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Sivanandam, S. N.
- Hybridization of Modified Ant Colony Optimization and Intelligent Water Drops Algorithm for Job Scheduling in Computational Grid
Abstract Views :151 |
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Authors
Affiliations
1 Department of Computer Science and Engineering, Sri Ramakrishna Engineering College, IN
2 Department of Computer Science and Engineering, Karpagam College of Engineering, IN
3 PSG College of Technology, IN
1 Department of Computer Science and Engineering, Sri Ramakrishna Engineering College, IN
2 Department of Computer Science and Engineering, Karpagam College of Engineering, IN
3 PSG College of Technology, IN
Source
ICTACT Journal on Soft Computing, Vol 4, No 1 (2013), Pagination: 651-655Abstract
As grid is a heterogeneous environment, finding an optimal schedule for the job is always a complex task. In this paper, a hybridization technique using intelligent water drops and Ant colony optimization which are nature-inspired swarm intelligence approaches are used to find the best resource for the job. Intelligent water drops involves in finding out all matching resources for the job requirements and the routing information (optimal path) to reach those resources. Ant Colony optimization chooses the best resource among all matching resources for the job. The objective of this approach is to converge to the optimal schedule faster, minimize the make span of the job, improve load balancing of resources and efficient utilization of available resources.Keywords
Grid Computing, Grid Scheduling, Ant Colony Optimization, Intelligent Water Drops, Pheromone.- Fuzzy Logic Based Optimization of Capacitor Value for Single Phase Open well Submersible Induction Motor
Abstract Views :164 |
PDF Views:0
Authors
Affiliations
1 Department of Electrical and Electronics Engineering, SNS College of Technology, Tamil Nadu, IN
2 Department of Computer Science and Engineering, PSG College of Technology, Tamil Nadu, IN
3 Small Industries Testing and Research Centre (SITARC), Tamil Nadu, IN
1 Department of Electrical and Electronics Engineering, SNS College of Technology, Tamil Nadu, IN
2 Department of Computer Science and Engineering, PSG College of Technology, Tamil Nadu, IN
3 Small Industries Testing and Research Centre (SITARC), Tamil Nadu, IN
Source
ICTACT Journal on Soft Computing, Vol 1, No 3 (2011), Pagination: 113-118Abstract
Purpose - The aim of this paper is to optimize the capacitor value of a single phase open well submersible motor operating under extreme voltage conditions using fuzzy logic optimization technique and compared with no-load volt-ampere method. This is done by keeping the displacement angle (α) between main winding and auxiliary winding near 90°, phase angle (φ) between the supply voltage and line current near 0°. The optimization work is carried out by using Fuzzy Logic Toolbox software built on the MATLAB technical computing environment with Simulink software. Findings - The optimum capacitor value obtained is used with a motor and tested for different supply voltage conditions. The vector diagrams obtained from the experimental test results indicates that the performance is improved from the existing value. Originality/value - This method will be highly useful for the practicing design engineers in selecting the optimum capacitance value for single phase induction motors to achieve the best performance for operating at extreme supply voltage conditions.Keywords
Single-Phase, Induction Motors, Capacitance, Optimization, Fuzzy.- Grid Scheduling Using Enhanced Ant Colony Algorithm
Abstract Views :159 |
PDF Views:0
Authors
Affiliations
1 Department of Computer Science and Engineering, PSG College of Technology, Tamil Nadu, IN
2 Akshaya College of Engineering and Technology, Tamil Nadu, IN
1 Department of Computer Science and Engineering, PSG College of Technology, Tamil Nadu, IN
2 Akshaya College of Engineering and Technology, Tamil Nadu, IN
Source
ICTACT Journal on Soft Computing, Vol 1, No 2 (2010), Pagination: 85-87Abstract
Grid computing is a high performance computing used to solve larger scale computational demands. Task scheduling is a major issue in grid computing systems. Scheduling of tasks is the NP hard problem. The heuristic approach provides optimal solution for NP hard problems .The ant colony algorithm provides optimal solution. The existing ant colony algorithm takes more time to schedule the tasks. In this paper ant colony algorithm improved by enhancing pheromone updating rule such that it schedules the tasks efficiently and better resource utilization. The simulation results prove that proposed method reduces the execution time of tasks compared to existing ant colony algorithm.Keywords
Pheromone, Swarm Intelligence, Inertia, Grid Scheduling.- Enhanced Hybrid PSO - ACO Algorithm for Grid Scheduling
Abstract Views :174 |
PDF Views:0
Authors
Affiliations
1 Department of Computer Science and Engineering, PSG College of Technology, Coimbatore, Tamil Nadu, IN
1 Department of Computer Science and Engineering, PSG College of Technology, Coimbatore, Tamil Nadu, IN