Multiobjective optimisation on a budget: Exploring surrogate modelling for robust multi-reservoir rules generation under hydrological uncertainty

[1]  Dennis P. Lettenmaier,et al.  Economic Value of Long-Lead Streamflow Forecasts for Columbia River Hydropower , 2002 .

[2]  Christine A. Shoemaker,et al.  Comparison of function approximation, heuristic, and derivative‐based methods for automatic calibration of computationally expensive groundwater bioremediation models , 2005 .

[3]  Olivier P. Le Maître,et al.  Polynomial chaos expansion for sensitivity analysis , 2009, Reliab. Eng. Syst. Saf..

[4]  Kourosh Behzadian,et al.  Stochastic sampling design using a multi-objective genetic algorithm and adaptive neural networks , 2009, Environ. Model. Softw..

[5]  Alcigeimes B. Celeste,et al.  Evaluation of stochastic reservoir operation optimization models , 2009 .

[6]  Gary B. Lamont,et al.  Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation) , 2006 .

[7]  William H. Press,et al.  Numerical recipes in C. The art of scientific computing , 1987 .

[8]  Holger R. Maier,et al.  Application of partial mutual information variable selection to ANN forecasting of water quality in water distribution systems , 2008, Environ. Model. Softw..

[9]  Osvaldo E. Sala,et al.  Climate Change Impacts on , 2008 .

[10]  Joshua D. Knowles Local-search and hybrid evolutionary algorithms for Pareto optimization , 2002 .

[11]  Demetris Koutsoyiannis,et al.  A decision support tool for the management of multi-reservoir systems , 2002 .

[12]  Breanndán Ó Nualláin,et al.  Parameter optimisation and uncertainty assessment for large-scale streamflow simulation with the LISFLOOD model , 2007 .

[13]  Yaochu Jin,et al.  Surrogate-assisted evolutionary computation: Recent advances and future challenges , 2011, Swarm Evol. Comput..

[14]  Bryan A. Tolson,et al.  A benchmarking framework for simulation-based optimization of environmental models , 2012, Environ. Model. Softw..

[15]  Christine A. Shoemaker,et al.  A Stochastic Radial Basis Function Method for the Global Optimization of Expensive Functions , 2007, INFORMS J. Comput..

[16]  Andy J. Keane,et al.  Statistical Improvement Criteria for Use in Multiobjective Design Optimization , 2006 .

[17]  William W.-G. Yeh,et al.  Reservoir Management and Operations Models: A State‐of‐the‐Art Review , 1985 .

[18]  Petros Koumoutsakos,et al.  Reducing the Time Complexity of the Derandomized Evolution Strategy with Covariance Matrix Adaptation (CMA-ES) , 2003, Evolutionary Computation.

[19]  Avi Ostfeld,et al.  A hybrid genetic—instance based learning algorithm for CE-QUAL-W2 calibration , 2005 .

[20]  B J Fregly,et al.  Parallel global optimization with the particle swarm algorithm , 2004, International journal for numerical methods in engineering.

[21]  Mahmood Ali,et al.  A decision support system for ERP implementation in small and medium-sized enterprises , 2013 .

[22]  Bryan A. Tolson,et al.  Pre-emption strategies for efficient multi-objective optimization: Application to the development of Lake Superior regulation plan , 2014, Environ. Model. Softw..

[23]  Andrea Castelletti,et al.  Data-driven dynamic emulation modelling for the optimal management of environmental systems , 2012, Environ. Model. Softw..

[24]  Andy J. Keane,et al.  Engineering Design via Surrogate Modelling - A Practical Guide , 2008 .

[25]  Holger R. Maier,et al.  Water Distribution System Optimization Using Metamodels , 2005 .

[26]  S. Larson Index-based tool for preliminary ranking of social and environmental impacts of hydropower and storage reservoirs , 2007 .

[27]  Michael S. Eldred,et al.  OVERVIEW OF MODERN DESIGN OF EXPERIMENTS METHODS FOR COMPUTATIONAL SIMULATIONS , 2003 .

[28]  Bryan A. Tolson,et al.  Pareto archived dynamically dimensioned search with hypervolume-based selection for multi-objective optimization , 2013 .

[29]  Christos Makropoulos,et al.  A multi-objective evolutionary programming approach to the ‘object location’ spatial analysis and optimisation problem within the urban water management domain , 2005 .

[30]  Barbara S. Minsker,et al.  Optimal groundwater remediation design using an Adaptive Neural Network Genetic Algorithm , 2006 .

[31]  B. Leupen,et al.  Design and analysis , 1997 .

[32]  Bryan A. Tolson,et al.  Reducing the computational cost of automatic calibration through model preemption , 2010 .

[33]  Michael T. M. Emmerich,et al.  Single- and multiobjective evolutionary optimization assisted by Gaussian random field metamodels , 2006, IEEE Transactions on Evolutionary Computation.

[34]  Kalyanmoy Deb,et al.  Faster Hypervolume-Based Search Using Monte Carlo Sampling , 2008, MCDM.

[35]  G. Gary Wang,et al.  Survey of modeling and optimization strategies to solve high-dimensional design problems with computationally-expensive black-box functions , 2010 .

[36]  Christine A. Shoemaker,et al.  Improved Strategies for Radial basis Function Methods for Global Optimization , 2007, J. Glob. Optim..

[37]  David W. Corne,et al.  Properties of an adaptive archiving algorithm for storing nondominated vectors , 2003, IEEE Trans. Evol. Comput..

[38]  Li Zheng,et al.  PGO: A parallel computing platform for global optimization based on genetic algorithm , 2007, Comput. Geosci..

[39]  Qingfu Zhang,et al.  Multiobjective evolutionary algorithms: A survey of the state of the art , 2011, Swarm Evol. Comput..

[40]  Rui Zou,et al.  Multiple-pattern parameter identification and uncertainty analysis approach for water quality modeling , 2009 .

[41]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[42]  Demetris Koutsoyiannis A generalized mathematical framework for stochastic simulation and forecast of hydrologic time series , 2000 .

[43]  Hirotaka Nakayama,et al.  Meta-Modeling in Multiobjective Optimization , 2008, Multiobjective Optimization.

[44]  C. Shoemaker,et al.  Assessing the impacts of parameter uncertainty for computationally expensive groundwater models , 2006 .

[45]  Demetris Koutsoyiannis,et al.  One decade of multi-objective calibration approaches in hydrological modelling: a review , 2010 .

[46]  Joshua D. Knowles,et al.  ParEGO: a hybrid algorithm with on-line landscape approximation for expensive multiobjective optimization problems , 2006, IEEE Transactions on Evolutionary Computation.

[47]  Avi Ostfeld,et al.  State of the Art for Genetic Algorithms and Beyond in Water Resources Planning and Management , 2010 .

[48]  Mark Watson Adaptive Neural Networks , 1991 .

[49]  Thomas Stützle,et al.  Exploratory Analysis of Stochastic Local Search Algorithms in Biobjective Optimization , 2010, Experimental Methods for the Analysis of Optimization Algorithms.

[50]  Bryan A. Tolson,et al.  Review of surrogate modeling in water resources , 2012 .

[51]  Carlos M. Fonseca,et al.  Inferential Performance Assessment of Stochastic Optimisers and the Attainment Function , 2001, EMO.

[52]  Carlos A. Coello Coello,et al.  A Review of Techniques for Handling Expensive Functions in Evolutionary Multi-Objective Optimization , 2010 .

[53]  Slobodan P. Simonovic,et al.  Reservoir Systems Analysis: Closing Gap between Theory and Practice , 1992 .

[54]  Christine A. Shoemaker,et al.  Watershed calibration using multistart local optimization and evolutionary optimization with radial basis function approximation , 2007 .

[55]  Demetris Koutsoyiannis,et al.  A parametric rule for planning and management of multiple‐reservoir systems , 1997 .

[56]  Lothar Thiele,et al.  A Tutorial on the Performance Assessment of Stochastic Multiobjective Optimizers , 2006 .

[57]  Nicola Beume,et al.  SMS-EMOA: Multiobjective selection based on dominated hypervolume , 2007, Eur. J. Oper. Res..

[58]  Holger R. Maier,et al.  Optimal Design of Water Distribution Systems including Water Quality and System Uncertainty , 2008 .

[59]  Raphael T. Haftka,et al.  Surrogate-based Analysis and Optimization , 2005 .

[60]  A. E. Eiben,et al.  Multiobjective Evolutionary Algorithms , 2015 .

[61]  Q. Kang,et al.  Optimization and uncertainty assessment of strongly nonlinear groundwater models with high parameter dimensionality , 2010 .

[62]  M. Sasena,et al.  Exploration of Metamodeling Sampling Criteria for Constrained Global Optimization , 2002 .

[63]  George Kourakos,et al.  Pumping optimization of coastal aquifers based on evolutionary algorithms and surrogate modular neural network models , 2009 .

[64]  Chuntian Cheng,et al.  Multiple criteria rainfall–runoff model calibration using a parallel genetic algorithm in a cluster of computers / Calage multi-critères en modélisation pluie–débit par un algorithme génétique parallèle mis en œuvre par une grappe d'ordinateurs , 2005 .

[65]  Avi Ostfeld,et al.  Evolutionary algorithms and other metaheuristics in water resources: Current status, research challenges and future directions , 2014, Environ. Model. Softw..

[66]  R. P. Oliveira,et al.  Operating rules for multireservoir systems , 1997 .

[67]  Yacov Y. Haimes,et al.  Sensitivity, responsivity, stability and irreversibility as multiple objectives in civil systems , 1977 .

[68]  John W. Labadie,et al.  Optimal Operation of Multireservoir Systems: State-of-the-Art Review , 2004 .

[69]  Bryan A. Tolson,et al.  A new multi-objective algorithm, pareto archived DDS , 2009, GECCO '09.

[70]  Vijay P. Singh,et al.  Rainfall-runoff modeling , 1988 .

[71]  Dirk Gorissen,et al.  Evolutionary Regression Modeling with Active Learning: An Application to Rainfall Runoff Modeling , 2009, International Conference on Adaptive and Natural Computing Algorithms.

[72]  Wolfgang Ponweiser,et al.  Multiobjective Optimization on a Limited Budget of Evaluations Using Model-Assisted -Metric Selection , 2008, PPSN.

[73]  Lothar Thiele,et al.  Quality Assessment of Pareto Set Approximations , 2008, Multiobjective Optimization.

[74]  Michael T. M. Emmerich,et al.  Hypervolume-based expected improvement: Monotonicity properties and exact computation , 2011, 2011 IEEE Congress of Evolutionary Computation (CEC).

[75]  Stefan M. Wild,et al.  Bayesian Calibration and Uncertainty Analysis for Computationally Expensive Models Using Optimization and Radial Basis Function Approximation , 2008 .

[76]  Guangtao Fu,et al.  Simulation of urban wastewater systems using artificial neural networks: embedding urban areas in integrated catchment modelling. , 2010 .

[77]  Wolfgang Ponweiser,et al.  On Expected-Improvement Criteria for Model-based Multi-objective Optimization , 2010, PPSN.

[78]  Kuolin Hsu,et al.  Uncertainty assessment of hydrologic model states and parameters: Sequential data assimilation using the particle filter , 2005 .

[79]  Aaron C. Zecchin,et al.  Hybrid discrete dynamically dimensioned search (HD‐DDS) algorithm for water distribution system design optimization , 2009 .

[80]  Howard C. Card,et al.  Stochastic Radial Basis Functions , 2001, Int. J. Neural Syst..

[81]  D. Krige A statistical approach to some basic mine valuation problems on the Witwatersrand, by D.G. Krige, published in the Journal, December 1951 : introduction by the author , 1951 .

[82]  Thomas Bartz-Beielstein,et al.  A Case Study on Multi-Criteria Optimization of an Event Detection Software under Limited Budgets , 2013, EMO.

[83]  William H. Press,et al.  The Art of Scientific Computing Second Edition , 1998 .

[84]  Piet Demeester,et al.  A Surrogate Modeling and Adaptive Sampling Toolbox for Computer Based Design , 2010, J. Mach. Learn. Res..

[85]  H. Levy Stochastic dominance and expected utility: survey and analysis , 1992 .

[86]  Rui Zou,et al.  An adaptive neural network embedded genetic algorithm approach for inverse water quality modeling , 2007 .

[87]  Bruno Sudret,et al.  Global sensitivity analysis using polynomial chaos expansions , 2008, Reliab. Eng. Syst. Saf..

[88]  Tom Dhaene,et al.  Fast calculation of multiobjective probability of improvement and expected improvement criteria for Pareto optimization , 2014, J. Glob. Optim..

[89]  Bryan A. Tolson,et al.  Dynamically dimensioned search algorithm for computationally efficient watershed model calibration , 2007 .

[90]  Demetris Koutsoyiannis,et al.  Evaluation of the parameterization‐simulation‐optimization approach for the control of reservoir systems , 2003 .

[91]  Dimitri P. Solomatine,et al.  Model Induction with Support Vector Machines: Introduction and Applications , 2001 .

[92]  André Luís Marques Marcato,et al.  Parallel computing applied to the stochastic dynamic programming for long term operation planning of hydrothermal power systems , 2013, Eur. J. Oper. Res..

[93]  Mark Fleischer,et al.  The measure of pareto optima: Applications to multi-objective metaheuristics , 2003 .

[94]  Peter M. A. Sloot,et al.  Application of parallel computing to stochastic parameter estimation in environmental models , 2006, Comput. Geosci..

[95]  Lucas Bradstreet,et al.  A Fast Way of Calculating Exact Hypervolumes , 2012, IEEE Transactions on Evolutionary Computation.

[96]  W. Yeh,et al.  An integrated optimization algorithm for parameter structure identification in groundwater modeling , 2008 .

[97]  Sh. Momtahen,et al.  Direct Search Approaches Using Genetic Algorithms for Optimization of Water Reservoir Operating Policies , 2007 .

[98]  Donald R. Jones,et al.  Efficient Global Optimization of Expensive Black-Box Functions , 1998, J. Glob. Optim..

[99]  Bryan A. Tolson,et al.  Numerical assessment of metamodelling strategies in computationally intensive optimization , 2012, Environ. Model. Softw..

[100]  Paul Schot,et al.  Multiple‐objective optimization of drinking water production strategies using a genetic algorithm , 2002 .

[101]  Eckart Zitzler,et al.  Objective Reduction in Evolutionary Multiobjective Optimization: Theory and Applications , 2009, Evolutionary Computation.

[102]  Andrea Castelletti,et al.  A general framework for Dynamic Emulation Modelling in environmental problems , 2012, Environ. Model. Softw..

[103]  Dong Jun Seo,et al.  Fast and efficient optimization of hydrologic model parameters using a priori estimates and stepwise line search , 2008 .

[104]  Zoran Kapelan,et al.  Reducing the Complexity of Multiobjective Water Distribution System Optimization through Global Sensitivity Analysis , 2012 .

[105]  L. L. Rogers,et al.  Optimal Groundwater Remediation , 2003 .

[106]  Andy J. Keane,et al.  Recent advances in surrogate-based optimization , 2009 .

[107]  Lothar Thiele,et al.  Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach , 1999, IEEE Trans. Evol. Comput..

[108]  Carlos M. Fonseca,et al.  On the Computation of the Empirical Attainment Function , 2011, EMO.

[109]  Jack P. C. Kleijnen,et al.  Kriging Metamodeling in Simulation: A Review , 2007, Eur. J. Oper. Res..

[110]  Marco Laumanns,et al.  Performance assessment of multiobjective optimizers: an analysis and review , 2003, IEEE Trans. Evol. Comput..

[111]  Christine A. Shoemaker,et al.  Parallel Stochastic Global Optimization Using Radial Basis Functions , 2009, INFORMS J. Comput..

[112]  Tom Dhaene,et al.  Towards Efficient Multiobjective Optimization: Multiobjective statistical criterions , 2012, 2012 IEEE Congress on Evolutionary Computation.

[113]  Barbara S. Minsker,et al.  Applying Dynamic Surrogate Models in Noisy Genetic Algorithms to Optimize Groundwater Remediation Designs , 2011 .

[114]  Demetris Koutsoyiannis,et al.  Hurst‐Kolmogorov Dynamics and Uncertainty 1 , 2011 .

[115]  Nicola Beume,et al.  S-Metric Calculation by Considering Dominated Hypervolume as Klee's Measure Problem , 2009, Evolutionary Computation.

[116]  Joseph R. Kasprzyk,et al.  Evolutionary multiobjective optimization in water resources: The past, present, and future , 2012 .

[117]  Nicola Beume,et al.  On the Complexity of Computing the Hypervolume Indicator , 2009, IEEE Transactions on Evolutionary Computation.

[118]  Zoran Kapelan,et al.  Evolutionary-based Meta-modelling: The Relevance of Using Approximate Models in Hydroinformatics , 2009 .

[119]  Raghavan Srinivasan,et al.  Approximating SWAT Model Using Artificial Neural Network and Support Vector Machine 1 , 2009 .