Search Results - (( java simulation optimization algorithm ) OR ( _ integration case algorithm ))
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1
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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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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5
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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6
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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7
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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Implementation of locust inspired scheduling algorithm with huge number of servers for energy efficiency in a cloud datacenter
Published 2019“…Cloudsim is used as Discrete Event Simulation tool and Java as coding language to evaluate LACE algorithm. …”
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9
Hybrid Spatial-Artificial Intelligence Approach for Renewable Energy Sources Sites Identification and Integration in Sarawak State
Published 2022“…In each case, the Traveling Salesman Problem-Genetic Algorithm (TSP-GA) algorithm is applied to determine a minimum total distance of RES sites integration. …”
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10
SG-PBFS : Shortest Gap-Priority Based Fair Scheduling technique for job scheduling in cloud environment
Published 2024“…To conduct this experiment, we employed the CloudSim simulator, which is implemented using the Java programming language.…”
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11
Isochronous explicit time integration framework: illustration to thermal stress problems involving both first- and second-order transient systems
Published 2014“…Similar to the implicit case, the same adaptation process of the isochronous integration is valid for the explicit case. …”
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12
Backstepping Integral Super Twisting Sliding Mode Control Algorithm For Autonomous Underwater Glider
Published 2019“…As a result, a robust and reliable has been designed using back-stepping integral super twisting sliding mode control algorithm (BISTSMC) for nonlinear model of longitudinal plane of an AUG. …”
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13
Development of multi-objective optimization methods for integrated scheduling of handling equipment (AGVs, QCs, SP-AS/RS) in automated container terminals
Published 2012“…Therefore, two meta-heuristic algorithms, namely genetic algorithm (GA) and simulated annealing (SA) algorithm, were developed to optimize the integrated scheduling of handling equipment. …”
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14
SecPath: Energy efficient path reconstruction in wireless sensor network using iterative smoothing
Published 2019“…To achieve energy efficiency, it compresses the packet information by using GZIP tools in JAVA. SecPath is evaluated with several variations using 400 nodes in WSN deployments as well as large-scale simulations. …”
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15
An integrated algorithm of analytical network process with case-based reasoning to support the selection of an ideal football team formation and players
Published 2021“…A new combined decision model which integrates Analytical Network Process and Case-based Reasoning was developed. …”
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16
Energy Management in Integrated Microgrids: An Optimal Schedule Controller Utilizing Gradient Descent Algorithm
Published 2024“…Weather data, including wind, solar, fuel, and battery status, is integrated into the BGD algorithm for optimizing ON and OFF schedules. …”
Conference Paper -
17
A modified artificial bee colony algorithm to optimise integrated assembly sequence planning and assembly line balancing
Published 2019“…This paper proposed a modified Artificial Bee Colony algorithm (MABC) to optimise the integrated ASP and ALB problem. …”
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An interactive analytics approach for sustainable and resilient case studies: a machine learning perspective
Published 2023“…To show the methods applicability, this paper uses the proposed algorithm in three sustainable and resilient case studies. …”
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Energy efficient path reconstruction in wireless sensor network using iPath
Published 2019“…To achieve energy efficiency, it compresses the packet information by using GZIP tools in JAVA. Energy efficient iPath (E-iPath) is evaluated with several variations of nodes in WSN deployments as well as large-scale simulations. …”
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20
Development of an integrated scheduling model for handling equipment in automated port container terminals
Published 2014“…In addition, results proved that the modified meta-heuristic algorithm is able to find near optimal solutions and on average, the solutions found by the GA algorithm are only 0.2% worse than the optimal solutions and in the worst case in the test cases this difference is 2.3% which is acceptable. …”
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