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  1. 1
    Schelkunoff array synthesis methods using adaptive-iterative algorithm

    Schelkunoff array synthesis methods using adaptive-iterative algorithm by Abdul Latiff, Nurul Mu'azzah

    Published 2003
    “…Basically, this algorithm is a combination of iterative algorithm with adaptive algorithm. …”
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  2. 2
    Augmentation of basic-line-search and quick-simplex-method algorithms to enhance linear programming computational performance

    Augmentation of basic-line-search and quick-simplex-method algorithms to enhance linear programming computational performance by Nor Azlan, Nor Asmaa Alyaa

    Published 2021
    “…Whereas, the validation from the real-world of the two datasets showed that the new augmentation algorithm steadily producing lesser iteration number compared to the conventional Simplex, QSM and BLSA algorithms. …”
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  3. 3
    A combined filter line search and trust region method for nonlinear programming

    A combined filter line search and trust region method for nonlinear programming by Chin, Choong Ming, Halim, Abdul, Rashid, A. H. A., Nor, K. M.

    Published 2006
    “…In the event when the step size has reached a minimum threshold such that the trial iterate is rejected by the filter, the algorithm temporarily exits to a trust region based algorithm to generate iterates that approach the feasible region and also acceptable to the filter. …”
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  4. 4
    Evolution Performance of Symbolic Radial Basis Function Neural Network by Using Evolutionary Algorithms

    Evolution Performance of Symbolic Radial Basis Function Neural Network by Using Evolutionary Algorithms by Alzaeemi, Shehab Abdulhabib, Tay, Kim Gaik, Huong, Audrey, Sathasivam, Saratha, Majahar Ali, Majid Khan

    Published 2023
    “…The SRBFNN’s objective function that corresponds to Satisfiability logic programming can be minimized by different algorithms, including Genetic Algorithm (GA), Evolution Strategy Algorithm (ES), Differential Evolution Algorithm (DE), and Evolutionary Programming Algorithm (EP). …”
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  5. 5
    Evolution Performance of Symbolic Radial Basis Function Neural Network by Using Evolutionary Algorithms

    Evolution Performance of Symbolic Radial Basis Function Neural Network by Using Evolutionary Algorithms by Shehab Abdulhabib Alzaeemi, Shehab Abdulhabib Alzaeemi, Kim Gaik Tay, Kim Gaik Tay, Audrey Huong, Audrey Huong, Saratha Sathasivam, Saratha Sathasivam, Majahar Ali, Majid Khan

    Published 2023
    “…The SRBFNN’s objective function that corresponds to Satisfiability logic programming can be minimized by different algorithms, including Genetic Algorithm (GA), Evolution Strategy Algorithm (ES), Differential Evolution Algorithm (DE), and Evolutionary Programming Algorithm (EP). …”
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  6. 6
    Evolution Performance of Symbolic Radial Basis Function Neural Network by Using Evolutionary Algorithms

    Evolution Performance of Symbolic Radial Basis Function Neural Network by Using Evolutionary Algorithms by Shehab Abdulhabib Alzaeemi, Shehab Abdulhabib Alzaeemi, Kim Gaik Tay, Kim Gaik Tay, Audrey Huong, Audrey Huong, Saratha Sathasivam, Saratha Sathasivam, Majid Khan bin Majahar Ali, Majid Khan bin Majahar Ali

    Published 2023
    “…The SRBFNN’s objective function that corresponds to Satisfiability logic programming can be minimized by different algorithms, including Genetic Algorithm (GA), Evolution Strategy Algorithm (ES), Differential Evolution Algorithm (DE), and Evolutionary Programming Algorithm (EP). …”
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  7. 7
    Evolution Performance of Symbolic Radial Basis Function Neural Network by Using Evolutionary Algorithms

    Evolution Performance of Symbolic Radial Basis Function Neural Network by Using Evolutionary Algorithms by Shehab Abdulhabib Alzaeemi, Shehab Abdulhabib Alzaeemi, Kim Gaik Tay, Kim Gaik Tay, Audrey Huong, Audrey Huong, Saratha Sathasivam, Saratha Sathasivam, Majahar Ali, Majid Khan

    Published 2023
    “…The SRBFNN’s objective function that corresponds to Satisfiability logic programming can be minimized by different algorithms, including Genetic Algorithm (GA), Evolution Strategy Algorithm (ES), Differential Evolution Algorithm (DE), and Evolutionary Programming Algorithm (EP). …”
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  8. 8
    Evolution Performance of Symbolic Radial Basis Function Neural Network by Using Evolutionary Algorithms

    Evolution Performance of Symbolic Radial Basis Function Neural Network by Using Evolutionary Algorithms by Shehab Abdulhabib Alzaeemi, Shehab Abdulhabib Alzaeemi, Kim Gaik Tay, Kim Gaik Tay, Audrey Huong, Audrey Huong, Saratha Sathasivam, Saratha Sathasivam, Majahar Ali, Majid Khan

    Published 2023
    “…The SRBFNN’s objective function that corresponds to Satisfiability logic programming can be minimized by different algorithms, including Genetic Algorithm (GA), Evolution Strategy Algorithm (ES), Differential Evolution Algorithm (DE), and Evolutionary Programming Algorithm (EP). …”
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  9. 9
    Evolution Performance of Symbolic Radial Basis Function Neural Network by Using Evolutionary Algorithms

    Evolution Performance of Symbolic Radial Basis Function Neural Network by Using Evolutionary Algorithms by Alzaeemi, Shehab Abdulhabib, Kim Gaik Tay, Kim Gaik Tay, Audrey Huong, Audrey Huong, Saratha Sathasivam, Saratha Sathasivam, Majahar Ali, Majid Khan

    Published 2023
    “…The SRBFNN’s objective function that corresponds to Satisfiability logic programming can be minimized by different algorithms, including Genetic Algorithm (GA), Evolution Strategy Algorithm (ES), Differential Evolution Algorithm (DE), and Evolutionary Programming Algorithm (EP). …”
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  10. 10
    Flowshop scheduling using artificial bee colony (ABC) algorithm with varying onlooker bees approaches

    Flowshop scheduling using artificial bee colony (ABC) algorithm with varying onlooker bees approaches by Mohd Pauzi, Nur Fazlinda

    Published 2015
    “…The main objective of this research is to develop a computer program with capability of manipulating the onlooker bee approaches in ABC Algorithm for solving flowshop scheduling problem. …”
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  11. 11
    Comparison of Some Numerical Methods for Solving Real-Life Nonlinear Equations by Using Python Programming / Annie Gorgey ... [et al.]

    Comparison of Some Numerical Methods for Solving Real-Life Nonlinear Equations by Using Python Programming / Annie Gorgey ... [et al.] by Gorgey, Annie, Zulkifly, Zul Hafiezy, Hassim, Haslinah, Azim, Farah Adilla

    Published 2024
    “…The study focuses on four types of iterative methods, such as Newton-Raphson, Bisection, Secant, and Algorithm 1, a novel fourth-order and derivative-free root-finding algorithm exhibiting different convergence orders. …”
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  12. 12
    Solving Large System Of Linear Equation Using Iterative Methods (Successive Over-Relaxation, Conjugate Gradient And Preconditioned Conjugate Gradient) / Nurhanani Abu Bakar

    Solving Large System Of Linear Equation Using Iterative Methods (Successive Over-Relaxation, Conjugate Gradient And Preconditioned Conjugate Gradient) / Nurhanani Abu Bakar by Abu Bakar, Nurhanani

    Published 2017
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  13. 13
    Comparison Between Linear Programming And Integer Linear Programming: A Review

    Comparison Between Linear Programming And Integer Linear Programming: A Review by Sam, Mei Lee, Saptari, Adi, Salleh, Mohd Rizal, Mohamad, Effendi

    Published 2018
    “…For ILP, available algorithms can be classified into exact algorithms and heuristic algorithms. …”
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  14. 14
    An iterative procedure for production-inventory-distribution routing problem

    An iterative procedure for production-inventory-distribution routing problem by Dicky Lim, T.K., Moin, N.H.

    Published 2015
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  15. 15
    Optimizing the placement of fire department in Kulim using greedy heuristic and simplex method / Muhammad Abu Syah Mohd Suzaly

    Optimizing the placement of fire department in Kulim using greedy heuristic and simplex method / Muhammad Abu Syah Mohd Suzaly by Mohd Suzaly, Muhammad Abu Syah

    Published 2023
    “…The binary integer programming (BIP) issue is a rare form of integer programming problem in which the value of variable xi is only 0 or 1. …”
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  16. 16
    Hybrid evolutionary-dolphin echolocation programming for sizing optimization of stand-alone photovoltaic systems / Zulkifli Othman

    Hybrid evolutionary-dolphin echolocation programming for sizing optimization of stand-alone photovoltaic systems / Zulkifli Othman by Othman, Zulkifli

    Published 2021
    “…Initially, Iterative-based Sizing Algorithm (ISA) which uses the non-computational intelligence-based approach is presented to serve as the benchmark for computational intelligence (CI)-based sizing algorithm. …”
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  17. 17
    Sizing optimization of large-scale grid connected photovoltaic system using dolphin echolocation algorithm / Muhammad Zakyizzuddin Rosselan

    Sizing optimization of large-scale grid connected photovoltaic system using dolphin echolocation algorithm / Muhammad Zakyizzuddin Rosselan by Rosselan, Muhammad Zakyizzuddin

    Published 2018
    “…Besides DEA, Evolutionary Programming (EP), Firefly Algorithm (FA) and Cuckoo Search Algorithm (CS) were also incorporated in the sizing algorithm for performance comparison. …”
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  18. 18
    Linear array beampattern gain optimization techniques

    Linear array beampattern gain optimization techniques by Nagi F.

    Published 2023
    “…In the LMS algorithm the error between the reference and the iterated beampattern is reduced. …”
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  19. 19
    Shuffled frog leaping algorithm for solving economic load dispatch problem / Mohammad Ariffin Aizat Ezanee

    Shuffled frog leaping algorithm for solving economic load dispatch problem / Mohammad Ariffin Aizat Ezanee by Ezanee, Mohammad Ariffin Aizat

    Published 2015
    “…This method provides a better result compared to Classical Method using lamda iteration and Genetic Algorithm (GA).…”
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  20. 20
    Peningkatan halaju suapan maksimum penginterpolasi CNC dedenyut rujukan menggunakan pengaturcaraan selari

    Peningkatan halaju suapan maksimum penginterpolasi CNC dedenyut rujukan menggunakan pengaturcaraan selari by Ahmad Fakhri, Ab. Nasir

    Published 2011
    “…This situation is also required in interpolator algorithm. The existing interpolator is not using parallel programming which every task have not been execute simultaneous in concurrent manner although using multi-cores processor computer, thus, the more time is needed for the iteration time especially for a complex and large scale of process. …”
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