پیوندهای مفید
آمار
| تعداد نشریات | 7 |
| تعداد شماره‌ها | 406 |
| تعداد مقالات | 5,432 |
| تعداد مشاهده مقاله | 5,615,915 |
| تعداد دریافت فایل اصل مقاله | 5,051,919 |
ارائه روشی مبتنی بر الگوریتم‌های بهینه‌ساز گرگ خاکستری و رقابت استعماری در فرآیند بهره‌برداری بهینه از مخزن سد | ||
| نشریه مهندسی عمران امیرکبیر | ||
| مقاله 1، دوره 52، شماره 5، مرداد 1399، صفحه 1027-1046 اصل مقاله (1.58 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22060/ceej.2018.15049.5818 | ||
| نویسندگان | ||
| سمیه امامی* 1؛ یحیی چوپان2؛ فرزین سلماسی3 | ||
| 1گروه مهندسی آب دانشگاه تبریز | ||
| 2گروه مهندسی آب دانشگاه علوم کشاورزی‌و منابع طبیعی گرگان | ||
| 3دانشیار گروه مهندسی آب دانشگاه تبریز | ||
| چکیده | ||
| در طول سه دهه اخیر، مسئله بهره برداری بهینه از مخازن سد ها در بین پژوهشگران مدیریت منابع آب از توجه زیادی برخوردار بوده است. در همین راستا، با توجه به کارایی و قابلیت های بالای الگوریتم های فراابتکاری در این پژوهش به پیش‌بینی ذخیره مخزن سد شهرچای ارومیه و ارائه یک برنامه پیش بینی کوتاه مدت چند سال آتی، با استفاده از الگوریتم بهینه ساز گرگ خاکستری GWO ) ) پرداخته شده است. الگوریتم GWO از سلسله مراتب رهبری و سازوکار شکار گرگ های خاکستری در طبیعت تقلید می کند. در این الگوریتم از چهار نوع گرگ خاکستری شامل آلفا، بتا، دلتا و امگا برای شبیه سازی سلسله مراتب رهبری استفاده شده است. در این پژوهش با در نظر گرفتن افق دید برنامه ریزی یک ساله و بازه های زمانی ماهانه، ابتدا الگوریتم GWO برای مسئله پیش بینی ذخیره مخزن سد شهرچای ارومیه طی دوره آماری 93-1385 ،به خوبی ارزیابی و نتایج با روش بهینه سازی الگوریتم رقابت استعماری ICA )) مقایسه شد. نتایج نشان داد الگوریتم GWO ،با دقت 90 %نتایج بسیار مطلوب تری در یافتن جواب بهینه، سرعت همگرایی و هزینه ی محاسباتی کم تری در مقایسه با الگوریتم ICA ارائه می کند. نتایج این پژوهش نشان داد الگوریتم GWO ،الگوریتمی مناسب در حل مسئله بهره‌برداری بهینه از سیستم مخزن سد می‌باشد. | ||
| کلیدواژه‌ها | ||
| الگوریتم گرگ خاکستری؛ الگوریتم رقابت استعماری؛ بهره برداری بهینه؛ پیش بینی؛ سد شهرچای | ||
| موضوعات | ||
| آب و سازه های هیدرولیکی؛ سیستم های انتقال آب؛ مخازن سدها؛ مدیریت منابع آب | ||
| عنوان مقاله [English] | ||
| Presentation of a Method Based on Gray Wolf Optimizer and Imperialist Competitive Algorithms in Optimal Operation of Dam Reservoir | ||
| نویسندگان [English] | ||
| Somayeh Emami1؛ Yahya Choopan2؛ Farzin Salmasi3 | ||
| 1Water Engineering Department of Tabriz | ||
| 2Water Engineering Department of Gorgan university | ||
| 3Associate Professor Water Engineering Department of Tabriz University | ||
| چکیده [English] | ||
| In recent decades, the optimal use of dam reservoirs among water resource management researchers has been of great interest. So, due to the high performance and capabilities of evolutionary algorithms, in this study, using gray wolf optimizer algorithm (GWO) to predict Urmia Shaharchay dam reservoir and present a short-term forecast program for next years. The gray wolf algorithm imitates the hierarchy of leadership and the mechanism of hunting gray wolves in nature. In this algorithm, four types of gray wolves consist of alpha, beta, delta, and omega have been used to simulate the hierarchy of leadership. In this study, considering the annual planning and monthly intervals, the GWO algorithm was firstly evaluated for prediction storage of Urmia Shaharchay reservoir during 2006-2014 years and the results compared with the ICA algorithm. The results showed that the GWO algorithm, with a high accuracy of 90%, provides better results in finding optimal response, convergence rate, and lower computational cost compared to the ICA algorithm. The results of this study indicated that GWO algorithm, an appropriate algorithm to solve the optimal operation of the dam reservoir system problem. | ||
| کلیدواژه‌ها [English] | ||
| Gray Wolf Algorithm, Imperialist Competitive Algorithm, Optimal Operation, Prediction, Shaharchay Dam | ||
| مراجع | ||
|
[1] S. Emami, Predict of dam reservoir using Imperialist Competitive Algorithm (case study: Umia Shaharchay dam), Water Engineering, BS.C. Thesis, Urmia University, (2014). [2] M. M. Mirsanjouri, F. Mohammadyari, R. Basiri, F. Hamidipour, Modeling the EC, SAr and TDS in groundwater using artificial neural network (case study:Mehran and Dehloran plain). Human and Environmental Quarterly Journal, 42 (2015) pp. 1-12. (In Persian) [3] Haupt, R. L. and Haupt, S. E, Practical genetic algorithms, John Wiley & Sons, (2004). [4] J. Kennedy, R. Eberhart, Particle swarm optimization, Proceedings of ICNN’9- International Conference on Neural Networks, (1995) pp. 1942–1948. [5] M. Dorigo, V. Maniezzo, A. Colorni, Ant system: Optimization by a colony of cooperating agents, IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 26(1) (1996) pp. 29–41. [6] D. Karaboga, B. Basturk, A powerful and efficient algorithm for numerical function optimization : artificial bee colony (ABC) algorithm, Journal of Global Optimization, 39, (2007) 459–471. [7] R. Moieni, Performance Evaluation of the Ant Colony Optimization Algorithm for the Optimal Operation of a Multi-Reservoir System: Comparing Four Algorithms, Iran-Water Resources Research, 11(2) (2015) 29-46. [8] www.azmet.ir [9] S. Samadianfard, R. Delirhasannia, Forecasting Shaharchay river flow in lake Urmia basin using Genetic programming and M5 model tree. Journal of Water and Soil, 29(5) (2015) 1190-1206. [10] Anonymous, Technical report of Shaharchay reservoir dam, Sakoo Consulting Engineers, Regional Water Company of West-Azerbaijan, Iran, (2008). [11] S. Mirjalili, S. M. Mirjalili A. Lewis, Grey Wolf Optimizer, Advances in Engineering Software, 69, 920140 46-61. [12] L. D. Mech, Alpha Status, dominance, and division of labor in wolf packs, Canadian Journal of Zoology, 77(8) (1999) 1196-1203. [13] C. Muro, R. Escobedo, L. Spector and R. Coppinger, wolf-pack (canis lupus) hunting strategies emerge from simple rules in computational simulations, Behavioural Processes, 88(3) (2011) 192-197. [14] ssM. Khishe, M. R. Mosavi, A. Ghamgosar, M. J.Ghalandari, Classification of sonar data set using the gray wolf optimizer algorithm, Electronics Industries Quartely, 7(1) (2016) 27-41. [15] E. Atashpaz-Gargari C. Lucas, Imperialist competitive algorithm: An algorithm for optimization inspired by imperialistic competition, IEEE Congress on Evolutionary Computation, CEC, (2007) pp. 4661–4667. [16] H. Emami, Prediction of dam reservoir volume using Election algorithm. 1st National Conference on New Technologies in Electrical and Computer Engineering. University of Isfahan, 27 January, (2018). [17] Y. Dinpashoh, M. T. Sattari, S. Ebrahimi, S. Darbandi, Optimum operation of reservoir using the Genetic Algorithm and Particle Swarm optimization (case study: Alavian dam). Soil and Water Journal, (2017) 27(2) 17-29. [18] J. Anand, A. K. Gosain, R. Khosa, Optimisation of multipurpose reservoir operation by coupling SWAT and Genetic algorithm for optimal operating policy (Case Study: Ganga river basin). Preprints, (2018) doi:10.20944/ preprints201803.0199.v1. [19] A. S. Parlikar, P. D. Dahe, M. Vaidya, K. Sharma, Reservoir releases using Genetic algorithm. International Journal of Innovations & Advancement in Computer Science, 6 (11) (2017) 363-366. [20] A. Kangrang, H. Prasanchum, R. Hormwichian, Development of future rule curves for multipurpose reservoir operation using conditional Genetic and Tabu search algorithms. Advances in Civil Engineering, 2018 (2018) Article ID 6474870, 10 pages. [21] S. M. Ghadami, B. Ghahreman, M. Sharifi, H. Rajabi Mashhadi, Optimization of the operation of multireservoir systems using Genetic algorithm, Iranian Water Resources Research, 2(10) (2009) 1-15. [22] E. Atashpaz Gargari, Development of social optimization algorithm and its efficiency review. Master’s Thesis, Faculty of Electrical and Computer Engineering, University of Tehran, (2007). [23] M. A. Samandizadeh, A. Adib, A. Ahmadianfar, Optimal operation of Amirkabir reservoir using genetic algorithm and dynamic programming for Markov scenarios and observation series, 4th Water Resources Management Conference, Amirkabir University, Tehran, (2011). [24] A. Mehdipour, O. Bozorgh-Hadad, Operation optimization of multi-objective dams using particle optimization algorithm, Journal of Water and Wastewater, 4 (2012) 97-105. [25] R. Wardlaw, M. Sharif, Evaluation of Genetic Algorithm for optimal reservoir system operation, Journal of Water Resources Planning and Management, 125 (1) (1999). [26] Jian–Xia, Chang, H. Qiang Wang, Yi–Min, Genetic Algorithms for optimal reservoir dispatching, Journal Water Resource Managament, ASCE, 19 (2005) 321-331. [27] H. S. Fahmy, J. P. King, M. W. Wentzel, J. A. Seton, Economic optimization of river management using algerthms, Paper No. 943034, ASAE, 1994 Int. Summer Meating , Am. Soc of Agricultural Engrs. St. Joseph. [28] R. Moeini, M. H. Afshar, Extension of the constrained Ant Colony optimization Algorithms for optimal operation of multi-reservoir systems, Journal of Hydro-informatics, 15(1) (2013) 155-173. [29] J. A. Adeyemo, Reservoir operation using multi-objective evolutionary Algorithms a review, Asian Journal of scientific Research, 4(1) (2011) 16-27. [30] T. D. Asfaw, S. Saiedi, Optimal Short-term cascade reservoir operation using Genetic Algorithm. Asian Journal of scientific Research, 4(3) (2011) 297-305. [31] O. Bozorg Haddad, M. Moravej, H. A. Loáiciga, Application of the Water Cycle Algorithm to the Optimal Operation of Reservoir Systems, Journal of Irrigation and Drainage Engineering, 141(5) (2014) 04014064. [32] S. Sonaliya, T. M. V. Suryanarayana, International Journal of Innovative Research in Science Engineering and Technology, 3(6) (2014) 13681-13687. [33] K. Saber-Chenari, H. Abghari, M. Erfanian, Efficiency of Genetic Algorithm in optimal operation of dam reservoir in drought condition, 9th National Conference on Watershed Management Sciences, Watershed Management in Arid Areas (Flood and Drought), Department of Watershed of Watershed Management, Faculty of Natural Resources and Desertification, Yazd University, (2013). [34] A. Moghaddam, M. Montaseri, H. Rezaei, The application of GA, SMPSO and HGAPSO in optimal reservoirs operation, Journal of Water and Soil, 30(4) (2016) 11021113. [35] A. Afshar, M. J. E. Skardi, F. Masoumi, Optimizing water supply and hydropower reservoir operation rule curves: An imperialist competitive algorithm approach, Engineering Optimization, vol. 47(9) (2015) 1208– 1225. [36] M. H. Afshar R. Moeini, Partially and fully constrained ant algorithms for the optimal solution of large scale reservoir operation problems, Water Resource Management, 22(12) (2008) 1835- 1857.
| ||
|
آمار تعداد مشاهده مقاله: 2,331 تعداد دریافت فایل اصل مقاله: 3,003 |
||