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Ant colony algorithm for job scheduling in grid computing
Published 2010“…Scheduling jobs to resources in grid computing 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.This will lead to resourccs having high workload and stagnation may occur if computational times of the processed jobs are high.This paper proposed an enhanced ant colony optimization algorithm for jobs and resources scheduling in grid computing.The proposed ant colony algorithm for job scheduling in the grid environment combines the techniques from Ant Colony System and Max - Min Ant System.The algorithm focuses on local pheromone trail update and the trail limit values. …”
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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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Ant colony optimization algorithm for dynamic scheduling of jobs in computational grid
Published 2012“…Job scheduling problem is classified as an NP-hard problem.Such a problem can be solved only by using approximate algorithms such as heuristic and meta-heuristic algorithms.Among different optimization algorithms for job scheduling, ant colony system algorithm is a popular meta-heuristic algorithm which has the ability to solve different types of NP-hard problems.However, ant colony system algorithm has a deficiency in its heuristic function which affects the algorithm behavior in terms of finding the shortest connection between edges.This research focuses on a new heuristic function where information about recent ants’ discoveries has been considered.The new heuristic function has been integrated into the classical ant colony system algorithm.Furthermore, the enhanced algorithm has been implemented to solve the travelling salesman problem as well as in scheduling of jobs in computational grid.A simulator with dynamic environment feature to mimic real life application has been development to validate the proposed enhanced ant colony system algorithm. …”
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Comparison between ant colony and genetic algorithm using traveling salesman problem
Published 2013“…The aim of this study is compare the effect of using two distributed algorithm which are ant colony as a Swarm intelligence algorithm and genetic algorithm. …”
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Scheduling jobs in computational grid using hybrid ACS and GA approach
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Optimizing large scale combinatorial problems using multiple ant colonies algorithm based on pheromone evaluation technique
Published 2008“…The new algorithm can effectively be used to tackle large scale optimization problems.Computational tests show promises of the new algorithm.…”
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Low and high level hybridization of ant colony system and genetic algorithm for job scheduling in grid computing
Published 2015“…Hybrid metaheuristic algorithms have the ability to produce better solution than stand-alone approach and no algorithm could be concluded as the best algorithm for scheduling algorithm or in general, for combinatorial problems.This study presents the low and high level hybridization of ant colony system and genetic algorithm in solving the job scheduling in grid computing.Two hybrid algorithms namely ACS(GA) as a low level and ACS+GA as a high level are proposed.The proposed algorithms were evaluated using static benchmarks problems known as expected time to compute model. …”
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Enhancement of Ant System Algorithm for Course Timetabling Problem
Published 2009“…As the requirement of the Ant System Algorithm, the problem is modeled as a graph that can be used by the ant to deliver its pheromone. …”
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New heuristic function in ant colony system for job scheduling in grid computing
Published 2012“…Job scheduling is one of the main factors affecting grid computing performance. Job scheduling problem classified as an NP-hard problem.Such a problem can be solved only by using approximate algorithms such as heuristic and meta-heuristic algorithms.Ant colony system algorithm is a meta-heuristic algorithm which has the ability to solve different types of NP-hard problems.However, ant colony system algorithm has a deficiency in its heuristic function which affects the algorithm behavior in terms of finding the shortest connection between edges.This paper focuses on enhancing the heuristic function where information about recent ants’ discoveries will be taken into account.Experiments were conducted using a simulator with dynamic environment features to mimic the grid environment.Results show that the proposed enhanced algorithm produce better output in term of utilization and make span.…”
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Heuristic factors in ant system algorithm for course timetabling problem
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Stagnation control using interacted multiple ant colonies
Published 2009“…Computational tests show that the proposed approach is competitive with other state of art ant algorithms.…”
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Load balancing using enhanced ant algorithm in grid computing
Published 2010“…Load balancing is one of the critical issues that must he considered in managing a grid computing environment.It is complicated due to the distributed and heterogeneous nature of the resources.An enhanced ant algorithm for load balancing in grid computing is proposed in this papcr.The proposed algorithm will determine the best resource to he allocated to the jobs based on job characteristics and resource capacity, and at the same time to balance the entire resources.The proposed algorithm focuses on local pheromone trail update and trail limit. …”
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Grid load balancing using enhance ant colony optimization
Published 2011“…This study presents a new algorithm based on ant colony optimization for load balancing management in grid computing. …”
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Food tour recommendation using modified ant colony algorithm
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New heuristic function in ant colony system for the travelling salesman problem
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Hybrid ant colony system and genetic algorithm approach for scheduling of jobs in computational grid
Published 2015“…Metaheuristic algorithms have been used to solve scheduling problems in grid computing.However, stand-alone metaheuristic algorithms do not always show good performance in every problem instance. …”
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