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  1. 1
    Enhancing Wearable-Based Human Activity Recognition with Binary Nature-Inspired Optimization Algorithms for Feature Selection

    Enhancing Wearable-Based Human Activity Recognition with Binary Nature-Inspired Optimization Algorithms for Feature Selection by Norfadzlan, Yusup, Izzatul Nabila, Sarbini, Dayang Nurfatimah, Awang Iskandar, Azlan, Mohd Zain, Didik Dwi, Prasetya

    Published 2026
    “…The experiment results show how these algorithms could be used to improve methods for recognizing human activities using wearables technology, such as feature selection, parameter adjustment, and model optimization.…”
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  2. 2
    Modeling time series data using Genetic Algorithm based on Backpropagation Neural network

    Modeling time series data using Genetic Algorithm based on Backpropagation Neural network by Haviluddin

    Published 2018
    “…This study showed the task of optimizing the topology structure and the parameter values (e.g., weights) used in the BPNN learning algorithm by using the GA. …”
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  3. 3
    A novel peak detection algorithm using particle swarm optimization for chew count estimation of a contactless chewing detection

    A novel peak detection algorithm using particle swarm optimization for chew count estimation of a contactless chewing detection by Selamat, Nur Asmiza, Md. Ali, Sawal Hamid, Minhad, Khairun Nisa’, Ahmad, Siti Anom, Sampe, Jahariah

    Published 2022
    “…In accordance with the result, this article proposes a novel chew count estimation based on particle swarm optimization (PSO). First, the base of the algorithm is developed based on counting the peak of the chewing signal. …”
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  4. 4
    Application of nature-inspired algorithms and artificial intelligence for optimal efficiency of horizontal axis wind turbine / Md. Rasel Sarkar

    Application of nature-inspired algorithms and artificial intelligence for optimal efficiency of horizontal axis wind turbine / Md. Rasel Sarkar by Md. Rasel, Sarkar

    Published 2019
    “…There is no particular study which focuses on the optimization and prediction of blades parameters using natural inspired algorithms namely Ant Colony Optimization (ACO), Artificial Bee Colony (ABC) and Particle Swarm Optimization (PSO) and Adaptive Neuro-fuzzy Interface System (ANFIS) respectively for optimal power coefficient (�436�45D ). …”
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  5. 5
    A novel peak detection algorithm using particle swarm optimization for chew count estimation of a contactless chewing detection

    A novel peak detection algorithm using particle swarm optimization for chew count estimation of a contactless chewing detection by Selamat, Nur Asmiza, Md. Ali, Sawal Hamid, Minhad, Khairun Nisa’, Ahmad, Siti Anom

    Published 2022
    “…In accordance with the result, this article proposes a novel chew count estimation based on particle swarm optimization (PSO). First, the base of the algorithm is developed based on counting the peak of the chewing signal. …”
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  6. 6
    Voltage constrained optimal power flow based using genetic algorithm

    Voltage constrained optimal power flow based using genetic algorithm by Yassir Asnawi, Teuku Hasannuddin

    Published 2015
    “…Simulation results of the three-phase voltage profi le along the tested feeder using the proposed method provide better results than other mentioned methods. …”
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  7. 7
    Optimal power flow using hybrid firefly and particle swarm optimization algorithm

    Optimal power flow using hybrid firefly and particle swarm optimization algorithm by Khan, Abdullah, Hizam, Hashim, Abdul Wahab, Noor Izzri, Othman, Mohammad Lutfi

    Published 2020
    “…In this paper, a novel, effective meta-heuristic, population-based Hybrid Firefly Particle Swarm Optimization (HFPSO) algorithm is applied to solve different non-linear and convex optimal power flow (OPF) problems. …”
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  8. 8
    Feature Selection and Classifier Parameter Estimation for Egg Signal Peak Detection using Gravitational Search Algorithm

    Feature Selection and Classifier Parameter Estimation for Egg Signal Peak Detection using Gravitational Search Algorithm by Zuwairie, Ibrahim, Mohd Zaidi, Mohd Tumari, Asrul, Adam, Norrima, Mokhtar, Marizan, Mubin, Mohd Ibrahim, Shapiai

    Published 2014
    “…This study focuses on using GSA method, a new computational intelligence algorithm. …”
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  9. 9
    Dynamic control and performance of dual active bridge converter based particle swarm optimization

    Dynamic control and performance of dual active bridge converter based particle swarm optimization by Suliana, Ab Ghani, Hamdan, Daniyal, Norazila, Jaalam, Norhafidzah, Mohd Saad, Nur Huda, Ramlan

    Published 2022
    “…This paper presents analysis of the dynamic response of the isolated bidirectional dual active bridge (DAB) DC-DC converter using particle swarm optimization (PSO) algorithm. …”
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  10. 10
    Novel chewing cycle approach for peak detection algorithm of chew count estimation

    Novel chewing cycle approach for peak detection algorithm of chew count estimation by Selamat, Nur Asmiza, Md Ali, Sawal Hamid, Ismail, Ahmad Ghadafi, Ahmad, Siti Anom, Minhad, Khairun Nisa'

    Published 2025
    “…This work proposes a novel approach to chew count estimation using particle swarm optimization (PSO) combined with a peak detection algorithm. …”
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  11. 11
    Novel chewing cycle approach for peak detection algorithm of chew count estimation

    Novel chewing cycle approach for peak detection algorithm of chew count estimation by Selamat, Nur Asmiza, Md Ali, Sawal Hamid, Ismail, Ahmad Ghadafi, Ahmad, Siti Anom, Minhad, Khairun Nisa’

    Published 2025
    “…This work proposes a novel approach to chew count estimation using particle swarm optimization (PSO) combined with a peak detection algorithm. …”
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  12. 12
    Activity recognition using optimized reduced kernel extreme learning machine (OPT-RKELM) / Yang Dong Rui

    Activity recognition using optimized reduced kernel extreme learning machine (OPT-RKELM) / Yang Dong Rui by Yang , Dong Rui

    Published 2019
    “…ReliefF can solve the problem of large feature dimension in the existing RKELM. By using clustering method K-Means, we have found the best center point position to calculate Kernel matrix. at last, we have employed Quantum-behaved Particle Swarm Optimization (QPSO) to get the optimal kernel parameter in the proposed model. …”
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  13. 13
    Modeling flood occurences using soft computing technique in southern strip of Caspian Sea Watershed

    Modeling flood occurences using soft computing technique in southern strip of Caspian Sea Watershed by Borujeni, Sattar Chavoshi

    Published 2012
    “…Genetic algorithm (GA) was employed to adjust parameters of FES and optimize the system. …”
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  14. 14
    Multivariable PID controller design tuning using bat algorithm for activated sludge process

    Multivariable PID controller design tuning using bat algorithm for activated sludge process by Nur Atikah, Nor’Azlan, Nur Asmiza, Selamat, Nafrizuan, Mat Yahya

    Published 2018
    “…The selected optimization technique is Bat Algorithm (BA). All the MPID-BA tuning result will be compared and analyzed. …”
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  15. 15
    Water level forecasting using feed forward neural networks optimized by African Buffalo Algorithm (ABO)

    Water level forecasting using feed forward neural networks optimized by African Buffalo Algorithm (ABO) by Ahmed, Ehab Ali

    Published 2019
    “…This research proposed a swarm intelligence training algorithm, Improved African Buffalo Optimization algorithm (IABO) based on the Metaheuristic method called the African Buffalo Optimization algorithm (ABO). …”
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  16. 16
    Sliding mode controller optimization-based three-phase rectifier: review study

    Sliding mode controller optimization-based three-phase rectifier: review study by Mahmood, Omar Talal, Wan Hasan, Wan Zuha, Ismail, Luthffi Idzhar, Ramli, Hafiz Rashidi Harun

    Published 2024
    “…This active rectifier is used in our project to optimize the power system for the alternator used in our unmanned ground vehicle (UGV) as an oil palm autonomous grabber.…”
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  17. 17
    Optimization of fast fourier transform based on twiddle factor using genetic algorithm on raspberry pi

    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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  18. 18
    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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  19. 19
    Dynamic smart grid communication parameters based cognitive radio network

    Dynamic smart grid communication parameters based cognitive radio network by Haider H.T., Muhsen D.H., Shahadi H.I., See O.H., Elmenreich W.

    Published 2023
    “…A differential evolution algorithm is used to select the optimal transmission parameters for given communication modes based on a fitness function that combines multiple objectives based on appropriate weights. …”
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  20. 20
    Short term traffic forecasting based on hybrid of firefly algorithm and least squares support vector machine

    Short term traffic forecasting based on hybrid of firefly algorithm and least squares support vector machine by Yusof, Yuhanis, Ahmad, Farzana Kabir, Kamaruddin, Siti Sakira, Omar, Mohd Hasbullah, Mohamed, Athraa Jasim

    Published 2015
    “…The goal of an active traffic management is to manage congestion based on current and predicted traffic conditions.This can be achieved by utilizing traffic historical data to forecast the traffic flow which later supports travellers for a better journey planning.In this study, a new method that integrates Firefly algorithm (FA) with Least Squares Support Vector Machine (LSSVM) is proposed for short term traffic speed forecasting, which is later termed as FA-LSSVM.In particular, the Firefly algorithm which has the advantage in global search is used to optimize the hyper-parameters of LSSVM for efficient data training. …”
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