Search Results - parallel selection ((based algorithm) OR (modified algorithm))*

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  1. 1
    Parallel algorithms for numerical simulations of EHD ion-drag micropump on distributed parallel computing systems

    Parallel algorithms for numerical simulations of EHD ion-drag micropump on distributed parallel computing systems by Shakeel Ahmed, Kamboh

    Published 2014
    “…The DPA-EHD is further modified by utilizing the pipelining parallelism to reduce the computing iterations and named as data parallel and pipelining algorithm (DPPA-EHD). …”
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  2. 2
    Parallel computation of maass cusp forms using mathematica

    Parallel computation of maass cusp forms using mathematica by Chan, Kar Tim

    Published 2013
    “…Our parallel programme comprises of two important parts namely the pullback algorithm and also the Maass cusp form algorithm. …”
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  3. 3
    OPTIMIZATION OF HYBRID-FUZZY CONTROLLER FOR SERVOMOTOR CONTROL USING A MODIFIED GENETIC ALGORITHM

    OPTIMIZATION OF HYBRID-FUZZY CONTROLLER FOR SERVOMOTOR CONTROL USING A MODIFIED GENETIC ALGORITHM by WAHYUNGGORO, OYAS WAHYUNGGORO

    Published 2011
    “…In this thesis, a new optimization GA-based algorithm that emanates from modification of conventional GA to reduce the iterations number and the duration time, namely, semi-parallel operation genetic algorithm (SPOGA) is proposed. …”
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  4. 4
    A New Technique To Design Coating Structure For Energy Saving Glass Using The Genetic Algorithm

    A New Technique To Design Coating Structure For Energy Saving Glass Using The Genetic Algorithm by Azmin, Farah Ayuni

    Published 2017
    “…This research proposed a modified regular shape which is based on the best selected regular shape, modified using genetic algorithm on the central processing unit (CPU). …”
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  5. 5
    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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  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
    Exploring the High Performance Computing-Enablement of a Suite of Gene-Knockout Based Genetic Engineering Applications

    Exploring the High Performance Computing-Enablement of a Suite of Gene-Knockout Based Genetic Engineering Applications by Li, Zhenya, Sinnott, Richard O., Choon, Yee Wen, Sjaugi, Muhammad Farhan, Mohd Saberi, Mohamad, Safaai, Deris, Suhaimi, Napis, Omatu, Sigeru, Corchado, Juan Manuel, Zuwairie, Ibrahim, Zulkifli, Md. Yusof

    Published 2016
    “…Genetic engineering provides methods to modify the genes of microorganisms to achieve desired effects. …”
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  8. 8
    New methods of partial transmit sequence for reducing the high peak-to-average-power ratio with low complexity in the ofdm and f-ofdm systems

    New methods of partial transmit sequence for reducing the high peak-to-average-power ratio with low complexity in the ofdm and f-ofdm systems by Abduljabbar, Yasir Amer

    Published 2019
    “…Secondly, two novel algorithms, Grouping Complex iterations algorithm (G-C-PTS), and Gray Code Phase Factor algorithm (Gray-PF-PTS) have been developed to reduce the computational complexity for finding the optimum phase rotation factors in the time domain part of the PTS structure. …”
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  9. 9
    A review of genetic algorithms and parallel genetic algorithms on Graphics Processing Unit (GPU)

    A review of genetic algorithms and parallel genetic algorithms on Graphics Processing Unit (GPU) by Mohd Johar, Fauzi, Azmin, Farah Ayuni, Suaidi, Mohd Kadim, Shibghatullah, Abdul Samad, Ahmad, Badrul Hisham, Salleh, Siti Nadzirah, Abdul Aziz, Mohd Zainol Abidin, Md Shukor, Mahfuzah

    Published 2013
    “…GAs is one of the optimization tools used widely in solving problems based on natural selection and genetics. This paper is intended to cover the study of GA and parallel GA and analyses its usage in CPU and GPU. …”
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  10. 10
    Hybrid harmony search-artificial intelligence models in credit scoring

    Hybrid harmony search-artificial intelligence models in credit scoring by Goh, Rui Ying

    Published 2019
    “…Then, the two types of features importance computed from RF algorithm are utilized for the attributes explanation. …”
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  11. 11
    An integrated priority-based cell attenuation model for dynamic cell sizing

    An integrated priority-based cell attenuation model for dynamic cell sizing by Amphawan, Angela, Omar, Mohd Nizam, Din, Roshidi

    Published 2012
    “…A new, robust integrated priority-based cell attenuation model for dynamic cell sizing is proposed and simulated using real mobile traffic data.The proposed model is an integration of two main components; the modified virtual community – parallel genetic algorithm (VC-PGA) cell priority selection module and the evolving fuzzy neural network (EFuNN) mobile traffic prediction module.The VC-PGA module controls the number of cell attenuations by ordering the priority for the attenuation of all cells based on the level of mobile level of mobile traffic within each cell.The EFuNN module predicts the traffic volume of a particular cell by extracting and inserting meaningful rules through incremental, supervised real-time learning.The EFuNN module is placed in each cell and the output, the predicted mobile traffic volume of the particular cell, is sent to local and virtual community servers in the VC-PGA module.The VC-PGA module then assigns priorities for the size attenuation of all cells within the network, based on the predicted mobile traffic levels from the EFuNN module at each cell.The performance of the proposed module was evaluated on five adjacent cells in Selangor, Malaysia. …”
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  12. 12
    Automated plant recognition system based on multi-objective parallel genetic algorithm and neural network

    Automated plant recognition system based on multi-objective parallel genetic algorithm and neural network by Sefidgar, Seyed Mohammad Hossein

    Published 2014
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  13. 13
    Image classification using two dimensional wavelet coefficients with parallel computing

    Image classification using two dimensional wavelet coefficients with parallel computing by Ong, Yew Fai

    Published 2020
    “…This research algorithm demonstrated a very promising result with Support Vector Machines, this algorithm produces a 90% of accuracies whereas the decision tree algorithm gets 100% accuracies. …”
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  14. 14
    Diverse cell interaction in dynamic cell sizing and VC-PGA cell priority selection method

    Diverse cell interaction in dynamic cell sizing and VC-PGA cell priority selection method by Amphawan, Angela, G, Ezra Moris Abraham

    Published 2002
    “…In this paper, the diverse interaction among cells is discussed.The preliminary description of forward link capacity gain on a single cell serves as groundwork for discussions on bi-directional impact of cell-pairs and the multifarious interactions of cells in a CDMA network.The diverse interaction of cells is then made analogous to that of community members in a virtual community in a genetic algorithm.The cell to be given highest priority cell over shrinking in the virtual community is selected by the virtual community parallel genetic algorithm (VC-PGA).By this method, the number of cell attenuations may be controlled, the emergence of coverage holes reduced and thus, the quality of service increased.…”
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  15. 15
    PMT : opposition based learning technique for enhancing metaheuristic algorithms performance

    PMT : opposition based learning technique for enhancing metaheuristic algorithms performance by Hammoudeh, S. Alamri

    Published 2020
    “…Like existing OBL-based approaches, the PMT generates new potential solutions based on the currently selected candidate. …”
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  16. 16
    Improved TLBO-JAYA Algorithm for Subset Feature Selection and Parameter Optimisation in Intrusion Detection System

    Improved TLBO-JAYA Algorithm for Subset Feature Selection and Parameter Optimisation in Intrusion Detection System by Aljanabi, Mohammad, Mohd Arfian, Ismail, Mezhuyev, Vitaliy

    Published 2020
    “…The proposed method combined the improved teaching-learning-based optimisation (ITLBO) algorithm, improved parallel JAYA (IPJAYA) algorithm, and support vector machine. …”
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  17. 17
    PMT: opposition-based learning technique for enhancing meta-heuristic performance

    PMT: opposition-based learning technique for enhancing meta-heuristic performance by Alamri, Hammoudeh S., Kamal Z., Zamli

    Published 2019
    “…Like existing OBL-based approaches, the PMT generates new potential solutions based on the currently selected candidate. …”
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  18. 18
    A Parallel-Model Speech Emotion Recognition Network Based on Feature Clustering

    A Parallel-Model Speech Emotion Recognition Network Based on Feature Clustering by Li-Min Zhang, Giap Weng Ng, Yu-Beng Leau, Hao Yan

    Published 2023
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  19. 19
    A parallel-model speech emotion recognition network based on feature clustering

    A parallel-model speech emotion recognition network based on feature clustering by Li-Min Zhang, Giap Weng Ng, Yu-Beng Leau, Hao Yan

    Published 2023
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  20. 20
    The Parallel Fuzzy C-Median Clustering Algorithm Using Spark for the Big Data

    The Parallel Fuzzy C-Median Clustering Algorithm Using Spark for the Big Data by Mallik, Moksud Alam, Zulkurnain, Nurul Fariza, Siddiqui, Sumrana, Sarkar, Rashel

    Published 2024
    “…Therefore, we develop a Parallel Fuzzy C-Median Clustering Algorithm Using Spark for Big Data that can handle large datasets while maintaining high accuracy and scalability. …”
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