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  1. 1
    Optimization of modified Bouc–Wen model for magnetorheological damper using modified cuckoo search algorithm

    Optimization of modified Bouc–Wen model for magnetorheological damper using modified cuckoo search algorithm by Rosmazi, Rosli, Zamri, Mohamed

    Published 2021
    “…This article presents a new modified cuckoo search algorithm with dynamic discovery probability and step-size factor for optimizing the Bouc–Wen Model in magnetorheological damper application. …”
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  2. 2
    Optimization of job scheduling in a machine shop using genetic algorithm

    Optimization of job scheduling in a machine shop using genetic algorithm by Adhikari, A., Biswas, C.K., Adhikari, N.

    Published 2002
    “…A modified version of GA known as string GA has been used to get the near optimal cycle time for permutation analysis. …”
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  3. 3
    Optimization of job scheduling in a machine shop using genetic algorithm

    Optimization of job scheduling in a machine shop using genetic algorithm by Adhikari, A., Biswas, C.K., Adhikari, N.

    Published 2002
    “…A modified version of GA known as string GA has been used to get the near optimal cycle time for permutation analysis. …”
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  4. 4
    VLSI floor planning optimization using genetic algorithm and cross entropy method / Angeline Teoh Szu Fern

    VLSI floor planning optimization using genetic algorithm and cross entropy method / Angeline Teoh Szu Fern by Angeline Teoh, Szu Fern

    Published 2012
    “…These two models are based on topological placement method. DM is optimized using genetic algorithm (GA). GA is a widely used optimization algorithm based on the concept of survival of the fittest. …”
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  5. 5
    Modified ant colony optimization algorithms for deterministic and stochastic inventory routing problems / Lily Wong

    Modified ant colony optimization algorithms for deterministic and stochastic inventory routing problems / Lily Wong by Lily , Wong

    Published 2018
    “…The classical Ant Colony Optimization (ACO) is modified by including the inventory cost in the global pheromones updating is proposed in this study. …”
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  6. 6
    An optimized aggregate marker algorithm for bandwidth fairness improvement in classifying traffic networks

    An optimized aggregate marker algorithm for bandwidth fairness improvement in classifying traffic networks by Al-Kharasani, Ameen Mohammed Abdulkarem, Othman, Mohamed, Abdullah, Azizol, Kweh, Yeah Lun

    Published 2016
    “…This article analyses and evaluates a new time sliding window traffic marker algorithm called the Optimized time sliding window Three Colour Marker (OtswTCM). …”
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  7. 7
    Fair bandwidth distribution marking and scheduling algorithm in network traffic classification

    Fair bandwidth distribution marking and scheduling algorithm in network traffic classification by Al-Kharasani, Ameen Mohammed Abdulkarem

    Published 2019
    “…Finally, propose a new method of obtaining optimal parameters dropping functions for Random Early Detection (RED) algorithm. …”
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  8. 8
    Fuzzy adaptive teaching learning-based optimization for solving unconstrained numerical optimization problems

    Fuzzy adaptive teaching learning-based optimization for solving unconstrained numerical optimization problems by Din, Fakhrud, Khalid, Shah, Fayaz, Muhammad, Gwak, Jeonghwan, Kamal Z., Zamli, Mashwani, Wali Khan

    Published 2022
    “…To overcome these drawbacks and to achieve an appropriate percentage of exploitation and exploration, this study presents a new modified teaching learning-based optimization algorithm called the fuzzy adaptive teaching learning-based optimization algorithm. …”
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  9. 9
    Comparison between four non-classical conjugate gradient method for solving unconstrained optimization problem / Nik Nurshahfini Daud and Farah Ellyna Mohamad Fadzal

    Comparison between four non-classical conjugate gradient method for solving unconstrained optimization problem / Nik Nurshahfini Daud and Farah Ellyna Mohamad Fadzal by Daud, Nik Nurshahfini, Mohamad Fadzal, Farah Ellyna

    Published 2019
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  10. 10
    Data-driven brain emotional learning-based intelligent controller-PID control of MIMO systems based on a modified safe experimentation dynamics algorithm

    Data-driven brain emotional learning-based intelligent controller-PID control of MIMO systems based on a modified safe experimentation dynamics algorithm by Shahrizal, Saat, Mohd Ashraf, Ahmad, Mohd Riduwan, Ghazali

    Published 2025
    “…The safe experimentation dynamics algorithm (SEDA) is one such method that optimizes controller parameters using data-driven techniques. …”
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  11. 11
    Data-driven brain emotional learning-based intelligent controller-PID control of MIMO systems based on a modified safe experimentation dynamics algorithm

    Data-driven brain emotional learning-based intelligent controller-PID control of MIMO systems based on a modified safe experimentation dynamics algorithm by Shahrizal, Saat, Mohd Ashraf, Ahmad, Mohd Riduwan, Ghazali

    Published 2025
    “…The safe experimentation dynamics algorithm (SEDA) is one such method that optimizes controller parameters using data-driven techniques. …”
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  12. 12
    On solving unconstrained optimization problem using three term conjugate gradient method / Nor Amila Sofiya Abdullah and Nurul Nadia Mohd Jalil

    On solving unconstrained optimization problem using three term conjugate gradient method / Nor Amila Sofiya Abdullah and Nurul Nadia Mohd Jalil by Abdullah, Nor Amila Sofiya, Mohd Jalil, Nurul Nadia

    Published 2019
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  13. 13
    Optimization of Simultaneous Scheduling for Machines and Automated Guided Vehicles Using Fuzzy Genetic Algorithm

    Optimization of Simultaneous Scheduling for Machines and Automated Guided Vehicles Using Fuzzy Genetic Algorithm by Badakhshian, Mostafa

    Published 2009
    “…While the scheduling of AGVs and machines are highly related; simultaneous scheduling of machines and AGVs has been proposed in the literature. Genetic algorithm (GA) proposed as a robust tool for optimization of scheduling problems. …”
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  14. 14
    Application of Evolutionary Algorithm for Assisted History Matching

    Application of Evolutionary Algorithm for Assisted History Matching by Zahari, Muhammad Izzat

    Published 2014
    “…Today, tremendous efforts are made to develop Automatic History Matching algorithms. While the automatic method focus on optimization which is normally computer based. …”
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  15. 15
    Performance Enhancement Of Artificial Bee Colony Optimization Algorithm

    Performance Enhancement Of Artificial Bee Colony Optimization Algorithm by Abro, Abdul Ghani

    Published 2013
    “…Artificial Bee Colony (ABC) algorithm is a recently proposed bio-inspired optimization algorithm, simulating foraging phenomenon of honeybees. …”
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  16. 16
    Comparative study of modified BFGS and new scale modified BFGS for solving unconstrained optimization / Shahirah Atikah Mohamad Husnin

    Comparative study of modified BFGS and new scale modified BFGS for solving unconstrained optimization / Shahirah Atikah Mohamad Husnin by Mohamad Husnin, Shahirah Atikah

    Published 2018
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  17. 17
    Hybridization of Strength Pareto Multiobjective Optimization with Modified Cuckoo Search Algorithm for Rectangular Array

    Hybridization of Strength Pareto Multiobjective Optimization with Modified Cuckoo Search Algorithm for Rectangular Array by Abdul Rani, Khairul Najmi, Abdulmalek, Mohamed Fareq, Rahim, Hasliza, Siew Chin, Neoh, Abd Wahab, Alawiyah

    Published 2017
    “…In addition, the MCS algorithm is hybridized with the particle swarm optimization (PSO) and hill climbing (HC) stochastic techniques along with the standard strength Pareto evolutionary algorithm (SPEA) forming the MCSPSOSPEA and MCSHCSPEA, respectively.All the proposed MCS-based algorithms are examined to perform MO optimization on Zitzler–Deb–Thiele’s (ZDT’s) test functions. …”
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  18. 18
    An ensemble of neural network and modified grey wolf optimizer for stock prediction

    An ensemble of neural network and modified grey wolf optimizer for stock prediction by Das, Debashish

    Published 2019
    “…Additionally, the research restricts the number of variables through feature selection to enhance the performance of the algorithm. Subsequently, the research attempts to construct an ensemble model applying Modified Grey Wolf Optimizer (MGWO) and neural network for stock prediction. …”
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  19. 19
    Numerical comparison between classical conjugate gradient and modified conjugate gradient / Mohd Afif Hamran

    Numerical comparison between classical conjugate gradient and modified conjugate gradient / Mohd Afif Hamran by Hamran, Mohd Afif

    Published 2018
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  20. 20
    Comparison of three modified Conjugate Gradient methods to solve unconstrained optimization problems / Siti Aminah Abdullah & Nur Anisah Najwa Mohd Arizi

    Comparison of three modified Conjugate Gradient methods to solve unconstrained optimization problems / Siti Aminah Abdullah & Nur Anisah Najwa Mohd Arizi by Abdullah, Siti Aminah, Mohd Arizi, Nur Anisah Najwa

    Published 2019
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