Comparison of genetic and binary particle swarm optimization algorithms on system maintenance scheduling using prognostics information

Engineering Optimization

Volumn 41, Issue 2, 2009, Pages 119-136

  Camci, Fatih ^a  

^a FATIH UNIVERSITY   (Turkey)

Author keywords

Binary particle swarm optimization; Condition based maintenance; Genetic algorithm; Maintenance planning; Maintenance scheduling; Prognostics

Indexed keywords

GENETIC ALGORITHMS; MACHINERY; MAINTENANCE; NEURAL NETWORKS; QUALITY ASSURANCE; SCHEDULING; SCHEDULING ALGORITHMS; TRAFFIC SIGNALS;

BINARY PARTICLE SWARM OPTIMIZATION; CONDITION-BASED MAINTENANCE; MAINTENANCE PLANNING; MAINTENANCE SCHEDULING; PROGNOSTICS;

PARTICLE SWARM OPTIMIZATION (PSO);

EID: 60749103685     PISSN: 0305215X     EISSN: 10290273     Source Type: Journal    
DOI: 10.1080/03052150802368807     Document Type: Article

References (18)

1. Afshinmanesh, F., Marandi, A., and Rahimi-Kian, A., 2005. A novel binary particle swarm optimization method using artificial immune system In: International Conference on Computer as a Tool, EUROCON 2005, 22-24 November 2005, Belgrade, 217-220.
2. Belegundu, A.D. and Chandrupatla, T.R., 1999. Optimization concepts and applications in engineering. Upper Saddle River, NJ: Prentice-Hall.
3. Byington, C.S., Watson, M., Roemer, M.J., Galic, T.R., and McGroarty, J.J., 2003. Prognostic enhancements to gas turbine diagnostic systems. In: Aerospace Conference, 2003. Proceedings. 2003 IEEE, 8-15 March 2003, Big Sky, MT, 3247-3255.
4. Camci, F., 2008. System maintenance scheduling with prognostics information using genetic algorithm. Technical Report TR001, Fatih University, Istanbul, Turkey.
5. Chuang, L., Chang. H.W., Tu, C.J., and Yang, C.H., 2008. Improved binary PSO for feature selection using gene expression data. Computational Biology and Chemistry 32, 29-38.
6. Eberhart, R. and Kennedy, J., 1995. A new optimizer using particles swarm theory. In: Proceedings of the Sixth IEEE International Symposium on Micro Machine and Human Science, Nagoya, Japan. Piscataway, NJ: IEEE Service Center, 39-43.
7. Evans, J.R. and Olson, D.L., 1998. Introduction to simulation and risk analysis. Upper Saddle River, NJ: Prentice Hall, 108-111.
8. Holland, J.H., 1962. Adaptation in natural and artificial systems. Ann Arbor, MI: University of Michigan.
9. Jiang, M., Luo, Y.P., and Yang, S.Y., 2007. Stochastic convergence analysis and parameter selection of the standard particle swarm optimization algorithm. Information Processing Letters 102, 8-16.
10. Jin, Y.X., Cheng, H.Z., Yan, J.Y., and Zhang L., 2007. New discrete method for particle swarm optimization and its application in transmission network expansion planning. Electric Power Systems Research 77, 227-233.
11. Kennedy, J. and Eberhart, R.C., 1997. A discrete binary version of the particle swarm algorithm. In: Proceedings of the International Conference on Systems, Man, Cybernetics. Piscataway, NJ, 4104-4109.
12. Kennedy, J. and Eberhart, R., 2001. Swarm intelligence. San Francisco, CA: Morgan Kaufmann Publishers, Inc.
13. Kennedy, K. and Spears, W.M., 1998. Matching algorithms to problems: an experimental test of the particle swarm and some genetic algorithms on the multimodal problem generator. In: Evolutionary Computation Proceedings. Piscataway, NJ, 78-83.
14. Khanesar, M.A., Teshnehlab, M., and Shoorehdeli, M.A., 2007. A novel binary particle swarm optimization. In: Proceedings of the 15th Mediterranean Conference on Control and Automation, 27-29 July, Athens, 1-6.
15. Montgomery, D.C., 1997. Introduction to statistical quality control. New York: Wiley & Sons, 22-28.
16. Ocak, H., Loparo, K.A., and Discenzo, F.M., 2007. Online tracking of bearing wear using wavelet packet decomposition and probabilistic modeling: a method for bearing prognostics. Journal of Sound and Vibration, 302 (4/5), 951-961.
17. Sadri, J. and Suen, C.Y., 2006. A genetic binary particle swarm optimization model. In: IEEE Congress on Evolutionaiy Computation, 16-21 July, Vancouver, 656-663.
18. Ting, T.O., Rao, M.V.C., and Loo, C.K., 2006. A novel approach for unit commitment problem via an effective hybrid particle swarm optimization. IEEE Transactions on Power Systems, 21 (1), 411-418.