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  1. 1
    Optimization of PID parameters for hydraulic positioning system utilizing variable weight Grey-Taguchi and particle swarm optimization

    Optimization of PID parameters for hydraulic positioning system utilizing variable weight Grey-Taguchi and particle swarm optimization by Nur Iffah, Mohamed Azmi

    Published 2014
    “…Particle swarm optimization algorithm (PSO) is one of the artificial intelligence methods. …”
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  2. 2
    Optimal parameter estimation of permanent magnet synchronous motor by using Mothflame optimization algorithm / Abdolmajid Dejamkhooy and Sajjad Asefi

    Optimal parameter estimation of permanent magnet synchronous motor by using Mothflame optimization algorithm / Abdolmajid Dejamkhooy and Sajjad Asefi by Dejamkhooy, Abdolmajid, Asefi, Sajjad

    Published 2018
    “…In the next step, the parameter identification as an optimization problem is solved by Moth-flame optimization, which is a novel nature-inspired heuristic algorithm. …”
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  3. 3
    Modeling and multi-objective optimal sizing of standalone photovoltaic system based on evolutionary algorithms

    Modeling and multi-objective optimal sizing of standalone photovoltaic system based on evolutionary algorithms by Ridha, Hussein Mohammed

    Published 2020
    “…The IEM algorithm uses the attraction-repulsion mechanism to change the positions of solutions towards the optimality. …”
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  4. 4
    An Optimized PID Parameters for LFC in Interconnected Power Systems Using MLSL Optimization Algorithm

    An Optimized PID Parameters for LFC in Interconnected Power Systems Using MLSL Optimization Algorithm by Najeeb, Mushtaq, Shahooth, Mohammed, Mohaisen, Arrak, Ramdan, Razali, Hamdan, Daniyal

    Published 2016
    “…In order to enhance the dynamic performance, the optimal parameters of the PID scheme which optimized by the proposed MLSL algorithm are compared with that one’s obtained by GA algorithm. …”
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  5. 5
    Design of low order quantitative feedback theory and H-infinity-based controllers using particle swarm optimisation for a pneumatic actuator system

    Design of low order quantitative feedback theory and H-infinity-based controllers using particle swarm optimisation for a pneumatic actuator system by Ali, Hazem I.

    Published 2010
    “…The particle swarm optimization (PSO) method is used to tune the parameters of the controller and weighting functions subject to QFT and/or constraints. …”
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  6. 6
    Optimization of Turning Parameters to Minimize Production Cost using Genetic Algorithm

    Optimization of Turning Parameters to Minimize Production Cost using Genetic Algorithm by M. F. F., Ab Rashid, Shah, Izwandi

    Published 2009
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  7. 7
    Refinement of Tuned Mass Damper parameters on machine support structure using dynamic Cuckoo Search algorithm

    Refinement of Tuned Mass Damper parameters on machine support structure using dynamic Cuckoo Search algorithm by Ahmad Muinuddin, Mahmood, Zamri, Mohamed, Rosmazi, Rosli

    Published 2025
    “…This study proposes the use of a dynamic Cuckoo Search (CS) algorithm, a nature-inspired optimization method, to enhance the accuracy of TMD parameters when external factors, such as excitation frequency or structural properties, change. …”
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  8. 8
    A hybrid controller method with genetic algorithm optimization to measure position and angular for mobile robot motion control

    A hybrid controller method with genetic algorithm optimization to measure position and angular for mobile robot motion control by Shamsudin, Shamsudin, Mohamaddan, Shahrol

    Published 2023
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  9. 9
    Enhanced Harris's Hawk algorithm for continuous multi-objective optimization problems

    Enhanced Harris's Hawk algorithm for continuous multi-objective optimization problems by Yasear, Shaymah Akram

    Published 2020
    “…Harris’s hawk multi-objective optimizer (HHMO) algorithm is a MOSIbased algorithm that was developed based on the reference point approach. …”
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  10. 10
    A hybrid controller method with genetic algorithm optimization to measure position and angular for mobile robot motion control

    A hybrid controller method with genetic algorithm optimization to measure position and angular for mobile robot motion control by Razali, Muhammad Razmi, Mohd Faudzi, Ahmad Athif, Shamsudin, Abu Ubaidah, Shahrol Mohamaddan, Shahrol Mohamaddan

    Published 2023
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  11. 11
    A hybrid controller method with genetic algorithm optimization to measure position and angular for mobile robot motion control

    A hybrid controller method with genetic algorithm optimization to measure position and angular for mobile robot motion control by Razali, Muhammad Razmi, Mohd Faudzi, Ahmad Athif, Shamsudin, Abu Ubaidah, Mohamaddan, Shahrol

    Published 2023
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  12. 12
    A hybrid controller method with genetic algorithm optimization to measure position and angular for mobile robot motion control

    A hybrid controller method with genetic algorithm optimization to measure position and angular for mobile robot motion control by Razali, Muhammad Razmi, Mohd Faudzi, Ahmad Athif, Shamsudin, Abu Ubaidah, Mohamaddan, Shahrol

    Published 2023
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  13. 13
    A hybrid controller method with genetic algorithm optimization to measure position and angular for mobile robot motion control

    A hybrid controller method with genetic algorithm optimization to measure position and angular for mobile robot motion control by Razali, Muhammad Razmi, Mohd Faudzi, Ahmad Athif, Shamsudin, Abu Ubaidah, Mohamaddan, Shahrol

    Published 2022
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  14. 14
    A hybrid controller method with genetic algorithm optimization to measure position and angular for mobile robot motion control

    A hybrid controller method with genetic algorithm optimization to measure position and angular for mobile robot motion control by Razali, Muhammad Razmi, Mohd Faudzi, Ahmad Athif, Shamsudin, Abu Ubaidah, Mohamaddan, Shahrol

    Published 2023
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  15. 15
    A hybrid controller method with genetic algorithm optimization to measure position and angular for mobile robot motion control

    A hybrid controller method with genetic algorithm optimization to measure position and angular for mobile robot motion control by Razali, Muhammad Razmi, Mohd Faudz, Ahmad Athif, Shamsudin, Abu Ubaidah, Mohamaddan, Shahrol

    Published 2023
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  16. 16
    A hybrid controller method with genetic algorithm optimization to measure position and angular for mobile robot motion control

    A hybrid controller method with genetic algorithm optimization to measure position and angular for mobile robot motion control by Razali, Muhammad Razmi, Mohd Faudzi, Ahmad Athif, Shamsudin, Abu Ubaidah, Mohamaddan, Shahrol

    Published 2023
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  17. 17
    A hybrid controller method with genetic algorithm optimization to measure position and angular for mobile robot motion control

    A hybrid controller method with genetic algorithm optimization to measure position and angular for mobile robot motion control by Razali, Muhammad Razmi, Mohd Faudzi, Ahmad Athif, Shamsudin, Abu Ubaidah, Mohamaddan, Shahrol

    Published 2023
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  18. 18
    Design optimization of valve timing at various engine speeds using Multi-Objective Genetic Algorithm (MOGA)

    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
    “…In preprocessing of optimization, modeFrontier Response Surface Method (RSM) is able to model the behavior of engine performances corresponding to the change of design variables.…”
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  19. 19
    Investigation of firefly algorithm and chaos firefly algorithm for load prequency control / Zaid Najid

    Investigation of firefly algorithm and chaos firefly algorithm for load prequency control / Zaid Najid by Zaid, Najid

    Published 2015
    “…Result shows that the CFA is the best optimization method due to its robustness and consistency. …”
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  20. 20
    Alternative method for economic dispatch utilizing grey wolf optimizer

    Alternative method for economic dispatch utilizing grey wolf optimizer by Wong, Lo Ing

    Published 2015
    “…In addition, the flexibility of this algorithm is a merit to solve different problems by only setting few parameters such as number of population and number of iteration without any special changes in the structure of the algorithm. …”
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