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  1. 1
    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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  2. 2
    Distributed parallel deep learning with a hybrid backpropagation-particle swarm optimization for community detection in large complex networks

    Distributed parallel deep learning with a hybrid backpropagation-particle swarm optimization for community detection in large complex networks by Shing, Chiang Ta, Mohammed Al-Andoli, Mohammed Nasser, Wooi, Ping Cheah

    Published 2022
    “…Next, the method is integrated with two optimization algorithms: (1) backpropagation (BP), which optimizes deep learning locally within each local chunk of the CN; (2) particle swarm optimization (PSO), which is used to improve the BP optimization involving all CN chunks. …”
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  3. 3
    Extreme Learning Machines: A new approach for prediction of reference evapotranspiration

    Extreme Learning Machines: A new approach for prediction of reference evapotranspiration by Abdullah S.S., Malek M.A., Abdullah N.S., Kisi O., Yap K.S.

    Published 2023
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  4. 4
    Twofold Integer Programming Model for Improving Rough Set Classification Accuracy in Data Mining.

    Twofold Integer Programming Model for Improving Rough Set Classification Accuracy in Data Mining. by Saeed, Walid

    Published 2005
    “…The accuracy for rules and classification resulted from the TIP method are compared with other methods such as Standard Integer Programming (SIP) and Decision Related Integer Programming (DRIP) from Rough Set, Genetic Algorithm (GA), Johnson reducer, HoltelR method, Multiple Regression (MR), Neural Network (NN), Induction of Decision Tree Algorithm (ID3) and Base Learning Algorithm (C4.5); all other classifiers that are mostly used in the classification tasks. …”
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  5. 5
    Efficient Sequential and Parallel Routing Algorithms in Optical Multistage Interconnection Network

    Efficient Sequential and Parallel Routing Algorithms in Optical Multistage Interconnection Network by Abduh Kaid, Monir Abdullah

    Published 2005
    “…This routing problem is an NPhard problem. Many algorithms are designed by many researchers to perform this routing such as window method, sequential algorithm, degree-descending algorithm, simulated annealing algorithm, genetic algorithm and ant colony algorithm.This thesis explores two approaches, sequential and parallel approaches. …”
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  6. 6
    Workflow optimization in distributed computing environment for stream-based data processing model / Saima Gulzar Ahmad

    Workflow optimization in distributed computing environment for stream-based data processing model / Saima Gulzar Ahmad by Saima Gulzar, Ahmad

    Published 2017
    “…The second algorithm is a variation in first algorithm in which data parallelism is introduced in each partition. …”
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  7. 7
    WCDMA teletraffic performance improvement via power resource optimization using distributed parallel genetic algorithm

    WCDMA teletraffic performance improvement via power resource optimization using distributed parallel genetic algorithm by Prajindra S.K., Tiong S.K., Johnny Koh S.P., Yap D.F.W.

    Published 2023
    “…The algorithm works by finding the minimum transmitter power with the help of Distributed Parallel Genetic Algorithm (DPGA) employed on an offload microcontroller system to form optimal beam coverage to reduce power usage of adaptive antenna at WCDMA base station. …”
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  8. 8
    Design and implementation of a real-time adaptive learning algorithm controller for a 3-DOF parallel manipulator / Mustafa Jabbar Hayawi

    Design and implementation of a real-time adaptive learning algorithm controller for a 3-DOF parallel manipulator / Mustafa Jabbar Hayawi by Hayawi, Mustafa Jabbar

    Published 2015
    “…An electronic board, transistor relay driver circuit, is designed for the purpose of establishing communication interface between the computer, adaptive learning algorithm and the actuator mechanism. Design and development an adaptive learning algorithm controller ALAC of position the actuators is presented in real time parallel manipulator based on artificial neural network ANN. …”
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  9. 9
    Development of a heuristic procedure for balancing mixed-model parallel assembly line type II

    Development of a heuristic procedure for balancing mixed-model parallel assembly line type II by Esmaeilian, Gholamreza

    Published 2010
    “…To solve these problems, two heuristic algorithms were developed and coded in MATLAB®. The first one allocates each model to only one parallel assembly line and achieves the initial arrangement of tasks with the minimum number of workstations for each line. …”
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  10. 10
    Fast and efficient sequential learning algorithms using direct-link RBF networks

    Fast and efficient sequential learning algorithms using direct-link RBF networks by Asirvadam , Vijanth Sagayan, McLoone, Sean, Irwin, George

    Published 2003
    “…The dynamic DRBF network is trained using the recently proposed decomposed/parallel recursive Levenberg Marquardt (PRLM) algorithm by neglecting the interneuron weight interactions. …”
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  11. 11
    A GPU accelerated parallel genetic algorithm for the traveling salesman problem

    A GPU accelerated parallel genetic algorithm for the traveling salesman problem by Binjubier, Mohammed, Mohd Arfian, Ismail, Tusher, Ekramul Haque, Aljanabi, Mohammad

    Published 2024
    “…The problem was classified as NP-Hard. Several different algorithms were developed to solve the problem; among them, the Genetic Algorithm was used to deal with it. …”
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  12. 12
    Voting algorithms for large scale fault-tolerant systems

    Voting algorithms for large scale fault-tolerant systems by Karimi, Abbas

    Published 2011
    “…To solve this problem and gain benefits of this algorithm, we employed parallel algorithm technique and by using optimal number of processors, we could propose optimal algorithms known as Parallel Average Voting and Parallel Weighted Average Voting which both have optimal time complexity and less calculation cost. …”
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  13. 13
    Harmonic reduction in three-phase parallel connected inverter

    Harmonic reduction in three-phase parallel connected inverter by Younis, M.A.A., Rahim, N.A., Mekhilef, Saad

    Published 2009
    “…Series resistors added to the inverter output to maintain same current in each inverter of the two parallel inverters, and to reduce the circulating current in the parallel inverters to the minimum. …”
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  14. 14
    Parallel backpropagation neural network training for face recognition

    Parallel backpropagation neural network training for face recognition by Omarov B., Suliman A., Tsoy A.

    Published 2023
    “…In this paper, we describe implementation of ANN training process using backpropagation learning algorithm for exploiting the high performance SIMD architecture of GPU using CUDA. …”
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  15. 15
    Distributed generation system using parallel inverters supplied by unstable DC source

    Distributed generation system using parallel inverters supplied by unstable DC source by Younis, M.A.A., Rahim, N.A., Mekhilef, Saad

    Published 2009
    “…A full distributed generation system has been implemented and tested, the testing of the system proves that the system operates with minimum circulating current on the parallel connected inverter and minimum harmonic distortion on the inverter output voltage and current. …”
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  16. 16
    Parallel power load abnormalities detection using fast density peak clustering with a hybrid canopy-K-means algorithm

    Parallel power load abnormalities detection using fast density peak clustering with a hybrid canopy-K-means algorithm by Al-Jumaili A.H.A., Muniyandi R.C., Hasan M.K., Singh M.J., Paw J.K.S., Al-Jumaily A.

    Published 2025
    “…The results showed that using canopy as a preprocessing step cut the time it proceeds to deal with the significant number of power load abnormalities found in parallel using a fast density peak dataset and the time it proceeds for the k-means algorithm to run. …”
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  17. 17
    Parallel batch self-organizing map on graphics processing unit using CUDA

    Parallel batch self-organizing map on graphics processing unit using CUDA by Daneshpajouh, H., Delisle, P., Boisson, J.-C., Krajecki, M., Zakaria, N.

    Published 2018
    “…The most computationally expensive parts of its training algorithm (such as steps to compute distance between each data vector and neuron, and determining the Best Matching Unit based on minimum distance) are identified and mapped on GPU to be processed in parallel. …”
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  18. 18
    Parallel batch self-organizing map on graphics processing unit using CUDA

    Parallel batch self-organizing map on graphics processing unit using CUDA by Daneshpajouh, H., Delisle, P., Boisson, J.-C., Krajecki, M., Zakaria, N.

    Published 2018
    “…The most computationally expensive parts of its training algorithm (such as steps to compute distance between each data vector and neuron, and determining the Best Matching Unit based on minimum distance) are identified and mapped on GPU to be processed in parallel. …”
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  19. 19
    Distributed generation with parallel connected inverter

    Distributed generation with parallel connected inverter by Younis, U.A.A., Rahirn, N.A., Mekhilef, Saad

    Published 2009
    “…Third harmonic injection PWM (THIPWM) reduces the total harmonic distortion and to make maximum use of the voltage source. DSP was used to generate the THIPWM and the control algorithm for the converter. …”
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  20. 20
    Parallel connected inverter for fuel cell system

    Parallel connected inverter for fuel cell system by Younis, M.A.A., Rahim, N.A., Mekhilef, Saad

    Published 2008
    “…Third harmonic injection PWM (THIPWM) reduces the total harmonic distortion and to make maximum use of the voltage source. DSP was used to create the THIPWM and the control algorithm for the converter. …”
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