Search Results - (( using optimization based algorithm ) OR ( using allocation parallel algorithm ))

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  1. 1
    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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  2. 2
    Application of intelligence based genetic algorithm for job sequencing problem on parallel mixed-model assembly line

    Application of intelligence based genetic algorithm for job sequencing problem on parallel mixed-model assembly line by Noroziroshan, Alireza, Mohd Ariffin, Mohd Khairol Anuar, Ismail, Napsiah

    Published 2010
    “…Conclusion/Recommendations: The results obtained from intelligence based genetic algorithm were used as an initial point for fine-tuning by simulated annealing to increase the quality of solution. …”
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  3. 3
    Dynamic partitioning and data allocation method on heterogeneous architecture / Muhammad Helmi Rosli

    Dynamic partitioning and data allocation method on heterogeneous architecture / Muhammad Helmi Rosli by Rosli, Muhammad Helmi

    Published 2015
    “…Each experiment highlight the advantages and disadvantages of the experimental architecture.The disadvantages from each experiment prompts the design of dynamic parallel partitioning and allocating framework. The case study use for this experiment is Sobel edge detection algorithm. …”
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  4. 4
    Load-Balancing Models for Scheduling Divisible Load on Large Scale Data Grids

    Load-Balancing Models for Scheduling Divisible Load on Large Scale Data Grids by Abduh Kaid, Monir Abdullah

    Published 2009
    “…The third stage integrates the proposed DLT based models with GA algorithm to solve the time consuming problem. …”
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  5. 5
    Deep Learning-Driven Mobility And Utility-Based Resource Management In Mm-Wave Enable Ultradense Heterogeneous Networks

    Deep Learning-Driven Mobility And Utility-Based Resource Management In Mm-Wave Enable Ultradense Heterogeneous Networks by Ukasyah bin Mahamod

    Published 2025
    thesis::doctoral thesis
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  6. 6
    Scheduling dynamic cellular manufacturing systems in the presence of cost uncertainty using heuristic method

    Scheduling dynamic cellular manufacturing systems in the presence of cost uncertainty using heuristic method by Delgoshaei, Aidin

    Published 2016
    “…Then, design of experiments is used to examine the sensitivity of the parameters of each solving algorithm using Taguchi method. …”
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  7. 7
    The division free parallel algorithm for finding determinant

    The division free parallel algorithm for finding determinant by Karim, Sharmila, Omar, Zurni, Ibrahim, Haslinda

    Published 2013
    “…A cross multiplication method for determinant was generalized for any size of square matrices using a new permutation strategy.The permutation is generated based on starter sets.However, via permutation, the time execution of sequential algorithm became longer.Thus, in order to reduce the computation time, a parallel strategy was developed which is suited for master and slave paradigm of the high performance computer.A parallel algorithm is integrated with message passing interface.The numerical results showed that the parallel methods computed the determinants faster than the sequential counterparts particularly when the tasks were equally allocated.…”
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  8. 8
    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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  9. 9
    Parallel guided image processing model for ficus deltoidea (Jack) moraceae varietal recognition

    Parallel guided image processing model for ficus deltoidea (Jack) moraceae varietal recognition by Abdul Rahman, Prof. Dr. Mohd Nordin

    Published 2018
    “…From the results, the sequential complex image processing model and computational flow design are significantly improved when executed through parallel model under multi-cores computer system. As the number of cores increases, the computational time taken by the parallel algorithm becomes less. © 2018, Springer Nature Singapore Pte Ltd.…”
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  10. 10
    Optimization of Workload Allocation Problem in a Network of Heterogeneous Computer Systems

    Optimization of Workload Allocation Problem in a Network of Heterogeneous Computer Systems by Rahela, Abdul Rahim

    Published 2005
    “…Multiple Queue Multiple Server Queueing models are used to model workload allocation problems in a network of computers. …”
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  11. 11
    New Sequential and Parallel Division Free Methods for Determinant of Matrices

    New Sequential and Parallel Division Free Methods for Determinant of Matrices by Sharmila, Karim

    Published 2013
    “…Numerical results showed that the parallel methods were able to compute determinants faster than the sequential counterparts, particularly when the tasks were equally allocated. …”
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  12. 12
    Image reconstruction using iterative transpose algorithm for optical tomography

    Image reconstruction using iterative transpose algorithm for optical tomography by Md. Yunos, Yusri, Abd. Rahim, Ruzairi, Green, R. G., Fazalul Rahiman, Mohd. Hafiz

    Published 2007
    “…This paper describes a transpose algorithm for use with an optical tomography system. …”
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  13. 13
    Improving energy consumption in cloud computing datacenters using a combination of energy-aware resource allocation and scheduling mechanism

    Improving energy consumption in cloud computing datacenters using a combination of energy-aware resource allocation and scheduling mechanism by Khalil Abd., Sura

    Published 2017
    “…Nowadays, DNA plays a vital role in many computing applications due to the massive processing parallelism. In addition, using fuzzy theory in genetic algorithm reduces the iteration of producing the population and assigning the suitable resources to the tasks-based and task length in the node capacity. …”
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  14. 14
    Simulated Kalman Filter algorithms for solving optimization problems

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

    Published 2019
    “…The proposed population-based SKF algorithm and the single solution-based SKF algorithm use the scalar model of discrete Kalman filter algorithm as the search strategy to overcome these flaws. …”
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  15. 15
    Parallel guided image processing model for ficus deltoidea (Jack) moraceae varietal recognition

    Parallel guided image processing model for ficus deltoidea (Jack) moraceae varietal recognition by Ahmad Fakhri, Ab. Nasir, Ahmad Shahrizan, Abdul Ghani, M. Nordin, A. Rahman

    Published 2018
    “…From the results, the sequential complex image processing model and computational flow design are significantly improved when executed through parallel model under multi-cores computer system. As the number of cores increases, the computational time taken by the parallel algorithm becomes less.…”
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  16. 16
    Sizing optimization of large-scale grid connected photovoltaic system using dolphin echolocation algorithm / Muhammad Zakyizzuddin Rosselan

    Sizing optimization of large-scale grid connected photovoltaic system using dolphin echolocation algorithm / Muhammad Zakyizzuddin Rosselan by Rosselan, Muhammad Zakyizzuddin

    Published 2018
    “…Later, an Iterative-based Sizing Algorithm (ISA) was developed to determine the optimal sizing solution which was later used as benchmark for sizing algorithms using optimization methods. …”
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  17. 17
    An improved method using fuzzy system based on hybrid boahs for phishing attack detection

    An improved method using fuzzy system based on hybrid boahs for phishing attack detection by Noor Syahirah, Nordin

    Published 2022
    “…The algorithms involved were Genetic Algorithm, Differential Evolution Algorithm, Particle Swarm Optimization, Butterfly Optimization Algorithm, Teaching-Learning-Based Optimization Algorithm, Harmony Search Algorithm and Gravitational Search Algorithm. …”
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  18. 18
    Optimization of cost-based hybrid flowshop scheduling using teaching-learning-based optimization algorithm

    Optimization of cost-based hybrid flowshop scheduling using teaching-learning-based optimization algorithm by Ullah, Wasif, Muhammad Ammar, Nik Mu’tasim, Mohd Fadzil Faisae, Ab Rashid

    Published 2024
    “…This paper investigates the optimization of a CHFS problem using the Teaching Learning-Based Optimization (TLBO) algorithm. …”
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  19. 19
    An enhanced swap sequence-based particle swarm optimization algorithm to solve TSP

    An enhanced swap sequence-based particle swarm optimization algorithm to solve TSP by Bibi Aamirah Shafaa Emambocus, Muhammed Basheer Jasser, Muzaffar Hamzah, Aida Mustapha, Angela Amphawan

    Published 2021
    “…Since there is no known polynomial-time algorithm for solving large scale TSP, metaheuristic algorithms such as Ant Colony Optimization (ACO), Bee Colony Optimization (BCO), and Particle Swarm Optimization (PSO) have been widely used to solve TSP problems through their high quality solutions. …”
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  20. 20
    Rule-Based Multi-State Gravitational Search Algorithm for Discrete Optimization Problem

    Rule-Based Multi-State Gravitational Search Algorithm for Discrete Optimization Problem by Ismail, Ibrahim, Zuwairie, Ibrahim, Zulkifli, Md. Yusof

    Published 2015
    “…In this study, rule-based multi-state gravitational search algorithm (RBMSGSA) algorithm is proposed to solve discrete combinatorial optimization problems. …”
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