Use of Multiobjective Particle Swarm Optimization in Water Resources Management
Publication: Journal of Water Resources Planning and Management
Volume 134, Issue 3
Abstract
Water resources management presents a large variety of multiobjective problems that require powerful optimization tools in order to fully characterize the existing trade-offs. Different optimization methods, based on mathematical programming at first and on evolutionary computation more recently, have been applied with various degrees of success. This paper explores the use of a relatively recent heuristic technique called particle swarm optimization (PSO), which has been found to perform very well in a wide spectrum of optimization problems. Many extensions of the single-objective PSO to handle multiple objectives have been proposed in the evolutionary computation literature. This paper presents an implementation of multiobjective particle swarm optimization (MOPSO) that evaluates alternative solutions based on Pareto dominance, using an external repository to store nondominated solutions, a fitness sharing approach to promote diversity, and a mutation operator to improve global search. The MOPSO solver is used on three applications: (1) test function for comparison with results of other MOPSO and other evolutionary algorithms reported in the literature; (2) multipurpose reservoir operation problem with up to four objectives; and (3) problem of selective withdrawal from a thermally stratified reservoir with three objectives. In the test function application, standard performance metrics were used to measure closeness to the true Pareto front and evenness of coverage of the nondominated set. Results for the other two applications are compared to Pareto solutions obtained using the -constraint method with nonlinear optimization ( -NLP). MOPSO performed very well when compared with other evolutionary algorithms for the test function and also provided encouraging results on the water management applications with the advantage of being much simpler than the -NLP approach.
Get full access to this article
View all available purchase options and get full access to this article.
Acknowledgments
The writers would like to acknowledge the support of the CNPq, a Brazilian Government agency dedicated to the development of science and technology, which has funded the Ph.D. studies of the first writer.
References
Baltar, A. M., and Fontane, D. G. (2006). “Exploring solutions of multi-objective optimization and multi-criteria decision analysis: The interactive compromise coordinate method.” J. Multi-Criter. Decis. Anal., in press.
Benitez, J. E. A., Everson, R. M., and Fieldsend, J. E. (2005). “A MOPSO algorithm based exclusively on Pareto dominance concepts.” Proc., Evolutionary Multi-Criterion Optimization 3rd Int. Conf., Springer, New York, 459–473.
Bohan, J. P., and Grace, J. L. (1973). “Selective withdrawal from man-made lakes.” Technical Rep. No. H-73-4, U.S. Army Engineer Waterways Experiment Station, Vicksburg, Miss.
Coello Coello, C. A. (2001). A short tutorial on evolutionary multiobjective optimization, Lecture Notes in Computer Science, Vol. 1993, Springer, Berlin, 21–40.
Coello Coello, C. A., and Lechuga, M. S. (2002). “MOPSO: A proposal for multiple objective particle swarm optimization.” Proc., Congress on Evolutionary Computation, Vol. 2, IEEE, Piscataway, N.J., 1051–1056.
Coello Coello, C. A., Pulido, G. T., and Lechuga, M. S. (2004). “Handling multiple objectives with particle swarm optimization.” IEEE Trans. Evol. Comput., 8(3), 256–279.
Coello Coello, C. A., Veldhuizen, D. A. V., and Lamont, G. B. (2002). Evolutionary algorithms for solving multi-objective problems, Kluwer Academic, New York.
Cohon, J. L., and Marks, D. H. (1975). “A review and evaluation of multiobjective programming techniques.” Water Resour. Res., 11(2), 208–220.
Deb, K., Agrawal, S., Pratab, A., and Meyarivan, T. (2000). “A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II.” KanGAL Rep. No. 200001, Indian Institute of Technology, Kanpur, India.
Elbeltagi, E., Hegazy, T., and Grierson, D. (2005). “Comparison among five evolutionary-based optimization algorithms.” Adv. Eng. Inf., 19(1), 43–53.
Fieldsend, J. E., and Singh, S. (2002). “A multi-objective algorithm based upon particle swarm optimization, an efficient data structure and turbulence.” Proc., 2002 U.K. Workshop on Computational Intelligence, Birmingham, U.K., 37–44.
Fontane, D. G. (2002). “Multi-objective simulation and optimization of reservoir operation using excel.” Proc., 2nd Joint Federal Interagency Hydrologic Modeling Conf., Las Vegas.
Fontane, D. G., and Schneider, M. L. (1994). “Description of the application of goal programming for selective withdrawal structure operation.” Proc., 21st Water Resources Planning and Management Division Conf., D. G. Fontane, and H. N. Tuvel, eds., ASCE, New York.
Gill, M. K., Kaheil, Y. H., Khalil, A., McKee, M., and Bastidas, L. (2006). “Multiobjective particle swarm optimization for parameter estimation in hydrology.” Water Resour. Res., 42(7), W07417 .
Goicoechea, A., Hansen, D. R., and Duckstein, L. (1982). Multiobjective decision analysis with engineering and business applications, Wiley, New York.
Hu, X., Eberhart, R. C., and Shi, Y. (2003). “Particle swarm with extended memory for multiobjective optimization.” Proc., IEEE Swarm Intelligence Symp., IEEE, Piscataway, N.J., 193–197.
Huang, V. L., Suganthan, P. N., and Liang, L. L. (2006). “Comprehensive learning particle swarm optimizer for solving multiobjective optimization problems.” Int. J. Intell. Syst., 21(2), 209–226.
Jung, B. S., and Karney, B. W. (2006). “Hydraulic optimization of transient protection devices using GA and PSO approaches.” J. Water Resour. Plann. Manage., 132(1), 44–52.
Kennedy, J., and Eberhart, R. (1995). “Particle swarm optimization.” Proc., 4th IEEE Int. Conf. on Neural Networks, IEEE, Piscataway, N.J., 1942–1948.
Kita, H., Yabumoto, Y., Mori, N., and Nishikawa, Y. (1996). “Multi-objective optimization by means of the thermodynamical genetic algorithm.” Parallel problem solving from nature-PPSN IV, H.-M. Voigt, W. Ebeling, I. Rechenberg, and H.-P. Schwefel, eds., Springer, New York, 504–512.
Lechuga, M. S., and Rowe, J. E. (2005). “Particle swarm optimization and fitness sharing to solve multi-objective optimization problems.” Proc., IEEE Congress on Evolutionary Computation CEC’05, IEEE, Piscataway, N.J., 1204–1211.
Li, X. (2003). “A non-dominated sorting particle swarm optimizer for multiobjective optimization.” Proc., Genetic and Evolutionary Computation Conf. GECCO 2003, Springer, New York, 37–48.
McMahon, T. A., and Mein, R. G. (1988). River and reservoir yield, Water Resources Publications, Littleton, Colo.
Mendes, R., Kennedy, J., and Neves, J. (2004). “The fully informed particle swarm: Simpler, maybe better.” IEEE Trans. Evol. Comput., 8(3), 204–210.
Miettinen, K. (1999). Nonlinear multiobjective optimization, Kluwer Academic, Boston.
Moore, J., and Chapman, R. (1999). Application of particle swarm to multiobjective optimization, Department of Computer Science and Software Engineering, Auburn University Press, Auburn, A19.
Mostaghim, S., and Teich, J. (2003). “Strategies for finding good local guides in multiobjective particle swarm optimization (MOPSO).” Proc., IEEE Swarm Intelligence Symposium, IEEE, Piscataway, N.J., 26–33.
Parsopoulos, K. E., and Vrahatis, M. N. (2002). “Particle swarm optimization method in multiobjective problems.” Proc., ACM Symp. on Applied Computing, ACM, New York, 603–607.
Pulido, G. T. (2005). “On the use of self-adaptation and elitism for multiobjective particle swarm optimization.” Doctoral thesis, Centro de Investigacíon y Estudios Avanzados del Instituto Politécnico Nacional, Mexico City, Mexico.
Pulido, G. T., and Coello Coello, C. A. (2004). “Using clustering techniques to improve the performance of a multi-objective particle swarm optimizer.” Proc., of the Genetic and Evolutionary Computation Conf. GECCO 2004, Springer, New York, 225–237.
Ray, T., and Liew, K. M. (2002). “A swarm metaphor for multiobjective design optimization.” Eng. Optimiz., 34(2), 141–153.
Schott, J. R. (1995). “Fault tolerant design using single and multicriteria genetic algorithm optimization.” MS thesis, MIT, Cambridge, Mass.
Srinivas, N., and Deb, K. (1994). “Multiobjective optimization using nondominated sorting in genetic algorithms.” Evol. Comput., 2(3), 221–248.
Van Veldhuizen, D. A., and Lamont, G. B. (1998). “Multiobjective evolutionary algorithm research: A history and analysis.” Technical Rep. No. TR-98-03, Air Force Institute of Technology, Wright-Patterson AFB, Ohio.
Villalobos-Arias, M. A., Pulido, G. T., and Coello Coello, C. A. (2005). “A Proposal to use stripes to maintain diversity in a multi-objective particle swarm optimizer.” Proc., IEEE Swarm Intelligence Symp. SIS05, IEEE, Piscataway, N.J., 22–29.
Information & Authors
Information
Published In
Journal of Water Resources Planning and Management
Volume 134 • Issue 3 • May 2008
Pages: 257 - 265
Copyright
© 2008 ASCE.
History
Received: Sep 25, 2006
Accepted: Jun 8, 2007
Published online: May 1, 2008
Published in print: May 2008
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.