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  1. 1
    Adaptive differential evolution algorithm with fitness based selection of parameters and mutation strategies / Rawaa Dawoud Hassan Al-Dabbagh

    Adaptive differential evolution algorithm with fitness based selection of parameters and mutation strategies / Rawaa Dawoud Hassan Al-Dabbagh by Rawaa Dawoud Hassan, Al-Dabbagh

    Published 2015
    “…The second taxonomy is a new taxonomy proposed to classify the adaptive DE algorithms in particular into two categories (DE with adaptive parameters and DE with adaptive parameters and strategies) considering the adaptive components used in this algorithm. …”
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  2. 2
    Taguchi-Grey Relational Analysis Method for Parameter Tuning of Multi-objective Pareto Ant Colony System Algorithm

    Taguchi-Grey Relational Analysis Method for Parameter Tuning of Multi-objective Pareto Ant Colony System Algorithm by Muthana, Shatha Abdulhadi, Ku Mahamud, Ku Ruhana

    Published 2023
    “…This research aims to find the optimal parameter values for the Pareto Ant Colony System (PACS) algorithm, which is used to obtain solutions for the generator maintenance scheduling problem. …”
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  3. 3
    Finite impulse response optimizers for solving optimization problems

    Finite impulse response optimizers for solving optimization problems by Tasiransurini, Ab Rahman

    Published 2019
    “…Selecting optimal parameters’ values may improve an algorithm’s performance. …”
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  4. 4
    Finite impulse response optimizers for solving optimization problems

    Finite impulse response optimizers for solving optimization problems by Ab Rahman, Tasiransurini

    Published 2019
    “…Selecting optimal parameters’ values may improve an algorithm’s performance. …”
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  5. 5
    Hybrid ACO and SVM algorithm for pattern classification

    Hybrid ACO and SVM algorithm for pattern classification by Alwan, Hiba Basim

    Published 2013
    “…The first two algorithms, ACOR-SVM and IACOR-SVM, tune the SVM parameters while the second two algorithms, ACOMV-R-SVM and IACOMV-R-SVM, tune the SVM parameters and select the feature subset simultaneously. …”
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  6. 6
    Semiparametric inference procedure for the accelarated failure time model with interval-censored data

    Semiparametric inference procedure for the accelarated failure time model with interval-censored data by Karimi, Mostafa

    Published 2019
    “…The findings of this research provide two new iterative algorithms for estimating the parameters of the AFT model with interval-censored data, and also two new resampling techniques for estimating the covariance matrix of estimators. …”
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  7. 7
    Development of optimization Alghorithm for uncertain non-linear dynamical system

    Development of optimization Alghorithm for uncertain non-linear dynamical system by Abdul Aziz, Mohd. Ismail, Yaacob, Nazeeruddin, Mohd. Said, Norfarizan, Hamzah, Nor Hazadura

    Published 2004
    “…The new algorithms also boast faster contractions. Both new algorithms performed better than DISOPE. …”
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    Monograph
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  8. 8
    Dynamic social behavior algorithm for real-parameter optimization problems and optimization of hyper beamforming of linear antenna arrays

    Dynamic social behavior algorithm for real-parameter optimization problems and optimization of hyper beamforming of linear antenna arrays by Prajindra�Sankar K., Kiong T.S., Siaw�Paw J.K.

    Published 2023
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  9. 9
    New Parameter Reduction of Soft Sets

    New Parameter Reduction of Soft Sets by Ma, Xiuqin

    Published 2012
    “…However, the algorithm involves a great amount of computation. In this thesis, a New Efficient Normal Parameter Reduction algorithm (NENPR) of soft sets is proposed based on the new theorems, which have been proved and presented. …”
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  10. 10
    A new cryptographic algorithm based on decomposition problem in elliptic curve cryptography / Hilyati Hanina Zazali

    A new cryptographic algorithm based on decomposition problem in elliptic curve cryptography / Hilyati Hanina Zazali by Hilyati Hanina, Zazali

    Published 2012
    “…The algorithms work by arranging parameters using finite field group in elliptic curve E. …”
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  11. 11
    A Hybrid Adaptive Leadership GWO Optimization with Category Gradient Boosting on Decision Trees Algorithm for Credit Risk Control Classification

    A Hybrid Adaptive Leadership GWO Optimization with Category Gradient Boosting on Decision Trees Algorithm for Credit Risk Control Classification by Suihai, Chen, Chih How, Bong, Po Chan, Chiu

    Published 2024
    “…Thirdly, this study uses SHAP framework to improve the interpretability of the new algorithm (EBGWO-CatBoost), and solves the problem of the weak interpretability of the new algorithm. …”
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  12. 12
    A new HMCR parameter of harmony search for better exploration

    A new HMCR parameter of harmony search for better exploration by Mansor, N.F., Abas, Z.A, Rahman, A.F.N.A, Shibghatullah, A.S., Sidek, S.

    Published 2016
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  13. 13
    A new HMCR parameter of harmony search for better exploration

    A new HMCR parameter of harmony search for better exploration by Nur Farraliza, Mansor, Abas, Z.A, Rahman, A.F.N.A, Shibghatullah, A.S., Sidek, S

    Published 2015
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  14. 14
    Solving 0/1 Knapsack Problem Using Hybrid HS and Jaya Algorithms

    Solving 0/1 Knapsack Problem Using Hybrid HS and Jaya Algorithms by Alomoush, Alaa A., Alsewari, Abdulrahman A., Alamri, Hammoudeh S., Kamal Z., Zamli

    Published 2018
    “…The new hybrid algorithm has been applied on different cases of Knapsack problem with different dimensions. 20 case studies have been evaluated by the new hybrid algorithm. …”
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  15. 15
    A hybrid SP-QPSO algorithm with parameter free adaptive penalty method for constrained global optimization problems

    A hybrid SP-QPSO algorithm with parameter free adaptive penalty method for constrained global optimization problems by Fatemeh, D. B., Loo, C. K., Kanagaraj, G., Ponnambalam, S. G.

    Published 2018
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  16. 16
    A coordinated design of PSSs and UPFC-based stabilizer using Genetic Algorithm

    A coordinated design of PSSs and UPFC-based stabilizer using Genetic Algorithm by Hassan, L.H., Moghavvemi, M., Almurib, H.A.F., Muttaqi, K.M.

    Published 2013
    “…This paper details a new coordinated design between Power System Stabilizers (PSSs) and Unified Power Flow Controller (UPFC) using Genetic Algorithm (GA). …”
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  17. 17
    Optimization of turning parameters using genetic algorithm method

    Optimization of turning parameters using genetic algorithm method by Shah Izwandi, Mohd Zawawi

    Published 2008
    “…This study about development of optimization for turning parameters based on the Genetic Algorithm (GA). This method was demonstrated for the optimization of machining parameters for turning operation using conventional lathe machines. …”
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    Undergraduates Project Papers
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  18. 18
    Machining optimization using Firefly Algorithm / Farhan Md Jasni

    Machining optimization using Firefly Algorithm / Farhan Md Jasni by Md Jasni, Farhan

    Published 2020
    “…When any of these targets is not achieved, creating big problem to company and a total loss. The metaheuristic algorithm which being called as Firefly Algorithm (FA) is often used to solve various optimization problem in our daily life. …”
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    Student Project
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  19. 19
    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
    “…The third component is the ACO-based adaptive parameter selection algorithm to solve the parameterization problem which relies on quality, exploration and unified criteria in assigning rewards to promising parameters. …”
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  20. 20
    Mixed-variable ant colony optimisation algorithm for feature subset selection and tuning support vector machine parameter

    Mixed-variable ant colony optimisation algorithm for feature subset selection and tuning support vector machine parameter by Alwan, Hiba Basim, Ku-Mahamud, Ku Ruhana

    Published 2017
    “…ACOMV-SVM algorithm is able to simultaneously tune SVM parameters and feature subset selection. …”
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