# Reservoir Operation Management with New Multi-Objective (MOEPO) and Metaheuristic (EPO) Algorithms

^{1}

^{2}

^{3}

^{*}

## Abstract

**:**

_{ba}) and logical operators (EPO

_{ad}) modified HR and SOP policies. Multi-Objective EPO (MPOEPO) and GEP with trigonometric functions were used to create a multi-objective policies formula. The results showed that the generation of the operation rules with EPO

_{ad}increased the dam reservoir Performance Indexes (Vulnerability and Reliability Indexes) compared to EPO

_{ba}. Moreover, HR application compared to SOP improves the mean dam reservoir’s Performance Indexes by about 12 and 33% in the baseline and 12 and 21% in the future period (climate change conditions), respectively. The MOO method (MOEPO) improved the Vulnerability and Reliability Indexes by about 36 and 25% in the baseline and by 31 and 26% in the future, respectively, compared to SOP.

## 1. Introduction

- Investigating the reservoir Performance Indexes through the change of operation policies (HR, SOP, and multi-objective optimization)
- Comparing operation policies generated by GEP logical and arithmetic operators
- Developing appropriate policies for the future period.

## 2. Materials and Methods

#### 2.1. Study Area and Input Data

#### 2.1.1. Baseline and Future Temperature and Precipitation

#### 2.1.2. Simulate Inflow to Dam Reservoir

#### 2.1.3. Future Agriculture and Domestic Demands

#### 2.2. Research Models

#### 2.2.1. Reservoir Operation Using Standard Operation Policy (SOP)

- MAE: mean absolute errors as objective function
- RSPt: total demand per month
- rspt: total output based on SOP (observational) in t period.

#### 2.2.2. Reservoir Operation Using Hedging Rule (HR)

_{p}) are obtained using the EPO optimization algorithm based on LSR minimization. These coefficients are the slope of the operation line in a given month (see Appendix A.2 for more details).

#### 2.2.3. Multi-Objective Optimization

#### 2.2.4. Gene Expression Programming

#### 2.2.5. Emperor Penguin Optimization (EPO) and Multi-Objective Emperor Penguin Optimization (MOEPO)

#### 2.2.6. Reservoir Operation Rule Generation Using EPO, MOEPO Algorithms and GEP

_{ad}) and elementary arithmetic operators (EPO

_{ba}) and the coupling of MOEPO with GEP by trigonometric functions. It is worth mentioning that the GEP model performs better in producing output formulas related to the HR and SOP, except for the four elementary arithmetic operators (×, ÷, +, −), there are several other operators such as the multi-criteria function (≤, ≥, <, >) operators of logical functions (if, and) and Boolean function were considered as logical operators. This condition is known as modified GEP in this study.

- The first scenario, development of baseline rules based on the volume of available water in the reservoir using EPO
_{ba}in the baseline condition. - The second scenario, development of baseline rules based on the volume of available water in the reservoir using the EPO
_{ad}in the baseline condition. - The third scenario, development of future rules based on the volume of available water in the reservoir using the EPO
_{ba}under future condition. - The fourth scenario, development of future rules based on the volume of available water in the reservoir using the EPO
_{ad}under future condition.

#### 2.3. Vulnerability and Reliability Indexes

- D
_{t}: Demand volume in the t period - D
_{max}: Maximum demand in the under-review period. - Re
_{t}: the released volume from the reservoir in the t period.

## 3. Results

#### 3.1. Integrate SOP and HR Using EPO_{ad} and EPO_{ba}

#### 3.1.1. Validation of the SOP Simulation with EPO

_{ad}and EPO

_{ba}algorithms. According to the Figure, the convergence rate of EPO

_{ad}and EPO

_{ba}are almost the same; both algorithms reach the final result of the objective function after about 400 iterations; EPO

_{ad}reaches the objective function of 0.32 while the EPO

_{ba}reaches the objective function of 0.75.

_{ad}and EPO

_{ba}algorithms are presented in Table 1. It shows the higher accuracy of EPO

_{ad}in simulating the SOP.

_{ba}and EPO

_{ad}approaches with the minimum objective function value for the baseline period are presented.

- RSP
_{t}: Total released water based on Reservoir System Policy in the t period - AW
_{t}: Available water in the dam reservoir (in the t period).

_{ad}algorithm will improve the performance in the baseline and future conditions.

_{ad}algorithm has a better performance in simulating the SOP, only the EPOad results (after this, referred to as the “SOP”) were used in the continuation.

#### 3.1.2. Validation of the HR Simulation with EPO

_{ad}and EPO

_{ba}algorithms are shown in Figure 5 and Table 3. As can be seen in the Figure, the EPO

_{ad}by objective function of 0.87 compare with EPO

_{ba}by objective function of 0.98 has a better performance in minimizing the MAE index. Examination of the results of the implementation of the HR by the algorithms shows that when using the EPO

_{ad}algorithm in simulating, the total output from the reservoir is more balanced with the demand, so the less objective function is obtained. Comparing the results in Table 2 also shows that the EPO

_{ba}outputs are more similar to the SOP.

_{ba}and EP

_{ad}approach with the minimum objective function value (see Equation (A6) for more details) for the baseline are presented.

#### 3.1.3. Comparison of the Results of HR and SOP in Extracting Allocation Rules in Four Scenarios

_{ad}diagram, the storage volume for 75% of the study period is more than 49 MCM and less than 120 MCM. The released volume from the reservoir for 25% of the study period is less than 20 MCM and more than 42 MCM. For 75% of the study period, the deficiencies amount is less than 1.2 MCM. Corresponding to the EPO

_{ba}diagram, the storage magnitude for 75% of the study period is more than 96 MCM and less than 171 MCM. For 50% of the study period, the released volume is between 22 and 40 MCM. For 75% of the study period, the amount of deficiencies is less than 1.8 MCM. According to the SOP diagram, for 50% of the months, the reservoir storage is about 92–41 MCM. The released volume for 75% of the study period is more than 18 MCM and less than 44 MCM. For 75% of the period, the deficit is less than 2.2 MCM.

_{ad}in Figure 7, the storage is over 33 MCM and less than 65 MCM in 75% of the study period. For 50% of the study period, the released volume is between 38 and 77 MCM. There are fewer than 1.9 MCM of deficiencies in 75% of the months. Based on EPO

_{ba}, in 75% of the study period, the storage is less than 68 MCM. For 75% of the months, the released water from the dam reservoir is less than 30 MCM. Deficiencies are less than 4.4 MCM in 75% of months. According to the SOP diagram, 50% of the time, the storage volume is between 12 and 30 MCM. In 75% of the months, the dam reservoir’s release is greater than 11 MCM and less than 29 MCM. Deficiencies are less than 8.2 MCM in 75% of months. As shown in part (a) of Figure 6 and Figure 7, the EPO

_{ad}performed better EPO

_{ba}in extracting the total output rule from the reservoir (total discharge, deficit and storage volume from the reservoir) in the baseline and future.

_{ad}, has increased the reliability index in the baseline by 11% and decreased the vulnerability index (improvement) by 40% compared to the EPO

_{ba}. These changes (using EPO

_{ad}instead of EPO

_{ba}algorithm) in future conditions (third and fourth scenarios) were 12% increasing the Reliability and 5% decreasing the vulnerability indexes. The results also show in the second and fourth scenarios (EPO

_{ad}) the reliability and vulnerability indexes will decline by 43% and increase by 44%, respectively. In other words, the status of reservoir performance indexes in the future will be much worse than the baseline.

#### 3.2. Comparison of the Results of Multi-Objective Optimization (MOEPO) and SOP in Extracting Allocation Rules in Four Scenarios

#### Operation Rules and Three Considered Scenarios

## 4. Discussion

## 5. Conclusions

_{ad}and EPO

_{ba}, was used for the simulation of this part. The validation results of algorithms in the extraction of the SOP indicated an appropriate performance of the EPO

_{ad}. In other words, the EPO

_{ad}improved the objective function by 57% over the EPO

_{ba}in reconstructing the SOP and decreased RMSE by 1.27%, and NS increased by 4% compared to the EPO

_{ba}.

_{ad}and EPO

_{ba}approach, it was used to derive the HR. The result of this part is also the EPO

_{ad}will improve the objective function by 15% compared to the EPO

_{ba}approach. Next, the optimal allocation rules (storge and deficit volume changes) based on EPO

_{ad}and EPO

_{ba}approaches for baseline and futures were compared in four scenarios. The results indicated the higher performance of the EPO

_{ad}.

_{ad}resulted in an 11% increase in reliability, a 40% decrease in vulnerability indexes in the baseline. Additionally, in the third and fourth scenarios, the use of EPO

_{ad}increased the reliability index by 12% and reduced the vulnerability index by 5%. The results showed that in the second and fourth scenarios using EPO

_{ad}(future period), the reliability and vulnerability indexes in the future compared to the baseline will increase by 43% and decrease by 44%, respectively. Meanwhile, in the first and third scenarios, the above indexes will decrease by 44% and increase by 26%, respectively. In other words, the status of Reservoir Performance Indexes in both algorithms declined in future compared to the baseline period.

- The boolean function increased the accuracy and performance of the generated allocation rules.
- The multi-objective optimization policy, SOP, and HR were classified from the most to the least based on improving the Performance Indexes.
- To increase the performance of the dam reservoir, it is necessary to generate particular management policies for each interval.
- The suggestions for future study are:
- Comparing this Metaheaustric algorithm with other well-known in terms of solving time consumption, convergence, etc.
- Investigating the Agriculture adaptation strategies (Deficit Irrigation, Changing cultivation date, etc.) in improving the system performance.
- Investigating other decision variables in Performance Indexes.

## Author Contributions

## Funding

## Data Availability Statement

## Conflicts of Interest

## Appendix A

#### Appendix A.1. Standard Operation Policy

- AW
_{t}: Available water volume during period t - S
_{t}: Reservoir storage volume in the t period - Q
_{t}: Inflow volume during the period t

- E
_{t}: Evaporation depth from the surface of the reservoir during the t period - A
_{t}and A_{t+1}: the reservoir surface areas at the beginning and end of the t th period, which use Equations (A3) and (A4), respectively.$${A}_{t}={a}_{0}+{a}_{1}{S}_{t}\forall t=1,2,\dots ,T$$$${A}_{t+1}={a}_{0}+{a}_{1}{S}_{t+1}\forall t=1,2,\dots ,T$$_{0}and a_{1}are constant coefficients of the surface-volume curve of the reservoir.

- rsp
_{t}: total output based on SOP (observational) in t period - D: the average volume of demand over the entire period of operation.
- S
_{max}: the maximum volume or reservoir capacity (constant number).

#### Appendix A.2. Hedging Rule

#### Appendix A.2.1. Objective Function

- LSR: Long-term Shortage Ratio (as objective function)
- RSPH
_{t}: total output (sum of release and overflow) based on the HR in t period. - D
_{t}: the demand in t. - D
_{max}: the highest demand during t.

#### Appendix A.2.2. Constraint

- S
_{t}: the reservoir storage in t. - S
_{min}: reservoir dead volume.

#### Appendix A.3. Multi Objective Optimization of Dam Reservoir Operation

- F(u
_{1}): Objective function related to the vulnerability index - F(u
_{2}): Objective function related to the reliability index - D
_{t}: Demand volume in the t period - D
_{max}: Maximum demand in the under-review period. - $R{e}_{t}$: the released volume from the reservoir in t period.

_{min}is the minimum volume or dead volume of the reservoir, which can be obtained from the continuity equation (Equation (A19)). In this equation, time steps are considered monthly.

_{t+}

_{1}and S

_{t}are reservoir storage volumes at the beginning and end of t and t + 1 periods, respectively, Q

_{t}is amount of inflow volume to the reservoir during the t period, R

_{et}is the volume of release from the reservoir during the t period, SP

_{t}is the amount of overflow volume from the reservoir at the beginning of the t period (Equation (A20)) and LE

_{t}is the volume of losses due to evaporation from reservoir surface during t period.

_{max}is the maximum volume of reservoir capacity and S

_{t+}

_{1}is reservoir storage volume at the beginning and end of the t + 1 the period.

## References

- Raje, D.; Mujumdar, P.P. Reservoir Performance under Uncertainty in Hydrologic Impacts of Climate Change. Adv. Water Resour.
**2010**, 33, 312–326. [Google Scholar] [CrossRef] - Neelakantan, T.; Sasireka, K. Hydropower Reservoir Operation Using Standard Operating and Standard Hedging Policies. Citeseer
**2013**, 5, 1191–1196. [Google Scholar] - Men, B.; Wu, Z.; Li, Y.; Liu, H. Reservoir Operation Policy Based on Joint Hedging Rules. Water
**2019**, 11, 419. [Google Scholar] [CrossRef][Green Version] - Zhao, J.; Cai, X.; Wang, Z. Optimality Conditions for a Two-stage Reservoir Operation Problem. Water Resour. Res.
**2011**, 47. [Google Scholar] [CrossRef] - Alimohammadi, H.; Massah Bavani, A.R.; Roozbahani, A. Mitigating the Impacts of Climate Change on the Performance of Multi-Purpose Reservoirs by Changing the Operation Policy from SOP to MLDR. Water Resour. Manag.
**2020**, 34, 1495–1516. [Google Scholar] [CrossRef] - Sattari, M.T.; Apaydin, H.; Ozturk, F. Operation Analysis of Eleviyan Irrigation Reservoir Dam by Optimization and Stochastic Simulation. Stoch. Environ. Res. Risk Assess.
**2009**, 23, 1187–1201. [Google Scholar] [CrossRef] - Allawi, M.F.; Jaafar, O.; Hamzah, F.M.; El-Shafie, A. Novel Reservoir System Simulation Procedure for Gap Minimization between Water Supply and Demand. J. Clean. Prod.
**2019**, 206, 928–943. [Google Scholar] [CrossRef] - Mansouri, S.; Fathian, H.; Shahbazi, A.N.; Lour, M.A.; Asareh, A. Multi Objective Simulation-Optimization Operation of Dam Reservoir in Low Water Regions Based on Hedging Principles. Res. Sq.
**2022**. [Google Scholar] [CrossRef] - Allawi, M.F.; Jaafar, O.; Mohamad Hamzah, F.; Abdullah, S.M.S.; El-Shafie, A. Review on Applications of Artificial Intelligence Methods for Dam and Reservoir-Hydro-Environment Models. Environ. Sci. Pollut. Res.
**2018**, 25, 13446–13469. [Google Scholar] [CrossRef] - Hue, C.; Boullé, M.; Lemaire, V. Online Learning of Aweighted Selective Naive Bayes Classifier with Non-Convex Optimization. Stud. Comput. Intell.
**2017**, 665, 3–17. [Google Scholar] [CrossRef] - Cruz-Duarte, J.M.; Amaya, I.; Ortiz-Bayliss, J.C.; Pillay, N. Naïve Hyper-Heuristic Online Learning to Generate Unfolded Population-Based Metaheuristics to Solve Continuous Optimization Problems. In Proceedings of the 2021 IEEE Symposium Series on Computational Intelligence, SSCI 2021, Orlando, FL, USA, 5–7 December 2021. [Google Scholar] [CrossRef]
- Goodarzian, F.; Kumar, V.; Abraham, A. Hybrid Meta-Heuristic Algorithms for a Supply Chain Network Considering Different Carbon Emission Regulations Using Big Data Characteristics. Soft Comput.
**2021**, 25, 7527–7557. [Google Scholar] [CrossRef] - Fathollahi-Fard, A.M.; Dulebenets, M.A.; Hajiaghaei-Keshteli, M.; Tavakkoli-Moghaddam, R.; Safaeian, M.; Mirzahosseinian, H. Two Hybrid Meta-Heuristic Algorithms for a Dual-Channel Closed-Loop Supply Chain Network Design Problem in the Tire Industry under Uncertainty. Adv. Eng. Inform.
**2021**, 50, 101418. [Google Scholar] [CrossRef] - Rabbani, M.; Oladzad-Abbasabady, N.; Akbarian-Saravi, N. Ambulance Routing in Disaster Response Considering Variable Patient Condition: NSGA-II and MOPSO Algorithms. J. Ind. Manag. Optim.
**2022**, 18, 1035–1062. [Google Scholar] [CrossRef] - Dulebenets, M.A. An Adaptive Polyploid Memetic Algorithm for Scheduling Trucks at a Cross-Docking Terminal. Inf. Sci.
**2021**, 565, 390–421. [Google Scholar] [CrossRef] - Pasha, J.; Nwodu, A.L.; Fathollahi-Fard, A.M.; Tian, G.; Li, Z.; Wang, H.; Dulebenets, M.A. Exact and Metaheuristic Algorithms for the Vehicle Routing Problem with a Factory-in-a-Box in Multi-Objective Settings. Adv. Eng. Inform.
**2022**, 52, 101623. [Google Scholar] [CrossRef] - Nawi, N.M.; Khan, A.; Rehman, M.Z.; Chiroma, H.; Herawan, T. Weight Optimization in Recurrent Neural Networks with Hybrid Metaheuristic Cuckoo Search Techniques for Data Classification. Math. Probl. Eng.
**2015**, 2015, 868375. [Google Scholar] [CrossRef][Green Version] - Panda, N.; Majhi, S.K. Effectiveness of Swarm-Based Metaheuristic Algorithm in Data Classification Using Pi-Sigma Higher Order Neural Network. Adv. Intell. Syst. Comput.
**2021**, 1199, 77–88. [Google Scholar] [CrossRef] - Tu, L.-T.; Bradai, A. On the Performance of Physical Layer Security of RIS-Aided Communications. In Proceedings of the 2021 IEEE Conference on Antenna Measurements & Applications (CAMA); IEEE: Piscataway, NJ, USA, 2021; pp. 570–574. [Google Scholar]
- Gomes, L.S.; Maia, A.G.; de Medeiros, J.D.F. Fuzzified Hedging Rules for a Reservoir in the Brazilian Semiarid Region. Environ. Chall.
**2021**, 4, 100125. [Google Scholar] [CrossRef] - Djebedjian, B.; Abdel-Gawad, H.A.A.; Ezzeldin, R.M. Global Performance of Metaheuristic Optimization Tools for Water Distribution Networks. Ain Shams Eng. J.
**2021**, 12, 223–239. [Google Scholar] [CrossRef] - Lee, H.M.; Jung, D.; Sadollah, A.; Lee, E.H.; Kim, J.H. Performance Comparison of Metaheuristic Optimization Algorithms Using Water Distribution System Design Benchmarks. In Harmony Search and Nature Inspired Optimization Algorithms; Springer: New York, NY, USA, 2019; pp. 97–104. [Google Scholar]
- Bilal; Pant, M. Parameter Optimization of Water Distribution Network–A Hybrid Metaheuristic Approach. Mater. Manuf. Processes
**2020**, 35, 737–749. [Google Scholar] [CrossRef] - NA, S. Groundwater Vulnerability Mapping Using the Modified DRASTIC Model: The Metaheuristic Algorithm Approach. Environ. Monit. Assess.
**2021**, 193, 1–19. [Google Scholar] - Karimi-Rizvandi, S.; Goodarzi, H.V.; Afkoueieh, J.H.; Chung, I.-M.; Kisi, O.; Kim, S.; Linh, N.T.T. Groundwater-Potential Mapping Using a Self-Learning Bayesian Network Model: A Comparison among Metaheuristic Algorithms. Water
**2021**, 13, 658. [Google Scholar] [CrossRef] - Al-Fugara, A.; Ahmadlou, M.; Shatnawi, R.; AlAyyash, S.; Al-Adamat, R.; Al-Shabeeb, A.A.-R.; Soni, S. Novel Hybrid Models Combining Meta-Heuristic Algorithms with Support Vector Regression (SVR) for Groundwater Potential Mapping. Geocarto Int.
**2022**, 37, 2627–2646. [Google Scholar] [CrossRef] - Samantaray, S.; Das, S.S.; Sahoo, A.; Satapathy, D.P. Evaluating the Application of Metaheuristic Approaches for Flood Simulation Using GIS: A Case Study of Baitarani River Basin, India. Mater. Today Proc.
**2022**, 61, 452–465. [Google Scholar] [CrossRef] - Yin, J.; Guo, S.; Wu, X.; Yang, G.; Xiong, F.; Zhou, Y. A Meta-Heuristic Approach for Multivariate Design Flood Quantile Estimation Incorporating Historical Information. Hydrol. Res.
**2019**, 50, 526–544. [Google Scholar] [CrossRef] - Song, C.; Yao, L.; Hua, C.; Ni, Q. Comprehensive Water Quality Evaluation Based on Kernel Extreme Learning Machine Optimized with the Sparrow Search Algorithm in Luoyang River Basin, China. Environ. Earth Sci.
**2021**, 80, 1–10. [Google Scholar] [CrossRef] - Almubaidin, M.A.A.; Ahmed, A.N.; Sidek, L.B.M.; Elshafie, A. Using Metaheuristics Algorithms (MHAs) to Optimize Water Supply Operation in Reservoirs: A Review. Arch. Comput. Methods Eng.
**2022**, 2022, 1–35. [Google Scholar] [CrossRef] - Dogani, A.; Dourandish, A.; Ghorbani, M.; Shahbazbegian, M.R. A Hybrid Meta-Heuristic for a Bi-Objective Stochastic Optimization of Urban Water Supply System. IEEE Access
**2020**, 8, 135829–135843. [Google Scholar] [CrossRef] - Chong, K.L.; Lai, S.H.; Ahmed, A.N.; Zaafar, W.Z.W.; Rao, R.V.; Sherif, M.; Sefelnasr, A.; El-Shafie, A. Review on Dam and Reservoir Optimal Operation for Irrigation and Hydropower Energy Generation Utilizing Meta-Heuristic Algorithms. IEEE Access
**2021**, 9, 19488–19505. [Google Scholar] [CrossRef] - Sharifi, M.R.; Akbarifard, S.; Madadi, M.R.; Qaderi, K.; Akbarifard, H. Application of MOMSA Algorithm for Optimal Operation of Karun Multi Objective Multi Reservoir Dams with the Aim of Increasing the Energy Generation. Energy Strategy Rev.
**2022**, 42, 100883. [Google Scholar] [CrossRef] - Donyaii, A.; Sarraf, A.; Ahmadi, H. Application of a New Approach in Optimizing the Operation of the Multi-Objective Reservoir. J. Hydraul. Struct.
**2020**, 6, 1–20. [Google Scholar] - Donyaii, A.; Sarraf, A.; Ahmadi, H. 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. Water Resour. Eng.
**2020**, 13, 85–100. [Google Scholar] - Babamiri, O.; Marofi, S. A Multi-Objective Simulation–Optimization Approach for Water Resource Planning of Reservoir–River Systems Based on a Coupled Quantity–Quality Model. Environ. Earth Sci.
**2021**, 80, 1–19. [Google Scholar] [CrossRef] - Azari, A.; Hamzeh, S.; Naderi, S. Multi-Objective Optimization of the Reservoir System Operation by Using the Hedging Policy. Water Resour. Manag.
**2018**, 32, 2061–2078. [Google Scholar] [CrossRef] - Zhao, S.Z.; Suganthan, P.N. Two-Lbests Based Multi-Objective Particle Swarm Optimizer. Eng. Optim.
**2011**, 43, 1–17. [Google Scholar] [CrossRef] - Sun, Y.; Guan, Y.; Wang, H.; Wu, G. Autotrophic Nitrogen Removal in Combined Nitritation and Anammox Systems through Intermittent Aeration and Possible Microbial Interactions by Quorum Sensing Analysis. Bioresour. Technol.
**2019**, 272, 146–155. [Google Scholar] [CrossRef] - Ravi, V.; Pradeepkumar, D.; Deb, K. Financial Time Series Prediction Using Hybrids of Chaos Theory, Multi-Layer Perceptron and Multi-Objective Evolutionary Algorithms. Swarm Evol. Comput.
**2017**, 36, 136–149. [Google Scholar] [CrossRef] - Qi, R.; Qian, F.; Li, S.; Wang, Z. Chaos-Genetic Algorithm for Multiobjective Optimization. In Proceedings of the World Congress on Intelligent Control and Automation (WCICA), Dalian, China, 21–23 June 2006; Volume 1, pp. 1563–1566. [Google Scholar]
- Marichelvam, M.K.; Prabaharan, T.; Yang, X.S. A Discrete Firefly Algorithm for the Multi-Objective Hybrid Flowshop Scheduling Problems. IEEE Trans. Evol. Comput.
**2014**, 18, 301–305. [Google Scholar] [CrossRef] - Patel, V.K.; Savsani, V.J. A Multi-Objective Improved Teaching-Learning Based Optimization Algorithm (MO-ITLBO). Inf. Sci.
**2016**, 357, 182–200. [Google Scholar] [CrossRef] - Rashedi, E.; Rashedi, E.; Nezamabadi-pour, H. A Comprehensive Survey on Gravitational Search Algorithm. Swarm Evol. Comput.
**2018**, 41, 141–158. [Google Scholar] [CrossRef] - Cheng, J.; Yen, G.G.; Zhang, G. A Grid-Based Adaptive Multi-Objective Differential Evolution Algorithm. Inf. Sci.
**2016**, 367–368, 890–908. [Google Scholar] [CrossRef] - Yosefipoor, P.; Saadatpour, M.; Solis, S.S.; Afshar, A. An Adaptive Surrogate-Based, Multi-Pollutant, and Multi-Objective Optimization for River-Reservoir System Management. Ecol. Eng.
**2022**, 175, 106487. [Google Scholar] [CrossRef] - Wang, Y.; Xie, J.; Xu, Y.-P.; Guo, Y.; Wang, Y. Scenario-Based Multi-Objective Optimization of Reservoirs in Silt-Laden Rivers: A Case Study in the Lower Yellow River. Sci. Total Environ.
**2022**, 829, 154565. [Google Scholar] [CrossRef] [PubMed] - Talatahari, S.; Aalami, M.T.; Parsiavash, R. Multi-Objective Optimization of Double Curvature Arch Dams Subjected to Seismic Loading Using Charged System Search. arXiv
**2022**, arXiv:arXiv.2207.04366. [Google Scholar] - Wolpert, D.H.; Macready, W.G. No Free Lunch Theorems for Optimization. IEEE Trans. Evol. Comput.
**1997**, 1, 67–82. [Google Scholar] [CrossRef][Green Version] - Solgi, M.; Bozorg-Haddad, O.; Loáiciga, H.A. A Multi-Objective Optimization Model for Operation of Intermittent Water Distribution Networks. Water Supply
**2020**, 20, 2630–2647. [Google Scholar] [CrossRef] - Kumar, V.; Yadav, S.M. Multi-Objective Reservoir Operation of the Ukai Reservoir System Using an Improved Jaya Algorithm. Water Supply
**2022**, 22, 2287–2310. [Google Scholar] [CrossRef] - Yoosefdoost, I.; Khashei-Siuki, A.; Tabari, H.; Mohammadrezapour, O. Runoff Simulation under Future Climate Change Conditions: Performance Comparison of Data-Mining Algorithms and Conceptual Models. Water Resour. Manag.
**2022**, 2022, 1–25. [Google Scholar] [CrossRef] - Viertel, K. The Development of the Concept of Uniform Convergence in Karl Weierstrass’s Lectures and Publications between 1861 and 1886. Arch. Hist. Exact Sci.
**2021**, 75, 455–490. [Google Scholar] [CrossRef] - Stefánsson, A.; Končar, N.; Jones, A.J. A Note on the Gamma Test. Neural Comput. Appl.
**1997**, 5, 131–133. [Google Scholar] [CrossRef] - Doorenbos, J.; Pruitt, W.O. Guidelines for Predicting Crop Water Requirements. FAO Irrig. Drain. Pap.
**1977**, 24, 144. [Google Scholar] - Allen, R.G.; Pereira, L.S.; Raes, D.; Smith, M. Crop Evapotranspiration-Guidelines for Computing Crop Water Requirements-FAO Irrigation and Drainage Paper 56. Fao Rome
**1998**, 300, D05109. [Google Scholar] - Mohammadrezapour, O.; Yoosefdoost, I.; Ebrahimi, M. Cuckoo Optimization Algorithm in Optimal Water Allocation and Crop Planning under Various Weather Conditions (Case Study: Qazvin Plain, Iran). Neural Comput. Appl.
**2017**, 31, 1879–1892. [Google Scholar] [CrossRef] - Ferreira, C. undefined Gene Expression Programming: A New Adaptive Algorithm for Solving Problems. Complex Syst.
**2001**, 13, 87–129. [Google Scholar] - Dhiman, G.; Kumar, V. Emperor Penguin Optimizer: A Bio-Inspired Algorithm for Engineering Problems. Knowl. -Based Syst.
**2018**, 159, 20–50. [Google Scholar] [CrossRef] - Kaur, H.; Rai, A.; Bhatia, S.S.; Dhiman, G. MOEPO: A Novel Multi-Objective Emperor Penguin Optimizer for Global Optimization: Special Application in Ranking of Cloud Service Providers. Eng. Appl. Artif. Intell.
**2020**, 96, 104008. [Google Scholar] [CrossRef] - Zhou, M.; Chen, J.; Huang, H.; Zhang, D.; Zhao, S.; Shadabfar, M. Multi-Source Data Driven Method for Assessing the Rock Mass Quality of a NATM Tunnel Face via Hybrid Ensemble Learning Models. Int. J. Rock Mech. Min. Sci.
**2021**, 147, 104914. [Google Scholar] [CrossRef] - Ghorbani, H.; Wood, D.A.; Mohamadian, N.; Rashidi, S.; Davoodi, S.; Soleimanian, A.; Shahvand, A.K.; Mehrad, M. Adaptive Neuro-Fuzzy Algorithm Applied to Predict and Control Multi-Phase Flow Rates through Wellhead Chokes. Flow Meas. Instrum.
**2020**, 76, 101849. [Google Scholar] [CrossRef] - Hashimoto, T.; Stedinger, J.R.; Loucks, D.P. Reliability, Resiliency, and Vulnerability Criteria for Water Resource System Performance Evaluation. Water Resour. Res.
**1982**, 18, 14–20. [Google Scholar] [CrossRef][Green Version] - Zou, H.; Liu, D.; Guo, S.; Xiong, L.; Liu, P.; Yin, J.; Zeng, Y.; Zhang, J.; Shen, Y. Quantitative Assessment of Adaptive Measures on Optimal Water Resources Allocation by Using Reliability, Resilience, Vulnerability Indicators. Stoch. Environ. Res. Risk Assess.
**2019**, 34, 103–119. [Google Scholar] [CrossRef] - Wang, Z.; Zhen, H.-L.; Deng, J.; Zhang, Q.; Li, X.; Yuan, M.; Zeng, J. Multiobjective Optimization-Aided Decision-Making System for Large-Scale Manufacturing Planning. IEEE Trans. Cybern.
**2021**, 52, 8326–8339. [Google Scholar] [CrossRef] [PubMed] - Zhou, Y.; Kang, J.; Kwong, S.; Wang, X.; Zhang, Q. An Evolutionary Multi-Objective Optimization Framework of Discretization-Based Feature Selection for Classification. Swarm Evol. Comput.
**2021**, 60, 100770. [Google Scholar] [CrossRef] - Ashofteh, P.-S.; Haddad, O.B.; Akbari-Alashti, H.; Mariño, M.A. Determination of Irrigation Allocation Policy under Climate Change by Genetic Programming. J. Irrig. Drain. Eng.
**2014**, 141, 04014059. [Google Scholar] [CrossRef][Green Version]

**Figure 2.**Average monthly inflow volume to the reservoir, average monthly evaporation depth, and the average monthly volume of water demand in the baseline and under climate change conditions.

**Figure 6.**Change in output, storage, deficit volume using EPO

_{ad}and EPO

_{ba}algorithms in the first and second scenarios: (

**a**) Released water and demand Volume (MCM); (

**b**) Reservoir storage (MCM); (

**c**) Deficit, demand and reservoir inflow volumes (MCM).

**Figure 7.**Change in output, storage, deficit volume using EPO

_{ad}and EPO

_{ba}algorithms in the third and fourth scenarios: (

**a**) Released water and demand Volume (MCM); (

**b**) Reservoir storage (MCM); (

**c**) Deficit, demand and reservoir inflow volumes (MCM).

**Figure 8.**Comparison of the Pareto curve of two objective functions (vulnerability and reliability Indexes) in the baseline and future periods.

**Figure 9.**Comparison of: (

**a**) Released volume; (

**b**) Storage volume; (

**c**) Deficit volume, corresponding to the first scenario.

**Figure 10.**Comparison of: (

**a**) Released volume; (

**b**) Storage volume; (

**c**) Deficit volume, corresponding to the second scenario.

**Figure 11.**Comparison of: (

**a**) Released volume; (

**b**) Storage volume; (

**c**) Deficit volume, corresponding to the third scenario.

Algorithm | MAD ^{1} | MSE ^{2} | RMSE ^{3} | MAPE ^{4} | R(XY) ^{5} | NS ^{6} | MAE ^{7} | R^{2} | SSE ^{8} |
---|---|---|---|---|---|---|---|---|---|

EPO_{ad} | 0.774 | 1.513 | 1.230 | 4.487 | 0.999 | 0.995 | −0.774 | 0.997 | 381.386 |

EPO_{ba} | 0.511 | 12.276 | 3.504 | 2.124 | 0.98 | 0.961 | −0.24 | 0.961 | 3093.48 |

^{1}Mean Absolute Deviation.

^{2}Mean Square Error.

^{3}Root Mean Square Error.

^{4}Mean Absolute Percentage Error.

^{5}Correlation Coefficient between X and Y.

^{6}Nash–Sutcliffe.

^{7}Mean Absolute Error.

^{8}Sum of Squared Errors.

Scenarios | Reliability (%) | Vulnerability (%) |
---|---|---|

First | 43.56 | 9.44 |

Second | 55.88 | 6.73 |

Third | 29.74 | 23.45 |

Fourth | 36.65 | 14.65 |

Algorithm | MAD | MSE | RMSE | MAPE | R(XY) | NS | MAE | R^{2} | SSE |
---|---|---|---|---|---|---|---|---|---|

EPO_{ad} | 1.734 | 22.838 | 4.779 | 10.245 | 0.97 | 0.927 | −0.98 | 0.93 | 5755.17 |

EPO_{ba} | 1.113 | 9.656 | 3.107 | 5.308 | 0.99 | 0.966 | −0.98 | 0.95 | 2433.26 |

Sc. | Reliability % | Vulnerability % | Parameter Changes in Scenarios | Reliability Change % | Vulnerability Change % |
---|---|---|---|---|---|

First | 48.41 | 8.15 | Comparison of the first and second | 11.60 | −40.03 |

Second | 54.76 | 5.82 | Comparison of the third and fourth | 12.44 | −5.51 |

Third | 33.49 | 11.11 | Comparison of the first and third | −44.55 | 26.64 |

Fourth | 38.25 | 10.53 | Comparison of the second and fourth | −43.16 | 44.73 |

Method | Sc. | Vulnerability % | Reliability % | Parameter Changes in Scenarios | Vulnerability Change % | Reliability Change % |
---|---|---|---|---|---|---|

MOEPO | First Scenario | 4.33 | 48 | Comparison of the first and third | 0 | −4.3478 |

Second Scenario | 5.98 | 46 | Comparison of the Second and third | −38.106 | 0 | |

Third Scenario | 4.33 | 46 | Comparison of the first and second | 27.592 | −4.3478 | |

SOP | Baseline Condition | 14 | 48 | Comparison of the first scenario and Baseline condition | −35.714 | 25 |

Future Condition | 16 | 46 | Comparison of the Second scenario and Baseline condition | −31.25 | 26.087 |

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |

© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Yoosefdoost, I.; Basirifard, M.; Álvarez-García, J.
Reservoir Operation Management with New Multi-Objective (MOEPO) and Metaheuristic (EPO) Algorithms. *Water* **2022**, *14*, 2329.
https://doi.org/10.3390/w14152329

**AMA Style**

Yoosefdoost I, Basirifard M, Álvarez-García J.
Reservoir Operation Management with New Multi-Objective (MOEPO) and Metaheuristic (EPO) Algorithms. *Water*. 2022; 14(15):2329.
https://doi.org/10.3390/w14152329

**Chicago/Turabian Style**

Yoosefdoost, Icen, Milad Basirifard, and José Álvarez-García.
2022. "Reservoir Operation Management with New Multi-Objective (MOEPO) and Metaheuristic (EPO) Algorithms" *Water* 14, no. 15: 2329.
https://doi.org/10.3390/w14152329