A Heuristic Dynamically Dimensioned Search with Sensitivity Information (HDDS-S) and Application to River Basin Management

River basin simulation and multi-reservoir optimal operation have been critical for river basin management. Due to the intense interaction between human activities and river basin systems, the river basin model and multi-reservoir operation model are complicated with a large number of parameters. Therefore, fast and stable optimization algorithms are required for river basin management under the changing conditions of climate and current human activities. This study presents a new global optimization algorithm, named as heuristic dynamically dimensioned search with sensitivity information (HDDS-S), to effectively perform river basin simulation and multi-reservoir optimal operation during river basin management. The HDDS-S algorithm is built on the dynamically dimensioned search (DDS) algorithm; and has an improved computational efficiency while maintaining its search capacity compared to the original DDS algorithm. This is mainly due to the non-uniform probability assigned to each decision variable on the basis of its changing sensitivity to the optimization objectives during the adaptive change from global to local search with dimensionality reduced. This study evaluates the new algorithm by comparing its performance with the DDS algorithm on a river basin model calibration problem and a multi-reservoir optimal operation problem. The results obtained indicate that the HDDS-S algorithm outperforms the DDS algorithm in terms of search ability and computational efficiency in the two specific problems. In addition; similar to the DDS algorithm; the HDDS-S algorithm is easy to use as it does not require any parameter tuning and automatically adjusts its search to find good solutions given an available computational budget.

[1]  Angus R. Simpson,et al.  Genetic Algorithms for Reliability-Based Optimization of Water Distribution Systems , 2004 .

[2]  John R. Williams,et al.  LARGE AREA HYDROLOGIC MODELING AND ASSESSMENT PART I: MODEL DEVELOPMENT 1 , 1998 .

[3]  Soroosh Sorooshian,et al.  Comment on "Dynamically dimensioned search algorithm for computationally efficient watershed model calibration" by Bryan A. Tolson and Christine A. Shoemaker , 2008 .

[4]  Huicheng Zhou,et al.  Diurnal temperature range variation and its causes in a semiarid region from 1957 to 2006 , 2014 .

[5]  Christine A. Shoemaker,et al.  Impact of human activities on stream flow in the Biliu River basin, China , 2013 .

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

[7]  F. Feng,et al.  Reply to "Comment on , 1977 .

[8]  J. Lund,et al.  Optimal Hedging and Carryover Storage Value , 2004 .

[9]  Guangtao Fu,et al.  Multiple objective optimal control of integrated urban wastewater systems , 2008, Environ. Model. Softw..

[10]  Najmeh Mahjouri,et al.  Optimal Inter-Basin Water Allocation Using Crisp and Fuzzy Shapley Games , 2010 .

[11]  T. R. Neelakantan,et al.  Design of water distribution networks using particle swarm optimization , 2006 .

[12]  Jianshi Zhao,et al.  Improved Dynamic Programming for Hydropower Reservoir Operation , 2014 .

[13]  Jasper A Vrugt,et al.  Improved evolutionary optimization from genetically adaptive multimethod search , 2007, Proceedings of the National Academy of Sciences.

[14]  B. Santer,et al.  Human-Induced Changes in the Hydrology of the Western United States , 2008, Science.

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

[16]  Joseph R. Kasprzyk,et al.  Optimal Design of Water Distribution Systems Using Many-Objective Visual Analytics , 2013 .

[17]  Xuesong Zhang,et al.  On the use of multi‐algorithm, genetically adaptive multi‐objective method for multi‐site calibration of the SWAT model , 2010 .

[18]  Nikolaus Hansen,et al.  Completely Derandomized Self-Adaptation in Evolution Strategies , 2001, Evolutionary Computation.

[19]  Ben-de Wang,et al.  Assessments of Impacts of Climate Change and Human Activities on Runoff with SWAT for the Huifa River Basin, Northeast China , 2012, Water Resources Management.

[20]  Idel Montalvo,et al.  A diversity-enriched variant of discrete PSO applied to the design of water distribution networks , 2008 .

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

[22]  Chi Zhang,et al.  Human-Induced Runoff Change in Northeast China , 2015 .

[23]  Angus R. Simpson,et al.  Ant Colony Optimization Applied to Water Distribution System Design: Comparative Study of Five Algorithms , 2007 .

[24]  Christine A. Shoemaker,et al.  Reply to comment on “Dynamically dimensioned search algorithm for computationally efficient watershed model calibration” by Ali Behrangi et al. , 2008 .

[25]  S. Sorooshian,et al.  Shuffled complex evolution approach for effective and efficient global minimization , 1993 .

[26]  S. Sorooshian,et al.  Effective and efficient global optimization for conceptual rainfall‐runoff models , 1992 .

[27]  Wei Xu,et al.  A two stage Bayesian stochastic optimization model for cascaded hydropower systems considering varying uncertainty of flow forecasts , 2014 .

[28]  Dragan Savic,et al.  Genetic Algorithms for Least-Cost Design of Water Distribution Networks , 1997 .

[29]  Zoran Kapelan,et al.  Fuzzy probabilistic design of water distribution networks , 2011 .

[30]  Q. J. Wang.  THE GENETIC ALGORITHM AND ITS APPLICAYTION TO CALIBRATING CONCEPUTAL RAINFALL-RUNOFF MODELS , 1991 .

[31]  Pan Liu,et al.  Derivation of Aggregation-Based Joint Operating Rule Curves for Cascade Hydropower Reservoirs , 2011 .

[32]  Chi Zhang,et al.  Regional differences in hydrological response to canopy interception schemes in a land surface model , 2014 .

[33]  Q. J. Wang.  The Genetic Algorithm and Its Application to Calibrating Conceptual Rainfall-Runoff Models , 1991 .

[34]  Angus R. Simpson,et al.  Application of two ant colony optimisation algorithms to water distribution system optimisation , 2006, Math. Comput. Model..

[35]  Angus R. Simpson,et al.  Genetic algorithms compared to other techniques for pipe optimization , 1994 .

[36]  Soroosh Sorooshian,et al.  Optimal use of the SCE-UA global optimization method for calibrating watershed models , 1994 .

[37]  Angus R. Simpson,et al.  Ant Colony Optimization for Design of Water Distribution Systems , 2003 .

[38]  Paul F. Boulos,et al.  Using Genetic Algorithms to Rehabilitate Distribution Systems , 2001 .

[39]  Pan Liu,et al.  Deriving multiple near‐optimal solutions to deterministic reservoir operation problems , 2011 .

[40]  Guo-li Wang,et al.  A Negotiation-Based Multi-Objective, Multi-Party Decision-Making Model for Inter-Basin Water Transfer Scheme Optimization , 2012, Water Resources Management.

[41]  Guangtao Fu,et al.  The impacts of climate change on water diversion strategies for a water deficit reservoir , 2014 .

[42]  Jean-Luc Probst,et al.  Evidence for global runoff increase related to climate warming , 2004 .

[43]  Jeffrey G. Arnold,et al.  Model Evaluation Guidelines for Systematic Quantification of Accuracy in Watershed Simulations , 2007 .

[44]  Zhifang Yin,et al.  Assessment of crop growth and soil water modules in SWAT2000 using extensive field experiment data in an irrigation district of the Yellow River Basin , 2008 .

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

[46]  Yunzhong Jiang,et al.  Optimization of Water Diversion Based on Reservoir Operating Rules: Analysis of the Biliu River Reservoir, China , 2014 .