Advertisement

Use of Multiobjective Evolutionary Algorithms in Water Resources Engineering

  • Francisco Venícius Fernandes Barros
  • Eduardo Sávio Passos Rodrigues Martins
  • Luiz Sérgio Vasconcelos Nascimento
  • Dirceu Silveira ReisJr.
Part of the Studies in Computational Intelligence book series (SCI, volume 261)

Abstract

In Engineering, and more specifically in water resources, the need of representation of complex natural phenomena through models is of crucial importance for water resources planning and management. Through the use of these models, it is possible to understand the natural processes and to evaluate the system response to different scenarios, providing support to the decision making process. In this chapter, we investigate the use of this models to water resource engineering.

Keywords

Pareto Front Multiobjective Optimization Validation Period Operating Policy Multiobjective Problem 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Loucks, D.P., Beek, E.V.: Water Resources Systems Planning and Management: An Introduction to Methods, Models and Applications. In: Studies and Reports in Hydrology. UNESCO Publishing, Turin (2005)Google Scholar
  2. 2.
    Oliveira, R., Loucks, D. P.: Operating Rules for Multireservoir Systems. Water Resources Research 33(4), 839–852 (1997)CrossRefGoogle Scholar
  3. 3.
    Duan, Q.: Global Optimization for Watershed Model Calibration. In: Calibration of Watershed Models. AGU, Washington DC (2002)Google Scholar
  4. 4.
    Vrugt, J.A., Gupta, H.V., Bastidas, L.A., Bouten, W., Sorooshian, S.: Effective and Efficient Algorithm for Multiple Objective Optimization of Hydrological Model. Water Resources Research 39(8) (2003), doi:10.1029/2002WR001746Google Scholar
  5. 5.
    Duan, Q., Gupta, V.K., Sorooshian, S.: Effective and Efficient Global Optimization for Conceptual Rainfall-runoff Models. Water Resources Research 28(4), 1015–1031 (1992)CrossRefGoogle Scholar
  6. 6.
    Duan, Q., Gupta, V.K., Sorooshian, S.: A Shuffled Complex Evolution Approach for Effective and Efficient Global Minimization. J. Optim. Theory Appl. 76(3), 501–521 (1993)zbMATHCrossRefMathSciNetGoogle Scholar
  7. 7.
    Duan, Q., Sorooshian, S., Gupta, V.K.: Optimal Use of the SCE-UA Global Optimization Method for Calibrating Watershed Models. Journal of Hydrology 158, 265–284 (1994)CrossRefGoogle Scholar
  8. 8.
    Coello, C.A.C., Pulido, G.T., Lechuga, M.S.: Handling Multiple Objectives With Particle Swarm Optimization. IEEE Transactions on Evolutionary Computation 8(3), 256–279 (2004)CrossRefGoogle Scholar
  9. 9.
    Reyes-Sierra, M., Coello, C.A.C.: Multi-objective particle swarm optimizers: A survey of the state-of-the-art. International Journal of Computational Intelligence Research 2(3), 287–308 (2006)MathSciNetGoogle Scholar
  10. 10.
    Das, I., Dennis, J.: A Closer Look at Drawbacks of Minimizing Weighted Sums of Objectives for Pareto Set Generation in Multicriteria Optimization Problems. Structural Optimization 14(1), 63–69 (1997)CrossRefGoogle Scholar
  11. 11.
    Jin, Y., Okabe, T., Sendhoff, B.: Dynamic weighted aggregation for evolutionary multiobjective optimization: Why does it work and how? In: Spector, L., Goodman, E.D., Wu, A., Langdon, W.B., Voigt, H.-M., Gen, M., Sen, S., Dorigo, M., Pezeshk, S., Garzon, M.H., Burke, E. (eds.) Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2001). Morgan Kaufmann Publishers, San Francisco (2001)Google Scholar
  12. 12.
    Baumgartner, U., Magele, C., Renhart, W.: Pareto Optimality and Particle Swarm Optimization. IEEE Transactions on Magnetics 40(2), 1172–1175 (2004)CrossRefGoogle Scholar
  13. 13.
    Hu, X., Eberhart, R.C., Shi, Y.: Particle swarm with extended memory for multiobjective optimization. In: Proceedings of the 2003 IEEE Swarm Intelligence Symposium. IEEE Service Center, Indianapolis (2003)Google Scholar
  14. 14.
    Schaffer, J.D.: Multiple Objective Optimization with Vector Evaluated Genetic Algorithms. In: Genetic Algorithms and their Applications: Proceedings of the First International Conference on Genetic Algorithms, Lawrence Erlbaum, Mahwah (1985)Google Scholar
  15. 15.
    Knowles, J.D., Corne, D.W.: The Pareto Archived Evolution Strategy: A New Baseline Algorithm for Multiobjective Optimization. In: 1999 Congress on Evolutionary Computation. IEEE Service Center, Washington D.C (1999)Google Scholar
  16. 16.
    Knowles, J.D., Corne, D.W.: Approximating the Nondominated Front Using the Pareto Archived Evolution Strategy. Evolutionary Computation 8(2), 149–172 (2000)CrossRefGoogle Scholar
  17. 17.
    Nascimento, L.S.V., Reis Jr, D., Martins, E.: Avaliao do Algoritmo Evoluvionrio MOPSO na Calibrao Multiobjetivo do Modelo SMAP no Estado do Cear. In: Anais do XVII Simpsio Brasileiro de Recursos Hdricos, So Paulo (2007)Google Scholar
  18. 18.
    Xiao-Hua, Z., Hong-Yun, M., Li-Cheng, J.: Intelligent particle swarm optimization in multiobjective optimization. In: Congress on Evolutionary Computation (CEC 2005). IEEE Press, Edinburgh (2005)Google Scholar
  19. 19.
    Yapo, P.O., Gupta, H.V., Sorooshian, S.: Multiobjective Global Optimization for Hydrologic Models. Journal of Hydrology 204, 83–97 (1998)CrossRefGoogle Scholar
  20. 20.
    Gupta, V.K., Sorooshian, S., Yapo, P.O.: Toward Improved Calibration of Hydrologic Models: Multiple and Noncommensurable Measures of Information. Water Resources Research 34(4), 751–763 (1998)CrossRefGoogle Scholar
  21. 21.
    Bastidas, L.A., Gupta, V.K., Sorooshian, S., Shuttleworth, W.J., Yang, Z.L.: Sensitvity Analysis of a Land Surface Scheme using Multi-Criteria Methods. Journal of Geophysical Research 104(D16), 481–490 (1999)CrossRefGoogle Scholar
  22. 22.
    Boyle, D.P., Gupta, H.V., Sorooshian, S.: Toward Improved Calibration Models: Combining the Strengths of Manual and Automatic Methods. Water Resources Research 36(12), 3663–3674 (2000)CrossRefGoogle Scholar
  23. 23.
    Boyle, D.P., Gupta, H.V., Sorooshian, S.: Toward Improved Streamflow Forecasts: Value of Semi-distributed Modeling. Water Resources Research 37(11), 2749–2759 (2001)CrossRefGoogle Scholar
  24. 24.
    Wagener, T., Boyle, D.P., Lees, M.J., Wheater, H.S., Gupta, H.V., Sorooshian, S.: A Framework for Development and Application of Hydrological Models. Hydrol. Earth Sys. Sci. 5(1), 13–26 (2001)CrossRefGoogle Scholar
  25. 25.
    Fonseca, C.M., Fleming, P.J.: Genetic Algorithms for Multiobjective Optimization: Formulation, Discussion and Generalization. In: Forrest, S. (ed.) Proceedings of the Fifth International Conference on Genetic Algorithms. Morgan Kauffman Publishers, San Mateo (1993)Google Scholar
  26. 26.
    Horn, J., Nafpliotis, N., Goldberg, D.E.: A Niched Pareto Genetic Algorithm for Multiobjetive Optimization. In: Proceedings of the First IEEE Conference on Evolutionary Computation, IEEE World Congress on Computational Intelligence. IEEE Service Center, Piscataway (1994)Google Scholar
  27. 27.
    Srinivas, N., Deb, K.: Multiobjective Optimization Using Nondominated Sorting in Genetic Algorithms. Evolutionary Computation 2(3), 221–248 (1994)CrossRefGoogle Scholar
  28. 28.
    Zitzler, E., Thiele, L.: Multiobjective Evolutionary Algorithms: A Comparative Case Study and the Strength Pareto Approach. IEEE Transactions on Evolutionary Computation 3(4), 257–271 (1999)CrossRefGoogle Scholar
  29. 29.
    Deb, K.: Multi-objective Genetic Algorithms: Problem Difficulties and Construction of Test Problems. Evolutionary Computation 7(3), 205–230 (1999)CrossRefGoogle Scholar
  30. 30.
    Haddad, O.B., Afshar, A., Marino, M.A.: Honey-Bees Mating Optimization (HBMO) Algorithm: A new heuristic approach for water resources optimization. Water Resources Management 20(5), 661–680 (2006)CrossRefGoogle Scholar
  31. 31.
    Abbass, H.A.: A Pleometrosis MBO Approach to Satisfiability. In: Proceeding of International Conference on Computational Intelligence for Modeling, Control and Automation, CIMCA 2001, Las Vegas, USA (2001)Google Scholar
  32. 32.
    Lacerda, E.G.M.: Carvalho ACPLF, Introdução aos Algoritmos Genéticos. In: Galvão, C. O., Valença, M.J. S. (eds.) Sistemas Inteligentes: Aplicações a Recursos Hídricos e Ciências Ambientais. Ed. Universidade/UFRGS/ABRH, Porto Alegre (1999)Google Scholar
  33. 33.
    Kennedy, J., Eberhart, R.C.: Swarm Intelligence. Morgan Kaufmann Publishers, California (2000)Google Scholar
  34. 34.
    Alvarez-Benitz, J.E., Everson, R.M., Fieldsend, J.E.: A MOPSO algorithm based exclusively on pareto dominance concepts. In: Coello, C.A.C., Hernández Aguirre, A., Zitzler, E. (eds.) EMO 2005. LNCS, vol. 3410, pp. 459–473. Springer, Heidelberg (2005)Google Scholar
  35. 35.
    Thiemann, M., Trosset, M., Gupta, H., Sorooschian, S.: Bayesian Recursive Parameter Estimation for Hydrological Models. Water Resources Research 37(10), 2521–2535 (2001)CrossRefGoogle Scholar
  36. 36.
    Gelman, A., Rubin, D.B.: Inference from Iterative Simulation Using Multiple Sequences. Stat. Sci. 7, 457–472 (1992)CrossRefGoogle Scholar
  37. 37.
    Seber, G.A.F.: Multivariate Observations. Wiley, New York (1994)Google Scholar
  38. 38.
    Spath, H.: Cluster Dissection and Analysis: Theory, FORTRAN Programs, Examples, translated by J. Goldschmidt. Halsted Press, New York (1995)Google Scholar
  39. 39.
    Goldberg, D.E.: Genetic Algorithms in search, optimization and machine learning. Addison-Wesley, Reading (1989)zbMATHGoogle Scholar
  40. 40.
    Coello, C.A.C., Lechuga, M.S.: MOPSO: A proposal for multiple objective particle swarm optimization. In: Congress on Evolutionary Computation (CEC 2002). IEEE Service Center, Piscataway (2002)Google Scholar
  41. 41.
    Hu, X., Eberhart, R.: Multiobjective optimization using dynamic neighborhood particle swarm optimization. In: Congress on Evolutionary Computation (CEC 2002). IEEE Service Center, Piscataway (2002)Google Scholar
  42. 42.
    Parsopoulos, K.E., Vrahatis, M.N.: Particle swarm optimization method in multiobjective problems. In: Proceedings of the 2002 ACM Symposium on Applied Computing (SAC 2002). ACM Press, Madrid (2002)Google Scholar
  43. 43.
    Fieldsend, J.E., Singh, S.: A multiobjective algorithm based upon particle swarm optimization, an efficient data structure and turbulence. In: Proceedings of the 2002 U.K. Workshop on Computational Intelligence, Birmingham, UK (2002)Google Scholar
  44. 44.
    Li, X.: A non-dominated sorting particle swarm optimizer for multiobjective optimization. In: Cantú-Paz, E., Foster, J.A., Deb, K., Davis, L., Roy, R., O’Reilly, U.-M., Beyer, H.-G., Kendall, G., Wilson, S.W., Harman, M., Wegener, J., Dasgupta, D., Potter, M.A., Schultz, A., Dowsland, K.A., Jonoska, N., Miller, J., Standish, R.K., et al. (eds.) GECCO 2003. LNCS, vol. 2724. Springer, Heidelberg (2003)Google Scholar
  45. 45.
    Barros, F.V.F.: The Use of Evolutionary Algorithms in the Calibration of Watershed Models and in the Optimization of Reservoirs’ System Operation, MSc. Thesis, Department of Hydraulic and Environmental Engineering, Federal University of Ceara, Fortaleza, CE, Brazil (2007)(in Portuguese)Google Scholar
  46. 46.
    Yapo, P.O., Gupta, H.V., Sorooshian, S.: Automatic Calibration of Conceptual Rainfall-Runoff Models: Sensitivity to Calibration Data. Journal of Hydrology 181, 23–48 (1996)CrossRefGoogle Scholar
  47. 47.
    Moore, R.J.: The probability-distributed principle and runoff production at point and basin scale. Hydrological Sciences Journal 30(2), 273–297 (1985)Google Scholar
  48. 48.
    FUNCEME, Optimization of the Reservoirs’s System of the Metropolitan Region of Fortaleza to Minimize Pumping Costs, Report. Fortaleza, Brazil (2007)(in Portuguese)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Francisco Venícius Fernandes Barros
    • 1
  • Eduardo Sávio Passos Rodrigues Martins
    • 1
  • Luiz Sérgio Vasconcelos Nascimento
    • 1
  • Dirceu Silveira ReisJr.
    • 1
  1. 1.Research Institute for Meteorology and Water Resources FUNCEMEFortalezaBrazil

Personalised recommendations