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    Attribute reduction based scheduling algorithm with enhanced hybrid genetic algorithm and particle swarm optimization for optimal device selection by Nwogbaga, Nweso Emmanuel, Latip, Rohaya, Affendey, Lilly Suriani, Abdul Rahiman, Amir Rizaan

    Published 2022
    “…Then canonical Polyadic decomposition-based attribute reduction is applied to the offload-able task to reduce the data size. Enhance hybrid genetic algorithm and particle Swarm optimization are developed to select the optimal device in either fog or cloud. …”
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    Article
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    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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    Long term energy demand forecasting based on hybrid, optimization: Comparative study by Musa, Wahab, Ku-Mahamud, Ku Ruhana, Yasin, Azman

    Published 2012
    “…The objective of this research is to develop a long term energy demand forecasting model that used hybrid optimization.To accomplish this goal, a hybrid algorithm that combined a genetic algorithm and a local search algorithm method has been developed to overcome premature convergence.Model performances of hybrid algorithm were compared with former single algorithm model in estimating parameter values of an objective function to measure the goodness-of-fit between the observed data and simulated results.Averages error between two models was adopt to select the proper model for future projection of energy demand.…”
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    Article
  4. 4

    Optimal demand response of solar energy generation using Genetic Algorithm / Muhammad Asyraaf Adlan by Adlan, Muhammad Asyraaf

    Published 2025
    “…The aim of this study is to optimize the demand response of solar energy generation using Genetic Algorithm (GA) to minimize the daily yield loss caused by load shedding. …”
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    Thesis
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    Mathematical models and optimization algorithms for low-carbon Location-Inventory-Routing Problem with uncertainty by Liu, Lihua

    Published 2024
    “…Given the NP-Hard nature of the three models proposed in this thesis, two metaheuristic algorithms have been developed. A hybrid Particle Swarm Optimization-Bacterial Foraging Algorithm is developed for solving the single objective LIRP model. …”
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    Thesis
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    A review on the applications of PSO-based algorithm in demand side management: Challenges and opportunities by Sulaima, Mohamad Fani, Zaini, Farah Anishah, Wan Abdul Razak, Intan Azmira, Zulkafli, Nur Izyan, Mokhlis, Hazlie

    Published 2023
    “…The proposed PSO-based algorithm can optimize consumers’ consumption curves, reducing the peak demand and hence minimizing the electricity cost when integrated with the DR programs or EE measures. …”
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    Article
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    Optimization of operational policies for the Minab Reservoir, Southern Iran by Gholampoor, Mohammad

    Published 2012
    “…To solve the shortage of water during drought, the Limiting Hedging Rules model and Genetic Algorithm (GA) were developed to determine the optimal allocation for agricultural demand. …”
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    Thesis
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    Techno-economic optimization and modelling of grid-connected photovoltaic and battery energy storage system by Gopinath Subramani

    Published 2023
    “…Optimization was performed via MATLAB using particle swarm optimization (PSO) and Genetic Algorithms (GA) techniques. …”
    text::Thesis
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    An Optimized Binary Scheduling Controller for Microgrid Energy Management Considering Real Load Conditions by Mannan M., Roslan M.F., Reza M.S., Mansor M., Jern K.P., Hossain M.J., Hannan M.A.

    Published 2024
    “…This study presents an optimal schedule controller for microgrid energy management, utilizing the Binary Particle Swarm algorithm (BPSO) to minimize costs and ensure optimal power delivery to loads. …”
    Conference Paper
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    An ensemble learning method for spam email detection system based on metaheuristic algorithms by Behjat, Amir Rajabi

    Published 2015
    “…In the second phase, a classifier ensemble learning model is proposed consisting of separate outputs: (i) To select a relevant subset of original features based on Binary Quantum Gravitational Search Algorithm (QBGSA), (ii) To mine data streams using various data chunks and overcome a failure of single classifiers based on SVM, MLP and K-NN algorithms. …”
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    Thesis
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