Search Results - (( using application based algorithm ) OR ( parameter optimization based algorithm ))

Refine Results
  1. 1

    Integrated optimal control and parameter estimation algorithms for discrete-time nonlinear stochastic dynamical systems by Kek, Sie Long

    Published 2011
    “…The main idea is the integration of optimal control and parameter estimation. In this work, a simplified model-based optimal control model with adjustable parameters is constructed. …”
    Get full text
    Get full text
    Thesis
  2. 2

    On Adopting Parameter Free Optimization Algorithms for Combinatorial Interaction Testing by Kamal Z., Zamli, Alsariera, Yazan A., Nasser, Abdullah B., Alsewari, Abdulrahman A.

    Published 2015
    “…In doing so, this paper reviews two existing parameter free optimization algorithms involving Teaching Learning Based Optimization (TLBO) and Fruitfly Optimization Algorithm (FOA) in an effort to promote their adoption for CIT.…”
    Get full text
    Get full text
    Get full text
    Article
  3. 3

    Finite impulse response optimizers for solving optimization problems by Ab Rahman, Tasiransurini

    Published 2019
    “…In this work, three new estimation-based metaheuristic algorithms are introduced. The first algorithm is a single-agent-based algorithm, named Single-agent FIR optimizer (SAFIRO). …”
    Get full text
    Get full text
    Thesis
  4. 4

    Finite impulse response optimizers for solving optimization problems by Tasiransurini, Ab Rahman

    Published 2019
    “…In this work, three new estimation-based metaheuristic algorithms are introduced. The first algorithm is a single-agent-based algorithm, named Single-agent FIR optimizer (SAFIRO). …”
    Get full text
    Get full text
    Thesis
  5. 5

    Effectiveness of nature-inspired algorithms using ANFIS for blade design optimization and wind turbine efficiency by Sarkar, Md. Rasel, Julai, Sabariah, Chong, Wen Tong, Toha @ Tohara, Siti Fauziah

    Published 2019
    “…It was found that the optimized blade design parameters were obtained using an ABC algorithm with the maximum value power coefficient higher than ACO and PSO. …”
    Get full text
    Get full text
    Get full text
    Get full text
    Get full text
    Article
  6. 6

    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. …”
    Get full text
    Get full text
    Get full text
    Get full text
    Thesis
  7. 7

    Optimization of turning parameters using genetic algorithm method by Shah Izwandi, Mohd Zawawi

    Published 2008
    “…This study about development of optimization for turning parameters based on the Genetic Algorithm (GA). …”
    Get full text
    Get full text
    Undergraduates Project Papers
  8. 8
  9. 9

    Enhanced gravitational search algorithm for nano-process parameter optimization problem / Norlina Mohd Sabri by Mohd Sabri, Norlina

    Published 2020
    “…Based on the capabilities of the metaheuristic algorithms, this research is proposing the enhanced Gravitational Search Algorithm (eGSA) to solve the nano-process parameter optimization problem. …”
    Get full text
    Get full text
    Thesis
  10. 10

    Optimization Of Pid Controller Using Grey Wolf Optimzer And Dragonfly Algorithm by Nik Mohamed Hazli, Nik Muhammad Aiman

    Published 2018
    “…However, to fully utilize the algorithm, the parameter of the algorithm need to be set properly. …”
    Get full text
    Get full text
    Monograph
  11. 11

    Effectiveness of Nature-Inspired Algorithms using ANFIS for Blade Design Optimization and Wind Turbine Efficiency by Sarkar, Md Rasel, Julai, Sabariah, Chong, Wen Tong, Toha, Siti Fauziah

    Published 2019
    “…It was found that the optimized blade design parameters were obtained using an ABC algorithm with the maximum value power coefficient higher than ACO and PSO. …”
    Get full text
    Get full text
    Article
  12. 12
  13. 13

    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. …”
    Get full text
    Get full text
    Thesis
  14. 14

    Optimization of milling parameters using ant colony optimization by Mohd Saupi, Mohd Sauki

    Published 2008
    “…The simulation based on ACO algorithm are successful develop and the optimization of parameters values is to maximize the production rate is obtain from the simulation.…”
    Get full text
    Get full text
    Undergraduates Project Papers
  15. 15

    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 second one is a wide range variation when the load mass M=0.1 to 100 kg which is required for the pneumatic servo actuators when they are used in missiles applications. PSO based QFT control algorithm is proposed for pneumatic servo actuator system. …”
    Get full text
    Get full text
    Thesis
  16. 16

    DC MOTOR CONTROL USING GENETIC ALGORITHM BASED PID by SHAHRONI, NURUL ASHIKIN

    Published 2011
    “…This dissertation presents the research that had been done on the chosen topic, works done and results acquired throughout the Final Year Project for two semesters, about the DC Motor Control using Genetic Algorithm based PID. The objectives of this project are to optimize speed control of the DC motor by using Genetic Algorithm based PID, to improve the performances of the DC motor controller in term of rise time, settling time, maximum overshoot and Integral of Time Absolute Error (ITAE) and to decide the best parameters to be used for Genetic Algorithm that can optimize the performance of a DC Motor ( eg: population size, mutation rate and crossover value). …”
    Get full text
    Get full text
    Final Year Project
  17. 17

    Identification of continuous-time model of hammerstein system using modified multi-verse optimizer by Most. Julakha, Jahan Jui

    Published 2021
    “…It has been successfully implemented and used in various areas such as machine learning applications, engineering applications, network applications, parameter control, and other similar applications to solve optimization problems. …”
    Get full text
    Get full text
    Thesis
  18. 18

    Simulated Kalman Filter: A Novel Estimation-based Metaheuristic Optimization Algorithm by Zuwairie, Ibrahim, Nor Hidayati, Abd Aziz, Nor Azlina, Ab. Aziz, Saifudin, Razali, Mohd Saberi, Mohamad

    Published 2016
    “…In this paper, a new population-based metaheuristic optimization algorithm, named Simulated Kalman Filter (SKF) is introduced. …”
    Get full text
    Get full text
    Get full text
    Conference or Workshop Item
  19. 19

    Application of conjugate gradient approach for nonlinear optimal control problem with model-reality differences by Leong, Wah June, Sie, Long Kek, Teo, Kok Lay, Sim, Sy Yi

    Published 2018
    “…Specifically, the modified model-based optimal control problem is resulted. Here, the conjugate gradient approach is used to solve the modified model-based optimal control problem, where the optimal solution of the model used is calculated repeatedly, in turn, to update the adjusted parameters on each iteration step. …”
    Get full text
    Get full text
    Get full text
    Article
  20. 20

    Application of conjugate gradient approach for nonlinear optimal control problem with model-reality difference by Sie, Long Kek, Wah, June Leong, Sy, Yi Sim, Kok, Lay Teo

    Published 2018
    “…Specifically, the modified model-based optimal control problem is resulted. Here, the conjugate gradient approach is used to solve the modified model-based optimal control problem, where the optimal solution of the model used is calculated repeatedly, in turn, to update the adjusted parameters on each iteration step. …”
    Get full text
    Get full text
    Get full text
    Article