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Performance Assessment of Grasshopper Optimization Algorithm for Optimizing Coefficients of Sediment Rating Curve | ||
| AUT Journal of Civil Engineering | ||
| مقاله 4، دوره 2، شماره 1، شهریور 2018، صفحه 39-48 اصل مقاله (633.71 K) | ||
| نوع مقاله: Research Article | ||
| شناسه دیجیتال (DOI): 10.22060/ajce.2018.14511.5480 | ||
| نویسندگان | ||
| M.J Zeynali* ؛ A. Shahidi | ||
| Department of Water Engineering College of Agriculture University of Birjand, Birjand, Iran | ||
| چکیده | ||
| One of the most common methods for estimating suspended sediment of rivers is sediment rating curve. For better estimation of the amount of suspended sediment based on the sediment curve rating equation, it is possible to optimize its coefficients. One of the methods used for optimizing the coefficients of the sediment curve rating equation is taking advantage of meta-heuristic algorithms. The main objective of this research is the use of grasshopper optimisation algorithm to optimize the relationship between discharge and sediment discharge and comparison the results of this model with genetic algorithms and particle swarm. With respect to the objective function, which minimizes the difference between the measured values of the sediment and the calculated values of that, the optimal values of these coefficients are determined. The results of this research indicated since the objective function, grasshopper optimisation algorithm compared with Genetic algorithm and particle swarm optimization has a good performance. So that grasshopper optimisation algorithm with 7694507 values has the best performance in this problem and then PSO and GA algorithms with 7702357 and 7703750 have a good performance and finally this value in sediment rating curve is equal to 9163544. | ||
| کلیدواژه‌ها | ||
| genetic algorithm؛ Met-Heuristic Algorithm؛ Particle swarm algorithm؛ Suspended Sediment | ||
| مراجع | ||
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[1] A. Altunkaynak, Sediment load prediction by genetic algorithms, Advances in Engineering Software, 40(9) (2009) 928-934. [2] O.M.R. Pour, L.T. Shui, A.A. Dehghani, Genetic algorithm model for the relation between flow discharge and suspended sediment load (Gorgan river in Iran), Electronic Journal of Geotechnical Engineering, 16 (2011) 539-553. [3] H. Ebrahimi, E. Jabbari, M. Ghasemi, Application of Honey-Bees Mating Optimization algorithm on Estimation of Suspended Sediment Concentration, World Appl Sci J, 22(11) (2013) 1630-1638.
[4] O. Mohammadrezapour, M. JavadZeynali, Comparison of Ant Colony, Elite Ant system and Maximum–Minimum Ant system Algorithms for Optimizing Coefficients of Sediment Rating Curve (Case Study: Sistan River), Journal of Applied Hydrology, 1(2) (2014) 55-66.
[5] A. Talebi, A. Bahrami, M. Mardian, J. Mahjoobi, Determination of optimized sediment rating equation and its relationship with physical characteristics of watershed in semiarid regions: A case study of Pol-Doab watershed, Iran, Desert, 20(2) (2015) 135-144. [6] M. Nazeri Tahroudi, K. Khalili, M. Abbaszadeh Afshar, Z. Nazeri Tahroudi, F. Ahmadi, M. Motallebian, Evaluation of Univariate, Multivariate and Combined Time Series Model to Prediction and Estimation the Mean Annual Sediment (Case Study: Sistan River), Quarterly Journal of Environmental Erosion Research, 6(1) (2016) 52-70. [7] K.J. Gregory, D.E. Walling, Fluvial processes in instrumented watersheds, Institute of British Geographers, 1974. [8] D. Walling, Suspended sediment and solute response characteristics of the river Exe, Devon, England, Research in fluvial systems, (1978) 169-197. [9] C.M. Topaz, A.J. Bernoff, S. Logan, W. Toolson, A model for rolling swarms of locusts, The European Physical Journal Special Topics, 157(1) (2008) 93-109. [10] S. Saremi, S. Mirjalili, A. Lewis, Grasshopper optimisation algorithm: theory and application, Advances in Engineering Software, 105 (2017) 30-47. [11] R. Eberhart, J. Kennedy, A new optimizer using particle swarm theory, in: Micro Machine and Human Science, 1995. MHS’95., Proceedings of the Sixth International Symposium on, IEEE, 1995, pp. 39-43. | ||
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