Search Results - (( parameter optimization method algorithm ) OR ( practice application based algorithm ))

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  1. 1

    Algorithmic Loan Risk Prediction Method Based on PSO-EBGWO-Catboost by Chen, Suihai, Bong, Chih How, Chiu, Po Chan

    Published 2024
    “…PSO-EBGWO is used to optimize the parameters of the CatBoost model. In this method, the Gray Wolf optimized algorithm (EBGWO) is further optimized by particle swarm optimization (PSO), and when combined with it, the convergence performance of the model is improved, the parameters of the model are reduced, and the model is simplified. …”
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    Article
  2. 2

    Coordination of PSS and PID controller for power system stability enhancement - overview by Kasilingam G., Pasupuleti J.

    Published 2023
    “…From this view, many optimization methods and algorithms have been employed to tune the PID gains and PSS parameters. …”
    Article
  3. 3

    Safe experimentation dynamics algorithm for data-driven PID controller of a class of underactuated systems by Nor Sakinah, Abdul Shukor

    Published 2019
    “…So far, the tuning methods used for data-driven PID for the underactuated systems are mostly based on the multi-agent-based optimization, which means that the design requires substantial computation time and make it not practical for on-line tuning applications. …”
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    Thesis
  4. 4

    Safe experimentation dynamics algorithm for data-driven PID controller of a class of underactuated systems by Nor Sakinah, Abdul Shukor

    Published 2019
    “…So far, the tuning methods used for data-driven PID for the underactuated systems are mostly based on the multi-agent-based optimization, which means that the design requires substantial computation time and make it not practical for on-line tuning applications. …”
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    Thesis
  5. 5

    PID TUNING OF DC MOTOR USING SWARM ITELLIGENCE ALGORITHM by Hasdi Aimon, Arhimny

    Published 2012
    “…In this project, Particle Swarm Optimization (PSO) as one of Swarm Intelligence Algorithm based has proposed to be integrated with PID (Proportional, Integral, Derivative) Controller in order to achieve optimal tuning method. …”
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    Final Year Project
  6. 6

    Taguchi's method for optimized neural network based autoreclosure in extra high voltage lines by K.S.R., Rao, Z.F., Desta

    Published 2008
    “…The fault identification prior to reclosing is based on optimized artificial neural network associated with Levenberg Marquardt algorithm to train the ANN and Taguchi's Method to find optimal parameters of the algorithm and number of hidden neurons. …”
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    Conference or Workshop Item
  7. 7

    Design optimization of valve timing at various engine speeds using Multi-Objective Genetic Algorithm (MOGA) by Mohiuddin, A. K. M., Ashour, Ahmed Aly Ibrahim Shaaban, Yap, Haw Shin

    Published 2008
    “…This paper aims to demonstrate the effectiveness of Multi- Objective Genetic Algorithm Optimization and its robust practical application on the automobile engine valve timing where the variation of performance parameters required for finest tuning to obtain the optimal engine performances. …”
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    Proceeding Paper
  8. 8
  9. 9

    Firefly analytical hierarchy algorithm for optimal allocation and sizing of distributed generation in radial distribution network by Bujal, Noor Ropidah

    Published 2022
    “…Practically, this FAHA has great potential applications for the optimal allocation and sizing of DG in the radial distribution network.…”
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    Thesis
  10. 10

    A Stepper Motor Design Optimization Using by Wong, Chin Wei

    Published 2005
    “…In order to achieve the optimum design, Genetic Algorithms (GAs) approach has been applied. GAs approach is selected because it is a powerful and broadly applicable stochastic search and optimization techniques that works for many problems that are very difficult to solve by conventional methods. …”
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    Monograph
  11. 11

    Global Algorithms for Nonlinear Discrete Optimization and Discrete-Valued Optimal Control Problems by Woon, Siew Fang

    Published 2009
    “…These problems are known as discrete-valued optimal control problems. Most practical discrete-valued optimal control problems have multiple local minima and thus require global optimization methods to generate practically useful solutions. …”
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    Thesis
  12. 12

    Zero root-mean-square error for single- and double-diode photovoltaic models parameter determination by Mohammed Ridha, Hussein, Hizam, Hashim, Mirjalili, Seyedali, Othman, Mohammad Lutfi, Ya’acob, Mohammad Effendy

    Published 2022
    “…This may be extremely helpful both practically and theoretically in the PV system simulation, optimization, and evaluation. Numerous stochastic methods have been widely utilized in recent decades to tackle the parameter extraction optimization problem. …”
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    Article
  13. 13

    Discrete-time system identification using genetic algorithm with single parent-based mating technique by Zainuddin, Farah Ayiesya

    Published 2024
    “…The SPM technique offers researchers and practitioners a powerful tool for achieving faster convergence, better optimization, and more accurate models. This study not only advances the theoretical understanding of GA but also provides practical methodologies that can be applied to real-world problems, making it a valuable contribution to both academic research and practical applications.…”
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    Thesis
  14. 14

    Development an accurate and stable range-free localization scheme for anisotropic wireless sensor networks by Han, Fengrong

    Published 2022
    “…It has the potential significance to be guided and applied in practical location-based applications for anisotropic wireless sensor networks.…”
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    Thesis
  15. 15

    Model Predictive Control Design for f Nonlinear Four-Tank System by Ansarpanahi, Shadi

    Published 2008
    “…Regarding to investigate the performance of this algorithm and due to application of “nonlinear Four-Tank system” in control theory and industry, this system is considered as a plant to be examined under this method. …”
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    Thesis
  16. 16

    Variable Neighborhood Descent and Whale Optimization Algorithm for Examination Timetabling Problems at Universiti Malaysia Sarawak by Emily Sing Kiang, Siew

    Published 2025
    “…The original WOA is modified by replacing the equations designed for continuous problem domains with local search methods, enhancing its adaptability to discrete optimization problems. …”
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    Thesis
  17. 17

    Mental stress classification based on selected electroencephalography channels using correlation coefficient of Hjorth parameters by Hag, Ala, Al-Shargie, Fares, Handayani, Dini Oktarina Dwi, Asadi, Houshyar

    Published 2023
    “…Such limitations encompass computational complexity, potential overfitting, and the prolonged setup time for electrode placement, all of which can hinder practical applications. To address these challenges, this study presents the novel CCHP method, aimed at identifying and ranking commonly optimal EEG channels based on their sensitivity to the mental stress state. …”
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    Article
  18. 18

    Parameter extraction of solar photovoltaic modules using penalty-based differential evolution by Ishaque, K., Salam, Z., Mekhilef, Saad, Shamsudin, A.

    Published 2012
    “…The analyses carried out using synthetic current-voltage (I-V) data set showed that the proposed P-DE outperforms other Evolutionary Algorithm methods, namely the simulated annealing (SA), genetic algorithm (GA), and particle swarm optimization (PSO). …”
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    Article
  19. 19

    To develop an efficient variable speed compressor motor system by Mohd. Yatim, Abdul Halim, Mulyo Utomo, Wahyu

    Published 2007
    “…To achieve a robust controller from variation of motor parameters, a real-time or on-line learning algorithm based on a second order optimization Levenberg-Marquardt is employed. …”
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    Other
  20. 20

    Optimization-driven extreme learning machine for floating photovoltaic power prediction: A teaching learning-based approach by Mohd Redzuan, Ahmad, Nor Farizan, Zakaria, Mohd Shawal, Jadin, Mohd Herwan, Sulaiman

    Published 2025
    “…This study presents a novel Teaching–Learning-Based Optimization enhanced Extreme Learning Machine (TLBO-ELM) framework that achieves optimal parameter configuration without algorithmic tuning while maintaining computational efficiency for real-time deployment. …”
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    Article