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  1. 1
    Performance evaluation of PID controller parameters gain optimization for wheel mobile robot based on bat algorithm and particle swarm optimization

    Performance evaluation of PID controller parameters gain optimization for wheel mobile robot based on bat algorithm and particle swarm optimization by Nur Aisyah Syafinaz, Suarin, Pebrianti, Dwi, Nurnajmin Qasrina, Ann, Bayuaji, Luhur, Syafrullah, Muhammad, Riyanto, Indra

    Published 2019
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  2. 2
    Performance evaluation of PID controller parameters gain optimization for wheel mobile robot based on bat algorithm and particle swarm optimization

    Performance evaluation of PID controller parameters gain optimization for wheel mobile robot based on bat algorithm and particle swarm optimization by Nur Aisyah Syafinaz, Suarin, Pebrianti, Dwi, Nurnajmin Qasrina, Ann, Luhur, Bayuaji, Muhammad, Syafrullah, Indra, Riyanto

    Published 2019
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  3. 3
    3D virtual modelling and stabilization control of triple links inverted pendulum on two-wheeled system using enhanced interval type-2 fuzzy logic control

    3D virtual modelling and stabilization control of triple links inverted pendulum on two-wheeled system using enhanced interval type-2 fuzzy logic control by Muhammad Firdaus, Masrom

    Published 2020
    “…Two optimization algorithms are presented in this work which are Spiral Dynamic Algorithm (SDA) and Particle Swarm Optimization (PSO). …”
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  4. 4
    Hardware-in-the-loop study of a hybrid active force control scheme of an upper-limb exoskeleton for passive stroke rehabilitation

    Hardware-in-the-loop study of a hybrid active force control scheme of an upper-limb exoskeleton for passive stroke rehabilitation by Anwar, P. P. Abdul Majeed

    Published 2018
    “…A hardware-in-the-loop simulation is carried out to evaluate the appropriate gains of both the PD and the AFC inertial parameter gained that is tuned via the SKF algorithm. …”
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  5. 5
    Hardware-in-the-loop study of a hybrid active force control scheme of an upper-limb exoskeleton for passive stroke rehabilitation

    Hardware-in-the-loop study of a hybrid active force control scheme of an upper-limb exoskeleton for passive stroke rehabilitation by Anwar, P. P. Abdul Majeed

    Published 2018
    “…A hardware-in-the-loop simulation is carried out to evaluate the appropriate gains of both the PD and the AFC inertial parameter gained that is tuned via the SKF algorithm. …”
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  6. 6
    Particle Swarm Optimization for Bluetooth Scatternet Formation

    Particle Swarm Optimization for Bluetooth Scatternet Formation by Kamel , Nidal

    Published 2005
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  7. 7
    Performance evaluation of PID controller optimisation for wheel mobile robot using Bat based optimisation algorithm

    Performance evaluation of PID controller optimisation for wheel mobile robot using Bat based optimisation algorithm by ,, Dwi Pebrianti, Ann Ayop azmi, Nurnajmi Qasrina, Bayuaji, Luhur, Suarin, Nur Aisyah Syafinaz, ,, Muhammad Syafrullah

    Published 2022
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  8. 8
    Bio-inspired for Features Optimization and Malware Detection

    Bio-inspired for Features Optimization and Malware Detection by Razak, Mohd Faizal Ab, Anuar, Nor Badrul, Othman, Fazidah, Firdaus, Ahmad, Afifi, Firdaus, Salleh, Rosli

    Published 2018
    “…The features were optimized from 378 to 11 by using bio-inspired algorithm: particle swarm optimization (PSO). The evaluation utilizes 5000 Drebin malware samples and 3500 benign samples. …”
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  9. 9
    Bio-inspired for Features Optimization and Malware Detection

    Bio-inspired for Features Optimization and Malware Detection by Mohd Faizal, Ab Razak, Nor Badrul, Anuar, Fazidah, Othman, Ahmad, Firdaus, Firdaus, Afifi, Rosli, Salleh

    Published 2018
    “…The features were optimized from 378 to 11 by using bio-inspired algorithm: particle swarm optimization (PSO). The evaluation utilizes 5000 Drebin malware samples and 3500 benign samples. …”
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  10. 10
    Seamless vertical handover technique for vehicular ad-hoc networks using artificial bee colony-particle swarm optimisation

    Seamless vertical handover technique for vehicular ad-hoc networks using artificial bee colony-particle swarm optimisation by Abdulwahhab, Mohanad Mazin

    Published 2019
    “…Firstly, we proposed a multi-criteria artificial bee colony hybrid with particle swarm optimisation algorithm (MC ABC-PSO) for evaluating the information gathered from the mobile nodes in the handover. …”
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  11. 11
    Genetic Algorithms And Particle Swarm Optimization For Interference Minimization In Mobile Network Channel Assignment Problem

    Genetic Algorithms And Particle Swarm Optimization For Interference Minimization In Mobile Network Channel Assignment Problem by Loh, Ser Lee, Keek, Joe Siang, Wong, Yan Chiew, Woo, Xiu Juan, Lee, Wei Wen

    Published 2021
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  12. 12
    Multi-objective AGV scheduling in an FMS using a hybrid of genetic algorithm and particle swarm optimization

    Multi-objective AGV scheduling in an FMS using a hybrid of genetic algorithm and particle swarm optimization by Mousavi, M., Yap, Hwa Jen, Musa, S.N., Tahriri, F., Md Dawal, Siti Zawiah

    Published 2017
    “…In the main draw of the research, a mathematical model was developed and integrated with evolutionary algorithms (genetic algorithm (GA), particle swarm optimization (PSO), and hybrid GA-PSO) to optimize the task scheduling of AGVs with the objectives of minimizing makespan and number of AGVs while considering the AGVs' battery charge. …”
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  13. 13
    Artificial Bee Colony algorithm in estimating kinetic parameters for yeast fermentation pathway

    Artificial Bee Colony algorithm in estimating kinetic parameters for yeast fermentation pathway by Ahmad Muhaimin, Ismail, Muhammad Akmal, Remli, Yee Wen, Choon, Nurul Athirah, Nasarudin, Norsyahidatul Nazirah, Ismail, Mohd Arfian, Ismail, Mohd Saberi, Mohamad

    Published 2023
    “…Fitting the simulated model into the experimental data is categorized under the parameter estimation problem. …”
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  14. 14
    GENETIC ALGORITHM WITH DEEP NEURAL NETWORK SURROGATE FOR THE OPTIMIZATION OF ELECTROMAGNETIC STRUCTURE

    GENETIC ALGORITHM WITH DEEP NEURAL NETWORK SURROGATE FOR THE OPTIMIZATION OF ELECTROMAGNETIC STRUCTURE by MOHAMMED SHARIFF, NUR ATIQAH

    Published 2020
    “…The behavior of Genetic Algorithm (GA) where it generates and evolves the parameters towards a high-quality solution gives an advantage in obtaining ideal combination of parameters to fit in with the simulation. …”
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  15. 15
    Estimation in spot welding parameters using genetic algorithm

    Estimation in spot welding parameters using genetic algorithm by Lukman, Hafizi

    Published 2007
    “…The application has widespread in many areas especially in system and control engineering. Genetic algorithm (GA) used as parameter estimation method for a model structure. …”
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  16. 16
    Simultaneous computation of model order and parameter estimation for ARX model based on single swarm and multi swarm simulated Kalman filter

    Simultaneous computation of model order and parameter estimation for ARX model based on single swarm and multi swarm simulated Kalman filter by Kamil Zakwan, Mohd Azmi, Zuwairie, Ibrahim, Pebrianti, Dwi, Mohd Saberi, Mohamad

    Published 2017
    “…Simultaneous Model Order and Parameter Estimation (SMOPE) and Simultaneous Model Order and Parameter Estimation based on Multi Swarm (SMOPE-MS) are two techniques of implementing meta-heuristic algorithm to iteratively establish an optimal model order and parameters simultaneously for an unknown system. …”
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  17. 17
    Simulation algorithm of bayesian approach for choice-conjoint model

    Simulation algorithm of bayesian approach for choice-conjoint model by Zulhanif

    Published 2011
    “…Therefore this research propose simulation algorithm of Bayesian approach for estimating parameter in MPM by Bayesian analysis to avoid computational difficulties in computing the maximum likelihood estimates (MLE).…”
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  18. 18
    Parameter characterization of PEM fuel cell mathematical models using an orthogonal learning-based GOOSE algorithm

    Parameter characterization of PEM fuel cell mathematical models using an orthogonal learning-based GOOSE algorithm by Manoharan P., Ravichandran S., Kavitha S., Tengku Hashim T.J., Alsoud A.R., Sin T.C.

    Published 2025
    “…The orthogonal learning mechanism improves the performance of the original GOOSE algorithm. This FC model uses the root mean squared error as the objective function for optimizing the unknown parameters. …”
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  19. 19
    A simulation study of a parametric mixture model of three different distributions to analyze heterogeneous survival data

    A simulation study of a parametric mixture model of three different distributions to analyze heterogeneous survival data by Mohammed, Yusuf Abbakar, Yatim, Bidin, Ismail, Suzilah

    Published 2013
    “…In this paper a simulation study of a parametric mixture model of three different distributions is considered to model heterogeneous survival data.Some properties of the proposed parametric mixture of Exponential, Gamma and Weibull are investigated.The Expectation Maximization Algorithm (EM) is implemented to estimate the maximum likelihood estimators of three different postulated parametric mixture model parameters.The simulations are performed by simulating data sampled from a population of three component parametric mixture of three different distributions, and the simulations are repeated 10, 30, 50, 100 and 500 times to investigate the consistency and stability of the EM scheme.The EM Algorithm scheme developed is able to estimate the parameters of the mixture which are very close to the parameters of the postulated model.The repetitions of the simulation give parameters closer and closer to the postulated models, as the number of repetitions increases, with relatively small standard errors.…”
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  20. 20
    Parameter estimation and outlier detection in linear functional relationship model / Adilah Abdul Ghapor

    Parameter estimation and outlier detection in linear functional relationship model / Adilah Abdul Ghapor by Adilah, Abdul Ghapor

    Published 2017
    “…This research focuses on the parameter estimation, outlier detection and imputation of missing values in a linear functional relationship model (LFRM). …”
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