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2009 Fifth International Conference on Natural Computation
The Comparative Research of Solving Multi-task Scheduling Problems with GA and PSO
Tianjian, China
August 14-August 16
ISBN: 978-0-7695-3736-8
| ASCII Text | x | ||
| Tianchang Zhang, Wenbin Fan, Yanli Li, "The Comparative Research of Solving Multi-task Scheduling Problems with GA and PSO," 2013 International Conference on Computing, Networking and Communications (ICNC), vol. 4, pp. 459-463, 2009 Fifth International Conference on Natural Computation, 2009. | |||
| BibTex | x | ||
| @article{ 10.1109/ICNC.2009.206, author = {Tianchang Zhang and Wenbin Fan and Yanli Li}, title = {The Comparative Research of Solving Multi-task Scheduling Problems with GA and PSO}, journal ={2013 International Conference on Computing, Networking and Communications (ICNC)}, volume = {4}, year = {2009}, isbn = {978-0-7695-3736-8}, pages = {459-463}, doi = {http://doi.ieeecomputersociety.org/10.1109/ICNC.2009.206}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, } | |||
| RefWorks Procite/RefMan/Endnote | x | ||
| TY - CONF JO - 2013 International Conference on Computing, Networking and Communications (ICNC) TI - The Comparative Research of Solving Multi-task Scheduling Problems with GA and PSO SN - 978-0-7695-3736-8 SP459 EP463 A1 - Tianchang Zhang, A1 - Wenbin Fan, A1 - Yanli Li, PY - 2009 KW - Genetic algorithm KW - Particle swarm optimization KW - Multi-task KW - Project scheduling problem VL - 4 JA - 2013 International Conference on Computing, Networking and Communications (ICNC) ER - | |||
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICNC.2009.206
Genetic algorithm and particle swarm optimization both belong to the evolutionary algorithms; they have much in common, but also have some differences. The paper set out from Multi-task scheduling problem, discussed in detail the method of utilizing GA and PSO to equilibrium and optimize Multi-task scheduling problem under the constraints of resources separately. Through the analysis of comparative experiment, two kinds of intelligence-optimizing methods made very good results when solved a same problem, but in most cases, PSO had a faster rate of convergence than GA, but GA had a better convergent result than PSO.
Index Terms:
Genetic algorithm, Particle swarm optimization, Multi-task, Project scheduling problem
Citation:
Tianchang Zhang, Wenbin Fan, Yanli Li, "The Comparative Research of Solving Multi-task Scheduling Problems with GA and PSO," icnc, vol. 4, pp.459-463, 2009 Fifth International Conference on Natural Computation, 2009
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