithm. …”
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    Thesis
  15. 15

    Power System State Estimation In Large-Scale Networks by NURSYARIZAL MOHD NOR, NURSYARIZAL

    Published 2010
    “…Also the WLS algorithm is modified to include Unified Power Flow Controller (UPFC) parameters. …”
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    Thesis
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    Fair bandwidth distribution marking and scheduling algorithm in network traffic classification by Al-Kharasani, Ameen Mohammed Abdulkarem

    Published 2019
    “…Finally, propose a new method of obtaining optimal parameters dropping functions for Random Early Detection (RED) algorithm. …”
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    Thesis
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    Variable Neighborhood Descent and Whale Optimization Algorithm for Examination Timetabling Problems at Universiti Malaysia Sarawak by Emily Sing Kiang, Siew

    Published 2025
    “…A constructive algorithm was developed to generate an initial feasible solution, which was subsequently refined using two primary approaches to evaluate their efficiency: Iterative Threshold Pipe Variable Neighborhood Descent (IT-PVND), and a modified Whale Optimization Algorithm (WOA). …”
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    Thesis
  18. 18

    Optimization Method Using Modified Harmony Search For Coverage And Energy Efficiency In Wireless Sensor Network by Halim, Nurul Hamimi

    Published 2018
    “…However,the sink node position and size of data transmitted will not affect the performance of coverage area.This is because the coverage area value is fluctuated as the parameters value increases.Throughout the experiment conducted,sensor nodes deployed using Modified Harmony Search algorithm (MHS) gives better coverage area compared to other existing methods.The average coverage area percentage obtained by Modified Harmony Search is 63 %.The average coverage area percentage obtained by Modified Random is 48 % and the average coverage area percentage obtained by Harmony Search is 46 %.The highest coverage area recorded for Modified Harmony Search is 70 %.To enhance the energy efficiency,shortest path distance finder is added to each method.Throughout the research,Modified Harmony Search with shortest path distance finder gives optimum results.…”
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    Optimization of fast fourier transform based on twiddle factor using genetic algorithm on raspberry pi by Ghazi, Firas Faisal

    Published 2019
    “…Over the years the Genetic Algorithms (GA) proved to be one of the best methods for optimization. …”
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    Thesis