Search Results - (( variable integration based algorithm ) OR ( parameters optimization based algorithm ))

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  1. 1

    A meta-heuristics based input variable selection technique for hybrid electrical energy demand prediction models by ul Islam, B., Baharudin, Z.

    Published 2017
    “…Genetic algorithm and simulated annealing techniques are used to optimize the control parameters of the neural network. …”
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    Article
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    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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    Thesis
  4. 4

    Cloudlet deployment and task offloading in mobile edge computing using variable-length whale and differential evolution optimization and analytical hierarchical process for decisio... by Daba, Layth Muwafaq Abdulhussein

    Published 2023
    “…Unlike the existing optimization algorithm, VL-WIDE features the capability of searching different lengths of solutions to cover the variable number of cloudlets for deployment. …”
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    Thesis
  5. 5

    CAT CHAOTIC GENETIC ALGORITHM BASED TECHNIQUE AND HARDWARE PROTOTYPE FOR SHORT TERM ELECTRICAL LOAD FORECASTING by ISLAM, BADAR UL ISLAM

    Published 2017
    “…The solution set (i.e. optimized weight/bias matrix of ANN) provided by the optimized and improved genetic algorithm and modified BP based model is extracted and used in the design and development of a prototype device of the proposed model. …”
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    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
    “…This paper aims to demonstrate the effectiveness of Multi- Objective Genetic Algorithm Optimization and its robust practical application on the automobile engine valve timing where the variation of performance parameters required for finest tuning to obtain the optimal engine performances. …”
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    Proceeding Paper
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    Hyper-heuristic approaches for data stream-based iIntrusion detection in the Internet of Things by Hadi, Ahmed Adnan

    Published 2022
    “…Here, the memory consumption can be reduced by enabling a feature selection algorithm that excludes nonrelevant features and preserves the relevant ones. the algorithm is developed based on the variable length of the PSO. …”
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    Thesis
  10. 10

    Neural network modeling and optimization for spray-drying coconut milk using genetic algorithm and particle swarm optimization by Lee, Jesee Kar Ming

    Published 2022
    “…Firstly, using MATLAB program, the ANN model is developed based on optimized topology and is then furthered optimized by genetic algorithm (GA) and particle swarm optimization (PSO) using MINITAB program. …”
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    Optimization of surface quality of mild steel machined by wire EDM using simulated annealing algorithm by Khan, Ahsan Ali, Al Hazza, Muataz Hazza Faizi, Che Daud, Mohd Radzi, Mohamad Kamal, Nor Shahirah

    Published 2016
    “…The results have been used as input the Simulated annealing algorithm to determine the optimum value that can be achieved based on the scope of the research…”
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    Proceeding Paper
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    Optimization of medical image steganography using n-decomposition genetic algorithm by Al-Sarayefi, Bushra Abdullah Shtayt

    Published 2023
    “…To overcome these limitations, this study proposes a technique known as an n-decomposition genetic algorithm. This algorithm uses a variable-length search to identify the best location to embed the secret message by incorporating constraints to avoid local minimum traps. …”
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    Thesis
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    Enhanced stability and performance of the tidal energy conversion system using adaptive optimum relation-based MPPT algorithms by Noor Lina, Ramli, Mohd Rusllim, Mohamed, Wan Ismail, Ibrahim, Kurukuri, Peddakapu

    Published 2025
    “…The A-ORB algorithm integrates the optimum relation-based (ORB) approach with Hill Climb Search (HCS), along with an adaptive gain adjustment mechanism that dynamically tunes the parameter K based on power variation (ΔP). …”
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    Article
  17. 17

    Resource Efficient For Hybrid Fiber-Wireless Communications Links In Access Networks With Multi Response Optimization Algorithm by Wong, Adam Yoon Khang, Indra, Win Adiyansyah, Jamil Alsayaydeh, Jamil Abedalrahim, Mohamat Gani, Johar Akbar, M. Idrus, Sevia, Pusppanathan, Jaysuman

    Published 2021
    “…This work proposes a Multi response Optimization (MO) algorithm, named MO-LMMHOWAN that apply in Last Mile Mobile Hybrid Optical Wireless Access Network (LMMHOWAN). …”
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  18. 18

    Design Optimization of a Gas Turbine Engine for Marine Applications: Off-Design Performance and Control System Considerations by Machmudah, A., Lemma, T.A., Solihin, M.I., Feriadi, Y., Rajabi, A., Afandi, M.I., Abbasi, A.

    Published 2022
    “…The modeling of gains as a function of plant variables is presented. Meta-heuristic optimizations, namely a genetic algorithm (GA) and a whale optimization algorithm (WOA), are applied to optimize the designed control system. …”
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    Article
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    Data-driven variable tracking differentiation sigmoid proportional-integral-derivative controller for nonlinear multiple input multiple output system with white noise by Qian, Goh Ming, Mohd Riduwan, Ghazali, Mohd Ashraf, Ahmad

    Published 2025
    “…The research contribution is the integration of a Variable Tracking Differentiator (VTD) into a sigmoid-base PID structure, optimized using the Safe Experimentation Dynamics Algorithm (SEDA), enabling enhanced noise filtration and improved control accuracy. …”
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    Intelligence Integration Of Particle Swarm Optimization And Physical Vapour Deposition For Tin Grain Size Coating Process Parameters by Abdul Syukor, Mohamad Jaya, Mu*ath Ibrahim, Mohammad Jarrah, Mohd Asyadi Azam, Mohd Abid, Mohd Razali, Muhamad

    Published 2016
    “…Additionally,analysis of variance (ANOVA) was used to determine the significant factors influencing resultant TiN coating grain size.Based on that,a quadratic polynomial model equation was developed to represent the process variables and coating grain size.Then,in order to optimize the coating process parameters,genetic algorithms (GAs) were combined with the RSM quadratic model and used for optimization work.Finally,the models were validated using actual testing data to measure model performances in terms of residual error and prediction interval (PI).The result indicated that for RSM,the actual coating grain size of validation runs data fell within the 95% (PI) and the residual errors were less than 10 nm with very low values, the prediction accuracy of the model is 96.09%.In terms of optimization and reduction the experimental data,GAs could get the best lowest value for grain size then RSM with reduction ratio of ≈6%, ≈5%, respectively.…”
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