Εξελικτικός αλγόριθμος ανόπτησης-απλόκου εμπλουτισμένος με υποκατάστατα μοντέλα για αποδοτική και αποτελεσματική βελτιστοποίηση προβλημάτων υδατικών πόρων με περιορισμένο προϋπολογισμό

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.

[Εξελικτικός αλγόριθμος ανόπτησης-απλόκου εμπλουτισμένος με υποκατάστατα μοντέλα για αποδοτική και αποτελεσματική βελτιστοποίηση προβλημάτων υδατικών πόρων με περιορισμένο προϋπολογισμό]

[doc_id=1587]

[Αγγλικά]

Στα προβλήματα βελτιστοποίησης υδατικών πόρων, η στοχική συνάρτηση συνήθως προϋποθέτει πρώτα να τρέξει ένα μοντέλο προσομοίωσης και στη συνέχεια να αξιολογηθούν τα αποτελέσματά του. Ωστόσο, οι μεγάλοι χρόνοι προσομοίωσης μπορεί να θέσουν πολύ σοβαρά εμπόδια στην παραπάνω διαδικασία. Συχνά, για να παραληφθεί μια λύση σε λογικό χρόνο, ο χρήστης πρέπει να μειώσει δραστικά το επιτρεπόμενο πλήθος αποτιμήσεων της συνάρτησης, τερματίζοντας έτσι την αναζήτηση πολύ νωρίτερα από όσο χρειάζεται. Μια υποσχόμενη στρατηγική για την αντιμετώπιση αυτών των αδυναμιών είναι η χρήση τεχνικών υποκατάστατων μοντέλων. Εδώ εισάγουμε τον εξελικτικό αλγόριθμο ανόπτησης-απλόκου εμπλουτισμένο με υποκατάστατα μοντέλα (Surrogate-Enhanced Evolutionary Annealing-Simplex, SEEAS) που συνδυάζει τα ισχυρά σημεία των υποκατάστατων μοντέλων με την αποτελεσματικότητα και αποδοτικότητα της εξελικτικής μεθόδου ανόπτησης-απλόκου. Ο αλγόριθμος SEEAS συνδυάζει τρεις διαφορετικές προσεγγίσεις βελτιστοποίησης (εξελικτική αναζήτηση, προσομοιωμένη ανόπτηση, και κατερχόμενο άπλοκο). Η επίδοσή του συγκρίνεται με άλλους αλγορίθμους που βασίζονται σε υποκατάστατα, σε διάφορες συναρτήσεις ελέγχου και σε δύο εφαρμογές υδατικών πόρων (βαθμονόμηση μοντέλου, διαχείριση ταμιευτήρων). Τα αποτελέσματα αναδεικνύουν τις σημαντικές δυνατότητες της χρήσης του SEEAS σε απαιτητικά προβλήματα βελτιστοποίησης με περιορισμένο προϋπολογισμό.

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Σχετικές εργασίες:

Εργασίες μας στις οποίες αναφέρεται αυτή η εργασία:

1. A. Efstratiadis, and D. Koutsoyiannis, An evolutionary annealing-simplex algorithm for global optimisation of water resource systems, Proceedings of the Fifth International Conference on Hydroinformatics, Cardiff, UK, 1423–1428, doi:10.13140/RG.2.1.1038.6162, International Water Association, 2002.
2. D. Koutsoyiannis, and A. Economou, Evaluation of the parameterization-simulation-optimization approach for the control of reservoir systems, Water Resources Research, 39 (6), 1170, doi:10.1029/2003WR002148, 2003.
3. E. Rozos, A. Efstratiadis, I. Nalbantis, and D. Koutsoyiannis, Calibration of a semi-distributed model for conjunctive simulation of surface and groundwater flows, Hydrological Sciences Journal, 49 (5), 819–842, doi:10.1623/hysj.49.5.819.55130, 2004.
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.
5. A. Efstratiadis, and D. Koutsoyiannis, Fitting hydrological models on multiple responses using the multiobjective evolutionary annealing simplex approach, Practical hydroinformatics: Computational intelligence and technological developments in water applications, edited by R.J. Abrahart, L. M. See, and D. P. Solomatine, 259–273, doi:10.1007/978-3-540-79881-1_19, Springer, 2008.
6. A. Efstratiadis, and D. Koutsoyiannis, One decade of multiobjective calibration approaches in hydrological modelling: a review, Hydrological Sciences Journal, 55 (1), 58–78, doi:10.1080/02626660903526292, 2010.
7. D. Koutsoyiannis, Hurst-Kolmogorov dynamics and uncertainty, Journal of the American Water Resources Association, 47 (3), 481–495, doi:10.1111/j.1752-1688.2011.00543.x, 2011.
8. I. Nalbantis, A. Efstratiadis, E. Rozos, M. Kopsiafti, and D. Koutsoyiannis, Holistic versus monomeric strategies for hydrological modelling of human-modified hydrosystems, Hydrology and Earth System Sciences, 15, 743–758, doi:10.5194/hess-15-743-2011, 2011.
9. A. Efstratiadis, D. Bouziotas, and D. Koutsoyiannis, The parameterization-simulation-optimization framework for the management of hydroelectric reservoir systems, Hydrology and Society, EGU Leonardo Topical Conference Series on the hydrological cycle 2012, Torino, doi:10.13140/RG.2.2.36437.22243, European Geosciences Union, 2012.
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.
13. I. Tsoukalas, and C. Makropoulos, Multiobjective optimisation on a budget: Exploring surrogate modelling for robust multi-reservoir rules generation under hydrological uncertainty, Environmental Modelling and Software, 69, 396–413, doi:10.1016/j.envsoft.2014.09.023, 2015.
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.

Εργασίες μας που αναφέρονται σ' αυτή την εργασία:

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.
7. I. Tsoukalas, P. Kossieris, and C. Makropoulos, Simulation of non-Gaussian correlated random variables, stochastic processes and random fields: Introducing the anySim R-Package for environmental applications and beyond, Water, 12 (6), 1645, doi:10.3390/w12061645, 2020.
8. H. Elsayed, S. Djordjević, D. Savic, I. Tsoukalas, and C. Makropoulos, The Nile water-food-energy nexus under uncertainty: Impacts of the Grand Ethiopian Renaissance Dam, Journal of Water Resources Planning and Management - ASCE, 146 (11), 04020085, doi:10.1061/(ASCE)WR.1943-5452.0001285, 2020.
9. S. Tsattalios, I. Tsoukalas, P. Dimas, P. Kossieris, A. Efstratiadis, and C. Makropoulos, Advancing surrogate-based optimization of time-expensive environmental problems through adaptive multi-model search, Environmental Modelling and Software, 162, 105639, doi:10.1016/j.envsoft.2023.105639, 2023.
10. A. Efstratiadis, I. Tsoukalas, and P. Kossieris, Improving hydrological model identifiability by driving calibration with stochastic inputs, Advances in Hydroinformatics: Machine Learning and Optimization for Water Resources, edited by G. A. Corzo Perez and D. P. Solomatine, doi:10.1002/9781119639268.ch2, American Geophysical Union, 2024.

Άλλες εργασίες που αναφέρονται σ' αυτή την εργασία (αυτός ο κατάλογος μπορεί να μην είναι ενημερωμένος):

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.
3. Müller, R., and N. Schütze, Multi-objective optimization of multi-purpose multi-reservoir systems under high reliability constraints, Environmental Earth Sciences, 75:1278, doi:10.1007/s12665-016-6076-5, 2016.
4. #Christelis, V., V. Bellos, and G. Tsakiris, Employing surrogate modelling for the calibration of a 2D flood simulation model, Sustainable Hydraulics in the Era of Global Change: Proceedings of the 4th IAHR Europe Congress (Liege, Belgium, 27-29 July 2016), A. S. Erpicum, M. Pirotton, B. Dewals, P. Archambeau (editors), CRC Press, 2016.
5. Salazar, J. Z., P. M. Reed, J. D. Quinn, M. Giuliani, and A. Castelletti, Balancing exploration, uncertainty and computational demands in many objective reservoir optimization, Advances in Water Resources, 109, 196-210, doi:10.1016/j.advwatres.2017.09.014, 2017.
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.
7. #Thandayutham, K., E. Avital, N. Venkatesan, and A. Samad, Design and analysis of a marine current turbine, Proceedings of ASME 2017 Gas Turbine India Conference and Exhibition, GTINDIA2017-4912, V001T02A014, Bangalore, India, doi:10.1115/GTINDIA2017-4912, 2017.
8. Christelis, V., R. G. Regis, and A. Mantoglou, Surrogate-based pumping optimization of coastal aquifers under limited computational budgets, Journal of Hydroinformatics, 20(1), 164-176, doi:10.2166/hydro.2017.063, 2018.
9. Christelis, V., and A. G. Hughes, Metamodel-assisted analysis of an integrated model composition: an example using linked surface water – groundwater models, Environmental Modelling and Software, 107, 298-306, doi:10.1016/j.envsoft.2018.05.004, 2018.
10. Zischg, A. P., G. Felder, M. Mosimann, V. Röthlisberger, and R. Weingartner, Extending coupled hydrological-hydraulic model chains with a surrogate model for the estimation of flood losses, Environmental Modelling and Software, 108, 174-185, doi:10.1016/j.envsoft.2018.08.009, 2018.
11. Christelis, V., and A. Mantoglou, Pumping optimization of coastal aquifers using seawater intrusion models of variable-fidelity and evolutionary algorithms, Water Resources Management, 33(2), 555-558, doi:10.1007/s11269-018-2116-0, 2019.
12. Thandayutham, K., L. K. Mishra, and A. Samad, Optimal design of a marine current turbine using CFD and FEA, Proceedings of the Fourth International Conference in Ocean Engineering (ICOE2018), K. Murali, V. Sriram, A. Samad, N. Saha (editors), Lecture Notes in Civil Engineering, 23, 675-690, doi:10.1007/978-981-13-3134-3, 2019.
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.
14. Cai, X., L. Gao, X. Li, and H-. Qiu, Surrogate-guided differential evolution algorithm for high dimensional expensive problems, Swarm and Evolutionary Computation, 48, 288-311, doi:10.1016/j.swevo.2019.04.009, 2019.
15. Huot, P.-L., A. Poulin, C. Audet, and S. Alarie, A hybrid optimization approach for efficient calibration of computationally intensive hydrological models, Hydrological Sciences Journal, 64(9), 1204-1222, doi:10.1080/02626667.2019.1624922, 2019.
16. Jahandideh-Tehrani, M., O. Bozorg-Haddad, and H. A. Loáiciga, Application of non-animal–inspired evolutionary algorithms to reservoir operation: an overview, Environmental Monitoring and Assessment, 191:439, doi:10.1007/s10661-019-7581-2, 2019.
17. Sandoval, S., and J.-L. Bertrand-Krajewski, From marginal to conditional probability functions of parameters in a conceptual rainfall-runoff model: an event-based approach, Hydrological Sciences Journal, 64(11), 1340-1350, doi:10.1080/02626667.2019.1635696, 2019.
18. Zhao, C. S., T. L. Pan, J. Xi, S. T. Yang, J. Zhao, X. J. Gan, L. P. Hou, and S. Y. Ding, Streamflow calculation for medium-to-small rivers in data scarce inland areas, Science of The Total Environment, 693, 133571, doi:10.1016/j.scitotenv.2019.07.377, 2019.
19. Monteil, C., F. Zaoui, N. Le Moine, and F. Hendrickx, Multi-objective calibration by combination of stochastic and gradient-like parameter generation rules – the caRamel algorithm, Hydrology and Earth System Sciences, 24, 3189-3209, 10.5194/hess-24-3189-2020, 2020.
20. Muhammed, K. A., and R. Farmani, Energy optimization using a pump scheduling tool in water distribution systems, ARO – The Scientific Journal of Koya University, 8(1), 112-123, doi:10.14500/aro.10635, 2020.
21. #Castro-Gama M., C. Agudelo-Vera, and D. Bouziotas, A bird’s-eye view of data validation in the drinking water industry of the Netherlands, The Handbook of Environmental Chemistry, Springer, Berlin, Heidelberg, doi:10.1007/698_2020_609, 2020.
22. Xai, W., C. Shoemaker, T. Akhtar, and M.-T. Nguyen, Efficient parallel surrogate optimization algorithm and framework with application to parameter calibration of computationally expensive three-dimensional hydrodynamic lake PDE models, Environmental Modelling and Software, 135, 104910, doi:10.1016/j.envsoft.2020.104910, 2021.
23. Saadatpour, M., S. Javaheri, A. Afshar, and S. S. Solis, Optimization of selective withdrawal systems in hydropower reservoir considering water quality and quantity aspects, Expert Systems with Applications, 184, 115474, doi:10.1016/j.eswa.2021.115474, 2021.
24. Zhao, T., and B. Minsker, Efficient metamodel approach to handling constraints in nonlinear optimization for drought management, Journal of Water Resources Planning and Management, 147(12), doi:10.1061/(ASCE)WR.1943-5452.0001476, 2021.
25. Anahideh, H., J. Rosenberger, and V. Chen, High-dimensional black-box optimization under uncertainty, Computers & Operations Research, 137, 105444, doi:10.1016/j.cor.2021.105444, 2022.
26. Pang, M., E. Du, C. A. Shoemaker, and C. Zheng, Efficient, parallelized global optimization of groundwater pumping in a regional aquifer with land subsidence constraints, Journal of Environmental Management, 310, 114753, doi:10.1016/j.jenvman.2022.114753, 2022.
27. Lu, W., W. Xia, and C. A. Shoemaker, Surrogate global optimization for identifying cost-effective green infrastructure for urban flood control with a computationally expensive inundation model, Water Resources Research, 58(4), e2021WR030928, doi:10.1029/2021WR030928, 2022.
28. Kopsiaftis, G., M. Kaselimi, E. Protopapadakis, A. Voulodimos, A. Doulamis, N. Doulamis, and A. Mantoglou, Performance comparison of physics-based and machine learning assisted multi-fidelity methods for the management of coastal aquifer systems, Frontiers in Water, 5, 1195029, doi:10.3389/frwa.2023.1195029, 2023.
29. Christelis, V., G. Kopsiaftis. R. G. Regis, and A. Mantoglou, An adaptive multi-fidelity optimization framework based on co-Kriging surrogate models and stochastic sampling with application to coastal aquifer management, Advances in Water Resources, 180, 104537, doi:10.1016/j.advwatres.2023.104537, 2023.
30. Costabile, P., C. Costanzo, J. Kalogiros, and V. Bellos, Toward street‐level nowcasting of flash floods impacts based on HPC hydrodynamic modeling at the watershed scale and high‐resolution weather radar data, Water Resources Research, 59(10), e2023WR034599, doi:10.1029/2023WR034599, 2023.

Κατηγορίες: Βελτιστοποίηση, Λογισμικό