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  1. 1
    Data-driven brain emotional learning-based intelligent controller-PID control of MIMO systems based on a modified safe experimentation dynamics algorithm

    Data-driven brain emotional learning-based intelligent controller-PID control of MIMO systems based on a modified safe experimentation dynamics algorithm by Shahrizal, Saat, Mohd Ashraf, Ahmad, Mohd Riduwan, Ghazali

    Published 2025
    “…These methods present an effective alternative that treats plants as black-box systems without requiring explicit models. The safe experimentation dynamics algorithm (SEDA) is one such method that optimizes controller parameters using data-driven techniques. …”
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  2. 2
    Data-driven brain emotional learning-based intelligent controller-PID control of MIMO systems based on a modified safe experimentation dynamics algorithm

    Data-driven brain emotional learning-based intelligent controller-PID control of MIMO systems based on a modified safe experimentation dynamics algorithm by Shahrizal, Saat, Mohd Ashraf, Ahmad, Mohd Riduwan, Ghazali

    Published 2025
    “…These methods present an effective alternative that treats plants as black-box systems without requiring explicit models. The safe experimentation dynamics algorithm (SEDA) is one such method that optimizes controller parameters using data-driven techniques. …”
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  3. 3
    Hybrid scheduling and dual queue scheduling

    Hybrid scheduling and dual queue scheduling by Mahmood, Ahmad Kamil

    Published 2009
    “…Our research work involves the design and development of new CPU scheduling algorithms (the Hybrid Scheduling Algorithm and the Dual Queue Scheduling Algorithm) with a view to optimization. …”
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  4. 4
    Dynamic Economic Dispatch For Large Scale Power Systems: A Lagrangian Relaxation Approach

    Dynamic Economic Dispatch For Large Scale Power Systems: A Lagrangian Relaxation Approach by Ab Ghani, Mohd Ruddin, Hindi, K. S.

    Published 1991
    “…Otherwise, Dantzig-Wolfe decomposition is invoked, using almost all the information generated during subgradient optimization to ensure a speedy conclusion. The computational efficiency of the algorithm renders it suitable for on-line dispatch.…”
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  5. 5
    Hybrid scheduling and dual queue scheduling

    Hybrid scheduling and dual queue scheduling by A., Oxley, S.N.M., Shah, A.K., Mahmood

    Published 2009
    “…Our research work involves the design and development of new CPU scheduling algorithms (the Hybrid Scheduling Algorithm and the Dual Queue Scheduling Algorithm) with a view to optimization. …”
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  6. 6
    A Reference Based Surface Defect Segmentation Algorithm For Automatic Optical Inspection System

    A Reference Based Surface Defect Segmentation Algorithm For Automatic Optical Inspection System by Wong, Ze-Hao

    Published 2020
    “…However, present complex algorithms which are accurate require high processing power using a large size of learning dataset without labelling error. …”
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  7. 7
    Modified symbiotic organisms search optimization for automatic construction of convolutional neural network architectures

    Modified symbiotic organisms search optimization for automatic construction of convolutional neural network architectures by Jauro, Fatsuma, Abdullahi, Usman Ali, Abdulsalami, Aminu Onimisi, Ibrahim, Adamu Abubakar, Abdullahi, Mohammed, Chiroma, Haruna

    Published 2024
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  8. 8
    The advancement of artificial intelligence's application in hybrid solar and wind power plant optimization: a study of the literature

    The advancement of artificial intelligence's application in hybrid solar and wind power plant optimization: a study of the literature by Mauludin, Mochamad Subchan, Khairudin, Moh., Asnawi, Rustam, Mustafa, Wan Azani, Toha, Siti Fauziah

    Published 2024
    “…Furthermore, this inquiry delves into the optimization strategies of these systems leveraging artificial intelligence methodologies. …”
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  9. 9
    Training functional link neural network with ant lion optimizer

    Training functional link neural network with ant lion optimizer by Mohmad Hassim, Yana Mazwin, Ghazali, Rozaida

    Published 2020
    “…Functional Link Neural Network (FLNN) has becoming as an important tool used in machine learning due to its modest architecture. …”
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  10. 10
    Functional link neural network with modified bee-firefly learning algorithm for classification task

    Functional link neural network with modified bee-firefly learning algorithm for classification task by Mohmad Hassim, Yana Mazwin

    Published 2016
    “…The single layer property of FLNN also make the learning algorithm used less complicated compared to MLP network. …”
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  11. 11
    A modified generalized RBF model with EM-based learning algorithm for medical applications

    A modified generalized RBF model with EM-based learning algorithm for medical applications by Ma, Li Ya, Abdul Rahman, Abdul Wahab, Quek, Chai

    Published 2006
    “…Radial Basis Function (RBF) has been widely used in different fields, due to its fast learning and interpretability of its solution. …”
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  12. 12
    An improved artificial bee colony algorithm for training multilayer perceptron in time series prediction

    An improved artificial bee colony algorithm for training multilayer perceptron in time series prediction by Shah, Habib

    Published 2014
    “…Furthermore, here these algorithms used to train the MLP on two tasks; the seismic event's prediction and Boolean function classification. …”
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  13. 13
    Performance of 2-DOF PID controller in AGC of two area interconnected power system using PSO algorithm

    Performance of 2-DOF PID controller in AGC of two area interconnected power system using PSO algorithm by Peddakapu, Kurukuri, Mohd Rusllim, Mohamed, Srinivasarao, P., Kishore, Dokala Janandra Krishna, Koteswararao, D.A., Swamy, P. S.P.R.

    Published 2022
    “…Therefore, in this work, particle swarm optimization (PSO) algorithm is formulated for tuning the gain values of the suggested controllers in multi-area interconnected system. …”
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  14. 14
    Development of intelligent evaluation system for product end-of-life selection strategy

    Development of intelligent evaluation system for product end-of-life selection strategy by Zakri, Ghazalli

    Published 2011
    “…This study integrates the travelling salesman problem with genetic algorithm (TSP-GA) for finding the optimal disassembly sequence and disassembling the EOL product. …”
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  15. 15
    Success history moth flow optimization for multi-goal generation dispatching with nonlinear cost functions

    Success history moth flow optimization for multi-goal generation dispatching with nonlinear cost functions by Alam, Mohammad Khurshed, Mohd Herwan, Sulaiman, Sayem, Md. Shaoran, Ringku, Md Mahfuzer Akter, Imtiaz, Shahriar, Khan, Rahat

    Published 2023
    “…The valve-point loading causes oscillations in the input-output characteristics of generating units, hence rendering the CEED problem an imperfect optimization problem. …”
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  16. 16
    Integration of simulation for ergonomics assessment in operation control centre (railway industries) / Adib Zulfadhli Mohd Alias

    Integration of simulation for ergonomics assessment in operation control centre (railway industries) / Adib Zulfadhli Mohd Alias by Adib Zulfadhli, Mohd Alias

    Published 2019
    “…This study will focus on the translation of the CAD/Revit model into simulation software, either directly or through the intermediate stage of rendering package. Complete CAD/Revit model can be used to generate simulation model by straight forward translation of the whole model or with the algorithms for optimization. …”
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  17. 17
    Binary Coati Optimization Algorithm- Multi- Kernel Least Square Support Vector Machine-Extreme Learning Machine Model (BCOA-MKLSSVM-ELM): A New Hybrid Machine Learning Model for Predicting Reservoir Water Level

    Binary Coati Optimization Algorithm- Multi- Kernel Least Square Support Vector Machine-Extreme Learning Machine Model (BCOA-MKLSSVM-ELM): A New Hybrid Machine Learning Model for Pr... by Sammen S.S., Ehteram M., Sheikh Khozani Z., Sidek L.M.

    Published 2024
    “…For water level prediction, lagged rainfall and water level are used. In this study, we used extreme learning machine (ELM)-multi-kernel least square support vector machine (ELM-MKLSSVM), extreme learning machine (ELM)-LSSVM-polynomial kernel function (PKF) (ELM-LSSVM-PKF), ELM-LSSVM-radial basis kernel function (RBF) (ELM-LSSVM-RBF), ELM-LSSVM-Linear Kernel function (LKF), ELM, and MKLSSVM models to predict water level. …”
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  18. 18
    Evaluating the Efficacy of Intelligent Methods for Maximum Power Point Tracking in Wind Energy Harvesting Systems

    Evaluating the Efficacy of Intelligent Methods for Maximum Power Point Tracking in Wind Energy Harvesting Systems by Umar D.A., Alkawsi G., Jailani N.L.M., Alomari M.A., Baashar Y., Alkahtani A.A., Capretz L.F., Tiong S.K.

    Published 2024
    “…However, the output power of a wind turbine is affected by various factors, such as wind speed, wind direction, and generator design. In order to optimize the performance of a WEHS, it is important to track the maximum power point (MPP) of the system. …”
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  19. 19
    Differential evolution for neural networks learning enhancement

    Differential evolution for neural networks learning enhancement by Ismail Wdaa, Abdul Sttar

    Published 2008
    “…These algorithms can be used successfully in many applications requiring the optimization of a certain multi-dimensional function. …”
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  20. 20
    Dynamic training rate for backpropagation learning algorithm

    Dynamic training rate for backpropagation learning algorithm by Al-Duais, M. S., Yaakub, Abdul Razak, Yusoff, Nooraini

    Published 2013
    “…In this paper, we created a dynamic function training rate for the Back propagation learning algorithm to avoid the local minimum and to speed up training.The Back propagation with dynamic training rate (BPDR) algorithm uses the sigmoid function.The 2-dimensional XOR problem and iris data were used as benchmarks to test the effects of the dynamic training rate formulated in this paper.The results of these experiments demonstrate that the BPDR algorithm is advantageous with regards to both generalization performance and training speed. …”
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