Search Results - (( problem using ((grey algorithm) OR (graph algorithm)) ) OR ( java application using algorithm ))
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Multi-objective pareto ant colony system based algorithm for generator maintenance scheduling
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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Enhancement of Ant Colony Optimization for Grid Job Scheduling and Load Balancing
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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Ant colony optimization algorithm for load balancing in grid computing
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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Resource management in grid computing using ant colony optimization
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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Extrema Points Application In Determining Iris Region Of Interest
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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An Application of Grey Wolf Optimizer for Solving Combined Economic Emission Dispatch Problems
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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Hybrid Harmony Search Algorithm with Grey Wolf Optimizer and Modified Opposition-based Learning
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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Optimization Of Pid Controller Using Grey Wolf Optimzer And Dragonfly Algorithm
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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Optimal Overcurrent Relays Coordination using an Improved Grey Wolf Optimizer
Published 2018“…The optimization is performed using the Improved Grey Wolf Optimization (IGWO) algorithm. …”
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Graph processing hardware accelerator for shortest path algorithms in nanometer very large-scale integration interconnect routing
Published 2007“…Many challenging problems in Very Large-Scale Integration (VLSI) physical design automation are modeled using graphs. …”
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Inversed Control Parameter in Whale Optimization Algorithm and Grey Wolf Optimizer for Wrapper-Based Feature Selection: A Comparative Study
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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Inversed Control Parameter in Whale Optimization Algorithm and Grey Wolf Optimizer for Wrapper-Based Feature Selection: A Comparative Study
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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Opposition Based Competitive Grey Wolf Optimizer For EMG Feature Selection
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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Graph algorithm vertex coloring
Published 2019“…Graph coloring and its generalizations are useful tools in modelling a wide variety of scheduling and assignment problems.…”
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On some packing and partition problems in geometric graphs
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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Graph-Based Algorithm With Self-Weighted And Adaptive Neighbours Learning For Multi-View Clustering
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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Ant colony optimization based subset feature selection in speech processing: Constructing graphs with degree sequences
Published 2024journal::journal article -
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Branch and bound algorithm for finding the maximum clique problem
Published 2018“…We present a branch and bound algorithm for the maximum clique problem in arbitrary graphs. …”
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