Evolutionary multiobjective optimization in water resources: The past,present, and future |
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| Institution: | 1. Department of Electronics, Information and Bioengineering, Politecnico di Milano, Piazza L. da Vinci, 32, I-20133 Milano, Italy;2. Institute of Environmental Engineering, ETH Zurich, Stefano-Franscini-Platz 5, CH-8093 Zurich, Switzerland;1. Center for Watershed Sciences, University of California-Davis, Davis, California, USA;2. Department of Civil and Environmental Engineering, University of California-Davis, Davis, California, USA;3. Centre for Environmental Policy, Imperial College London, London SW7 2AZ, U.K.;4. Department of Physical Geography, Stockholm University, Stockholm, Sweden;1. Department of Electronics, Information and Bioengineering, Politecnico di Milano, Piazza L. da Vinci, 32, Milano, I-20133, Italy;2. Institute of Environmental Engineering, ETH Zurich, Wolfgang-Pauli-Str. 15, Zurich, CH-8093, Switzerland;3. Department of Environmental Sciences and Engineering, University of North Carolina, Chapel Hill, United States |
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| Abstract: | This study contributes a rigorous diagnostic assessment of state-of-the-art multiobjective evolutionary algorithms (MOEAs) and highlights key advances that the water resources field can exploit to better discover the critical tradeoffs constraining our systems. This study provides the most comprehensive diagnostic assessment of MOEAs for water resources to date, exploiting more than 100,000 MOEA runs and trillions of design evaluations. The diagnostic assessment measures the effectiveness, efficiency, reliability, and controllability of ten benchmark MOEAs for a representative suite of water resources applications addressing rainfall–runoff calibration, long-term groundwater monitoring (LTM), and risk-based water supply portfolio planning. The suite of problems encompasses a range of challenging problem properties including (1) many-objective formulations with four or more objectives, (2) multi-modality (or false optima), (3) nonlinearity, (4) discreteness, (5) severe constraints, (6) stochastic objectives, and (7) non-separability (also called epistasis). The applications are representative of the dominant problem classes that have shaped the history of MOEAs in water resources and that will be dominant foci in the future. Recommendations are given for the new algorithms that should serve as the benchmarks for innovations in the water resources literature. The future of MOEAs in water resources needs to emphasize self-adaptive search, new technologies for visualizing tradeoffs, and the next generation of computing technologies. |
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