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  1. 1
    Parameter Estimation Using Improved Differential Evolution And Bacterial Foraging Algorithms To Model Tyrosine Production In Mus Musculus(Mouse)

    Parameter Estimation Using Improved Differential Evolution And Bacterial Foraging Algorithms To Model Tyrosine Production In Mus Musculus(Mouse) by Jia, Xing Yeoh, Chuii, Khim Chong, Mohd Saberi, Mohamad, Yee, Wen Choon, Lian, En Chai, Safaai, Deris, Zuwairie, Ibrahim

    Published 2015
    “…Global optimisation method includes differential evolution algorithm, which will be used in this research. …”
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  2. 2
    Nonlinear identification of a small scale unmanned helicopter using optimized NARX network with multiobjective differential evolution

    Nonlinear identification of a small scale unmanned helicopter using optimized NARX network with multiobjective differential evolution by Tijani, Ismaila B., Akmeliawati, Rini, Legowo, Ari, Budiyono, Agus

    Published 2014
    “…This study proposes a hybrid of conventional back propagation training algorithm for the NARX network and multiobjective differential evolution (MODE) algorithm for identification of a nonlinear model of an unmanned small scale helicopter from experimental flight data.The proposed hybrid algorithm was able to produce models with Pareto-optimal compromise between the design objectives. …”
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  3. 3
    Optimization of handover algorithms in 3GPP long term evolution system

    Optimization of handover algorithms in 3GPP long term evolution system by Lin, Cheng-Chung, Sandrasegaran, Kumbesan, Mohd. Ramli, Huda Adibah, Basukala, Riyaj, Patachaianand, Rachod, Chen, Lu, Afrin, Toyoba Sohana

    Published 2011
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  4. 4
    Mixed Unscented Kalman Filter and differential evolution for parameter identification

    Mixed Unscented Kalman Filter and differential evolution for parameter identification by Legowo, Ari, Mohamad, Zahratu H., Park, HoonCheol

    Published 2013
    “…DE algorithm is integrated into the UKF algorithm to optimize the Kalman gain obtained. …”
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  5. 5
    Genetic algorithm to optimize routing problem modelled as the travelling salesman problem

    Genetic algorithm to optimize routing problem modelled as the travelling salesman problem by Muhammad Azrul Faiz , Nor Adzmi

    Published 2013
    “…Genetic algorithm conceptually follows steps inspired by the biological process of evolution. …”
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    Undergraduates Project Papers
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  6. 6
    Water level forecasting using feed forward neural networks optimized by African Buffalo Algorithm (ABO)

    Water level forecasting using feed forward neural networks optimized by African Buffalo Algorithm (ABO) by Ahmed, Ehab Ali

    Published 2019
    “…This research proposed a swarm intelligence training algorithm, Improved African Buffalo Optimization algorithm (IABO) based on the Metaheuristic method called the African Buffalo Optimization algorithm (ABO). …”
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  7. 7
    Identification and predictive control of spray tower system using artificial neural network and differential evolution algorithm

    Identification and predictive control of spray tower system using artificial neural network and differential evolution algorithm by Danzomo, Bashir A., Salami, Momoh Jimoh Eyiomika, Khan, Md. Raisuddin

    Published 2015
    “…This includes the use of an artificial neural network (ANN) based predictive control strategy and differential evolution (DE) optimization algorithm to determines the optimal control signal, uk (liquid droplet size, dD) by minimizing the cost function such that the output is set below the allowable PM concentration. …”
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  8. 8
    VISUALIZATION OF GENETIC ALGORITHM BASED ON 2-D GRAPH TO ACCELERATE THE SEARCHING WITH HUMAN INTERVENTIONS.

    VISUALIZATION OF GENETIC ALGORITHM BASED ON 2-D GRAPH TO ACCELERATE THE SEARCHING WITH HUMAN INTERVENTIONS. by FAROOQ, HUMERA

    Published 2012
    “…The aim of the proposed approach is to study the benefit of using visualization techniques to explorer Genetic Algorithm data based on gene values. …”
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  9. 9
    Using the evolutionary mating algorithm for optimizing deep learning parameters for battery state of charge estimation of electric vehicle

    Using the evolutionary mating algorithm for optimizing deep learning parameters for battery state of charge estimation of electric vehicle by Mohd Herwan, Sulaiman, Zuriani, Mustaffa, Nor Farizan, Zakaria, Mohd Mawardi, Saari

    Published 2023
    “…To show the effectiveness of EMA-DL, comparison studies were conducted among other metaheuristic optimizers that were also used to optimize the DL parameters viz, Particles Swarm Optimization (PSO), Genetic Algorithm (GA), Differential Evolution (DE) as well as the Adaptive Moment Estimation (ADAM). …”
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  10. 10
    Modeling and multi-objective optimal sizing of standalone photovoltaic system based on evolutionary algorithms

    Modeling and multi-objective optimal sizing of standalone photovoltaic system based on evolutionary algorithms by Ridha, Hussein Mohammed

    Published 2020
    “…The IEM algorithm uses the attraction-repulsion mechanism to change the positions of solutions towards the optimality. …”
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  11. 11
    B-spline curve fitting with different parameterization methods

    B-spline curve fitting with different parameterization methods by Kheng, Jia Shen

    Published 2020
    “…After generating control points, distance between the generated and original data points is used to identify the error of the algorithm. Later, genetic algorithm and differential evolution optimization are used to optimise the error of the curve. …”
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    Final Year Project / Dissertation / Thesis
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  12. 12
    Nonlinear modeling and control of a spark ignition engine idle speed / Hazem Mohamed

    Nonlinear modeling and control of a spark ignition engine idle speed / Hazem Mohamed by Hazem, Mohamed

    Published 1998
    “…The fuzzy controller is formulated as a radial basis function network trained by the orthogqnalleast squares algorithm to estimate the controller parameters. The train~ng data of the network is the entries of an optimal control table derived by analyzing the evolution of the state trajectories of the system under different initial conditions using the cell to cell mapping technique. …”
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  13. 13
    Robust Portfolio Mean-Variance Optimization for Capital Allocation in Stock Investment Using the Genetic Algorithm: A Systematic Literature Review

    Robust Portfolio Mean-Variance Optimization for Capital Allocation in Stock Investment Using the Genetic Algorithm: A Systematic Literature Review by Diandra Chika Fransisca, Sukono, Diah Chaerani, Nurfadhlina Abdul Halim

    Published 2024
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  14. 14
    Application of the Hybrid Artificial Neural Network Coupled with Rolling Mechanism and Grey Model Algorithms for Streamflow Forecasting Over Multiple Time Horizons

    Application of the Hybrid Artificial Neural Network Coupled with Rolling Mechanism and Grey Model Algorithms for Streamflow Forecasting Over Multiple Time Horizons by Yaseen, Zaher Mundher, Fu, Minglei, Wang, Chen, Mohtar, Wan Hanna Melini Wan, Deo, Ravinesh C., El-Shafie, Ahmed

    Published 2018
    “…The rolling mechanism method is applied to smooth out the dataset based on the antecedent values of the model inputs before being applied to the GM algorithm. …”
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  15. 15
    Parameter extraction of solar photovoltaic modules using penalty-based differential evolution

    Parameter extraction of solar photovoltaic modules using penalty-based differential evolution by Ishaque, K., Salam, Z., Mekhilef, Saad, Shamsudin, A.

    Published 2012
    “…The analyses carried out using synthetic current-voltage (I-V) data set showed that the proposed P-DE outperforms other Evolutionary Algorithm methods, namely the simulated annealing (SA), genetic algorithm (GA), and particle swarm optimization (PSO). …”
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  16. 16
    Time series forecasting of energy commodity using grey wolf optimizer

    Time series forecasting of energy commodity using grey wolf optimizer by Yusof, Yuhanis, Mustaffa, Zuriani

    Published 2015
    “…The ability to model and perform decision making is an essential feature of many real-world applications including the forecasting of commodity prices.In this study, a forecasting model based on a relatively new Swarm Intelligence (SI) behaviour, namely Grey Wolf Optimizer (GWO), is developed for short term time series forecasting.The model is built upon data obtained from the West Texas Intermediate (WTI) crude oil and gasoline price.Performance of the GWO model is compared against two other models which are developed based on Evolutionary Computation (EC) algorithms, namely the Artificial Bee Colony (ABC) and Differential Evolution (DE).Results showed that the GWO model outperformed DE in both crude oil and gasoline price forecasting. …”
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  17. 17
    Two level Differential Evolution algorithms for ARMA parameters estimatio

    Two level Differential Evolution algorithms for ARMA parameters estimatio by Salami, Momoh Jimoh Emiyoka, Tijani, Ismaila, Aibinu, Abiodun Musa

    Published 2013
    “…An Evolutionary Algorithm (EA) comprising two-level Differential Evolution (DE) optimization scheme is proposed. …”
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  18. 18
    Chiller power consumption forecasting for commercial building based on hybrid convolution neural networks-long short-term memory model with barnacles mating optimizer

    Chiller power consumption forecasting for commercial building based on hybrid convolution neural networks-long short-term memory model with barnacles mating optimizer by Mohd Herwan, Sulaiman, Zuriani, Mustaffa

    Published 2025
    “…The study compares the proposed CNN-LSTM-BMO against other metaheuristic optimization algorithms, including Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and Differential Evolution (DE). …”
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  19. 19
    Time Series Forecasting of Energy Commodity using Grey Wolf Optimizer

    Time Series Forecasting of Energy Commodity using Grey Wolf Optimizer by Zuriani, Mustaffa, Yuhanis, Yusof

    Published 2015
    “…Performance of the GWO model is compared against two other models which are developed based on Evolutionary Computation (EC) algorithms, namely the Artificial Bee Colony (ABC) and Differential Evolution (DE). …”
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  20. 20
    Reliably optimal PMU placement using disparity evolution-based genetic algorithm

    Reliably optimal PMU placement using disparity evolution-based genetic algorithm by Matsukawa, Yoshiaki, Othman, Mohammad Lutfi, Watanabe, Masayuki, Mitani, Yasunori

    Published 2017
    “…The optimization model is solved for IEEE 118 standard bus system. …”
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