Box-Constrained Optimization Methodology and Its Application for a Water Supply System Model
Publication: Journal of Water Resources Planning and Management
Volume 138, Issue 6
Abstract
This study introduces a new search method for box-constrained optimization problems called the search method for box optimization (SMBO). SMBO is a population heuristic-based search methodology that solves global optimization problems. SMBO represents the population as a probability density function (PDF) inside the problem bounds. The PDF shape is dynamically adapted during the process to guide to a “good” search domain. The applicability and the efficiency of the method are demonstrated using two benchmark sets, which include unimodal, multimodal, expanded, and hybrid composition functions. The performance of SMBO is compared with several genetic algorithms (GAs); the first benchmark compares it with nine codes of traditional/classic GAs, and the second compares SMBO with two recent variants of genetic algorithms. The results show that SMBO performs as well as or better than the GAs in both comparisons. The method is demonstrated on a nonlinear model for management of a water supply system (WSS), and the results are compared with the commercial GA toolbox of matrix laboratory (MATLAB).
Get full access to this article
View all available purchase options and get full access to this article.
References
Ahrari, A., Shariat-Panahi, M., and Atai, A. A. (2009). “Gem: A novel evolutionary optimization method with improved neighborhood search.” Appl. Math. Comput., 210(2), 376–386.
Colorado, S. G., Gordon, V. S., and Whitley, D. (1993). “Serial and parallel genetic algorithms as function optimizers.” Proc., 5th Int. Conf. on Genetic Algorithms, Morgan Kaufmann Publishers Inc., San Francisco, 177–183.
De Jong, K. A. (1975). “An analysis of the behavior of a class of genetic adaptive systems.” Ph.D. dissertation, Univ. of Michigan, Ann Arbor, MI.
Geem, Z. W., and Cho, Y. H. (2011). “Optimal design of water distribution networks using parameter-setting-free harmony search for two major parameters.” J. Water Resour. Plann. Manage., 137(4), 377–380.
Georgieva, A., and Jordanov, I. (2009). “Global optimization based on novel heuristics, low-discrepancy sequences and genetic algorithms.” Eur. J. Oper. Res., 196(2), 413–422.
Harik, G. R., Lobo, F. G., and Goldberg, D. E. (1999). “The compact genetic algorithm.” IEEE Trans. Evol. Comput., 3(4), 287–297.
Holland, J. H. (1975). Adaptation in natural and artificial systems, University of Michigan Press, Ann Arbor, MI.
Hsieh, S., Sun, T., and Liu, C. (2009). “Potential offspring production strategies: An improved genetic algorithm for global numerical optimization.” Expert Syst. with Appl., 36(8), 11,088–11,098.
Regis, R. G., and Shoemaker, C. A. (2007). “Parallel radial basis function methods for the global optimization of expensive functions.” Eur. J. Oper. Res, 182(2), 514–535.
Rubinstein, R. Y. (1981). Simulation and the Monte Carlo method, Wiley, New York.
Suganthan, P. N.,et al. (2005). “Problem definitions and evaluation criteria for the CEC’05 2005 special session on real parameter optimization.”, Nanyang Technological Univ. 〈http://www.ntu.edu.sg/home/epnsugan〉.
Tsai, J. T., Liu, T. K., and Chou, J. H. (2004). “Hybrid taguchi-genetic algorithm for global numerical optimization.” IEEE Trans. Evol. Comput, 8(4), 365–377.
Tu, Z., and Lu, Y. (2004). “A robust stochastic genetic algorithm (STGA) for global numerical optimization.” IEEE Trans. Evol. Comput., 8(5), 456–470.
Tvrdík, I. (2009). “Adaptation in differential evolution: A numerical comparison.” Appl. Soft Comput. J., 9(3), 1149–1155.
Information & Authors
Information
Published In
Journal of Water Resources Planning and Management
Volume 138 • Issue 6 • November 2012
Pages: 651 - 659
Copyright
© 2012 American Society of Civil Engineers.
History
Received: Jul 14, 2011
Accepted: Jan 5, 2012
Published online: Jan 12, 2012
Published in print: Nov 1, 2012
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.