Search Results - (( java simulation optimization algorithm ) OR ( using rough using algorithm ))
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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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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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3
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 -
4
OPTIMIZED MIN-MIN TASK SCHEDULING ALGORITHM FOR SCIENTIFIC WORKFLOWS IN A CLOUD ENVIRONMENT
Published 2023“…To achieve this, we propose a new noble mechanism called Optimized Min-Min (OMin-Min) algorithm, inspired by the Min-Min algorithm. …”
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Validation on 3D surface roughness algorithm for measuring roughness of psoriasis lesion
Published 2010“…Roughness index is calculated by using average roughness equation to the height map matrix. …”
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Thin Film Roughness Optimization In The Tin Coatings Using Genetic Algorithms
Published 2017“…Genetic algorithms were used in the optimization work of the coating process to optimize the coating roughness parameters. …”
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Embedded system for indoor guidance parking with Dijkstra’s algorithm and ant colony optimization
Published 2019“…This study proposes a car parking management system which applies Dijkstra’s algorithm, Ant Colony Optimization (ACO) and Binary Search Tree (BST) in structuring a guidance system for indoor parking. …”
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A comparison on optimization of surface roughness in machining AISI 1045 steel using Taguchi method, genetic algorithm and particle swarm optimization
“…While, particle swarm optimization give the lowest surface roughness when compared to Taguchi method and genetic algorithm and it can optimize faster than genetic algorithm.…”
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Features selection for intrusion detection system using hybridize PSO-SVM
Published 2016“…Hybridize Particle Swarm Optimization (PSO) as a searching algorithm and support vector machine (SVM) as a classifier had been implemented to cope with this problem. …”
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10
Surface roughness optimization in end milling using the multi objective genetic algorithm approach
Published 2012“…This paper presents the optimization of machining parameters in end milling processes by integrating the genetic algorithm (GA) with the statistical approach. Two objectives have been considered, minimum arithmetic mean roughness (Ra) and minimum Root-mean-square roughness (Rq). …”
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A GPGPU Approach to Accelerate Ant Swarm Optimization Rough Reducts (ASORR) Algorithm
Published 2012“…In order to find a reducts, a such applications of rough set uses a discernibility matrix. Ant Swarm Optimization Rough Reducts (ASORR) algorithm is used in rough reducts calculation for identifying significant attribute set optimally. …”
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Cutting temperature and surface roughness optimization in CNC end milling using multi objective genetic algorithm
Published 2012“…The mathematical models for the cutting temperature and surface roughness parameters have been developed, in terms of cutting speed, feed rate, and axial depth of cut by using Response Methodology Method (RSM). …”
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Optimisation of Surface Roughness in 3D Printing Using the Bees Algorithm
Published 2025“…The focus of this study is to determine the optimum input parameter of the 3D printer using the Bees Algorithm (BA). This study uses the Bees Algorithm to predict the best combination parameters to optimise the surface roughness of parts printed by a fused deposition modelling (FDM) machine. …”
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Book Chapter -
15
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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Monograph -
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Cutting Parameters Optimization of Mild Steel via AIS Heuristics Algorithm
Published 2014“…An infrared camera type (Flir E60) used for temperature measurement and a portable surface roughness device used for roughness measurement. …”
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Improvement of surface roughness in end milling of Ti6Al4V by coupling RSM with genetic algorithm
Published 2011“…MATLAB 7.0 toolbox for GA is used to develop GA program. The predicted results are in good agreement with the experimental one and hence the model can be efficiently used to achieve the minimum surface roughness value. …”
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Prediction of Optimum Cutting Conditions in Dry Turning Operations of S45C Mild Steel using AIS and PSO Intelligent Algorithm
Published 2014“…An infrared camera (Flir E60), a lathe tool dynamometer model USL-15 and a portable surface roughness device were respectively used to measure temperatures, cutting forces and surface roughness. …”
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3D surface roughness measurement for scaliness scoring of psoriasis lesions
Published 2013“…Clustering algorithms were used to classify the surface roughness measured using the proposed assessment method for PASI scaliness scoring. …”
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Optimization of cnc turning parameters for minimizing temperature rise in aluminum using a genetic algorithm
Published 2024“…To minimize the temperature rise during machining, the cutting speed, feed rate, depth of cut, and nose radius are optimized in this study using a single-objective genetic algorithm. In the second optimization process, machining parameters such as cutting speed, feed rate, and depth of cut are optimized using a multi-objective genetic algorithm to concurrently lower temperature rise and surface roughness. …”
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