Document Type : Original Article
Author
Abstract
In this study performance of evolutionary optimization methods for designing of cross section of heterogeneous earth dams is investigated. The methods applied were Artifical Fish Swarm (AFSA), Shuffled Complex Evolution (SCE) and Simulated Annealing (SA) algorithms. The model consisted of a nonlinear optimization function by applying different constraints such as slope stability constraints and geometrical dimensions. Design variables in optimization process were the geometrical parameters in cross section of earthen dam and stability safety factors constraints were determined as explicit functions according to design variables by using analyses results such as seepage and slopes stability analysis for a set of sample designs by using linear regression models. Efficiency of the optimization methods in identifying the global optimum point was compared according to mean performance and mean time required for calculations. After optimization of dimensions in Barzok dam by using SCE, AFSA and SA methods, dam volume was reduced 38, 37 and 30 percent respectively as compared to the primary design volume. Results showed that SCE method is more efficient than the SA and AFSA methods in achieving the optimal dimensions in cross section of earth dam.
Keywords: Artificial Fish Swarm, Earth Dam, Optimization, Shuffled Complex Evolution, Simulated Annealing
Keywords
AbdulHussain, I. A., Kashyap, D. and Hari Prasad, K. S. 2007. Seepage modeling assisted optimal design of a homogeneous earth dam: procedure evolution. Irrig. Drain. Eng. 133(2): 116-130.
Ajami, N. K., Gupta, H., Wagener, T. and Sorooshian, S. 2004. Calibration of a semi-distributed hydrologic model for streamflow estimation along a river system. J. Hydrol. 298, 112-135.
Anon. 2003. Engineering and design: slope stability. Engineering Manual. EM 1110-2-1902. U. S. Army Corps of Engineers. Washington, DC.
Barakat, S. A. and Altoubat, S. 2009. Application of evolutionary global optimization techniques in the design of RC water tanks. Eng. Struct. 31, 332-344.
Cerny, V. 1985. A thermodynamic approach to the traveling salesman problem: An efficient simulation. J. Optim. Theory Appl. 45, 41-51.
Chen, H., Wang, S., Li, J. and Li, Y. 2006. A hybrid of artificial fish swarm algorithm and particle swarm optimization for feed-forward neural network training. Proceeding of New Weaponry Technology & Application.
Cunha, M. C. and Sousa, J. 2001. Hydraulic infrastructures design using simulated annealing. J. Infrastruct. Syst. 7(1): 32-39.
Dougherty, D. E. and Marryott, R. A.1991. Optimal groundwater management: I. Simulated annealing. Water Resour. Res. 27(10): 2493-2508.
Duan, Q., Sorooshian, S. and Gupta, V. K. 1992. Effective and Efficient global optimization for conceptual rainfall-runoff models. Water Resour. Res. 28(4): 1015-1031.
Duan, Q., Gupta, V. K. and Sorooshian, S. 1993. A shuffled complex evolution approach for effective and efficient global minimization. J. Optimiz. Theory App. 76(3): 501-521.
Duan, Q., Sorooshian, S. and Gupta, V. K. 1994. Optimal use of the SCE-UA global optimization method for calibrating watershed models. J. Hydrol. 158(3-4): 265-284.
Goldman, F. E. and Mays, L.W. 1999. The application of simulated annealing to the optimal operation of water systems. Proceedings of the 26th Annual Water Resources Planning and Management Conference. June 6-9. Tempe. Arizona.
Hammouri, N. A. Malkawi, A. I. H. and Yamin, M. M. A. 2008. Stability analysis of slope using the finite element method and limiting equilibrium approach. Bulletin of Engineering Geology and the Environment.
Holland, J. 1992. Adaptation in Natural and Artificial Systems. 2nd Ed. Cambridge, Massachusetts: MIT Press.
Huang, X., Liao, W., Lei, X., Jia, Y., Wang, Y., Wang, X., Jiang, Y. and Wang, H. 2014. Parameter optimization of distributed hydrological model with a modified dynamically dimensioned search algorithm. J. Environ. Model. Softw. 52, 98-110.
Jiang, M., Wang, Y., Pfletschinger, S., Lagunas, M. A. and Yuan, D. 2007. Optimal Multiuser Detection with Artificial Fish Swarm Algorithm. Proceeding of 3rd International Conference on Intelligent Computing (ICIC). Aug. 21-24. Qingdao. China.
Ketabchi, H. and Ataie-Ashtiani, B. 2015. Evolutionary algorithms for the optimal management of coastal groundwater: a comparative study toward future challenges. J. Hydrol. 520, 193-213
Khodabakhshi, F., Ghirian, A. R. and Khakzad, N. 2009. Applying simulated annealing for optimal operation of multi-reservoir systems. J. Eng. Appl. Sci. 2(1): 80-87. (in Persian)
Kirkpatrick, S., Gelatt, C. D. Jr. and Vecchi, M. P. 1983. Optimization by simulated annealing. Science. 220, 671-680.
Lerma, N., Paredes-Arquiola, J., Andreu, J., Solera, A. and Sechi, G. M. 2015. Assessment of evolutionary algorithms for optimal operating rules design in real water resource systems. Environ. Modell. Softw. 69, 425-436.
Li, L. X., Shao, Z. J. and Qian, J. X. 2002. An optimizing method based on autonomous animate: fish swarm algorithm. Syst. Eng. Theory Pract. 11, 32-38.
Metropolise, N., Rosenbluth, A., Teller, A. and Teller, E. 1953. Equation of state calculations by fast computing machines. J. Chem. Phys. 21, 1087-1092.
Montaseri, M., Deiminiat, A. and Ghezelsofloo, A. A. 2010. Optimization of clay core dimensions in earth dams using genetic algorithm. Water Soil Sci. 20(3): 73-76. (in Persian)
Murthy, G. S. R., Murty, K. G. and Raghupathy, G. 2013. Designing Earth Dams Optimally. 40th Anniversary Vol. Indian Association for Productivity Quality Deformations of the Dam and Reliability (IAPQR).
Nelder, J. A. and Mead, R. 1965. A simplex method for function minimization. Comput. J. 7(4): 308-313.
Neshat, M., Sepidnam, G., Sargolzaei, M. and Najaran-Toosi, A. 2014. Artificial fish swarm algorithm: a survey of the state-of- the-art, hybridization, combinatorial and indicative applications. Artif. Intell. Rev. 42(4): 965-997.
Van Laarhoven, P. J. and Aarts, E. H. 1987. Simulated Annealing: Theory and Applications (Mathematics and Its Applications). Springer.
Price, W. L. 1987. Global optimizationa lgorithmsf or a CAD workstation. J. Optimiz. Theory App. 55(1): 133-146.
Ranjan, G. and Rao, A. S. R. 2000. Basic and Applied Soil Mechanics. New Age International Pub.
Skahill, B. E. and Doherty, J. 2006. Efficient accommodation of local minima in watershed model calibration. J. Hydrol. 329, 122-139.
Xiao, L. 2010. A clustering algorithm based on artificial fish school. 2nd International Conference on Computer Engineering and Technology. Apr. 16-18. Chengdu, China.
XU, Y. Q., Unami, K. and Kawachi. T. 2003. Optimal hydraulic design of earth dam cross section using saturated- unsaturated seepage flow model. Adv. Water Resour. 26, 1-7.
Yapo, P. O., Gupta, H. V. and Sorooshian, S. 1998. Multi- objective global optimization for hydrologic models. J. Hydrol. 204, 83-97.
Yazdani, D., Golyari, S. and Meybodi, M. R. 2010. A new hybrid approach for data clustering. 5th International Symposium on Telecommunication (IST). Tehran, Iran.
Yin, Z., Zong, Z., Sun, H., Wu, Z. and Yang, Z. 2012. A complexity-performance-balanced multiuser detector based on artificial fish swarm algorithm for DS-UWB systems in the AWGN and multipath environments. J. Adv. Signal Proc. Doi:10.1186/1687-6180-2012-229.
Zameer, A., Mirza, S. M. and Mirza, N. M. 2014. Core loading pattern optimization of a typical two-loop 300 MWe PWR using Simulated Annealing (SA), novel crossover Genetic Algorithms (GA) and hybrid GA (SA) schemes. Ann. Nucl. Energy. 65, 122-131.
)