A RBF-based constrained global optimization algorithm for problems with computationally expensive objective and constraints

Wu, Yongge; Yin, Qian; Jie, Haoxiang; Wang, Boxing; Zhao, Jianjun

doi:10.1007/s00158-018-1987-2

Cited by 20 publications

(8 citation statements)

References 55 publications

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Section: Adaptive Kalman Filtering Algorithmmentioning

confidence: 99%

“…Sage-Husa suboptimal unbiased maximum posterior (MAP) noise estimator has been widely used due to its advantages of simple calculation and clear principle [9]. However, Sage-Husa noise estimator cannot simultaneously estimate and measure system noise; otherwise, it will lead to filter divergence [9,10]. In the actual environment, the measurement noise can be obtained by the physical characteristics of the sensor, but due to the influence of measurement instrument precision, external interference, and other factors, it is difficult to obtain the system noise accurately [11][12][13].…”

Section: Adaptive Kalman Filtering Algorithmmentioning

confidence: 99%

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Application of Adaptive Robust CKF in SINS/GPS Initial Alignment with Large Azimuth Misalignment Angle

Zhang

Wang

et al. 2021

Mathematical Problems in Engineering

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When the strapdown inertial navigation system does not perform coarse alignment, the misalignment angle is generally a large angle, and a nonlinear error model and a nonlinear filtering method are required. For large azimuth misalignment, the initial alignment technology with a large azimuth misalignment angle is researched in this paper. The initial alignment technology with a large azimuth misalignment angle is researched in this paper. First, the SINS/GPS nonlinear error model is established. Secondly, in the view of observation gross errors and inaccurate noise statistical characteristics, an adaptive robust CKF algorithm is proposed. Finally, according to the simulation analysis and experiment, the adaptive robust CKF algorithm can augment the stability and improve the filter estimation precision and convergence rate.

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Section: Adaptive Kalman Filtering Algorithmmentioning

confidence: 99%

Section: Adaptive Kalman Filtering Algorithmmentioning

confidence: 99%

Application of Adaptive Robust CKF in SINS/GPS Initial Alignment with Large Azimuth Misalignment Angle

Zhang

Wang

et al. 2021

Mathematical Problems in Engineering

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“…A complex engineering model often takes several or more hours to run a single simulation. Also, design problems in engineering applications as described by the simulation models, are always "black-box" [32]. The search in constraint black-box optimization is difficult since there is not usually a priori knowledge about the feasible region and the fitness landscape.…”

Section: Related Workmentioning

confidence: 99%

A New Approach for Low-Dimensional Constrained Engineering Design Optimization Using Design and Analysis of Simulation Experiments

Parnianifard¹,

Chancharoen²,

Phanomchoeng³

et al. 2020

IJCIS

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The number of function evaluations in many industrial applications of simulation-based optimization problems is strictly limited. Therefore, only little analytical information on objective and constraint functions is available. This paper presents an adaptive algorithm called the Surrogate-Based Constrained Global-Optimization (SCGO) method to solve black-box constrained simulation-based optimization problems involving computationally expensive objective function and inequality constraints. Firstly, Kriging surrogate is constructed over a new overall objective function (called loss function) to approximate the behavior of a true model. Then, an adaptive approach is provided to improve the optimal results sequentially while enforcing a feasible solution. The SCGO method is tested on several classical engineering design problems namely design of a tension/compression spring, design of a welded beam, design of a pressure vessel, and three-bar truss design. The results demonstrate that SCGO has advantages in solving the costly constrained problems and needs less costly function evaluations. Optimization results prove that the proposed algorithm is very competitive compared to the state-of-the-art metaheuristic algorithms.

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“…However, in the field of automobile lightweight design, there is little research on passenger car rear seats. Therefore, it is of great significance to reduce the weight of the passenger car rear seat for the development of the automobile industry while satisfying the safety performance and riding comfort [5].…”

Section: Introductionmentioning

confidence: 99%

Multi-Response Weighted Adaptive Sampling Approach Based on Hybrid Surrogate Model

2021

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In order to improve the fitting accuracy and optimization efficiency of the surrogate model, a multi-response weighted adaptive sampling (MWAS) approach based on the hybrid surrogate model was proposed and implemented to a multi-objective lightweight design of car seats. In this approach, the sample discreteness index in the input design space was calculated by the maximum and minimum distance approach (MDA), the fitting uncertainty index of output response was calculated by a strategy based on the weighted prediction variance (WPV), and the two indices are combined by the weight coefficients. In the iterative process, the weight coefficients of the two indices were determined according to the accuracy of the hybrid surrogate model. The balance of global and local accuracy was realized by considering the sample dispersion and the fitting uncertainty of the surrogate model comprehensively. Numerical examples of single-response and multi-response systems showed that the proposed approach has excellent sampling efficiency and robustness. Moreover, the results of actual engineering application showed that the hybrid surrogate model constructed through MWAS could significantly improve the efficiency of model optimization. Hence, a high-precision optimization solution to the multi-objective lightweight design of passenger car rear seat was obtained.

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A RBF-based constrained global optimization algorithm for problems with computationally expensive objective and constraints

Cited by 20 publications

References 55 publications

Application of Adaptive Robust CKF in SINS/GPS Initial Alignment with Large Azimuth Misalignment Angle

Application of Adaptive Robust CKF in SINS/GPS Initial Alignment with Large Azimuth Misalignment Angle

A New Approach for Low-Dimensional Constrained Engineering Design Optimization Using Design and Analysis of Simulation Experiments

Multi-Response Weighted Adaptive Sampling Approach Based on Hybrid Surrogate Model

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