Least Squares Support Vector Machine Classifiers
Neural Processing Letters
Trust region model management in multidisciplinary design optimization
Journal of Computational and Applied Mathematics - Special issue on numerical analysis 2000 Vol. IV: optimization and nonlinear equations
A Knowledge-Based Approach To Response Surface Modelling in Multifidelity Optimization
Journal of Global Optimization
Asymptotic behaviors of support vector machines with Gaussian kernel
Neural Computation
Metamodel-based lightweight design of B-pillar with TWB structure via support vector regression
Computers and Structures
Power load forecasting using support vector machine and ant colony optimization
Expert Systems with Applications: An International Journal
A comparative study of metamodeling methods considering sample quality merits
Structural and Multidisciplinary Optimization
Multi-fidelity optimization for sheet metal forming process
Structural and Multidisciplinary Optimization
Cellular particle swarm optimization
Information Sciences: an International Journal
Measuring financial risk with generalized asymmetric least squares regression
Applied Soft Computing
Expert Systems with Applications: An International Journal
Finite Elements in Analysis and Design
Revenue forecasting using a least-squares support vector regression model in a fuzzy environment
Information Sciences: an International Journal
Hi-index | 12.05 |
Engineering design is usually a daunting optimization task which often involving time-consuming, even computation-prohibitive process. To reduce the computational expense, metamodels are commonly used to replace the actual expensive simulations or experiments. In this paper, a new and efficient metamodeling method named prior-knowledge input least square support vector regression (PKI-LSSVR) is developed, in which samples from different levels of fidelity are incorporated to gain an accurate approximation with limited times of the high-fidelity (HF) expensive simulations. The low-fidelity (LF) output serves as a prior-knowledge of the real response function, and then is used as the input variables of least square support vector regression (LSSVR). When the corresponding HF response is gained, a function that maps the LF outputs to HF outputs is constructed via LSSVR. The predictive accuracy of LSSVR models is highly dependent on their learning parameters. Therefore, a novel optimization method, cellular particle swarm optimization (CPSO), is exploited to seek the optimal hyper-parameters for PKI-LSSVR in order to improve its generalization capability. To get a better optimization performance, a new neighborhood function is developed for CPSO where the global and local search is efficiently balanced by adaptively varied neighbor radius. Several numerical experiments and one engineering case verify the efficiency of the proposed PKI-LSSVR method. Sample quality merits including sample sizes and noise, and metamodel performance evaluation measures incorporating accuracy, robustness, and efficiency are considered.