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Dynamic task scheduling with load balancing using parallel orthogonal particle swarm optimization (2009)
| Content Provider | CiteSeerX |
|---|---|
| Author | Visalakshi, P. Sivan, S. N. |
| Abstract | This paper presents a Hybrid Particle Swarm Optimization (HPSO) method for solving the Task Assignment Problem (TAP) which is an np-hard problem. Particle Swarm Optimization (PSO) is a recently developed population based heuristic optimization technique. The algorithm has been developed to dynamically schedule heterogeneous tasks on to heterogeneous processors in a distributed setup. Load balancing which is a major issue in task scheduling is also considered. The nature of the tasks are independent and non pre-emptive. The HPSO yields a better result than the Normal PSO when applied to the task assignment problem. The results Of PSO and HPSO is also compared with another popular heuristic optimization technique namely Genetic Algorithm ( GA). The results infer that the PSO performs better than the GA. |
| File Format | |
| Journal | Int. J. Bio-Inspired Comput |
| Language | English |
| Publisher Date | 2009-01-01 |
| Access Restriction | Open |
| Subject Keyword | Parallel Orthogonal Particle Swarm Optimization Dynamic Task Task Assignment Problem Major Issue Pso Performs Task Scheduling Heuristic Optimization Technique Particle Swarm Optimization Popular Heuristic Optimization Technique Genetic Algorithm Np-hard Problem Normal Pso Heterogeneous Task Hybrid Particle Swarm Optimization Distributed Setup Heterogeneous Processor |
| Content Type | Text |
| Resource Type | Article |
