Search Results - (( problem using ((grey algorithm) OR (graph algorithm)) ) OR ( java application using algorithm ))

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  1. 1

    Multi-objective pareto ant colony system based algorithm for generator maintenance scheduling by Shimailawi, Shatha Abdulhadi Muthana

    Published 2022
    “…The proposed models and algorithm can be used to solve the multi-objective GMS problem while the new parameters’ values can be used to obtain optimal or near optimal maintenance scheduling of generators. …”
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    Thesis
  2. 2

    Enhancement of Ant Colony Optimization for Grid Job Scheduling and Load Balancing by Husna, Jamal Abdul Nasir

    Published 2011
    “…The resource allocation problem is modeled as a graph that can be used by the ant to deliver its pheromone. …”
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  3. 3

    Ant colony optimization algorithm for load balancing in grid computing by Ku-Mahamud, Ku Ruhana, Mohamed Din, Aniza

    Published 2012
    “…The resource allocation problem is modelled as a graph that can be used by the ant to deliver its pheromone.This graph consists of four types of vertices which are job, requirement, resource and capacity that are used in constructing the grid resource management element. …”
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    Monograph
  4. 4

    Resource management in grid computing using ant colony optimization by Ku-Mahamud, Ku Ruhana, Mohamed Din, Aniza

    Published 2011
    “…Managing resources in grid computing system is complicated due to the distributed and heterogeneous nature of the resources.Stagnation in grid computing system may occur when all jobs require or are assigned to the same resources which lead to the resources having high workload or the time taken to process a job is high.This research proposes an Enhanced Ant Colony Optimization (EACO) algorithm that caters dynamic scheduling and load balancing in the grid computing system.The algorithm consists of three new mechanisms that organize the work of an ant colony i.e. initial pheromone value mechanism, resource selection mechanism and pheromone update mechanism.The resource allocation problem is modeled as a graph that can be used by the ant to deliver its pheromone.This graph consists of four types of vertices which are job, requirement, resource and capacity that are used in constructing the grid resource management element.The proposed EACO algorithm takes into consideration the capacity of resources and the characteristics of jobs in determining the best resource to process a job.EACO selects the resources based on the pheromone value on each resource which is recorded in a matrix form.The initial pheromone value of each resource for each job is calculated based on the estimated transmission time and execution time of a given job. …”
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    Monograph
  5. 5

    Extrema Points Application In Determining Iris Region Of Interest by Othman, Zuraini, Kasmin, Fauziah, Syed Ahmad, Sharifah Sakinah, Abdullah, Azizi

    Published 2019
    “…Hence, to address this issue, this paper proposed a method of iris localisation in the case of ideal and non-ideal iris images. In this study, the algorithm was based on finding the classification for the region of interest (ROI) with the help of a Support Vector Machine (SVM) by applying a histogram of grey level values as a descriptor in each region from the region growing technique. …”
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    Article
  6. 6

    An Application of Grey Wolf Optimizer for Solving Combined Economic Emission Dispatch Problems by Hong, Mee Song, M. H., Sulaiman, Mohd Rusllim, Mohamed

    Published 2014
    “…Grey Wolf Optimizer (GWO) is a newly proposed algorithm that developed based on inspiration of grey wolves (Canis Lupus). …”
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    Article
  7. 7

    Hybrid Harmony Search Algorithm with Grey Wolf Optimizer and Modified Opposition-based Learning by Alomoush, Alaa A., Alsewari, Abdulrahman A., Alamri, Hammoudeh S., Aloufi, Khalid, Kamal Z., Zamli

    Published 2019
    “…Many variants have been developed to cope with this problem and improve algorithm performance. In this paper, a hybrid algorithm of HS with grey wolf optimizer (GWO) has been developed to solve the problem of HS parameter selection. …”
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    Article
  8. 8

    Optimization Of Pid Controller Using Grey Wolf Optimzer And Dragonfly Algorithm by Nik Mohamed Hazli, Nik Muhammad Aiman

    Published 2018
    “…In this research, swarming intelligence is used to solve optimisation problem. Grey Wolf Optimizer and Dragonfly Algorithm were chosen. …”
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    Monograph
  9. 9
  10. 10

    Optimal Overcurrent Relays Coordination using an Improved Grey Wolf Optimizer by Noor Zaihah, Jamal, Mohd Herwan, Sulaiman, Omar, Aliman, Zuriani, Mustaffa

    Published 2018
    “…The optimization is performed using the Improved Grey Wolf Optimization (IGWO) algorithm. …”
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    Article
  11. 11
  12. 12

    Graph processing hardware accelerator for shortest path algorithms in nanometer very large-scale integration interconnect routing by Ch'ng, Heng Sun

    Published 2007
    “…Many challenging problems in Very Large-Scale Integration (VLSI) physical design automation are modeled using graphs. …”
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  13. 13

    Inversed Control Parameter in Whale Optimization Algorithm and Grey Wolf Optimizer for Wrapper-Based Feature Selection: A Comparative Study by Li Yu Yab, Li Yu Yab, Wahid, Noorhaniza, A Hamid, Rahayu

    Published 2023
    “…—Whale Optimization Algorithm (WOA) and Grey Wolf Optimizer (GWO) are well-perform metaheuristic algorithms used by various researchers in solving feature selection problems. …”
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    Article
  14. 14

    Inversed Control Parameter in Whale Optimization Algorithm and Grey Wolf Optimizer for Wrapper-Based Feature Selection: A Comparative Study by Li Yu Yab, Li Yu Yab, Wahid, Noorhaniza, A Hamid, Rahayu

    Published 2023
    “…Whale Optimization Algorithm (WOA) and Grey Wolf Optimizer (GWO) are well-perform metaheuristic algorithms used by various researchers in solving feature selection problems. …”
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    Article
  15. 15

    Opposition Based Competitive Grey Wolf Optimizer For EMG Feature Selection by Too, Jing Wei, Abdullah, Abdul Rahim

    Published 2020
    “…Four state-of-the-art algorithms include particle swarm optimization, flower pollination algorithm, butterfly optimization algorithm, and CBGWO are used to examine the effectiveness of proposed methods in feature selection. …”
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    Article
  16. 16

    Graph algorithm vertex coloring by Suyudi, M., Sukono, ., Mustafa, Mamat, Bon, A.T.

    Published 2019
    “…Graph coloring and its generalizations are useful tools in modelling a wide variety of scheduling and assignment problems.…”
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    Conference or Workshop Item
  17. 17

    On some packing and partition problems in geometric graphs by Trao, Hazim Michman

    Published 2018
    “…Graph packing problem refers to the problem of finding maximum number of edge-disjoint copies of a fixed subgraph in a given graph G. …”
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  18. 18

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

    Published 2024
    “…Although the swmcan algorithm solves the noise problem in multi-view data, its initial and final graphs are independent and cannot learn from each other. …”
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    Branch and bound algorithm for finding the maximum clique problem by Suyudi, M., Sukono, ., Mamat, M., Bon, A.T.

    Published 2018
    “…We present a branch and bound algorithm for the maximum clique problem in arbitrary graphs. …”
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    Conference or Workshop Item