I. Tsoukalas, P. Kossieris, A. Efstratiadis, and C. Makropoulos, Surrogate-enhanced evolutionary annealing simplex algorithm for effective and efficient optimization of water resources problems on a budget, *Environmental Modelling and Software*, 77, 122–142, doi:10.1016/j.envsoft.2015.12.008, 2016.

[doc_id=1587]

[English]

In water resources optimization problems, the objective function usually presumes to first run a simulation model and then evaluate its outputs. However, long simulation times may pose significant barriers to the procedure. Often, to obtain a solution within a reasonable time, the user has to substantially restrict the allowable number of function evaluations, thus terminating the search much earlier than required. A promising strategy to address these shortcomings is the use of surrogate modeling techniques. Here we introduce the Surrogate-Enhanced Evolutionary Annealing-Simplex (SEEAS) algorithm that couples the strengths of surrogate modeling with the effectiveness and efficiency of the evolutionary annealing-simplex method. SEEAS combines three different optimization approaches (evolutionary search, simulated annealing, downhill simplex). Its performance is benchmarked against other surrogate-assisted algorithms in several test functions and two water resources applications (model calibration, reservoir management). Results reveal the significant potential of using SEEAS in challenging optimization problems on a budget.

**Related works:**

- [doc_id=1520] Early presentation if EGU conference

**Our works referenced by this work:**

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4. | A. Efstratiadis, I. Nalbantis, A. Koukouvinos, E. Rozos, and D. Koutsoyiannis, HYDROGEIOS: A semi-distributed GIS-based hydrological model for modified river basins, Hydrology and Earth System Sciences, 12, 989–1006, doi:10.5194/hess-12-989-2008, 2008. |

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10. | P. Kossieris, A. Efstratiadis, and D. Koutsoyiannis, The use of stochastic objective functions in water resource optimization problems, Facets of Uncertainty: 5th EGU Leonardo Conference – Hydrofractals 2013 – STAHY 2013, Kos Island, Greece, doi:10.13140/RG.2.2.18578.66249, European Geosciences Union, International Association of Hydrological Sciences, International Union of Geodesy and Geophysics, 2013. |

11. | A. Efstratiadis, Y. Dialynas, S. Kozanis, and D. Koutsoyiannis, A multivariate stochastic model for the generation of synthetic time series at multiple time scales reproducing long-term persistence, Environmental Modelling and Software, 62, 139–152, doi:10.1016/j.envsoft.2014.08.017, 2014. |

12. | A. Efstratiadis, I. Nalbantis, and D. Koutsoyiannis, Hydrological modelling of temporally-varying catchments: Facets of change and the value of information, Hydrological Sciences Journal, 60 (7-8), 1438–1461, doi:10.1080/02626667.2014.982123, 2015. |

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14. | I. Tsoukalas, and C. Makropoulos, A surrogate based optimization approach for the development of uncertainty-aware reservoir operational rules: the case of Nestos hydrosystem, Water Resources Management, 29 (13), 4719–4734, doi:10.1007/s11269-015-1086-8, 2015. |

**Our works that reference this work:**

1. | E. Savvidou, A. Efstratiadis, A. D. Koussis, A. Koukouvinos, and D. Skarlatos, A curve number approach to formulate hydrological response units within distributed hydrological modelling, Hydrology and Earth System Sciences Discussions, doi:10.5194/hess-2016-627, 2016. |

2. | A. Tegos, N. Malamos, A. Efstratiadis, I. Tsoukalas, A. Karanasios, and D. Koutsoyiannis, Parametric modelling of potential evapotranspiration: a global survey, Water, 9 (10), 795, doi:10.3390/w9100795, 2017. |

3. | P. Kossieris, C. Makropoulos, C. Onof, and D. Koutsoyiannis, A rainfall disaggregation scheme for sub-hourly time scales: Coupling a Bartlett-Lewis based model with adjusting procedures, Journal of Hydrology, 556, 980–992, doi:10.1016/j.jhydrol.2016.07.015, 2018. |

4. | E. Savvidou, A. Efstratiadis, A. D. Koussis, A. Koukouvinos, and D. Skarlatos, The curve number concept as a driver for delineating hydrological response units, Water, 10 (2), 194, doi:10.3390/w10020194, 2018. |

5. | Ε. Psarrou, I. Tsoukalas, and C. Makropoulos, A Monte-Carlo-based method for the optimal placement and operation scheduling of sewer mining units in urban wastewater networks, Water, 10 (2), 200, doi:10.3390/w10020200, 2018. |

6. | I. Tsoukalas, A. Efstratiadis, and C. Makropoulos, Building a puzzle to solve a riddle: A multi-scale disaggregation approach for multivariate stochastic processes with any marginal distribution and correlation structure, Journal of Hydrology, 575, 354–380, doi:10.1016/j.jhydrol.2019.05.017, 2019. |

**Other works that reference this work (this list might be obsolete):**

1. | Dariane , A. B., and M. M. Javadianzadeh, Towards an efficient rainfall–runoff model through partitioning scheme, Water, 8, 63; doi:10.3390/w8020063, 2016. |

2. | Yaseen, Z. M., O. Jaafar, R. C. Deo, O. Kisi, J. Adamowski, J. Quilty, and A. El-Shafie, Boost stream-flow forecasting model with extreme learning machine data-driven: A case study in a semi-arid region in Iraq, Journal of Hydrology, 542, 603-614, doi:10.1016/j.jhydrol.2016.09.035, 2016. |

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6. | Christelis, V., and A. Mantoglou, Physics-based and data-driven surrogate models for pumping optimization of coastal aquifers, European Water, 57, 481–488, 2017. |

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13. | Christelis, V., G. Kopsiaftis, and A. Mantoglou, Performance comparison of multiple and single surrogate models for pumping optimization of coastal aquifers, Hydrological Sciences Journal, 64(3), 336-349, doi:10.1080/02626667.2019.1584400, 2019. |

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