Three decades of the Shuffled Complex Evolution (SCE-UA) optimization algorithm: Review and applications (original) (raw)
Related papers
2010
Three methods, Shuffled Complex Evolution (SCE), Simple Genetic Algorithm (SGA) and Micro-Genetic Algorithm (µGA), are applied in parameter calibration of a grid-based distributed rainfall-runoff model (GBDM) and compared by their performances. Ten and four historical storm events in the Yan-Shui Creek catchment, Taiwan, provide the database for model calibration and verification, respectively. The study reveals that the SCE, SGA and µGA have close calibration results, and none of them are superior with respect to all the performance measures, i.e. the errors of time to peak, peak discharge and the total runoff volume, etc. The performances of the GBDM for the verification events are slightly worse than those in the calibration events, but still quite satisfactory. Among the three methods, the SCE seems to be more robust than the other two approaches because of the smallest influence of different initial random number seeds on calibrated model parameters, and has the best performance of verification with a relatively small number of calibration events.
Parallel Shuffled Complex Evolution Algorithm for Calibration of Hydrological Models
2006
Calibration of hydrological models is an essential step for these models to simulate real world conditions as close as possible. Calibration is a tedious and timeconsuming process. An auto-calibration algorithm developed by Duan et al. [1] has been successfully used in hydrological modeling area. This algorithm suffers from high computational cost for complex hydrological models. In this study, the current serial version of this algorithm is modified to a parallel version and validated using simple functions.
Water Resources Research, 2003
1] Markov Chain Monte Carlo (MCMC) methods have become increasingly popular for estimating the posterior probability distribution of parameters in hydrologic models. However, MCMC methods require the a priori definition of a proposal or sampling distribution, which determines the explorative capabilities and efficiency of the sampler and therefore the statistical properties of the Markov Chain and its rate of convergence. In this paper we present an MCMC sampler entitled the Shuffled Complex Evolution Metropolis algorithm (SCEM-UA), which is well suited to infer the posterior distribution of hydrologic model parameters. The SCEM-UA algorithm is a modified version of the original SCE-UA global optimization algorithm developed by Duan et al. [1992]. The SCEM-UA algorithm operates by merging the strengths of the Metropolis algorithm, controlled random search, competitive evolution, and complex shuffling in order to continuously update the proposal distribution and evolve the sampler to the posterior target distribution. Three case studies demonstrate that the adaptive capability of the SCEM-UA algorithm significantly reduces the number of model simulations needed to infer the posterior distribution of the parameters when compared with the traditional Metropolis-Hastings samplers.
Water, 2015
Hydrologic Simulation Program-Fortran (HSPF) model calibration is typically done manually due to the lack of an automated calibration tool as well as the difficulty of balancing objective functions to be considered. This paper discusses the development and demonstration of an automated calibration tool for HSPF (HSPF-SCE). HSPF-SCE was developed using the open source software "R". The tool employs the Shuffled Complex Evolution optimization algorithm (SCE-UA) to produce a pool of qualified calibration parameter sets from which the modeler chooses a single set of calibrated parameters. Six calibration criteria specified in the Expert System for the Calibration of HSPF (HSPEXP) decision support tool were combined to develop a single, composite objective function for HSPF-SCE. The HSPF-SCE tool was demonstrated, and automated and manually calibrated model performance were compared using three Virginia watersheds, where HSPF models had been previously prepared for bacteria total daily maximum load (TMDL) development. The example applications demonstrate that HSPF-SCE can be an effective tool for calibrating HSPF.
Using shuffled complex evolution to calibrate water distribution network model
Calibration of water distribution network model is of paramount importance for the optimal management of water delivery systems. This includes the determination of network parameters such as pipe roughness coefficients and nodal demands. The parameters are not often exactly known and very much sensitive to the age of the pipe. The calibration is usually accomplished by mimicking the model results to the field conditions. However, it becomes tedious if this is performed manually. In this paper, a population based meta-heuristic evolutionary algorithm, Shuffled Complex Evolution (SCE), is applied to determine the network parameters. Two example problems have been analyzed to demonstrate the robustness of the model. The model results show that SCE is capable in reaching the optimal solution in an effective manner.
Water Science and Technology: Water Supply
The aim of this study is to improve the performance of the shuffled complex evolution (SCE) algorithm used in the optimization of hydropower generation in reservoirs as a complex issue in water resources. First, the SCE algorithm is merged with the differential evolution (DE) algorithm to form the SCE-DE algorithm. Then, a complex mathematical function is used as a benchmark to evaluate the performance and validate the SCE-DE algorithm and the outcomes are compared with the original SCE algorithm to show the superiority of the proposed SCE-DE algorithm. In addition, the two-reservoir system of Dez-Gotvand is considered as a real optimization problem to evaluate the performance of the SCE-DE algorithm. It is revealed that optimization by SCE-DE is much better than SCE. In conclusion, the results show that the proposed SCE-DE algorithm is a reasonable alternative to optimizing resource systems and can be used to solve complex issues of water resources.
Journal of Hydroinformatics, 2012
Formulation of an automatic calibration strategy for the MIKE 11/NAM rainfall-runoff model is outlined. The calibration scheme includes optimisation of multiple objectives that measure different aspects of the hydrograph: (1) overall water balance, (2) overall shape of the hydrograph, (3) peak flows, and (4) low flows. An automatic optimisation procedure based on the shuffled complex evolution algorithm is introduced for solving the multi-objective calibration problem. A test example is presented that illustrates the principles and implications of using multiple objectives in model calibration. Significant trade-offs between the different objectives are observed in this case and no single unique set of parameter values is able to optimise all objectives simultaneously. Instead, the solution to the calibration problem is given as a set of Pareto optimal solutions, which from a multi-objective viewpoint are equivalent. A large variability is observed in the Pareto optimal parameter sets, resulting in a large range of "equally good" simulated hydrographs. From the set of Pareto optimal solutions, one can draw a single solution according to priorities of the different objectives for the specific model application being considered. A balanced aggregated objective function is proposed, which provides a compromise solution that puts equal weights to the different objectives. ᭧
Effective and efficient algorithm for multiobjective optimization of hydrologic models
Water Resources Research, 2003
1] Practical experience with the calibration of hydrologic models suggests that any single-objective function, no matter how carefully chosen, is often inadequate to properly measure all of the characteristics of the observed data deemed to be important. One strategy to circumvent this problem is to define several optimization criteria (objective functions) that measure different (complementary) aspects of the system behavior and to use multicriteria optimization to identify the set of nondominated, efficient, or Pareto optimal solutions. In this paper, we present an efficient and effective Markov Chain Monte Carlo sampler, entitled the Multiobjective Shuffled Complex Evolution Metropolis (MOSCEM) algorithm, which is capable of solving the multiobjective optimization problem for hydrologic models. MOSCEM is an improvement over the Shuffled Complex Evolution Metropolis (SCEM-UA) global optimization algorithm, using the concept of Pareto dominance (rather than direct single-objective function evaluation) to evolve the initial population of points toward a set of solutions stemming from a stable distribution (Pareto set). The efficacy of the MOSCEM-UA algorithm is compared with the original MOCOM-UA algorithm for three hydrologic modeling case studies of increasing complexity.
A Shuffled Complex Evolution Metropolis Algorithm for Estimating
2003
Practical experience with hydrologic model calibration suggests that it is generally impossible to f'md a single best parameter set whose performance measure differs significantly from other feasible parameters sets. While considerable attention has been given to the development ofautomatic calibration methods which aim to successfully find a single best set of parameter values, much less attention has been paid to a real-istic assessment of parameter uncertainty in hydrologic models.
Hydraulic Analysis of Water Distribution Network Using Shuffled Complex Evolution
Journal of Fluids, 2014
Hydraulic analysis of water distribution networks is an important problem in civil engineering. A widely used approach in steadystate analysis of water distribution networks is the global gradient algorithm (GGA). However, when the GGA is applied to solve these networks, zero flows cause a computation failure. On the other hand, there are different mathematical formulations for hydraulic analysis under pressure-driven demand and leakage simulation. This paper introduces an optimization model for the hydraulic analysis of water distribution networks using a metaheuristic method called shuffled complex evolution (SCE) algorithm. In this method, applying if-then rules in the optimization model is a simple way in handling pressure-driven demand and leakage simulation, and there is no need for an initial solution vector which must be chosen carefully in many other procedures if numerical convergence is to be achieved. The overall results indicate that the proposed method has the capability of handling various pipe networks problems without changing in model or mathematical formulation. Application of SCE in optimization model can lead to accurate solutions in pipes with zero flows. Finally, it can be concluded that the proposed method is a suitable alternative optimizer challenging other methods especially in terms of accuracy.