Exploring the efficiency of parallel bacteria foraging metaheuristics for job shop scheduling problem optimization
Pages 88 - 94
Abstract
This paper suggest a model of parallel computation based on Bacteria Foraging metaheuristics for Job Shop Scheduling optimization problem. The parallel model is verified experimentally by Hybrid (MPI + OpenMP) programming implementations, run on heterogeneous computer cluster. The experimental study comprises parallel performance evaluation, parallel algorithm scalability analysis, profiling and evaluation of solution quality.
References
[1]
E. Alba, Parallel Metaheuristics -- A New Class of Algorithms, Wiley-Interscience, 2005
[2]
T. Gonzales, Handbook of Approximation Algorithms and Metaheuristics, Chapman & Hall/CRC Computer and Information Science Series, 2007
[3]
Goncalves, J. F., et al., A hybrid genetic algorithm for the Job Shop Scheduling Problem, European Journal of Operational Research 167 (1), 2005, pp.77--95.
[4]
P. Borovska, Yanchev G., The ParMetaOpt Experience: Performance of Parallel Metaheuristics on Scheduling Optimization, Proceedings of the 9th WSEAS International Conference on Applied Informatics and Communications (AIC'09), Moscow, Russia, August 20--22, 2009, ISSN 1790-5109, pp. 475--479
[5]
http://www.softcomputing.net/bfoa-chapter.pdf
Index Terms
- Exploring the efficiency of parallel bacteria foraging metaheuristics for job shop scheduling problem optimization
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Published: 16 June 2011
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CompSysTech '11
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- Austrian Comp Soc
- BPCSB
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- IOIACTBB
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- ERSVB
- FOSEUB
- UORB
- BBPSB
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- TECHUVB
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CompSysTech '11: 12th International Conference on Computer Systems and Technologies
June 16 - 17, 2011
Vienna, Austria
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