Search Results - (( developing application ant algorithm ) OR ( java simulation optimization algorithm ))
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Ant colony optimization algorithm for load balancing in grid computing
Published 2012“…The proposed algorithm is known as the enhance ant colony optimization (EACO). …”
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Monograph -
2
Enhancement of Ant Colony Optimization for Grid Job Scheduling and Load Balancing
Published 2011“…Global pheromone update is performed after the completion of processing the jobs in order to reduce the pheromone value of resources. 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“…Resources with high pheromone value are selected to process the submitted jobs.Global pheromone update is performed after completion processing the jobs in order to reduce the pheromone value of resources.A simulation environment was developed using Java programming to test the performance of the proposed EACO algorithm against other ant based algorithm, in terms of resource utilization.Experimental results show that EACO produced better grid resource management solution.…”
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An adaptive ant colony optimization algorithm for rule-based classification
Published 2020“…Classification is an important data mining task with different applications in many fields. Various classification algorithms have been developed to produce classification models with high accuracy. …”
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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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Monograph -
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DACS3:Embedding Individual Ant Behavior in Ant Colony System
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Ant system with heuristics for capacitated vehicle routing problem
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DACS3: Embedding Malaysian Individual Ant Behavior in Ant Colony System
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Improved Dynamic Ant Colony System (DACS) on Symmetric Traveling Salesman Problem (TSP).
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Attribute reduction based scheduling algorithm with enhanced hybrid genetic algorithm and particle swarm optimization for optimal device selection
Published 2022“…The simulation is implemented with iFogSim and java programming language. …”
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Hybrid ant colony optimization algorithm for container loading problem
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Embedding Malaysian House Red Ant Behavior into an Ant Colony System
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Citation Index Journal -
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Optimization of milling parameters using ant colony optimization
Published 2008“…An example to apply the Ant Colony Algorithm to the problem has been presented at the end of the paper to give clear picture from the application of the system and its efficiency. …”
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Undergraduates Project Papers -
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Reducing Iteration Using Candidate List
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Application of Moth-Flame Optimizer and Ant Lion Optimizer to Solve Optimal Reactive Power Dispatch Problems
Published 2018“…This paper presents the application of two nature-inspired meta-heuristic algorithms, namely moth-flame optimizer (MFO) and ant lion optimizer (ALO) in obtaining the optimal settings of control variables for solving optimal reactive power dispatch (ORPD) problems. …”
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Improvement DACS3 Searching Performance using Local Search
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