Evolutionary algorithms and other metaheuristics in water resources: current status, research challenges and future directions

Maier, H. R.; Kapelan, Z.; Ostfeld, A.; Savic, D.; Solomatine, D. P.; Vrugt, J. A.; Zecchin, A. C.; Minsker, B. S.; Barbour, E. J.; Kuczera, G.; Pasha, F.; Castelletti, A.; Kasprzyk, J.; Giuliani, M.; Reed, P. M.; Kollat, J.; Matott, L. S.; Cunha, M. C.; Dandy, G. C.; Gibbs, M. S.; Keedwell, E.; Marchi, A.

Title: Evolutionary algorithms and other metaheuristics in water resources: current status, research challenges and future directions
Creator: Maier, H. R.; Kapelan, Z.; Ostfeld, A.; Savic, D.; Solomatine, D. P.; Vrugt, J. A.; Zecchin, A. C.; Minsker, B. S.; Barbour, E. J.; Kuczera, G.; Pasha, F.; Castelletti, A.; Kasprzyk, J.; Giuliani, M.; Reed, P. M.; Kollat, J.; Matott, L. S.; Cunha, M. C.; Dandy, G. C.; Gibbs, M. S.; Keedwell, E.; Marchi, A.
Relation: Environmental Modelling & Software Vol. 62, p. 271-299
Publisher Link: http://dx.doi.org/10.1016/j.envsoft.2014.09.013
Publisher: Elsevier BV
Resource Type: journal article
Date: 2014
Description: The development and application of evolutionary algorithms (EAs) and other metaheuristics for the optimisation of water resources systems has been an active research field for over two decades. Research to date has emphasized algorithmic improvements and individual applications in specific areas (e.g. model calibration, water distribution systems, groundwater management, river-basin planning and management, etc.). However, there has been limited synthesis between shared problem traits, common EA challenges, and needed advances across major applications. This paper clarifies the current status and future research directions for better solving key water resources problems using EAs. Advances in understanding fitness landscape properties and their effects on algorithm performance are critical. Future EA-based applications to real-world problems require a fundamental shift of focus towards improving problem formulations, understanding general theoretic frameworks for problem decompositions, major advances in EA computational efficiency, and most importantly aiding real decision-making in complex, uncertain application contexts.
Subject: optimisation; water resources; evolutionary algorithms; metaheuristics; review; research directions
Identifier: http://hdl.handle.net/1959.13/1301479
Identifier: uon:20293
Identifier: ISSN:1364-8152
Language: eng
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