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

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....

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Published in:Environmental modelling & software : with environment data news 2014-12, Vol.62, p.271-299
Main Authors: Maier, H.R., Kapelan, Z., Kasprzyk, J., Kollat, J., Matott, L.S., Cunha, M.C., Dandy, G.C., Gibbs, M.S., Keedwell, E., Marchi, A., 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., Giuliani, M., Reed, P.M.
Format: Article
Language:English
Subjects:
Algorithms
Animal, plant and microbial ecology
Biological and medical sciences
Computational efficiency
Computer programs
Evolutionary algorithms
Fitness
Fundamental and applied biological sciences. Psychology
General aspects. Techniques
Heuristic methods
Landscapes
Metaheuristics
Methods and techniques (sampling, tagging, trapping, modelling...)
Optimisation
Research directions
Review
Water resources
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Summary: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.
ISSN:1364-8152
1873-6726
DOI:10.1016/j.envsoft.2014.09.013