Abstract
During the last two decades, the issue of optimal operation of dam reservoirs has received much attention among water resources management researchers. Also, the operation of dam reservoirs in terms of diversity of decision-making and target functions has complexities that sometimes cannot be solved with traditional optimization methods and requires a lot of time and money. Therefore, the use of new tools and advanced methods in solving such problems is inevitable. In this review article, 76 research articles from the most prestigious journals in the world between 2002 and 2021 have been reviewed. Meta-analysis method (PRISMA) has been used for systematic review and selection of the studied articles. This research includes a comprehensive review regarding the application of different optimization models in the exploitation of dam reservoirs and can provide a critical insight into the selection of used models and the accuracy of different modeling methods in the optimization of dam reservoirs. The investigated models include single-objective and multi-objective reservoirs, as well as single and multi-reservoirs. The results of this study show that researchers' interest and popularity in hybrid algorithms (HA) (18.68%) and GA (16.48%) were more than the traditional or improved versions. Also, hybrid algorithms showed better results than single meta-heuristic algorithms and traditional methods. According to the obtained results, it can be stated that the meta-heuristic algorithms used are capable of solving complex models in reservoir operation problems with a fast convergence rate.
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Abbreviations
- ABC:
-
Artificial Bee Colony
- ACDE:
-
Adaptive Chaotic Differential Evolution
- ACO:
-
Ant Colony Optimization
- ADE:
-
Adaptive Differential Evolution
- AFSA:
-
Artificial Fish Swarm Algorithm
- AGA:
-
Adaptive Genetic Algorithm
- AI:
-
Artificial Intelligence Algorithm
- ANFIS:
-
Adaptive Network-based Fuzzy Inference System
- ANN:
-
Artificial Neural Network
- AOA:
-
Accompanying Progressive Optimality
- APO:
-
Artificial physics optimization
- ARIW:
-
Adaptive random inertia weight
- BA:
-
Bat Algorithm
- BBO:
-
Biogeography-based optimization
- C-GA:
-
Chaos-genetic algorithm
- CGA:
-
Constrained genetic algorithm
- CIPSO:
-
Constrained version of IPSO algorithm
- COA:
-
Chaos Optimization Algorithm
- CPSO:
-
Chaotic particle swarm optimization
- CSA:
-
Clonal Selection Algorithm
- CSA:
-
Cuckoo Search Algorithm
- CSO:
-
Cat Swarm Optimization
- DE:
-
Differential Evolution
- DP:
-
Dynamic Programming
- ELM:
-
Extreme Learning Machine
- EMPSO:
-
Elitist-mutated particle swarm optimization
- FA:
-
Firefly algorithm
- FCACOA:
-
Fully Constrained Ant Colony Optimization
- FFNN:
-
Feed-forward neural network
- FIS:
-
Fuzzy Inference System
- FOA:
-
Fruit Fly Optimization Algorithm
- GA:
-
Genetic Algorithm
- GA–KNN:
-
Genetic Algorithm–K Nearest Neighborhood
- BSGA:
-
Bayesian Stochastic GA
- GAOM:
-
Genetic Algorithm Optimization Model
- GEP:
-
Gene Expression Programming
- GP:
-
Genetic Programming
- GPR:
-
Gaussian Process Regression
- GSA:
-
Gravity Search Algorithm
- GWO:
-
Grey Wolf Optimizer
- GWO:
-
Grey Wolf Optimizer
- HBMO:
-
Honey-Bee Mating Optimization
- HB-SA:
-
Hybrid bat–swarm algorithm
- HSA:
-
Harmony Search Algorithm
- HSLSO:
-
Hybridizing sum-local search optimizer
- HWGA:
-
Hybrid whale-genetic algorithm
- IBA:
-
Improved bat algorithm
- ICA:
-
Imperialist Competitive Algorithm
- IDEPSO:
-
Improved hybrid DE and PSO
- IDP:
-
Incremental Dynamic Programming
- IGWO:
-
Improved Grey Wolf Optimization
- IPSO:
-
Improved particle swarm optimization
- ISO:
-
Implicit stochastic reservoir optimization
- IWO:
-
Invasive weed optimization
- JA:
-
Jaya Algorithm
- KA:
-
Kidney Algorithm
- LBA:
-
Lévy Flight Bat Algorithm
- LFWOA:
-
Lévy flight and distribution
- LP:
-
Linear Programming
- LPA:
-
Lion Pride Algorithm
- LSO:
-
Lion Swarm Optimization
- LTHG:
-
Long Term Hydropower Generation
- LTMIF:
-
Long-Term Mean Inflow Forecast
- MA:
-
Metaheuristic algorithms
- MBA:
-
Monarch Butterfly Algorithm
- MFOA:
-
Modified Fruit Fly Optimization Algorithm
- ML:
-
Machine Learning
- MSA:
-
Moth Swarm Algorithm
- MS-DEPSO:
-
Multi-strategy
- NDSs:
-
Nondominated solutions
- NFIS:
-
Neuro-Fuzzy Inference System
- NFL:
-
No Free Lunch theorem
- NLP:
-
Non-Linear Programming
- NSGA-II:
-
Non-Dominated Sorting Genetic Algorithm-II
- NSGA-III:
-
Non-Dominated Sorting Genetic Algorithm-III
- PA-DDS:
-
Pareto Archived Dynamically Dimensioned Search
- PCACOA:
-
Partially Constrained Ant Colony Optimization Algorithm
- PFDO:
-
Perfect-Forecast Deterministic Optimization
- POA:
-
Progressive Optimization Algorithm
- PSO:
-
Particle Swarm Optimization
- R:
-
Correlation Coefficient
- R2 :
-
Coefficient of Determination
- RVM:
-
Relevance Vector Machine
- SA:
-
Simulated ANeuro-Fuzzynnealing
- SCE:
-
Shuffled Complex Evolution
- SDP:
-
Stochastic dynamic programming
- SLGA:
-
Self-Learning Genetic Algorithm
- SM:
-
Simulation Model
- SMA:
-
Spider Monkey Algorithm
- SMLA:
-
Shark Machine Learning Algorithm
- SOM:
-
Self-Organizing Map
- SOP:
-
Standard reservoir operating policy
- SOS:
-
Symbiotic Organisms Search
- SQP:
-
Sequential Quadratic Programming algorithm
- SVM:
-
Support Vector Machine
- SVR:
-
Support Vector Regression
- TLBO:
-
Teaching Learning Based Optimization
- VNS:
-
Variable Neighborhood Search
- WA:
-
Weed Algorithms
- WCA:
-
Water Cycle Algorithm
- WOA:
-
Weed Optimization Algorithm
- WOA:
-
Whale Optimization Algorithm
References
Abd Elaziz M, Dahou A, Abualigah L, Yu L, Alshinwan M, Khasawneh AM, Lu S (2021) Advanced metaheuristic optimization techniques in applications of deep neural networks: a review. Neural Comput Appl 33(21):14079–14099. https://doi.org/10.1007/s00521-021-05960-5
Abualigah L, Diabat A (2020) A comprehensive survey of the Grasshopper optimization algorithm: results, variants, and applications. Neural Comput Appl 32(19):15533–15556. https://doi.org/10.1007/s00521-020-04789-8
Abualigah L, Diabat A (2021) Advances in Sine Cosine Algorithm: A comprehensive survey. Artif Intell Rev 54(4):2567–2608. https://doi.org/10.1007/s10462-020-09909-3
Adeyemo J, Stretch D (2018) Review of hybrid evolutionary algorithms for optimizing a reservoir. S Afr J Chem Eng 25:22–31. https://doi.org/10.1016/j.sajce.2017.11.004
Afshar A, Masoumi F, Solis SS (2015) Reliability based optimum reservoir design by hybrid ACO-LP algorithm. Water Resour Manag 29(6):2045–2058. https://doi.org/10.1007/s11269-015-0927-9
Afshar AM, Emami Skardi MJ, Masoumi F (2014) Optimizing water supply and hydropower reservoir operation rule curves: An imperialist competitive algorithm approach. Eng Optim 47:1208–1225
Afshar AM, Emami Skardi MJ, Masoumi F (2015b) Optimizing water supply and hydropower reservoir operation rule curves: An imperialist competitive algorithm approach. Eng Optim 47:1208–1225
Afshar MH, Moeini R (2008) Partially and fully constrained ant algorithms for the optimal solution of large scale reservoir operation problems. Water Resour Manag 22(12):1835–1857. https://doi.org/10.1007/s11269-008-9256-6
Afshar MH, Shahidi M (2009) Optimal solution of large-scale reservoir-operation problems: Cellular-automata versus heuristic-search methods. Eng Optim 41(3):275–293. https://doi.org/10.1080/03052150802441273
Ahmad A, El-Shafie A, Razali SFM, Mohamad ZS (2014) Reservoir optimization in water resources: A review. Water Resour Manag 28(11):3391–3405. https://doi.org/10.1007/s11269-014-0700-5
Ahmadi M, Bozorg Haddad O, Mariño MA (2014) Extraction of flexible multi-objective real-time reservoir operation rules. Water Resour Manag 28:131–147. https://doi.org/10.1007/s11269-013-0476-z
Ahmadi A, Karamouz M, Moridi A (2010) Robust methods for identifying optimal reservoir operation strategies using deterministic and stochastic formulations. Water Resour Manag 24(11):2527–2552. https://doi.org/10.1007/s11269-009-9566-3
Ahmadianfar I, Adib A, Salarijazi M (2016) Optimizing multireservoir operation: Hybrid of bat algorithm and differential evolution. J Water Resour Plan Manag 142(2):05015010. https://doi.org/10.1061/(asce)wr.1943-5452.0000606
Ahmadianfar I, Khajeh Z, Asghari-Pari S-A, Chu X (2019) Developing optimal policies for reservoir systems using a multi-strategy optimization algorithm. Appl Soft Comput 80:888–903. https://doi.org/10.1016/j.asoc.2019.04.004
Ahmed JA, Sarma AK (2005) Genetic algorithm for optimal operating policy of a multipurpose reservoir. Water Resour Manag 19(2):145–161. https://doi.org/10.1007/s11269-005-2704-7
Akbarifard S, Sharifi MR, Qaderi K (2020) Data on optimization of the Karun-4 hydropower reservoir operation using evolutionary algorithms. Data Brief 29(105048):105048. https://doi.org/10.1016/j.dib.2019.105048
Akbarifard S, Sharifi MR, Qaderi K, Madadi MR (2021) Optimal operation of multi-reservoir systems: comparative study of three robust metaheuristic algorithms. Water Sci Technol Water Supply 21(2):941–958. https://doi.org/10.2166/ws.2020.368
Al-Aqeeli YH, Mahmood Agha OMA (2020) Optimal operation of multi-reservoir system for hydropower production using particle swarm optimization algorithm. Water Resour Manag 34(10):3099–3112. https://doi.org/10.1007/s11269-020-02583-8
Allawi MF, Jaafar O, Mohamad Hamzah F, Ehteram M, Hossain MS, El-Shafie A (2018) Operating a reservoir system based on the shark machine learning algorithm. Environ Earth Sci 77(10). https://doi.org/10.1007/s12665-018-7546-8
Asgari H-R, Bozorg Haddad O, Pazoki M, Loáiciga HA (2016) Weed optimization algorithm for optimal reservoir operation. J Irrig Drain Eng 142(2):04015055. https://doi.org/10.1061/(asce)ir.1943-4774.0000963
Ashofteh P-S, Bozorg-Haddad O, Loáiciga HA (2015) Evaluation of climatic-change impacts on multiobjective reservoir operation with multiobjective genetic programming. J Water Resour Plan Manag 141(11):04015030. https://doi.org/10.1061/(asce)wr.1943-5452.0000540
Atashpaz-Gargari E, Lucas C (2007a) Imperialist competitive algorithm: An algorithm for optimization inspired by imperialistic competition. IEEE Congress Evol Comput
Atashpaz-Gargari E, Lucas C (2007b) Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition, IEEE Congress on Evolutionary Computation, Singapore, 25–28 September, 4661–4667
Azad AS, Rahaman MSA, Watada J, Vasant P, Vintaned JAG (2020) Optimization of the hydropower energy generation using Meta-Heuristic approaches: A review. Energy Rep 6:2230–2248. https://doi.org/10.1016/j.egyr.2020.08.009
Azadi F, Ashofteh P-S, Chu X (2021) Evaluation of the effects of climate change on thermal stratification of reservoirs. Sustain Cities Soc 66:102531. https://doi.org/10.1016/j.scs.2020.102531
Azadi F, Ashofteh P-S, Shokri A, Loáiciga HA (2021b) Simulation-optimization of reservoir water quality under climate change. J Water Resour Plan Manag 147(9). https://doi.org/10.1061/%28ASCE%29WR.1943-5452.0001436
Azari A, Hamzeh S, Naderi S (2018) Multi-objective optimization of the reservoir system operation by using the hedging policy. Water Resour Manag 32(6):2061–2078. https://doi.org/10.1007/s11269-018-1917-5
Azizipour M, Ghalenoei V, Afshar MH, Solis SS (2016) Optimal operation of hydropower reservoir systems using weed optimization algorithm. Water Resour Manag 30(11):3995–4009. https://doi.org/10.1007/s11269-016-1407-6
Bahrami M, Bozorg-Haddad O, Chu X (2018) Application of cat swarm optimization algorithm for optimal reservoir operation. J Irrig Drain Eng 144(1):04017057. https://doi.org/10.1061/(asce)ir.1943-4774.000
Barros MTL, Tsai FT-C, Yang S-L, Lopes JEG, Yeh WW-G (2003) Optimization of large-scale hydropower system operations. J Water Resour Plan Manag 129(3):178–188. https://doi.org/10.1061/(asce)0733-9496(2003)129:3(178)
Bashiri-Atrabi H, Qaderi K, Rheinheimer DE, Sharifi E (2015) Application of Harmony search algorithm to reservoir operation optimization. Water Resour Manag 29(15):5729–5748. https://doi.org/10.1007/s11269-015-1143-3
Bellman R (1957) Dynamic programming. Princeton University Press, Princeton
Bhaskar NR, Whitlatch EE Jr (1980) Derivation of monthly reservoir release policies. Water Resour Res 16(6):987–993. https://doi.org/10.1029/wr016i006p00987
Bilal PM, Rani D (2021) Large scale reservoir operation through integrated meta-heuristic approach. Memetic Computing 13(3):359–382. https://doi.org/10.1007/s12293-021-00327-8
Bozorg-Haddad O, Afshar A, Mariño MA (2011) Multireservoir optimisation in discrete and continuous domains. Proc Inst Civil Eng Water Manag 164(2):57–72. https://doi.org/10.1680/wama.900077
Bozorg-Haddad O, Karimirad I, Seifollahi-Aghmiuni S, Loáiciga HA (2015a) Development and application of the bat algorithm for optimizing the operation of reservoir systems. J Water Resour Plan Manag 141(8):04014097. https://doi.org/10.1061/(asce)wr.1943-5452.0000498
Bozorg-Haddad O, Moravej M, Loáiciga HA (2015b) Application of the water cycle algorithm to the optimal operation of reservoir systems. J Irrig Drain Eng 141(5):04014064. https://doi.org/10.1061/(asce)ir.1943-4774.0000832
Bozorg-Haddad OB, Hosseini-Moghari S-M, Loáiciga HA (2016a) Biogeography-based optimization algorithm for optimal operation of reservoir systems. J Water Resour Plan Manag 142(1):04015034. https://doi.org/10.1061/(asce)wr.1943-5452.0000558
Bozorg-Haddad O, Janbaz M, Loáiciga HA (2016b) Application of the gravity search algorithm to multi-reservoir operation optimization. Adv Water Resour 98:173–185. https://doi.org/10.1016/j.advwatres.2016.11.001
Chang L-C, Chang F-J, Wang K-W, Dai S-Y (2010a) Constrained genetic algorithms for optimizing multi-use reservoir operation. J Hydrol 390(1–2):66–74. https://doi.org/10.1016/j.jhydrol.2010.06.031
Chang L-C, Chang F-J, Wang K-W, Dai S-Y (2010b) Constrained genetic algorithms for optimizing multi-use reservoir operation. J Hydrol 390:66e74
Chaves P, Chang F-J (2008) Intelligent reservoir operation system based on evolving artificial neural networks. Adv Water Resour 31(6):926–936. https://doi.org/10.1016/j.advwatres.2008.03.002
Chaves P, Kojiri T (2007) Stochastic fuzzy neural network: Case study of optimal reservoir operation. J Water Resour Plan Manag 133(6):509–518. https://doi.org/10.1061/(asce)0733-9496(2007)133:6(509)
Chen H-T, Wang W-C, Chen X-N, Qiu L (2020a) Multi-objective reservoir operation using particle swarm optimization with adaptive random inertia weights. Water Sci Eng 13(2):136–144. https://doi.org/10.1016/j.wse.2020.06.005
Chen J, Zhong P-A, Liu W, Wan X-Y, Yeh WW-G (2020b) A multi-objective risk management model for real-time flood control optimal operation of a parallel reservoir system. J Hydrol 590(125264):125264. https://doi.org/10.1016/j.jhydrol.2020.125264
Chiu Y-C, Chang L-C, Chang F-J (2007) Using a hybrid genetic algorithm–simulated annealing algorithm for fuzzy programming of reservoir operation. Hydrol Process 21(23):3162–3172. https://doi.org/10.1002/hyp.6539
Chong KL, Lai SH, Ahmed AN, Zaafar WZW, Rao RV, Sherif M, Sefelnasr A, El-Shafie A (2021) Review on dam and reservoir optimal operation for irrigation and hydropower energy generation utilizing meta-heuristic algorithms. IEEE Access: Practical Innovations, Open Solutions 9:19488–19505. https://doi.org/10.1109/access.2021.3054424
Choong S-M, El-Shafie A, Wan Mohtar WHM (2017) Optimisation of multiple hydropower reservoir operation using artificial bee colony algorithm. Water Resour Manag 31(4):1397–1411. https://doi.org/10.1007/s11269-017-1585-x
Chow VT, Cortes-Rivera G (1974) Applications of DDDP in water resources planning. Res Rep 78
Crawley PD, Dandy GC (1993) Optimal operation of multiple-reservoir system. J Water Resour Plan Manag 119(1):1–17. https://doi.org/10.1061/(asce)0733-9496(1993)119:1(1)
Dahe PD, Srivastava DK (2002) Multireservoir multiyield model with allowable deficit in annual yield. J Water Resour Plan Manag 128(6):406–414. https://doi.org/10.1061/(asce)0733-9496(2002)128:6(406)
Dariane AB, Karami F (2014) Deriving hedging rules of multi-reservoir system by online evolving neural networks. Water Resour Manag 28(11):3651–3665. https://doi.org/10.1007/s11269-014-0693-0
Diwold K, Beekman M, Middendorf M (2011) Honeybee optimisation – an overview and a new bee inspired optimisation scheme. In Adaptation, Learning, and Optimization (pp. 295–327). Springer Berlin Heidelberg
Dobson B, Wagener T, Pianosi F (2019) An argument-driven classification and comparison of reservoir operation optimization methods. Adv Water Resour 128:74–86. https://doi.org/10.1016/j.advwatres.2019.04.012
Eberhart RC, Kennedy J (1995) A new optimizer using particle swarm theory. Proc., 6th Symp. on Micro Machine and Human Science, IEEE Service Center, Piscataway, N.J., 39–43
Ehteram M, Mousavi S-F, Karami H et al (2017a) Fast convergence optimization model for single and multipurposes reservoirs using hybrid algorithm. Adv Eng Inform 32:287–298. https://doi.org/10.1016/j.aei.2017.04.001
Ehteram M, Karami H, Mousavi S-F, Farzin S, Kisi O (2017b) Optimization of energy management and conversion in the multi-reservoir systems based on evolutionary algorithms. J Clean Prod 168:1132–1142. https://doi.org/10.1016/j.jclepro.2017.09.099
Ehteram M, Mousavi S-F, Karami H, Farzin S, Emami M, Binti Othman F, Amini Z, Kisi O, El-Shafie A (2017c) Fast convergence optimization model for single and multi-purposes reservoirs using hybrid algorithm. Adv Eng Inform 32:287–298. https://doi.org/10.1016/j.aei.2017.04.001
Ehteram M, Karami H, Mousavi S-F, El-Shafie A, Amini Z (2017d) Optimizing dam and reservoirs operation based model utilizing shark algorithm approach. Knowl-Based Syst 122:26–38. https://doi.org/10.1016/j.knosys.2017.01.026
Ehteram M, Karami H, Mousavi SF, Farzin S, Celeste AB, Shafie A-E (2018a) Reservoir operation by a new evolutionary algorithm: Kidney algorithm. Water Resour Manag 32(14):4681–4706. https://doi.org/10.1007/s11269-018-2078-2
Ehteram M, Mousavi S-F, Karami H, Farzin S, Deo R, Othman FB, Chau K-W, Sarkamaryan S, Singh VP, El-Shafie A (2018b) Bat algorithm for dam–reservoir operation. Environ Earth Sci 77(13). https://doi.org/10.1007/s12665-018-7662-5
Ehteram M, Karami H, Farzin S (2018c) Reducing irrigation deficiencies based optimizing model for multi-reservoir systems utilizing spider monkey algorithm. Water Resour Manag 32(7):2315–2334. https://doi.org/10.1007/s11269-018-1931-7
Eiben AE, Smith JE (2003) Introduction to evolutionary computing. Berlin, Heidelberg: Springer Berlin Heidelberg
El Harraki W, Ouazar D, Bouziane A, Hasnaoui D (2021) Optimization of reservoir operating curves and hedging rules using genetic algorithm with a new objective function and smoothing constraint: application to a multipurpose dam in Morocco. Environ Monit Assess 193(4):196. https://doi.org/10.1007/s10661-021-08972-9
Elabd S, El-Ghandour HA (2014) Multiobjective optimization of Bigge reservoir operation in dry seasons. J Hydrol Eng 19(9):05014008. https://doi.org/10.1061/(asce)he.1943-5584.0000945
Fallah-Mehdipour E, Bozorg Haddad O, Mariño MA (2012) Real-time operation of reservoir system by genetic programming. Water Resour Manag 26(14):4091–4103. https://doi.org/10.1007/s11269-012-0132-z
Feng Z-K, Niu W-J, Cheng C-T (2019) China’s large-scale hydropower system: operation characteristics, modeling challenge and dimensionality reduction possibilities. Renew Energy 136:805–818. https://doi.org/10.1016/j.renene.2019.01.059
Geem ZW, Kim JH, Loganathan GV (2001) A New Heuristic Optimization Algorithm: Harmony Search. SIMULATION 76(2):60–68. https://doi.org/10.1177/003754970107600201
Garousi-Nejad I, Bozorg-Haddad O, Loáiciga HA, Mariño MA (2016) Application of the firefly algorithm to optimal operation of reservoirs with the purpose of irrigation supply and hydropower production. J Irrig Drain Eng 142(10):04016041. https://doi.org/10.1061/(asce)ir.1943-4774.0001064
Hashimoto T, Stedinger JR, Loucks DP (1982) Reliability, resiliency and vulnerability criteria for water resources system performance evaluation. Water Resour Res 18(1):14–20
He Y, Xu Q, Yang S, Liao L (2014) Reservoir flood control operation based on chaotic particle swarm optimization algorithm. Appl Math Model 38(17–18):4480–4492. https://doi.org/10.1016/j.apm.2014.02.030
Hossain MS, El-shafie A (2013) Intelligent systems in optimizing reservoir operation policy: A review. Water Resour Manag 27(9):3387–3407. https://doi.org/10.1007/s11269-013-0353-9
Hossain MS, El-shafie A (2014) Performance analysis of artificial bee colony (ABC) algorithm in optimizing release policy of Aswan High Dam. Neural Comput Appl 24(5):1199–1206. https://doi.org/10.1007/s00521-012-1309-3
Hosseini-Moghari S-M, Morovati R, Moghadas M, Araghinejad S (2015) Optimum operation of reservoir using two evolutionary algorithms: Imperialist competitive algorithm (ICA) and cuckoo optimization algorithm (COA). Water Resour Manag 29(10):3749–3769. https://doi.org/10.1007/s11269-015-1027-6
Jahandideh-Tehrani M, Bozorg-Haddad O, Loáiciga HA (2019) Application of non-animal-inspired evolutionary algorithms to reservoir operation: An overview. Environ Monit Assess 191(7):439. https://doi.org/10.1007/s10661-019-7581-2
Jalali MR, Afshar A, Mariño MA (2007) Multi-colony ant algorithm for continuous multi-reservoir operation optimization problem. Water Resour Manag 21:1429–1447. https://doi.org/10.1007/s11269-006-9092-5
Jamshidi J, Shourian M (2019) Hedging rules-based optimal reservoir operation using bat algorithm. Water Resour Manag 33(13):4525–4538. https://doi.org/10.1007/s11269-019-02402-9
Jothiprakash V, Shanthi G (2006) Single reservoir operating policies using genetic algorithm. Water Resour Manag 20(6):917–929. https://doi.org/10.1007/s11269-005-9014-y
Jothiprakash V, Shanthi G, Arunkumar R (2011) Development of operational policy for a multi-reservoir system in India using genetic algorithm. Water Resour Manag 25(10):2405–2423. https://doi.org/10.1007/s11269-011-9815-0
Karami H, Farzin S, Jahangiri A, Ehteram M, Kisi O, El-Shafie A (2019) Multi-reservoir system optimization based on hybrid gravitational algorithm to minimize water-supply deficiencies. Water Resour Manag 33(8):2741–2760. https://doi.org/10.1007/s11269-019-02238-3
Karamouz M, Houck MH (1987) Comparison of stochastic and deterministic dynamic programming for reservoir operating rule generation. J Am Water Resour Assoc 23(1):1–9. https://doi.org/10.1111/j.1752-1688.1987.tb00778.x
Karnatapu LK, Annavarapu SP, Nanduri UV (2020) Multi-objective reservoir operating strategies by genetic algorithm and nonlinear programming (GA–NLP) hybrid approach. J Inst Eng (India) Ser A 101(1):105–115. https://doi.org/10.1007/s40030-019-00419-2
Khorsandi M, Ashofteh P-S, Azadi F, Singh VP (2022) Multi-objective firefly integration with the K-nearest neighbor to reduce simulation model calls to accelerate the optimal operation of multi-objective reservoirs. Water Resour Manag 36:3283–3304. https://doi.org/10.1007/s11269-022-03201-5
Kirpatrick S, Gelatt CD, Vecchi MP (1983) Optimization by simulated annealing. Science 220(4598):671–680
Kumar V, Yadav SM (2018) Optimization of reservoir operation with a new approach in evolutionary computation using TLBO algorithm and Jaya algorithm. Water Resour Manag 32(13):4375–4391. https://doi.org/10.1007/s11269-018-2067-5
Lai V, Huang YF, Koo CH, Ahmed AN, El-Shafie A (2021) Optimization of reservoir operation at Klang Gate Dam utilizing a whale optimization algorithm and a Lévy flight and distribution enhancement technique. Eng Appl Comput Fluid Mech 15(1):1682–1702. https://doi.org/10.1080/19942060.2021.1982777
Larson RE (1968) State increment dynamic programming. Elsevier Science, New York
Loucks DP, Stedinger JR, Haith DA (1981) Water resource systems planning and analysis. PrenticeHall, Englewood Cliffs
Mehrabian AR, Lucas C (2006) A novel numerical optimization algorithm inspired from weed colonization. Eco Inform 1(4):355–366. https://doi.org/10.1016/j.ecoinf.2006.07.003
Mendoza Ramírez R, Arganis Juárez ML, Domínguez Mora R, Padilla Morales LD, Fuentes Mariles ÓA, Mendoza Reséndiz A, Carrizosa Elizondo E, Carmona Paredes RB (2021) Operation policies through dynamic programming and genetic algorithms, for a reservoir with irrigation and water supply uses. Water Resour Manag 35(5):1573–1586. https://doi.org/10.1007/s11269-021-02802-w
Mirjalili S, Hashim SZM (2010) A new hybrid PSOGSA algorithm for function optimization. Int Conf Comput Inform Appl
Mirjalili S, Lewis A (2016) The whale optimization algorithm. Adv Eng Softw (Barking, London, England: 1992) 95:51–67. https://doi.org/10.1016/j.advengsoft.2016.01.008
Misaghi M, Yaghoobi M (2019) Improved invasive weed optimization algorithm (IWO) based on chaos theory for optimal design of PID controller. J Comput Des Eng 6(3):284–295. https://doi.org/10.1016/j.jcde.2019.01.001
Moeini R, Babaei M (2020) Hybrid SVM-CIPSO methods for optimal operation of reservoir considering unknown future condition. Appl Soft Comput 95(106572):106572. https://doi.org/10.1016/j.asoc.2020.106572
Moghadam SH, Ashofteh P-S, Loáiciga HA (2022) Optimal water allocation of surface and ground water resources under climate change with WEAP and IWOA modeling. Water Resour Manag 36:3181–3205. https://doi.org/10.1007/s11269-022-03195-0
Mohammadi M, Farzin S, Mousavi S-F, Karami H (2019) Investigation of a new hybrid optimization algorithm performance in the optimal operation of multi-reservoir benchmark systems. Water Resour Manag 33(14):4767–4782. https://doi.org/10.1007/s11269-019-02393-7
Mohammadi M, Farzin S, Mousavi SF, Karami H (2019) Investigation of a new hybrid optimization algorithm performance in the optimal operation of multi-reservoir benchmark systems. Water Resour Manag 33(14):4767–4782. https://doi.org/10.1007/s11269-019-02393-7
Mohammadrezapour O, Yoosefdoost I, Ebrahimi M (2019) Cuckoo optimization algorithm in optimal water allocation and crop planning under various weather conditions (case study: Qazvin plain, Iran). Neural Comput Appl 31(6):1879–1892. https://doi.org/10.1007/s00521-017-3160-z
Mousavi H, Ramamurthy AS (2000) Optimal design of multi-reservoir systems for water supply. Adv Water Resour 23(6):613–624. https://doi.org/10.1016/s0309-1708(99)00053-6
Murray DM, Yakowitz SJ (1979) Constrained differential dynamic programming and its application to multireservoir control. Water Resour Res 15:1017–1027. https://doi.org/10.1029/WR015i005p01017
Nagesh Kumar D, Janga Reddy M (2007) Multipurpose reservoir operation using particle swarm optimization. J Water Resour Plan Manag 133(3):192–201. https://doi.org/10.1061/(asce)0733-9496(2007)133:3(192)
Nandalal KDW, Bogardi JJ (2007) Dynamic programming based operation of reservoirs: applicability and limits. Cambridge University Press, Camb
Neboh A, Adeyemo J, Enitan A, Olugbara O (2015) A review on application of evolutionary algorithms to reservoir operation for hydropower production. Int J Environ Chem Ecol Geol Geophys Eng 9(9):1115–1121
Needham JT, Watkins DW Jr, Lund JR, Nanda SK (2000) Linear programming for flood control in the Iowa and Des Moines rivers. J Water Resour Plan Manag 126(3):118–127. https://doi.org/10.1061/(asce)0733-9496(2000)126:3(118)
Nourani V, Rouzegari N, Molajou A, Hosseini Baghanam A (2020) An integrated simulation-optimization framework to optimize the reservoir operation adapted to climate change scenarios. J Hydrol 587:125018. https://doi.org/10.1016/j.jhydrol.2020.125018
Parsopoulos KE, Vrahatis MN (2004) Recent approaches to global optimization problems through Particle Swarm Optimization. Nat Comput 1:235–306. https://doi.org/10.1023/A:1016568309421
Pinthong P, Das Gupta A, Babel MS, Weesakul S (2009) Improved reservoir operation using hybrid genetic algorithm and neurofuzzy computing. Water Resour Manag 23(4):697–720. https://doi.org/10.1007/s11269-008-9295-z
Qaderi K, Akbarifard S, Madadi MR, Bakhtiari B (2018) Optimal operation of multi-reservoirs by water cycle algorithm. Proc Inst Civil Eng Water Manag 171(4):179–190. https://doi.org/10.1680/jwama.16.00034
Rahmati K, Ashofteh P-S, Loáiciga HA (2021) Application of the grasshopper optimization algorithm (GOA) to the optimal operation of hydropower reservoir systems under climate change. Water Resour Manag 35:4325–4348. https://doi.org/10.1007/s11269-021-02950-z
Rani D, Moreira MM (2010) Simulation–optimization modeling: A survey and potential application in reservoir systems operation. Water Resour Manag 24(6):1107–1138. https://doi.org/10.1007/s11269-009-9488-0
Rani D, Srivastava DK (2016) Optimal operation of Mula reservoir with combined use of dynamic programming and genetic algorithm. Sustain Water Resour Manag 2(1):1–12. https://doi.org/10.1007/s40899-015-0036-1
Reddy MJ, Kumar DN (2006) Optimal reservoir operation using multi-objective evolutionary algorithm. Water Resour Manag 20(6):861–878. https://doi.org/10.1007/s11269-005-9011-1
Rezaei-Estakhroueiyeh A, Jalalkamali N, Momeniroghabadi M (2020) Data on optimal operation of Safarud Reservoir using symbiotic organisms search (SOS) algorithm. Data Brief 29:105327. https://doi.org/10.1016/j.dib.2020.105327
Rouzegari N, Hassanzadeh Y, Sattari MT (2019) Using the hybrid simulated annealing-M5 tree algorithms to extract the if-then operation rules in a single reservoir. Water Resour Manag 33(10):3655–3672. https://doi.org/10.1007/s11269-019-02326-4
SaberChenari K, Abghari H, Tabari H (2016) Application of PSO algorithm in short-term optimization of reservoir operation. Environ Monit Assess 188(12):667. https://doi.org/10.1007/s10661-016-5689-1
Sharifazari S, Sadat-Noori M, Rahimi H, Khojasteh D, Glamore W (2021) Optimal reservoir operation using Nash bargaining solution and evolutionary algorithms. Water Sci Eng 14(4):260–268. https://doi.org/10.1016/j.wse.2021.10.002
Sharifi MR, Akbarifard S, Qaderi K, Madadi MR (2021) Comparative analysis of some evolutionary-based models in optimization of dam reservoirs operation. Sci Rep 11(1):15611. https://doi.org/10.1038/s41598-021-95159-4
Sheibani H, Alizadeh H, Shourian M (2019) Optimum design and operation of a reservoir and irrigation network considering uncertainty of hydrologic, agronomic and economic factors. Water Resour Manag 33(2):863–879. https://doi.org/10.1007/s11269-018-2148-5
Shenava N, Shourian M (2018) Optimal reservoir operation with water supply enhancement and flood mitigation objectives using an optimization-simulation approach. Water Resour Manag 32(13):4393–4407. https://doi.org/10.1007/s11269-018-2068-4
Silva Santos KM, Celeste AB, El-Shafie A (2019) ANNs and inflow forecast to aid stochastic optimization of reservoir operation. J Appl Water Eng Res 7(4):314–323. https://doi.org/10.1080/23249676.2019.1687017
Singh A (2012) An overview of the optimization modelling applications. J Hydrol 466–467:167–182. https://doi.org/10.1016/j.jhydrol.2012.08.004
Soghrati F, Moeini R (2020) Deriving optimal operation of reservoir proposing improved artificial bee colony algorithm: standard and constrained versions. J Hydroinf 22(2):263–280. https://doi.org/10.2166/hydro.2019.125
Suiadee W, Tingsanchali T (2007) A combined simulation–genetic algorithm optimization model for optimal rule curves of a reservoir: a case study of the Nam Oon Irrigation Project, Thailand. Hydrol Process 21(23):3211–3225. https://doi.org/10.1002/hyp.6528
Tabari MMR, Azadani MN, Kamgar R (2020) Development of operation multi-objective model of dam reservoir under conditions of temperature variation and loading using NSGA-II and DANN models: a case study of Karaj/Amir Kabir dam. Soft Comput 24(16):12469–12499. https://doi.org/10.1007/s00500-020-04686-1
Uzlu E, Akpınar A, Özturk HT, Nacar S, Kankal M (2014) Estimates of hydroelectric generation using neural networks with the artificial bee colony algorithm for Turkey. Energy (Oxford, England) 69:638–647. https://doi.org/10.1016/j.energy.2014.03.059
Vedula S, Mujumdar PP, Chandra Sekhar G (2005) Conjunctive use modeling for multicrop irrigation. Agric Water Manag 73(3):193–221. https://doi.org/10.1016/j.agwat.2004.10.014
Wang W, Jia B, Simonovic SP, Wu S, Fan Z, Ren L (2021) Comparison of representative heuristic algorithms for multi-objective reservoir optimal operation. Water Resour Manag 35(9):2741–2762. https://doi.org/10.1007/s11269-021-02864-w
Wardlaw R, Sharif M (1999) Evaluation of genetic algorithms for optimal reservoir system operation. J Water Resour Plan Manag 125 (1):25–33. https://doi.org/10.1061/(asce)0733-9496(1999)125:1(25)
Yakowitz S (1982) Dynamic programming applications in water resources. Water Resour Res 18(4):673–696. https://doi.org/10.1029/wr018i004p00673
Yang X-S (2010) A new metaheuristic bat-inspired algorithm. In nature inspired cooperative strategies for optimization (NICSO 2010) (pp. 65–74). Springer Berlin Heidelberg
Yang G, Guo S, Liu P, Li L, Liu Z (2017) Multiobjective cascade reservoir operation rules and uncertainty analysis based on PA-DDS algorithm. J Water Resour Plan Manag 143(7):04017025. https://doi.org/10.1061/(asce)wr.1943-5452.0000773
Yasar M (2016) Optimization of reservoir operation using Cuckoo Search algorithm: Example of Adiguzel Dam, Denizli, Turkey. Math Probl Eng 2016:1–7. https://doi.org/10.1155/2016/1316038
Yaseen ZM, Allawi MF, Karami H, Ehteram M, Farzin S, Ahmed AN, Koting SB, Mohd NS, Jaafar WZB, Afan HA, El-Shafie A (2019) A hybrid bat–swarm algorithm for optimizing dam and reservoir operation. Neural Comput Appl 31(12):8807–8821. https://doi.org/10.1007/s00521-018-3952-9
Yaseen ZM, Karami H, Ehteram M, Mohd NS, Mousavi SF, Hin LS, Kisi O, Farzin S, Kim S, El-Shafie A (2018) Optimization of reservoir operation using new hybrid algorithm. KSCE J Civ Eng 22(11):4668–4680. https://doi.org/10.1007/s12205-018-2095-y
Yavari HR, Robati A (2021) Developing water cycle algorithm for optimal operation in multi-reservoirs hydrologic system. Water Resour Manag 35(8):2281–2303. https://doi.org/10.1007/s11269-021-02781-y
Zarei A, Mousavi S-F, Eshaghi Gordji M, Karami H (2019) Optimal reservoir operation using bat and particle swarm algorithm and game theory based on optimal water allocation among consumers. Water Resour Manag 33(9):3071–3093. https://doi.org/10.1007/s11269-019-02286-9
Zhang D, Lin J, Peng Q, Wang D, Yang T, Sorooshian S, Liu X, Zhuang J (2018) Modeling and simulating of reservoir operation using the artificial neural network, support vector regression, deep learning algorithm. J Hydrol 565:720–736. https://doi.org/10.1016/j.jhydrol.2018.08.050
Zhang X, Yu X, Qin H (2016) Optimal operation of multi-reservoir hydropower systems using enhanced comprehensive learning particle swarm optimization. J Hydroenviron Res 10:50–63. https://doi.org/10.1016/j.jher.2015.06.003
Zhang Z, Jiang Y, Zhang S, Geng S, Wang H, Sang G (2014) An adaptive particle swarm optimization algorithm for reservoir operation optimization. Appl Soft Comput 18:167e177
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Behrang Beiranvand developed the theory and performed the computations. Parisa-Sadat Ashofteh verified the analytical methods. Parisa-Sadat Ashofteh encouraged Behrang Beiranvand to investigate a specific aspect. Parisa-Sadat Ashofteh supervised the findings of this work. All authors discussed the results and contributed to the final manuscript. Behrang Beiranvand wrote the manuscript with support from Parisa-Sadat Ashofteh. Parisa-Sadat Ashofteh conceived the original idea.
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Beiranvand, B., Ashofteh, PS. A Systematic Review of Optimization of Dams Reservoir Operation Using the Meta-heuristic Algorithms. Water Resour Manage 37, 3457–3526 (2023). https://doi.org/10.1007/s11269-023-03510-3
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DOI: https://doi.org/10.1007/s11269-023-03510-3