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  1. 1
    Tree Physiology Optimization tuning rule for Proportional-Integral control

    Tree Physiology Optimization tuning rule for Proportional-Integral control by Halim, A.H., Ismail, I.

    Published 2017
    “…TPO is a metaheuristic algorithm that has parallel search strategy inspired from plant growth system. …”
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  2. 2
    Integrated OBF-NN models with enhanced extrapolation capability for nonlinear systems

    Integrated OBF-NN models with enhanced extrapolation capability for nonlinear systems by H., Zabiri, M., Ramasamy, T. D. , Lemma, Maulud, Abdulhalim

    Published 2013
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    Citation Index Journal
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  3. 3
    The development of semantic meta-database: an ontology based semantic integration of biological databases

    The development of semantic meta-database: an ontology based semantic integration of biological databases by Samsudin, Ruhaidah, Deris, Safaai, Othman, Muhammad Razib, Md. Illias, Rosli

    Published 2007
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  4. 4
    Probabilistic ensemble fuzzy ARTMAP optimization using hierarchical parallel genetic algorithms

    Probabilistic ensemble fuzzy ARTMAP optimization using hierarchical parallel genetic algorithms by Loo, C.K., Liew, W.S., Seera, M., Lim, Einly

    Published 2015
    “…This was achieved by mitigating convergence in the genetic algorithms by employing a hierarchical parallel architecture. …”
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  5. 5
    Efficient Malware Detection And Response Model Using Enhanced Parallel Deep Learning (EPDL-MDR)

    Efficient Malware Detection And Response Model Using Enhanced Parallel Deep Learning (EPDL-MDR) by Chowdhury Sajadul Islam

    Published 2026
    “…Upon converting PE files to images, the deep learning pixel-matching algorithm identifies obscured malware features. The enhanced harmony search (EHS) algorithm is employed to tune hyperparameters and increase model performance while mitigating overfitting in the EPDL model. …”
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  6. 6
    Process Planning Optimization In Reconfigurable Manufacturing Systems

    Process Planning Optimization In Reconfigurable Manufacturing Systems by Musharavati, Farayi

    Published 2008
    “…The five (5) AADTs include; a variant of the simulated annealing algorithm that implements heuristic knowledge at critical decision points, two (2) cooperative search schemes based on a “loose hybridization” of the Boltzmann Machine algorithm with (i) simulated annealing, and (ii) genetic algorithm search techniques, and two (2) modified genetic algorithms. …”
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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
    “…An algorithm that definitely can satisfy the objectives is the Dynamic Integrated Systems Optimization and Parameter Estimation (DISOPE) algorithm. …”
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  8. 8
    Hybrid metaheuristics for QOS-aware service composition / Hadi Naghavipour

    Hybrid metaheuristics for QOS-aware service composition / Hadi Naghavipour by Hadi , Naghavipour

    Published 2022
    “…As a result of this mapping study, five major hybridization strategies were identified in which two-third of solutions have been based on modifying algorithm operators or integration with another metaheuristic. …”
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  9. 9
    Hyper-heuristic approaches for data stream-based iIntrusion detection in the Internet of Things

    Hyper-heuristic approaches for data stream-based iIntrusion detection in the Internet of Things by Hadi, Ahmed Adnan

    Published 2022
    “…Therefore, we propose a prominent approach that integrates each of the NN, a meta-heuristic based on an evolutionary genetic algorithm (GA), and a core online-offline clustering (Core). …”
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  10. 10
    An Improve k-NN Classifier using Similarity Distance Plot-Data Reduction and Dask for Big Datasets

    An Improve k-NN Classifier using Similarity Distance Plot-Data Reduction and Dask for Big Datasets by Abdul Muqtasid, Rushdi

    Published 2025
    “…Second, this results in slower classification speeds, as the algorithm must search through a large number of instances to classify each test sample. …”
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