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" Evolutionary algorithms and other metaheuristics in water resources: "
Maier, HR; Kapelan, Z; Kasprzyk, J; Kollat, J; Matott, LS; Cunha, MC; Dandy, GC; Gibbs, MS; Keedwell, E; Marchi, A; Ostfeld, A; Savic, D; Solomatine, DP; Vrugt, JA; Zecchin, AC; Minsker, BS; Barbour, EJ; Kuczera, G; Pasha, F; Castelletti, A; Giuliani, M; Reed, PM
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AL
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912077
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LA0hs5f9bf
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| Title & Author
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Evolutionary algorithms and other metaheuristics in water resources:. Current status, research challenges and future directions [Article]\ Maier, HR; Kapelan, Z; Kasprzyk, J; Kollat, J; Matott, LS; Cunha, MC; Dandy, GC; Gibbs, MS; Keedwell, E; Marchi, A; Ostfeld, A; Savic, D; Solomatine, DP; Vrugt, JA; Zecchin, AC; Minsker, BS; Barbour, EJ; Kuczera, G; Pasha, F; Castelletti, A; Giuliani, M; Reed, PM
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2014
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UC Irvine
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| Abstract
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© 2014 Elsevier Ltd. 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.
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