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Ant colony optimization algorithm for load balancing in grid computing
Published 2012“…This research proposes an enhancement of the ant colony optimization algorithm that caters for dynamic scheduling and load balancing in the grid computing system. …”
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Monograph -
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Enhancement of Ant Colony Optimization for Grid Job Scheduling and Load Balancing
Published 2011“…A simulation environment was developed using Java programming to test the performance of the proposed EACO algorithm against existing grid resource management algorithms such as Antz algorithm, Particle Swarm Optimization algorithm, Space Shared algorithm and Time Shared algorithm, in terms of processing time and resource utilization. …”
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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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Development of Algorithm for QoS Model in Cloud Computing
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Investigating and developing the best method in shortest path for implementing a geographical information system (e-map) for Peninsular Malaysia
Published 2005“…Assessment will be done by calculating the algorithm complexion and runtime using the computer. …”
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An Effective Fast Searching Algorithm for Internet Crawling Usage
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Weed recognition based on erosion and dilation segmentation algorithm
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Intelligent agent for e-commerce using genetic algorithm / Kok Sun Sun
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Pengesan lalu lintas dengan kaedah pemerosesan imej
Published 2001“…This study was conducted to develop efficient traffic detector using image-processing with a personal compute I: Traffic images were captured using ordinary analogue video camera and digitised using a video blastel: Four algorithms were developed i. e. algorithm for traffic counting, algorithm for vehicle classification. algorithm to determine the speed of the counted vehicles and algorithm for incident detection. …”
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A hybrid of Simple Constrained artificial bee colony algorithm and flux balance analysis for enhancing Lactate and Succinate in Escherichia Coli
Published 2018“…In this research, a hybrid algorithm is developed to seek optimal solutions in order to increase the overproduction of lactate and succinate by gene knockout in E. coli. …”
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Book Chapter -
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Comprehensive review of the development of the harmony search algorithm and its applications
Published 2019“…This paper presents a comprehensive overview of the development of the harmony search (HS) algorithm and its applications. …”
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Design and development of hybrid integrated thermal aware job scheduling on computational grid environment
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Hybrid scheduling and dual queue scheduling
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Identifying a Gene Knockout Strategy Using a Hybrid of Simple Constrained Artificial Bee Colony Algorithm and Flux Balance Analysis to Enhance the Production of Succinate and Lacta...
Published 2019“…Due to this, many computational approaches that perform genetic manipulation have been developed in the computational biology field. …”
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