Search Results - (( using adoption optimization algorithm ) OR ( using function method algorithm ))

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    Wavelet neural networks based solutions for elliptic partial differential equations with improved butterfly optimization algorithm training by Lee, Sen Tan, Zainuddin, Zarita, Ong, Pauline

    Published 2020
    “…Although the gradient information of the commonly used gradient descent training algorithm in WNNs may direct the search to optimal weight solutions that minimize the error function, the learning process is slow due to the complex calculation of the partial derivatives. …”
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    Article
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    Using genetic algorithms as image watermarking performance optimizer / Zuhaili Zahid by Zahid, Zuhaili

    Published 2008
    “…The process of embedding and retracting the data cause the original embedded data to be distorted as a side effect of the current method used. Due to the frequently use of rounding approach, embedded data or watermark hidden in an image is retrieved differently from the original watermark (Shih et al, 2004). …”
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    Thesis
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    Performance analysis of clustering based genetic algorithm by Najeeb, Athaur Rahman, Aibinu, Abiodun Musa, Nwohu, M. N., Salami, Momoh Jimoh Eyiomika, Salau, H Bello

    Published 2016
    “…The proposed CGA on which the performance analysis of this paper is based involve the use of two centroids based clustering technique as a new method of chromosomes selection at the reproduction stage in a typical Genetic Algorithm. …”
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    Proceeding Paper
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    Long term energy demand forecasting based on hybrid, optimization: Comparative study by Musa, Wahab, Ku-Mahamud, Ku Ruhana, Yasin, Azman

    Published 2012
    “…The objective of this research is to develop a long term energy demand forecasting model that used hybrid optimization.To accomplish this goal, a hybrid algorithm that combined a genetic algorithm and a local search algorithm method has been developed to overcome premature convergence.Model performances of hybrid algorithm were compared with former single algorithm model in estimating parameter values of an objective function to measure the goodness-of-fit between the observed data and simulated results.Averages error between two models was adopt to select the proper model for future projection of energy demand.…”
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    Article
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    A hyper-heuristic based strategy for image segmentation using multilevel thresholding by Luqman, Fakhrud Din, Shah Khalid, Kamal Zuhairi Zamli, Aftab Alam

    Published 2025
    “…EMCQ uses four low-level heuristic sets adopted from the teaching learning-based optimisation (TLBO) algorithm, flower pollination algorithm (FPA), genetic algorithm (GA), and Jaya algorithm. …”
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    Article
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    MOSDA: A proposal for multiple objective spiral dynamics algorithm by A. R.A., Azwan, Ahmad Nor Kasruddin, Nasir, Sha'akmal, S., A.R.M., Sawal

    Published 2018
    “…It has a good elitism strategy and has a simple structure. A method called “archive method” that is used in multi-objective particle swarm optimization (MOPSO) is adopted into SDA to develop its multiobjective (MO) type algorithm. …”
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    Article
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    Improved smoothed functional algorithmsoptimized pid controller for efficient speed regulation of wind turbines by Mohd Ashraf, Ahmad, Yoganathan, G., Muhammad Ikram, Mohd Rashid, Hao, Mok Ren, Mohd Zaidi, Mohd Tumari

    Published 2025
    “…This study introduces a novel approach for PID controller tuning in wind turbine systems using single-agent optimization methods, specifically the memory smoothed functional algorithm (MSFA) and norm-limited smoothed functional algorithm (NL-SFA). …”
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    Article
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    Harmony search-based robust optimal controller with prior defined structure by Rafieishahemabadi, Ali

    Published 2013
    “…In this approach, a combination of interacting two levels HS optimization algorithm is presented. In the first level, a new method for analytical formulation of integral square error cost function based on controller variables is elaborated for performance evaluation purposes by the proposed optimization algorithm. …”
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    Thesis
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    Space allocation for examination scheduling using Genetic Algorithm / Alya Kauthar Azman by Azman, Alya Kauthar

    Published 2025
    “…The system generates and evaluates potential schedules using fitness functions, selection, crossover, and mutation operators to iteratively improve scheduling efficiency. …”
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    Thesis
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    A hyper-heuristic based strategy for image segmentation using multilevel thresholding by Luqman, ., Fakhrud, Din, Shah, Khalid, Kamal Z., Zamli, Alam, Aftab

    Published 2025
    “…EMCQ uses four low-level heuristic sets adopted from the teaching learning-based optimisation (TLBO) algorithm, flower pollination algorithm (FPA), genetic algorithm (GA), and Jaya algorithm. …”
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    Article
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    Efficient genetic partitioning-around-medoid algorithm for clustering by Garib, Sarmad Makki Mohammed

    Published 2019
    “…The third set affirmed that the enhancement on the proposed algorithm, which made use of indexing method that suits the medoids, could boost the performance to about 9 to 27 times in terms of execution time depending on the complexity of the dataset. …”
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    Thesis
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    Optimal input features selection of wavelet-based EEG signals using GA by Mohd. Daud, Salwani, Yunus, Jasmy

    Published 2004
    “…A combination of genetic algorithm (GA) and artificial neural network (ANN) are used to select the relevant features. …”
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    Conference or Workshop Item
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    Hybrid optimization approach to estimate random demand by Wahab, Musa, Ku-Mahamud, Ku Ruhana, Yasin, Azman

    Published 2012
    “…The main objective of this study is to develop a demand forecasting model that should reflect the characteristics of random demand patterns.To accomplish this goal, a hybrid algorithm combining a genetic algorithm and a local search algorithm method was developed to overcome premature convergence in local optima problems.The performance of the hybrid algorithm was compared with a single algorithm model in estimating parameter values that minimize objective function which was used to measure the goodness-of-fit between the observed data and simulated results.However, two problems had to be overcome in the forecasting random demand model. …”
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    Conference or Workshop Item
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