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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Vydáno v:Environmental modelling & software : with environment data news Ročník 62; s. 271 - 299
Hlavní autoři: 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.
Médium: Journal Article
Jazyk:angličtina
Vydáno: Oxford Elsevier Ltd 01.12.2014
Elsevier
Témata:
ISSN:1364-8152
On-line přístup:Získat plný text
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Abstract 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.
AbstractList 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.
Author Matott, L.S.
Kollat, J.
Zecchin, A.C.
Minsker, B.S.
Ostfeld, A.
Keedwell, E.
Giuliani, M.
Reed, P.M.
Kuczera, G.
Marchi, A.
Cunha, M.C.
Kapelan, Z.
Pasha, F.
Savic, D.
Castelletti, A.
Kasprzyk, J.
Dandy, G.C.
Gibbs, M.S.
Solomatine, D.P.
Maier, H.R.
Vrugt, J.A.
Barbour, E.J.
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  surname: Maier
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  organization: School of Civil, Environmental and Mining Engineering, The University of Adelaide, Adelaide, SA 5005, Australia
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  givenname: Z.
  surname: Kapelan
  fullname: Kapelan, Z.
  organization: College of Engineering, Mathematics and Physical Sciences, University of Exeter, Harrison Building, North Park Road, Exeter EX4 4QF, United Kingdom
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  givenname: J.
  surname: Kasprzyk
  fullname: Kasprzyk, J.
  organization: Civil Environmental and Architectural Engineering Department, University of Colorado Boulder, UCB 428, Boulder, CO 80309, USA
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  surname: Kollat
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  organization: DecisionVis, 315 S. Allen St., Ste 321, State College, PA 16801, USA
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  organization: Center for Computational Research, University at Buffalo, Buffalo, NY 14203, USA
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  organization: Department of Civil Engineering, University of Coimbra, Rua Luís Reis Santos – Pólo II, 3030-788 Coimbra, Portugal
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  givenname: G.C.
  surname: Dandy
  fullname: Dandy, G.C.
  organization: School of Civil, Environmental and Mining Engineering, The University of Adelaide, Adelaide, SA 5005, Australia
– sequence: 8
  givenname: M.S.
  surname: Gibbs
  fullname: Gibbs, M.S.
  organization: School of Civil, Environmental and Mining Engineering, The University of Adelaide, Adelaide, SA 5005, Australia
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  givenname: E.
  surname: Keedwell
  fullname: Keedwell, E.
  organization: College of Engineering, Mathematics and Physical Sciences, University of Exeter, Harrison Building, North Park Road, Exeter EX4 4QF, United Kingdom
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  givenname: A.
  surname: Marchi
  fullname: Marchi, A.
  organization: School of Civil, Environmental and Mining Engineering, The University of Adelaide, Adelaide, SA 5005, Australia
– sequence: 11
  givenname: A.
  surname: Ostfeld
  fullname: Ostfeld, A.
  organization: Civil and Environmental Engineering, Technion – Israel Institute of Technology, Haifa 32000, Israel
– sequence: 12
  givenname: D.
  surname: Savic
  fullname: Savic, D.
  organization: College of Engineering, Mathematics and Physical Sciences, University of Exeter, Harrison Building, North Park Road, Exeter EX4 4QF, United Kingdom
– sequence: 13
  givenname: D.P.
  surname: Solomatine
  fullname: Solomatine, D.P.
  organization: UNESCO-IHE Institute for Water Education, Delft, The Netherlands
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  surname: Vrugt
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  organization: Department of Civil and Environmental Engineering, University of California Irvine, Irvine, CA, USA
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  givenname: A.C.
  surname: Zecchin
  fullname: Zecchin, A.C.
  organization: School of Civil, Environmental and Mining Engineering, The University of Adelaide, Adelaide, SA 5005, Australia
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  surname: Minsker
  fullname: Minsker, B.S.
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  surname: Barbour
  fullname: Barbour, E.J.
  organization: Integrated Catchment and Management Centre, Fenner School of Environment and Society, Australian National University, Canberra, ACT 0200, Australia
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  surname: Kuczera
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  organization: School of Engineering, University of Newcastle, Callaghan, NSW 2308, Australia
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  surname: Pasha
  fullname: Pasha, F.
  organization: Department of Civil and Geomatics Engineering, California State University, Fresno, CA 93740, USA
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  surname: Castelletti
  fullname: Castelletti, A.
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  surname: Reed
  fullname: Reed, P.M.
  organization: Department of Civil and Environmental Engineering, Cornell University, Ithaca, NY, USA
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Keywords Research directions
Evolutionary algorithms
Review
Optimisation
Metaheuristics
Water resources
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Water current
Algorithm
Optimization
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Snippet The development and application of evolutionary algorithms (EAs) and other metaheuristics for the optimisation of water resources systems has been an active...
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SubjectTerms Algorithms
Animal, plant and microbial ecology
Biological and medical sciences
Computational efficiency
Computer programs
computer software
decision making
environmental models
Evolutionary algorithms
Fitness
Fundamental and applied biological sciences. Psychology
General aspects. Techniques
Heuristic methods
Landscapes
Metaheuristics
Methods and techniques (sampling, tagging, trapping, modelling...)
Optimisation
planning
Research directions
Review
water distribution
Water resources
Title Evolutionary algorithms and other metaheuristics in water resources: Current status, research challenges and future directions
URI https://dx.doi.org/10.1016/j.envsoft.2014.09.013
https://www.proquest.com/docview/1647013367
https://www.proquest.com/docview/1660054180
https://www.proquest.com/docview/2000141539
Volume 62
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