An overview of the applications of particle swarm in water resources optimization

Advances in Intelligent Systems and Computing

Volumn 202 AISC, Issue VOL. 2, 2013, Pages 41-52

  Cyriac, Rosemary ^a   Rastogi, A K ^a  

^a INDIAN INSTITUTE OF TECHNOLOGY BOMBAY   (India)

Author keywords

Particle swarm optimization; Swarm intelligence; Water resources engineering

Indexed keywords

ALGORITHMS; ARTIFICIAL INTELLIGENCE; COMPUTATION THEORY; PARTICLE SWARM OPTIMIZATION (PSO); SOFT COMPUTING;

CONVENTIONAL OPTIMIZATION; ENGINEERING PROBLEMS; META HEURISTICS; OPTIMIZATION ALGORITHMS; OPTIMIZATION METHOD; RESOURCES OPTIMIZATION; SWARM INTELLIGENCE; WATER RESOURCES MANAGEMENT;

WATER RESOURCES;

EID: 84875087214     PISSN: 21945357     EISSN: None     Source Type: Book Series    
DOI: 10.1007/978-81-322-1041-2_4     Document Type: Conference Paper

References (24)

1. Clerc, M., The swarm and the queen: towards a deterministic and adaptive particle swarm optimization. Proc. Congress on Evolutionary Computation, 1999 Washington, DC, pp 1951 - 1957. Piscataway, NJ: IEEE Service Centre (1999)
2. Clerc, M., and Kennedy, J., The particle swarm explosion, stability, and convergence in a multi-dimensional complex space. IEEE Transactions on Evolutionary Computation, vol. 6, p. 58-73 (2002)
3. Eberhart, R.C., and Kennedy, J., A new optimizer using particle swarm theory. Proceedings of the Sixth International Symposium on Micro Machine and Human Science, Nagoya, Japan, 39-43. Piscataway, N J: IEEE Service Centre (1995)
4. Eberhart, R. C., and Shi, Y.,Evolving artificial neural networks.Proc. Conference on Neural Networks and Brain, 1998, Beijing, P.R.C., PL5-PL3. (1998)(a)
5. Eberhart, R. C., and Shi, Y., Comparing inertia weights and constriction factors in particle swarm optimization. Proc. Congress on Evolutionary Computation 2000, San Diego, CA, pp 84-88. Piscataway, NJ: IEEE Service Centre (2000)
6. Eberhart, R.C., and Shi, Y., Tracking and optimizing dynamic systems with particle swarms. Proc. Congress on Evolutionary Computation 2001, Seoul, Korea. Piscataway, NJ: IEEE Service Centre (2001a)
7. Eberhart, R.C., and Shi, Y., Particle swarm optimization: developments, applications and resources. Proc. Congress on Evolutionary Computation 2001, Seoul, Korea. Piscataway, NJ: IEEE Service Centre (2001b)
8. Gaur, S., Chahar, B.R., and Graillot, D., Analytic Element Method and particle swarm optimization based simulation-optimization model for groundwater management. Journal of Hydrology 402, 217-227 (2011)
9. Gill, K.M., Kaheil, Y.H., Khalil, A., McKee, M., and Bastidas,L. Multiobjective particle swarm optimization for parameter estimation in hydrology. Journal of Water Resources Research, 42, W07417, 14 PP (2006)
10. th International Conference on Computer Sciences and Convergence information Technology(ICCIT) 2010,Seoul,Korea (2010)
11. Izquierdo J., Montalvo. I, Perez R., and Tavero M., Optimization in Water Systems: a PSO approach. Proc. Spring Simulation Multi conference 2008, Ottawa, Canada (2008)
12. Kumar, D.N and Reddy, M.J., Multipurpose Reservoir Operation using Particle Swarm Optimization. Journal of Water Resources Planning and Management 133:3,192-201 (2007)
13. Loucks P. D., and Oliveira R. Operating rules for multi reservoir systems.Water Resources Research, vol. 33, no. 4, p. 839 (1997)
14. Mategaonkar M., and Eldho T. I., Groundwater remediation optimization using a point collocation method and particle swarm optimization. Environmental Modelling& Software 32,37-38 (2012)
15. Mathur, Y. P., Kumar.R., and Pawde, A., A binary particle swarm optimisation for generating optimal schedule of lateral canals.The IES Journal Part A: Civil & Structural Engineering, 3:2, 111-118 (2010)
16. Mattot S. L., Rabideau J.A., and Craig R. J., Pump-and-treat optimization using analytic element method flow models. Advances in Water Resources 29, 760-775 (2006)
17. Moradi, J.M., Marino, M.A., Afshar, A., Optimal design and operation of irrigation pumping station. Journal of Irrigation and Drainage Engineering129(3), 149-154 (2003)
18. Poli R., An Analysis of Publications on Particle Swarm Optimisation Applications Department of Computer Science University of Essex Technical Report CSM-469(2007)
19. Poli, R., Kennedy, J., and Blackwell, T., Particle swarm optimization. An overview.Swarm Intelligence, 1(1), 33-57 (2007)
20. Samuel P.M. and Jha K.M., Estimation of Aquifer Parameters from Pumping Test Data by Genetic Algorithm Optimization Technique. Journal of Irrigation and DrainageEngineering, 129(5): 348-359 (2003)
21. Theis, C. V., The relation between the lowering of the piezometric surface and the rate and duration of discharge of a well using ground-water storage. Trans. of American Geophysical Union 16:519-52 (1935)
22. Sharma, A.K., and Swamee, P.K., Cost considerations and general principles in the optimal design of water distribution systems. In: ASCE Conf. Proc., vol. 247, p.85 (2006)
23. Yuhui Shi., Particle Swarm Optimization. Electronic Data Systems, Inc. IEEE Neural Network Society Magazine (2004)
24. Zhou C.,Gao L., GaoHaibing, and Peng C., Pattern Classification and Prediction of Water Quality by Neural Network with Particle Swarm Optimization. Proceedings of the 6th World Congress on Control and Automation, June 21-23, 2006, Dalian, China (2006)