Evaluation of Whale, Fruit Fly and Cuckoo Search Algorithms in Optimizing Multi-Objective Operation of Golestan Dam Reservoir Based on Multi-Criteria Decision-Making Method
Subject Areas : Article frome a thesisAlireza Donyaii 1 , Amirpouya Sarraf 2 , hassan ahmadi 3
1 - Civil Engineering Department, Roudehen Branch, Islamic Azad University, Roudehen, Iran
2 - Department of Civil Engineering, Roudehen Branch, Islamic Azad university, Roudehen, Iran
3 - Department of civil engineering, islamic azad university, roudehen branch, roudehen, tehran
Keywords: Whale, cuckoo search and fruit fly Algorithm, Multi-Objective Optimal Operation, Flood control and Supplying Downstream Demand, Multi-Criteria decision-making method, Golestan Dam reservoir,
Abstract :
In this research, after introducing the Whale, Cuckoo search and fruit fly Multi-Objective Optimization Algorithms, their performance individually and compared to each other was evaluated to optimize the Golestan Dam reservoir operation policies with an approach to reduce downstream water demand shortages and flood storage volume management based on reliability, resilience, vulnerability and sustainability criteria. The results showed that the proposed algorithms act differently in optimizing the objective functions. So that the whale multi-objective optimization algorithm leads to better values of objective functions. Regarding to the optimization time, although, there are no significant differences between the cuckoo search and whale algorithms, both perform far better than the fruit fly algorithm and achieve much more convergence over time. When comparing the whale algorithm, as the most efficient algorithm, with the fruit fly algorithm, as the most inefficient algorithm, the model reliability and resilience indices increased by 44% and 52%, respectively, and its vulnerability decreased by 23%, indicating a better performance compared to the other algorithms. Therefore, Whale multi-objective optimization algorithm performs better and converges better than the other algorithms, and cuckoo search and fruit fly multi-objective algorithms ranked second and third.
1) Behzad, A. 2017. Impact of environmental factors on destruction of archaeological sites by TOPSIS model (case study archaeological sites of Darreh Shahr and Abdanan of Ilam province). 14(53): 1-20.
2) Bozorg Haddad, O., Azarnivand, A., Hosseini-Moghari, S. M. and Hugo, A. L. 2016. Development of a Comparative Multiple Criteria Framework for Ranking Pareto Optimal Solutions of a Multiobjective Reservoir Operation Problem, Int. J. Irrigation and Drainage Engineering. 142(7):04016019
3) Dashti, R., Sattari, M. and Nourani, V. 2016. Performance evaluation of differential evolution algorithm in optimum operating of Eleviyan single-reservoir dam system, Journal of Water and Soil Research Conservation. 6(3):61-76
4) Donyaii, A.R., Sarraf, A.P., and Ahmadi, H. 2020a. Multi-Objective Optimal Utilization Policy of Boostan Dam Reservoir Using Whale and NSGA-II Algorithms Based on Game Theory and Shannon Entropy Method, Iranian water researches Journal, In Press. [in Persian].
5) Donyaii, A.R., Sarraf, A.P., & Ahmadi, H. 2020b. Optimization of Reservoir Dam Operation Using Gray Wolf, Crow Search and Whale Algorithms Based on the Solution of the Nonlinear Programming Model Journal of Water and Soil Science, In Press. [in Persian].
6) Fallah Mehdipour and A., Bozorg Haddad, A. 2018. Optimization of Multipurpose Dam Reservoirs Using Particle Collection Optimization Method, Journal of Water and Wastewater. 23(4): 97-105.
7) Fallahi, F., Beheshti, M. and Marashi, S. 2017. Ranking the environmental sustainability in selected Iranian provinces: A comparison of AHP and TOPSIS methods. J. of Quantitative Economics. 14(1): 97-118.
8) Hafezparast, M., Araghinejad, Sh. and Sharifazari, S. 2015. Sustainability Criteria in Assessment of Integrated Water Resources Management in the Aras Basin Based on DPSIR Approach. J. of Water and Soil Conservation. 22(2): 61-77.
9) Hazim, I. and Mesut, G. 2014. Parameter Analysis on Fruit Fly Optimization Algorithm, Journal of Computer and Communications, 2, 137-141.
10) Hojjati, A., Hoseini, F., Ghahreman, B. and Alizadeh, A. 2013. The comparison of the application of heuristic methods in optimization of multi-objective water resources systems, Iranian Journal of Water & Environment Engineering. 1(2): 9- 14.
11) Hosseini Moghari, S.M. and Araghinezhad, sh. 2017 Application of Cuckoo optimization algorithm for optimal operation of hydroelectric ponds. Case study: reservoir of Karun4. Journal of Water Resources Engineering. 10, 19-32.
12) Hosseini Moghari, S.M. and Banihabib M.E. 2014. Optimizing operation of reservoir for agricultural water supply using firefly Algorithm, Journal of Conservation of Soil and Water Resources. 3(4): 17-31.
13) Kaveh, A. and Bakhshpoori, T. 2016. An efficient multi-objective cuckoo search algorithm for design optimization, Advances in Computational Design. 1(1): 87-103
14) Mirjalili, S.A. and Lewis, A. 2016. The Whale optimization algorithm, Advances in Engineering Software 1(95): 51-67.
15) Nabinejad, S. and Mousavi, S. 2013. Simulation-optimization for Basin-wide Optimum Water Allocation Considering System’s Performance and Equity Measures. Journal of Water and Wastewater (parallel title); Ab va Fazilab. 24(2): 70-79.
16) Pan, W.T. 2011. A new evolutionary computation approach: Fruit Fly Optimization Algorithm, Conference of Digital Technology and Innovation Management. Taipei.
17) Pan, Wen-Tsao. 2012. A new Fruit Fly Optimization Algorithm: Taking the financial distress model as an example. Elsevier, Knowledge-Based Systems 26, 69-74.
18) Parhizkari, M. and Mazandarani Zadeh, H. 2019. Multi-Objective Operation Optimization of Hydropower Reservoirs by MOPSO, Case Study: Karun 5 Dam, Iran-Water Resources. 15(10):384 – 381.
19) Parsamehr, A.H. and khosravani, Z. 2017. Determination of drought Using Multi Criteria Decision Making Based on TOPSIS Method (A case study of selective Isfahan Province Stations). Iranian J. of Rangelend and Desert Research. 24(1): 16-29.
20) Pourtabari, M., Maknoun, r. and Ebadi T. 2006. Multi-objective Optimal Model for Surface and Ground-water Conjunctive Use Management Using SGAs and NSGA-II, Journal of water and wastewater. 20 (69): 2 - 12
21) Rajabioun, R. 2011. Cuckoo optimization algorithm. Appl. Soft Computing. 11, 5508-5518.
22) Rani, D. and Moreira, M. 2010. Simulation–optimization modeling: a survey and potential application in reservoir systems operation. Water resources management. 24(6): 1107-1138.
23) Samadianfard, S., Jarhan, S., Salwana, E., Mosavi, A., Shamshirband, S. and Akib, S. 2019. Support Vector Regression Integrated with Fruit Fly Optimization Algorithm for River Flow Forecasting in Lake Urmia Basin. Water. 11, 1934.
24) Sandoval-Solis S, McKinney DC. and Loucks DP. 2011. Sustainability index for water resources planning and management. J Water Resour Plan 137(5):381–390
25) Shannon, C. E., and Weaver, W. 1947. The mathematical theory of communication. Champaign, IL: University of Illinois press.
26) Wang, L., Yuanlong, S. and Shan, L. 2015. An improved fruit fly optimization algorithm and its application to joint replenishment problems, Expert Systems with Applications 42, 4310–4323.
27) Yang, X-S. and Deb, S. 2009. Cuckoo Search via Lévy Flights. In: Proceedings of World congress on nature and biologically inspired computing (NaBIC), Coimbatore, India, pp 210–214.
28) Yang, X.S. and Deb, S. 2011. Multi-objective cuckoo search for design optimization. Computers & Operations Research.40(6): 1616-1624
29) Zaher, M.Y., Karami, H., Ehteram, M., Nuruol Syuhadaa, M., Mousavi, S.F. and Hin, L.S .2018. Optimization of Reservoir Operation using New Hybrid Algorithm, Journal of Civil Engineering. 22(11): 4668–4680.
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