scholarly journals Adaptive meta-modeling-based simulation optimization in basin-scale optimum water allocation: a comparative analysis of meta-models

2015 ◽  
Vol 18 (3) ◽  
pp. 446-465 ◽  
Author(s):  
Golnazalsadat Mirfenderesgi ◽  
S. Jamshid Mousavi
Keyword(s):  
River Basin ◽  
Water Allocation ◽  
Optimization Problems ◽  
Computational Cost ◽  
Search Space ◽  
Support Vector ◽  
Meta Model ◽  
Modeling Approach ◽  
Basin Scale ◽  
Meta Modeling

Incorporating river basin simulation models in heuristic optimization algorithms can help modelers address complex, basin-scale water resource problems. We have developed a hybrid optimization-simulation model by linking a stretching particle swarm optimization (SPSO) algorithm and the MODSIM river basin decision support system (DSS), and have used the SPSO-MODSIM model to optimize water allocation at basin scale. Due to high computational cost of the SPSO-MODSIM model, we have, subsequently, used four meta-model types of artificial neural networks (ANN), support vector machines (SVM), kriging and polynomial response functions, replacing the MODSIM DSS, in an adaptively learning meta-modeling approach. The performances of the meta-models are first compared in two Ackley and Dejong benchmark functions optimization problems, and the meta-models are then evaluated by solving the Atrak river basin water allocation optimization problem in Iran. The results demonstrate that independent of the meta-model type, the sequentially space-filling meta-modeling approach can improve the performance of meta-models in the course of optimization by adaptively locating the promising regions of the search space where more samples need to be generated. However, the ANN and SVM meta-models perform better than others in saving the number of costly, original objective function evaluations.

Evolutionary Algorithm Embedded With Bump-Hunting for Constrained Design Optimization

2020 ◽  
Vol 143 (2) ◽  
Author(s):  
Kamrul Hasan Rahi ◽  
Hemant Kumar Singh ◽  
Tapabrata Ray
Keyword(s):  
Design Optimization ◽  
Optimization Problem ◽  
Feasible Solution ◽  
Optimization Problems ◽  
Computational Cost ◽  
Search Space ◽  
Bump Hunting ◽  
Simulation Based ◽  
Engineering Problems ◽  
Turbine Design

Abstract Real-world design optimization problems commonly entail constraints that must be satisfied for the design to be viable. Mathematically, the constraints divide the search space into feasible (where all constraints are satisfied) and infeasible (where at least one constraint is violated) regions. The presence of multiple constraints, constricted and/or disconnected feasible regions, non-linearity and multi-modality of the underlying functions could significantly slow down the convergence of evolutionary algorithms (EA). Since each design evaluation incurs some time/computational cost, it is of significant interest to improve the rate of convergence to obtain competitive solutions with relatively fewer design evaluations. In this study, we propose to accomplish this using two mechanisms: (a) more intensified search by identifying promising regions through “bump-hunting,” and (b) use of infeasibility-driven ranking to exploit the fact that optimal solutions are likely to be located on constraint boundaries. Numerical experiments are conducted on a range of mathematical benchmarks and empirically formulated engineering problems, as well as a simulation-based wind turbine design optimization problem. The proposed approach shows up to 53.48% improvement in median objective values and up to 69.23% reduction in cost of identifying a feasible solution compared with a baseline EA.

Ensemble of Meta-Models Based on Local Error Measure Using Cross-Validation

2009 ◽  
Author(s):  
Dohyun Park ◽  
Yongbin Lee ◽  
Dong-Hoon Choi
Keyword(s):  
Prediction Accuracy ◽  
Cross Validation ◽  
Polynomial Regression ◽  
Computational Cost ◽  
Support Vector ◽  
Local Error ◽  
Test Cases ◽  
Meta Model ◽  
Lower Accuracy ◽  
Cross Validation Error

Many meta-models have been developed to approximate true responses. These meta-models are often used for optimization instead of computer simulations which require high computational cost. However, designers do not know which meta-model is the best one in advance because the accuracy of each meta-model becomes different from problem to problem. To address this difficulty, research on the ensemble of meta-models that combines stand-alone meta-models has recently been pursued with the expectation of improving the prediction accuracy. In this study, we propose a selection method of weight factors for the ensemble of meta-models based on v-nearest neighbors’ cross-validation error (CV). The four stand-alone meta-models we employed in this study are polynomial regression, Kriging, radial basis function, and support vector regression. Each method is applied to five 1-D mathematical examples and ten 2-D mathematical examples. The prediction accuracy of each stand-alone meta-model and the existing ensemble of meta-models is compared. Ensemble of meta-models shows higher accuracy than the worst stand-alone model among the four stand-alone meta-models at all test examples (30 cases). In addition, the ensemble of meta-models shows the highest accuracy for the 5 test cases. Although it has lower accuracy than the best stand-alone meta-model, it has almost same RMSE values (less than 1.1) as the best standalone model in 16 out of 30 test cases. From the results, we can conclude that proposed method is effective and robust.

scholarly journals An Enhanced Evolutionary Software Defect Prediction Method Using Island Moth Flame Optimization

Mathematics ◽  
2021 ◽  
Vol 9 (15) ◽  
pp. 1722
Author(s):  
Ruba Abu Khurma ◽  
Hamad Alsawalqah ◽  
Ibrahim Aljarah ◽  
Mohamed Abd Elaziz ◽  
Robertas Damaševičius
Keyword(s):  
Swarm Intelligence ◽  
Optimization Problems ◽  
Prediction Method ◽  
Search Space ◽  
Defect Prediction ◽  
Support Vector ◽  
Software Defect Prediction ◽  
Running Time ◽  
Software Defect ◽  
Software Modules

Software defect prediction (SDP) is crucial in the early stages of defect-free software development before testing operations take place. Effective SDP can help test managers locate defects and defect-prone software modules. This facilitates the allocation of limited software quality assurance resources optimally and economically. Feature selection (FS) is a complicated problem with a polynomial time complexity. For a dataset with N features, the complete search space has 2N feature subsets, which means that the algorithm needs an exponential running time to traverse all these feature subsets. Swarm intelligence algorithms have shown impressive performance in mitigating the FS problem and reducing the running time. The moth flame optimization (MFO) algorithm is a well-known swarm intelligence algorithm that has been used widely and proven its capability in solving various optimization problems. An efficient binary variant of MFO (BMFO) is proposed in this paper by using the island BMFO (IsBMFO) model. IsBMFO divides the solutions in the population into a set of sub-populations named islands. Each island is treated independently using a variant of BMFO. To increase the diversification capability of the algorithm, a migration step is performed after a specific number of iterations to exchange the solutions between islands. Twenty-one public software datasets are used for evaluating the proposed method. The results of the experiments show that FS using IsBMFO improves the classification results. IsBMFO followed by support vector machine (SVM) classification is the best model for the SDP problem over other compared models, with an average G-mean of 78%.

scholarly journals Process-based distributed modeling approach for analysis of sediment dynamics in a river basin

2010 ◽  
Vol 7 (4) ◽  
pp. 5685-5735
Author(s):  
M. A. Kabir ◽  
D. Dutta ◽  
S. Hironaka
Keyword(s):  
Sediment Transport ◽  
Soil Erosion ◽  
River Basin ◽  
Best Management Practices ◽  
Management Practices ◽  
Sediment Dynamics ◽  
Distributed Hydrological Model ◽  
Distributed Modeling ◽  
Modeling Approach ◽  
Basin Scale

Abstract. Modeling of sediment dynamics for developing best management practices of reducing soil erosion and of sediment control has become essential for sustainable management of watersheds. Precise estimation of sediment dynamics is very important since soils are a major component of enormous environmental processes and sediment transport controls lake and river pollution extensively. Different hydrological processes govern sediment dynamics in a river basin, which are highly variable in spatial and temporal scales. This paper presents a process-based distributed modeling approach for analysis of sediment dynamics at river basin scale by integrating sediment processes (soil erosion, sediment transport and deposition) with an existing process-based distributed hydrological model. In this modeling approach, the watershed is divided into an array of homogeneous grids to capture the catchment spatial heterogeneity. Hillslope and river sediment dynamic processes have been modeled separately and linked to each other consistently. Water flow and sediment transport at different surface grids and river nodes are modeled using one-dimensional kinematic wave approximation of Saint-Venant equations. The mechanics of sediment dynamics are integrated into the model using representative physical equations after a comprehensive review. The model has been tested on river basins in two different hydro climatic areas, the Abukuma River Basin, Japan and Latrobe River Basin, Australia. Sediment transport and deposition are modeled using Govers transport capacity equation. All spatial datasets, such as, Digital Elevation Model (DEM), land use and soil classification data, etc., have been prepared using raster "Geographic Information System (GIS)" tools. The results of relevant statistical checks (Nash-Sutcliffe efficiency and R-squared value) indicate that the model simulates basin hydrology and its associated sediment dynamics reasonably well. This paper presents the model including descriptions of the various components and the results of its application on case study areas.

Approximation based curvilinear local search for optimization problems

2016 ◽  
Vol 33 (2) ◽  
Author(s):  
Fatih Yaman ◽  
Asim Egemen Yilmaz ◽  
Kemal Leblebicioğlu
Keyword(s):  
Local Search ◽  
Design Methodology ◽  
Reference Data ◽  
Optimization Problems ◽  
Interpolation Method ◽  
Computational Cost ◽  
Search Space ◽  
Approximation Technique ◽  
Search Mechanism ◽  
Alpha Beta

Purpose At this work, we propose a local approximation based search method to optimize any function. For this purpose, an approximation method is combined with an estimation filter, and a new local search mechanism is constituted. Design/methodology/approach RBF network is very efficient interpolation method especially if we have sufficient reference data. Here, reference data refers to the exact value of the objective function at some points. Using this capability of RBFs, we can approximately inspect the vicinity each point in search space. Meanwhile, in order to obtain a smooth, rapid and better trajectory toward the global optimum, the alpha-beta filter can be integrated to this mechanism. For better description and visualization, the operations are defined in 2-dimensional search space; but the outlined procedure can be extended to higher dimensions without loss of generality. Findings When compared with our previous studies using conventional heuristic methods, approximation based curvilinear local search mechanism provide better minimization performance for almost all benchmark functions. Moreover computational cost of this method too less than heuristics. The number of iteration down to at least 1/10 compared to conventional heuristic algorithm. Additionally, the solution accuracy is very improved for majority of the test cases. Originality/value This paper proposes a new search approach to solve optimization problems with less cost. For this purpose, a new local curvilinear search mechanism is built using RBF based approximation technique and alpha-beta estimation filter.

Resampling Methods for Meta-Model Validation with Recommendations for Evolutionary Computation

2012 ◽  
Vol 20 (2) ◽  
pp. 249-275 ◽  
Author(s):  
B. Bischl ◽  
O. Mersmann ◽  
H. Trautmann ◽  
C. Weihs
Keyword(s):  
Model Validation ◽  
Optimization Problems ◽  
Fitness Function ◽  
Support Vector ◽  
Modeling Framework ◽  
Meta Modeling ◽  
Expensive Optimization Problems ◽  
And Performance ◽  
Classical Linear Regression ◽  
Expensive Optimization

Meta-modeling has become a crucial tool in solving expensive optimization problems. Much of the work in the past has focused on finding a good regression method to model the fitness function. Examples include classical linear regression, splines, neural networks, Kriging and support vector regression. This paper specifically draws attention to the fact that assessing model accuracy is a crucial aspect in the meta-modeling framework. Resampling strategies such as cross-validation, subsampling, bootstrapping, and nested resampling are prominent methods for model validation and are systematically discussed with respect to possible pitfalls, shortcomings, and specific features. A survey of meta-modeling techniques within evolutionary optimization is provided. In addition, practical examples illustrating some of the pitfalls associated with model selection and performance assessment are presented. Finally, recommendations are given for choosing a model validation technique for a particular setting.

scholarly journals Feature and Instance Selection Through Discriminant Analysis Criteria

2021 ◽  
Author(s):  
Fadi Dornaika ◽  
Abdelmalik Moujahid
Keyword(s):  
Discriminant Analysis ◽  
Nearest Neighbor ◽  
Optimization Problems ◽  
Computational Cost ◽  
Public Image ◽  
Support Vector ◽  
Instance Selection ◽  
Feature Subset ◽  
Learning Tasks ◽  
Analysis Theory

Abstract Feature selection and instance selection are two data preprocessing methods widely used in data mining and pattern recognition. The main goal is to reduce the computational cost of many learning tasks. Recently, joint feature and instance selection has been approached by solving some global optimization problems using meta-heuristics. This approach is not only computationally expensive, but also does not exploit the fact that the data usually has a structured manifold implicitly hidden in the data and its labels. In this paper, we address joint feature and instance selection using scores derived from discriminant analysis theory. We present three approaches for joint feature and instance selection. The first scheme is a wrapper technique, while the other two schemes are filtering techniques. In the filtering approaches, the search process uses a genetic algorithm where the evaluation criterion is mainly given by the discriminant analysis score. This score depends simultaneously on the feature subset candidate and the best corresponding subset of instances. Thus, the best feature subset and the best instances are determined by finding the best score. The performance of the proposed approaches is quantified and studied using image classification with Nearest Neighbor and Support Vector Machine Classifiers. Experiments are conducted on five public image datasets. We compare the performance of our proposed methods with several state-of-the-art methods. The experiments performed show the superiority of the proposed methods over several baseline methods.

scholarly journals Process-based distributed modeling approach for analysis of sediment dynamics in a river basin

2011 ◽  
Vol 15 (4) ◽  
pp. 1307-1321 ◽  
Author(s):  
M. A. Kabir ◽  
D. Dutta ◽  
S. Hironaka
Keyword(s):  
Sediment Transport ◽  
Soil Erosion ◽  
River Basin ◽  
Best Management Practices ◽  
Management Practices ◽  
Sediment Dynamics ◽  
Distributed Hydrological Model ◽  
Distributed Modeling ◽  
Modeling Approach ◽  
Basin Scale

Abstract. Modeling of sediment dynamics for developing best management practices of reducing soil erosion and of sediment control has become essential for sustainable management of watersheds. Precise estimation of sediment dynamics is very important since soils are a major component of enormous environmental processes and sediment transport controls lake and river pollution extensively. Different hydrological processes govern sediment dynamics in a river basin, which are highly variable in spatial and temporal scales. This paper presents a process-based distributed modeling approach for analysis of sediment dynamics at river basin scale by integrating sediment processes (soil erosion, sediment transport and deposition) with an existing process-based distributed hydrological model. In this modeling approach, the watershed is divided into an array of homogeneous grids to capture the catchment spatial heterogeneity. Hillslope and river sediment dynamic processes have been modeled separately and linked to each other consistently. Water flow and sediment transport at different land grids and river nodes are modeled using one dimensional kinematic wave approximation of Saint-Venant equations. The mechanics of sediment dynamics are integrated into the model using representative physical equations after a comprehensive review. The model has been tested on river basins in two different hydro climatic areas, the Abukuma River Basin, Japan and Latrobe River Basin, Australia. Sediment transport and deposition are modeled using Govers transport capacity equation. All spatial datasets, such as, Digital Elevation Model (DEM), land use and soil classification data, etc., have been prepared using raster "Geographic Information System (GIS)" tools. The results of relevant statistical checks (Nash-Sutcliffe efficiency and R–squared value) indicate that the model simulates basin hydrology and its associated sediment dynamics reasonably well. This paper presents the model including descriptions of the various components and the results of its application on two case study areas.

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