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  1. 1
    A Novel Discrete Filled Function Algorithm in Solving Discrete Optimization Problems (S/O: 12408)

    A Novel Discrete Filled Function Algorithm in Solving Discrete Optimization Problems (S/O: 12408) by Woon, Siew Fang, Karim, Sharmila, Mohamad, Mohd Saiful Adli

    Published 2016
    “…We focus our study on a recently developed method known as discrete filled function method. …”
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  2. 2
    Simulated Kalman Filter algorithms for solving optimization problems

    Simulated Kalman Filter algorithms for solving optimization problems by Nor Hidayati, Abdul Aziz

    Published 2019
    “…Its optimality has inspired the development of a metaheuristic algorithm called Heuristic Kalman Algorithm (HKA) in 2009. …”
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  3. 3
    Firefly analytical hierarchy algorithm for optimal allocation and sizing of distributed generation in radial distribution network

    Firefly analytical hierarchy algorithm for optimal allocation and sizing of distributed generation in radial distribution network by Bujal, Noor Ropidah

    Published 2022
    “…Finally, an AHP was integrated with FA to form Firefly Analytical Hierarchy Algorithm (FAHA) to automatically calculate the weight of each objective function based on the load flow outputs followed by the optimisation process. …”
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  4. 4
    Data-driven continuous-time Hammerstein modeling with missing data using improved Archimedes optimization algorithm

    Data-driven continuous-time Hammerstein modeling with missing data using improved Archimedes optimization algorithm by Islam, Muhammad Shafiqul, Mohd Ashraf, Ahmad

    Published 2024
    “…Across all experiments, the IAOA-based method demonstrated superior performance compared to AOA and other methods, including a hybrid approach combining the average multi-verse optimizer and sine cosine algorithm, particle swarm optimizer, the sine cosine algorithm, multi-verse optimizer and grey wolf optimizer. …”
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  5. 5
    Harmony search-based robust optimal controller with prior defined structure

    Harmony search-based robust optimal controller with prior defined structure by Rafieishahemabadi, Ali

    Published 2013
    “…In this approach, a combination of interacting two levels HS optimization algorithm is presented. In the first level, a new method for analytical formulation of integral square error cost function based on controller variables is elaborated for performance evaluation purposes by the proposed optimization algorithm. …”
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  6. 6
    New Quasi-Newton Equation And Method Via Higher Order Tensor Models

    New Quasi-Newton Equation And Method Via Higher Order Tensor Models by Gholilou, Fahimeh Biglari

    Published 2010
    “…Moreover, a new limited memory QN method to solve large scale unconstrained optimization is developed based on the modified BFGS updated formula. …”
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  7. 7
    Graph-Based Algorithm With Self-Weighted And Adaptive Neighbours Learning For Multi-View Clustering

    Graph-Based Algorithm With Self-Weighted And Adaptive Neighbours Learning For Multi-View Clustering by He, Yanfang

    Published 2024
    “…To address the noise problem in multi-view data, this study enhances the gbs method and develops a new self-weighted graph multi-view clustering algorithm (swmcan). …”
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  8. 8
    Liquid slosh suppression by implementing data-driven fractional order pid controller based on marine predators algorithm

    Liquid slosh suppression by implementing data-driven fractional order pid controller based on marine predators algorithm by Mohd Tumari, Mohd Zaidi Mohd, Mustapha, Nik Mohd Zaitul Akmal, Ahmad, Mohd Ashraf, Saat, Shahrizal, Ghazali, Mohd Riduwan

    Published 2023
    “…Thus, this research paper proposed the development of a data-driven fractional-order PID controller based on marine predators algorithm (MPA) for liquid slosh suppression system. …”
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  9. 9
    A Stepper Motor Design Optimization Using

    A Stepper Motor Design Optimization Using by Wong, Chin Wei

    Published 2005
    “…There is a need to fill this void in the area of small-motor design, and develop a program using Genetic Algorithms (GAs) as an approach to achieve optimization. …”
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  10. 10
    Safe experimentation dynamics algorithm for data-driven PID controller of a class of underactuated systems

    Safe experimentation dynamics algorithm for data-driven PID controller of a class of underactuated systems by Nor Sakinah, Abdul Shukor

    Published 2019
    “…So far, the tuning methods used for data-driven PID for the underactuated systems are mostly based on the multi-agent-based optimization, which means that the design requires substantial computation time and make it not practical for on-line tuning applications. …”
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  11. 11
    Safe experimentation dynamics algorithm for data-driven PID controller of a class of underactuated systems

    Safe experimentation dynamics algorithm for data-driven PID controller of a class of underactuated systems by Nor Sakinah, Abdul Shukor

    Published 2019
    “…So far, the tuning methods used for data-driven PID for the underactuated systems are mostly based on the multi-agent-based optimization, which means that the design requires substantial computation time and make it not practical for on-line tuning applications. …”
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  12. 12
    Liquid slosh suppression by implementing data-driven fractional order PID controller based on marine predators algorithm

    Liquid slosh suppression by implementing data-driven fractional order PID controller based on marine predators algorithm by Mohd Zaidi, Mohd Tumari, Mohd Ashraf, Ahmad, Mohd Riduwan, Ghazali, Nik Mohd Zaitul Akmal, Mustapha, Shahrizal, Saat

    Published 2023
    “…Thus, this research paper proposed the development of a data-driven fractional-order PID controller based on marine predators algorithm (MPA) for liquid slosh suppression system. …”
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  13. 13
    Discrete-time system identification using genetic algorithm with single parent-based mating technique

    Discrete-time system identification using genetic algorithm with single parent-based mating technique by Zainuddin, Farah Ayiesya

    Published 2024
    “…Performance indicators included the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), and Parameter Magnitude-based Information Criterion 2 (PMIC2). The findings showed that incorporating SPM with single-point crossover resulted in lower objective function (OF) values and improved error indices (EI) compared to traditional GA methods. …”
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  14. 14
    Developing an app for streamlined inventory tracking with barcode scanning and load planning optimization

    Developing an app for streamlined inventory tracking with barcode scanning and load planning optimization by Teng, Yan Xin

    Published 2025
    “…Keywords: Inventory Management, Barcode Scanning, Load Planning, Binary Tree Bin Packing Algorithm, Mobile Application Development. Subject Area: T58.5–58.64 Information Technology…”
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  15. 15
    Using genetic algorithms to optimise land use suitability

    Using genetic algorithms to optimise land use suitability by Pormanafi, Saeid

    Published 2012
    “…In this study, under environmentfriendliness objective, based on multi-agent genetic algorithms, was developed a geospatial model for the land use allocation. …”
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  16. 16
    Variable Neighborhood Descent and Whale Optimization Algorithm for Examination Timetabling Problems at Universiti Malaysia Sarawak

    Variable Neighborhood Descent and Whale Optimization Algorithm for Examination Timetabling Problems at Universiti Malaysia Sarawak by Emily Sing Kiang, Siew

    Published 2025
    “…A constructive algorithm was developed to generate an initial feasible solution, which was subsequently refined using two primary approaches to evaluate their efficiency: Iterative Threshold Pipe Variable Neighborhood Descent (IT-PVND), and a modified Whale Optimization Algorithm (WOA). …”
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  17. 17
    Development of an integrated scheduling model for handling equipment in automated port container terminals

    Development of an integrated scheduling model for handling equipment in automated port container terminals by Sadeghian, Syed Hamidreza

    Published 2014
    “…As the integrated scheduling of handling equipment is a “non-deterministic polynomialtime hard” (NP-hard) problem and also the computation time and ease of application are so important for real practices of the scheduling methods, So a meta-heuristic algorithm based on Genetic Algorithm is developed, in which, new operators create solutions considering the constraints of the problem and also a heuristic rule proposed which assigns the ALVs to the tasks. …”
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  18. 18
    Online Programming Judge System (UOJ)

    Online Programming Judge System (UOJ) by Tran , Quang Trung

    Published 2013
    “…This research conducts a study to build an Online Programming Judge system with a mechanism to generate test cases automatically using Particle Swarm Optimization (PSO) algorithm. The system has the function to judge programming code by evaluating the output that the program produced. …”
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  19. 19
    A genetic algorithm based fuzzy inference system for pattern classification and rule extraction

    A genetic algorithm based fuzzy inference system for pattern classification and rule extraction by Wong S.Y., Yap K.S., Li X.

    Published 2023
    “…This paper presents a genetic-algorithm-based fuzzy inference system for extracting highly comprehensible fuzzy rules to be implemented in human practices without detailed computation (hereafter denoted as GA-FIS). …”
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  20. 20
    Structural health monitoring using adaptive wavelet functions / Seyed Hossein Mahdavi

    Structural health monitoring using adaptive wavelet functions / Seyed Hossein Mahdavi by Seyed Hossein, Mahdavi

    Published 2016
    “…As a consequence, the computational performance of structural health monitoring strategies will be directly influenced by the higher computational competency and convergence rate of the proposed wavelet-based method for structural simulation. Accordingly, in the second part of this research, the procedure of structural identification and damage detection has been developed by employing the wavelet-based method through the modified genetic algorithms (GAs) to optimally solve inverse problems. …”
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