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  1. 1
    Reactive approach for automating exploration and exploitation in ant colony optimization

    Reactive approach for automating exploration and exploitation in ant colony optimization by Allwawi, Rafid Sagban Abbood

    Published 2016
    “…Ant colony optimization (ACO) algorithms can be used to solve nondeterministic polynomial hard problems. …”
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  2. 2
    Ant colony optimization in dynamic environments

    Ant colony optimization in dynamic environments by Chen, Fei Huang

    Published 2010
    “…The last objective of this thesis is to optimize the parameter settings of the best performing ant algorithm with local search. …”
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  3. 3
    Improvement of Centralized Routing and Scheduling Using Cross-Layer Design and Multi-Slot Assignment in Wimax Mesh Networks

    Improvement of Centralized Routing and Scheduling Using Cross-Layer Design and Multi-Slot Assignment in Wimax Mesh Networks by Al-Humairi, Ali Zuhair Abdulameer

    Published 2009
    “…This thesis proposes an optimized strategy namely cross-layer design in routing algorithms used find the best route for all SSs and scheduling algorithms, used to assign a time slot for each possible node transmission. …”
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  4. 4
    Prediction and multi-criteria-based schemes for seamless handover mechanism in mobile WiMAX networks

    Prediction and multi-criteria-based schemes for seamless handover mechanism in mobile WiMAX networks by Mubarak, Mohammed Awadh Ahmed Ben

    Published 2013
    “…It reduces the number of handovers by 29.7% and 26.9%, respectively, compared to the conventional RSSI based handover algorithm and the previous worked, mobility improved handover (MIHO) algorithm. …”
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  5. 5
    Electric vehicle battery state of charge estimation using metaheuristic-optimized CatBoost algorithms

    Electric vehicle battery state of charge estimation using metaheuristic-optimized CatBoost algorithms by Mohd Herwan, Sulaiman, Zuriani, Mustaffa, Ahmad Salihin, Samsudin, Amir Izzani, Mohamed, Mohd Mawardi, Saari

    Published 2025
    “…A comprehensive data preprocessing pipeline was implemented, including missing value treatment, outlier removal, and feature normalization using Min-Max scaling. Three distinct metaheuristic algorithms were investigated: Barnacles Mating Optimizer (BMO), Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Whale Optimization Algorithm (WOA), each integrated with CatBoost to optimize critical parameters including learning rate, tree depth, regularization, and bagging temperature. …”
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  6. 6
    Single Fitness Function to Optimize Energy using Genetic Algorithms for Wireless Sensor Network

    Single Fitness Function to Optimize Energy using Genetic Algorithms for Wireless Sensor Network by Ismail Abdullah, Kalid Abdlkader Marsal

    Published 2024
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  7. 7
    A meta-heuristics based input variable selection technique for hybrid electrical energy demand prediction models

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

    Published 2017
    “…The focus of the paper is to propose a hybrid approach for the selection of the most influential input variables for the training and testing of neural network based hybrid models. …”
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  8. 8
    Optimized scheme for efficient and scalable key management in IEEE 802.16e-based networks

    Optimized scheme for efficient and scalable key management in IEEE 802.16e-based networks by Sadeghi, Mohammad Mehdi Gilanian

    Published 2015
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  9. 9
    Design Optimization of a Gas Turbine Engine for Marine Applications: Off-Design Performance and Control System Considerations

    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
    “…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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  10. 10
    Effect of input variables selection on energy demand prediction based on intelligent hybrid neural networks

    Effect of input variables selection on energy demand prediction based on intelligent hybrid neural networks by Islam, B., Baharudin, Z., Nallagownden, P.

    Published 2015
    “…The efficacy of these models depends upon many factors such as, neural network architecture, type of training algorithm, input training and testing data set and initial values of synaptic weights. …”
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  11. 11
    The effect of pre-processing techniques and optimal parameters on BPNN for data classification

    The effect of pre-processing techniques and optimal parameters on BPNN for data classification by HUSSEIN, AMEER SALEH

    Published 2015
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  12. 12
    Power plant energy predictions based on thermal factors using ridge and support vector regressor algorithms

    Power plant energy predictions based on thermal factors using ridge and support vector regressor algorithms by Afzal, Asif, Alshahrani, Saad, Alrobaian, Abdulrahman, Buradi, Abdulrajak, Khan, Sher Afghan

    Published 2021
    “…Initially, the Ridge algorithm-based modeling is performed in detail, and then SVR-based LR, named as SVR (LR), SVR-based radial basis function—SVR (RBF), and SVR-based polynomial regression—SVR (Poly.) algorithms, are applied. …”
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  13. 13
    DC Motor Control using Ant Colony Optimization

    DC Motor Control using Ant Colony Optimization by Amr Mansour, Sara

    Published 2011
    “…Since 1995 various other extended versions of AS have been developed, induding Ant Colony System (ACS) and MAX-MIN Ant System (MMAS). In 1999 Dorigo proposed the Ant Colony Optimization (ACO) meta-heuristic that became the most successful and recognized algorithm based on ant behaviour [1]. …”
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  14. 14
    Multidimensional Minimization Training Algorithms for Steam Boiler Drum Level Trip Using Artificial Intelligence Monitoring System

    Multidimensional Minimization Training Algorithms for Steam Boiler Drum Level Trip Using Artificial Intelligence Monitoring System by Ismail, F. B., Al-Kayiem, Hussain H.

    Published 2010
    “…The selection of the relevant variables for the neural networks is based on merging between theoretical analysis base and the plant operator experience. …”
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  15. 15
    Robust PID anti-swing control of automatic gantry crane based on Kharitonov's stability

    Robust PID anti-swing control of automatic gantry crane based on Kharitonov's stability by Solihin, Mahmud Iwan, Martono, Wahyudi, Legowo, Ari, Akmeliawati, Rini

    Published 2009
    “…The proposed method employs Genetic Algorithm (GA) in min-max optimization to find the stable robust PID. …”
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  16. 16
    Improving neural networks training using experiment design approach

    Improving neural networks training using experiment design approach by Chong, Wei Kean

    Published 2005
    “…There are several methods of selecting training data from input space for neural networks which include D-optimal and Max-min design approaches. Consider a function approximation problem (Neural Network using Radial Basic Function structure) and limit the amount of training data, say (m) from N amount of possible data. …”
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  17. 17
    Design optimization of valve timing at various engine speeds using Multi-Objective Genetic Algorithm (MOGA)

    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
    “…The primary concern is to acquire the clear picture of the implementation of Multi-Objective Genetic Algorithm and the essential of variable valve timing effects on the engine performances in various engine speeds. …”
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    Proceeding Paper
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  18. 18
    Neural network based model predictive control for a steel pickling process

    Neural network based model predictive control for a steel pickling process by Kittisupakorn, P., Thitiyasook, P., Hussain, Mohd Azlan, Daosud, W.

    Published 2009
    “…The Levenberg-Marquardt algorithm is used to train the process models. In the control (MPC) algorithm, the feedforward neural network models are used to predict the state variables over a prediction horizon within the model predictive control algorithm for searching the optimal control actions via sequential quadratic programming. …”
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  19. 19
    Development Of Water Quality Index Prediction Model For Penang Rivers Using Artificial Neural Network

    Development Of Water Quality Index Prediction Model For Penang Rivers Using Artificial Neural Network by Mohd Hamdan, Eleena Yasmeen

    Published 2021
    “…Prior to the development of ANN-based WQI prediction model, the BR algorithm was chosen with two-, three-, four-, five- and six-neuron architectures for 60% and 70% training. …”
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    Monograph
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  20. 20
    Modeling and validation of base pressure for aerodynamic vehicles based on machine learning models

    Modeling and validation of base pressure for aerodynamic vehicles based on machine learning models by Quadros, Jaimon Dennis, Khan, Sher Afghan, Aabid, Abdul, Baig, Muneer

    Published 2023
    “…Based on the identical dataset, the GA-BP and PSO-BP algorithms are also compared to the PCA-BAS-ENN algorithm. …”
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