Search Results - (( simulation optimization function algorithm ) OR ( based application designing algorithm ))

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  1. 1
    DESIGN OPTIMIZATION OF A BLDC MOTOR BY GENETIC ALGORITHM AND SIMULATED ANNEALING

    DESIGN OPTIMIZATION OF A BLDC MOTOR BY GENETIC ALGORITHM AND SIMULATED ANNEALING by K. S. , Rama Rao, Azrul, Hisham Bin Othman

    Published 2007
    “…A constrained optimization on the objective function is performed using Genetic Algorithm (GA) and Simulated Annealing (SA), and optimal parameters are obtained. …”
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  2. 2
    OPTIMAL DESIGN AND ANALYSIS OF A DC–DC SYNCHRONOUS CONVERTER USING GENETIC ALGORITHM AND SIMULATED ANNEALING

    OPTIMAL DESIGN AND ANALYSIS OF A DC–DC SYNCHRONOUS CONVERTER USING GENETIC ALGORITHM AND SIMULATED ANNEALING by K., S. Rama Rao., C. , -K, Chew

    Published 2009
    “…Abstract This paper presents the application of Genetic Algorithm (GA) and Simulated Annealing (SA) techniques for optimal design and analysis of a DC–DC converter with synchronous rectification normally used in battery charge/discharge circuits in DC uninterruptible power supply systems. …”
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  3. 3
    OPTIMAL DESIGN AND ANALYSIS OF A DC–DC SYNCHRONOUS CONVERTER USING GENETIC ALGORITHM AND SIMULATED ANNEALING

    OPTIMAL DESIGN AND ANALYSIS OF A DC–DC SYNCHRONOUS CONVERTER USING GENETIC ALGORITHM AND SIMULATED ANNEALING by K.S., Rama Rao, C. K., Chew

    Published 2009
    “…This paper presents the application of Genetic Algorithm (GA) and Simulated Annealing (SA) techniques for optimal design and analysis of a DC–DC converter with synchronous rectification normally used in battery charge/discharge circuits in DC uninterruptible power supply systems. …”
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  4. 4
    Design optimization of a bldc motor by genetic algorithm and simulated annealing

    Design optimization of a bldc motor by genetic algorithm and simulated annealing by K.S.R., Rao, A.H.B., Othman

    Published 2007
    “…A constrained optimization on the objective function is performed using Genetic Algorithm (GA) and Simulated Annealing (SA), and optimal parameters are obtained. …”
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  5. 5
    An improved particle swarm optimization based on lévy flight and simulated annealing for high dimensional optimization problem

    An improved particle swarm optimization based on lévy flight and simulated annealing for high dimensional optimization problem by Basath, Samar Salem, Ismail, Amelia Ritahani, Alwan, Ali Amer, Amir Hussin, Amir 'Aatieff

    Published 2022
    “…On 500 dimensions, the algorithm obtains the optimal value on 14 out of 16 functions. …”
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  6. 6
    A firefly algorithm based hybrid method for structural topology optimization

    A firefly algorithm based hybrid method for structural topology optimization by Gebremedhen, H.S., Woldemichael, D.E., Hashim, F.M.

    Published 2020
    “…The lower and upper limit of design variables (0 and 1) were used to find initial material distribution to initialize the firefly algorithm based section of the hybrid algorithm. …”
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  7. 7
    An exponential based simulated kalman filter algorithm for data-driven PID tuning in liquid slosh controller

    An exponential based simulated kalman filter algorithm for data-driven PID tuning in liquid slosh controller by Mohd Falfazli, Mat Jusof, Ahmad Nor Kasruddin, Nasir, Mohd Ashraf, Ahmad, Zuwairie, Ibrahim

    Published 2018
    “…This paper presents an Exponent-based Simulated Kalman Filter (EbSKF) algorithm. …”
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  8. 8
    Optimal design of power system stabilizer for multimachine power system using farmland fertility algorithm

    Optimal design of power system stabilizer for multimachine power system using farmland fertility algorithm by Sabo, Aliyu, Abdul Wahab, Noor Izzri, Othman, Mohammad Lutfi, Mohd Jaffar, Mai Zurwatul Ahlam, Beiranvand, Hamzeh

    Published 2020
    “…In this research, the effective application and performance assessment of genetic algorithm (GA), particle swarm optimization (PSO) and a new metaheuristic-based farmland fertility algorithm (FFA) search algorithm for designing of coordinated multi PSSs in an interconnected multimachine power systems are presented and the limitations of the existing techniques (GA and PSO) are disclosed. …”
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  9. 9
    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
    “…This paper will report on an initial study of the usage of Genetic Algorithm (GA) merged with Deep Neural Network based surrogate model to optimize simulation for electromagnetic structure. …”
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  10. 10
    Process Planning Optimization In Reconfigurable Manufacturing Systems

    Process Planning Optimization In Reconfigurable Manufacturing Systems by Musharavati, Farayi

    Published 2008
    “…The five (5) AADTs include; a variant of the simulated annealing algorithm that implements heuristic knowledge at critical decision points, two (2) cooperative search schemes based on a “loose hybridization” of the Boltzmann Machine algorithm with (i) simulated annealing, and (ii) genetic algorithm search techniques, and two (2) modified genetic algorithms. …”
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  11. 11
    Genetic Algorithms In Optimizing Membership Function For Fuzzy Logic Controller

    Genetic Algorithms In Optimizing Membership Function For Fuzzy Logic Controller by Ismail, H. Muh Yusuf

    Published 2010
    “…GA have been successfully applied to solve many optimization problems. This research proposes a method that may help users to determine the membership function of FLC using the technique of GA optimization for the fastest processing in solving the problems. …”
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  12. 12
    Adaptable algorithms for performance optimization of dynamic batch manufacturing processes

    Adaptable algorithms for performance optimization of dynamic batch manufacturing processes by Teo, Kenneth Tze Kin

    Published 2018
    “…With nowadays high end computation ability, revolutionary changes of implementing precision measurement is expectable and applicable to obtain expensive products. Central to precision manufacturing is artificial intelligence as this thesis presents the performance characteristics of tuning-based, rule-based, learning-based and evolutionary-based algorithms. …”
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  13. 13
    Optimization of Digital Electronic Circuit Structure Design Using Genetic Algorithm

    Optimization of Digital Electronic Circuit Structure Design Using Genetic Algorithm by Chong, Kok Hen

    Published 2008
    “…The simulator called Optimal Digital Circuit Structure Designer (ODCSD) is also developed in this work. …”
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  14. 14
    New random approaches of modified adaptive bats sonar algorithm for reservoir operation optimization problems

    New random approaches of modified adaptive bats sonar algorithm for reservoir operation optimization problems by Nor Shuhada, Ibrahim

    Published 2024
    “…The increasing interest among researchers in the application of metaheuristic algorithms for search optimization has resulted in notable progress, especially in tackling single objective optimization problems. …”
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  15. 15
    A new algorithm for normal and large-scale optimization problems: Nomadic People Optimizer

    A new algorithm for normal and large-scale optimization problems: Nomadic People Optimizer by Alsewari, Abdul Rahman Ahmed, Sinan, Q. Salih

    Published 2019
    “…The algorithm is validated based on 36 unconstrained benchmark functions. …”
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  16. 16
    Dung Beetle Optimization algorithm for predicting damage to 2D truss structures

    Dung Beetle Optimization algorithm for predicting damage to 2D truss structures by Lan, Hoang Ton-That

    Published 2025
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  17. 17
    DESIGN AND EVALUATION OF RESOURCE ALLOCATION AND JOB SCHEDULING ALGORITHMS ON COMPUTATIONAL GRIDS

    DESIGN AND EVALUATION OF RESOURCE ALLOCATION AND JOB SCHEDULING ALGORITHMS ON COMPUTATIONAL GRIDS by MEHMOOD SHAH, SYED NASIR

    Published 2012
    “…Experimental evaluation confirmed that the proposed grid scheduling algorithms possess a high degree of optimality in performance, efficiency and scalability.…”
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  18. 18
    Hybridization of nonlinear sine cosine and safe experimentation dynamics algorithms for solving control engineering optimization problems

    Hybridization of nonlinear sine cosine and safe experimentation dynamics algorithms for solving control engineering optimization problems by Mohd Helmi, Suid

    Published 2024
    “…The empirical assessment of these proposed methods encompasses a diverse set of 23 benchmark functions, demonstrating their efficacy comparable to well-established metaheuristic algorithms such as as the Grey Wolf Optimizer (GWO), Multi-Verse Optimization (MVO), Sine Cosine Algorithm (SCA), Ant Lion Optimizer (ALO), Moth-Flame Optimization Algorithm (MFO), and Grasshopper Optimization Algorithm (GOA). …”
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  19. 19
    Novel farmland fertility algorithm based PIDPSS design for SMIB angular stability enhancement

    Novel farmland fertility algorithm based PIDPSS design for SMIB angular stability enhancement by Sabo, Aliyu, Abdul Wahab, Noor Izzri, Othman, Mohammad Lutfi, Mohd Jaffar, Mai Zurwatul Ahlam

    Published 2020
    “…For this study, design of proportional integral derivative (PID) PSS (PIDPSS) is proposed for damping oscillations and improve angular stability in single machine infinite bus (SMIB) power system modeled and simulated in MATLAB/SIMULINK. A new metaheuristics method called Farmland Fertility Algorithm (FFA) inspired by nature is proposed for optimal design of PIDPSS using a robust ISTSE objective function which had to be minimized. …”
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  20. 20
    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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