Search Results - (( model evaluation bees algorithm ) OR ( storage optimization based algorithm ))*

Refine Results
  1. 1

    Optimal water supply reservoir operation by leveraging the meta-heuristic Harris Hawks algorithms and opposite based learning technique by Lai V., Huang Y.F., Koo C.H., Ahmed A.N., Sherif M., El-Shafie A.

    Published 2024
    “…The HHO is still a promising method, as proven by its reliability and resilience indices compared to other published heuristic algorithms: at 62.50% and 1.56, respectively. The Artificial Bee Colony (ABC) outcomes satisfied demand at 61.36%, 59.47% with the Particle Swarm Optimisation (PSO), 55.68% with the real-coded Genetic Algorithm (GA), and 23.5 percent with the binary GA. …”
    Article
  2. 2

    Meta-heuristic approaches for reservoir optimisation operation and investigation of climate change impact at Klang gate dam by Lai, Vivien Mei Yen

    Published 2023
    “…The Whale Optimisation Algorithm (WOA), Harris Hawks Optimisation (HHO) Algorithm, Lévy Flight WOA (LFWOA) and the Opposition-Based Learning of HHO (OBL-HHO) were proposed to simulate the initial model’s response and optimise the Klang Gate Dam (KGD) release operation with observed inflow, water level (storage), release, and evaporation rate (loss). …”
    Get full text
    Get full text
    Final Year Project / Dissertation / Thesis
  3. 3
  4. 4
  5. 5

    Pumped-storage scheduling using particle swarm optimization / Amirul Asraf Razali by Razali, Amirul Asraf

    Published 2012
    “…This thesis presents the solution algorithm based on the particle swarm optimization (PSO) for solving the pumped-storage (P/S) scheduling problem. …”
    Get full text
    Get full text
    Thesis
  6. 6

    Group method of data handling with artificial bee colony in combining forecasts by Yahya, Nurhaziyatul Adawiyah, Samsudin, Ruhaidah, Darmawan, Irfan, Kasim, Shahreen

    Published 2018
    “…The weights for each individual model are calculated using ABC algorithm. In order to evaluate the proposed model, this study tested the proposed model on the International Airline Passengers data, and the performances are calculated using mean square error (MSE), mean average error (MAE) and mean average percentage error (MAPE). …”
    Get full text
    Get full text
    Article
  7. 7

    Binary Artificial Bee Colony Optimization For Weighted Random 2 Satisfiability In Discrete Hopfield Neural Network by Muhammad Sidik, Siti Syatirah

    Published 2023
    “…This thesis will be presented by implementing simulated, and benchmark data sets with multiple performance evaluation metrics. Based on the findings, the proposed model outperforms other models.…”
    Get full text
    Get full text
    Thesis
  8. 8
  9. 9
  10. 10

    Optimizing Cloud Storage Costs: Introducing the Pre-Evaluation-Based Cost Optimization (PECSCO) Mechanism by Alomari M.F., Mahmoud M.A., Gharaei N., Rasool S.M., Hasan R.A.

    Published 2025
    “…Nonetheless, cloud service providers impose fees on users based on the volume of data transmitted to and from cloud storage, resulting in elevated storage costs. …”
    Conference paper
  11. 11

    Hybrid Artificial Bees Colony algorithms for optimizing carbon nanotubes characteristics by Mohammad Jarrah, Mu'ath Ibrahim

    Published 2018
    “…Optimization is a crucial process to select the best parameters in single and multi-objective problems for manufacturing process.However,it is difficult to find an optimization algorithm that obtain the global optimum for every optimization problem.Artificial Bees Colony (ABC) is a well-known swarm intelligence algorithm in solving optimization problems.It has noticeably shown better performance compared to the state-of-art algorithms.This study proposes a novel hybrid ABC algorithm with β-Hill Climbing (βHC) technique (ABC-βHC) in order to enhance the exploitation and exploration process of the ABC in optimizing carbon nanotubes (CNTs) characteristics.CNTs are widely used in electronic and mechanical products due to its fascinating material with extraordinary mechanical,thermal,physical and electrical properties. …”
    Get full text
    Get full text
    Get full text
    Thesis
  12. 12

    Optimized Intelligent Controller for Energy Storage based Microgrid towards Sustainable Energy Future by Abu S.M., Hannan M.A., Mansor M., Ker P.J., Yaw Long C.

    Published 2024
    “…The optimization of HES performance is achieved through fine-tuning of the proportional-integral (PI) controller using the particle swarm optimization (PSO) algorithm. …”
    Conference Paper
  13. 13

    Assessment of energy storage and renewable energy sources-based two-area microgrid system using optimized fractional order controllers by Peddakapu, K., Mohd Rusllim, Mohamed, Srinivasarao, P., Arya, Yogendra

    Published 2024
    “…Simulation results reveal that the AOA-based CFOID-FOPIDN outperforms other existing algorithms, such as particle swarm optimization (PSO), bat algorithm (BAT), moth flame optimization (MFO), and whale optimization algorithm (WOA). …”
    Get full text
    Get full text
    Get full text
    Get full text
    Article
  14. 14

    Sediment load forecasting from a biomimetic optimization perspective: Firefly and Artificial Bee Colony algorithms empowered neural network modeling in �oruh River by Katipo?lu O.M., Kartal V., Pande C.B.

    Published 2025
    “…The estimation of SL values was achieved using inputs of previous SL and streamflow values provided to the models. Various statistical metrics were used to evaluate the accuracy of the established hybrid and stand-alone models. …”
    Article
  15. 15

    Switching Time Optimization via Time Optimal Control for Natural Gas Vehicle Refueling by Mahidzal Dahari, Mahidzal

    Published 2007
    “…In this thesis, a refueling algorithm using Time Optimal Control (TOC) technique is proposed as a basis for determining the optimal switching time in NGV refueling using the mass and mass flowrate as the state variables, measured using Coriolis flowmeter. …”
    Get full text
    Get full text
    Thesis
  16. 16
  17. 17
  18. 18
  19. 19
  20. 20