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A performance comparison of PSO and GA in scheduling hybrid flow-shops with multiprocessor tasks
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Source Symposium on Applied Computing archive
Proceedings of the 2008 ACM symposium on Applied computing table of contents
Fortaleza, Ceara, Brazil
SESSION: Applications of evolutionary computation table of contents
Pages: 1767-1771  
Year of Publication: 2008
ISBN:978-1-59593-753-7
Author
M Fikret Ercan  Singapore Polytechnic, Singapore
Sponsor
SIGAPP: ACM Special Interest Group on Applied Computing
Publisher
ACM  New York, NY, USA
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ABSTRACT

Applications in industry and computing require a proper scheduling of tasks to achieve good performance. The algorithms presented in this paper tackles task scheduling problem in a multi layer multiprocessor environment. Using the scheduling terminology, problem is defined as multiprocessor task scheduling in hybrid flow-shops. This paper presents a particle swarm optimization (PSO) algorithm for the solution. In order to improve the performance of PSO, hybrid techniques were also employed. The performance results, compared with other well known meta-heuristics from the literature, are reported. Results show that PSO and hybrid methods have merits in solving multiprocessor task scheduling in hybrid flow-shop environment.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
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