Adaptive infill sampling criterion for multi-fidelity gradient-enhanced kriging model

Public traffic has a great influence, especially with the background of COVID-19. Solving simulation-based optimization (SO) problem is efficient to study how to improve the performance of public traffic. Global optimization based on Kriging (KGO) is an efficient method for SO; to this end, this paper proposes a Kriging-based global optimization using multi-point infill sampling criterion. This method uses an infill sampling criterion which obtains multiple new design points to update the Kriging model through solving the constructed multi-objective optimization problem in each iteration. Then, the typical low-dimensional and high-dimensional nonlinear functions, and a SO based on 445 bus line in Beijing city, are employed to test the performance of our algorithm. Moreover, compared with the KGO based on the famous single-point expected improvement (EI) criterion and the particle swarm algorithm (PSO), our method can obtain better solutions in the same amount or less time. Therefore, the proposed algorithm expresses better optimization performance, and may be more suitable for solving the tricky and expensive simulation problems in real-world traffic problems.

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Efficient Optimization Design Method Using Kriging Model

Journal of Aircraft ◽

10.2514/1.c10485e ◽

2005 ◽

Vol 42 (5) ◽

pp. 1375-1375 ◽

Cited By ~ 1

Author(s):

Shinkyu Jeong ◽

Mitsuhiro Murayama ◽

Kazuomi Yamamoto

Keyword(s):

Optimization Design ◽

Design Method ◽

Kriging Model

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Study of Attenuation of Centimetric Waves of the Fifth Generation of Cellular Telephony in Vegetation Areas using the Kriging Model

2020 International Conference on Innovation and Intelligence for Informatics, Computing and Technologies (3ICT) ◽

10.1109/3ict51146.2020.9311959 ◽

2020 ◽

Author(s):

Paulo Tiburcio Pereira ◽

Fernando Sousa Ramos ◽

Glaucio Lopes Ramos

Keyword(s):

Kriging Model ◽

Fifth Generation ◽

Cellular Telephony

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Reliability Analysis of Wheel fatigue strength based on active learning Kriging Model

IOP Conference Series Materials Science and Engineering ◽

10.1088/1757-899x/1043/5/052057 ◽

2021 ◽

Vol 1043 (5) ◽

pp. 052057

Author(s):

Xin Cheng ◽

Xufeng Yang

Keyword(s):

Fatigue Strength ◽

Active Learning ◽

Reliability Analysis ◽

Kriging Model ◽

Strength Based

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Multi-objective optimization of squirrel cage fan for range hood based on Kriging model

Proceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science ◽

10.1177/0954406221995863 ◽

2021 ◽

pp. 095440622199586

Author(s):

Qianhao Xiao ◽

Jun Wang ◽

Boyan Jiang ◽

Weigang Yang ◽

Xiaopei Yang

Keyword(s):

Flow Rate ◽

Optimization Design ◽

Volume Flow Rate ◽

Kriging Model ◽

Volume Flow ◽

Design Parameters ◽

Multi Objective Optimization ◽

Nsga Ii ◽

Multi Objective ◽

Squirrel Cage

In view of the multi-objective optimization design of the squirrel cage fan for the range hood, a blade parameterization method based on the quadratic non-uniform B-spline (NUBS) determined by four control points was proposed to control the outlet angle, chord length and maximum camber of the blade. Morris-Mitchell criteria were used to obtain the optimal Latin hypercube sample based on the evolutionary operation, and different subsets of sample numbers were created to study the influence of sample numbers on the multi-objective optimization results. The Kriging model, which can accurately reflect the response relationship between design variables and optimization objectives, was established. The second-generation Non-dominated Sorting Genetic algorithm (NSGA-II) was used to optimize the volume flow rate at the best efficiency point (BEP) and the maximum volume flow rate point (MVP). The results show that the design parameters corresponding to the optimization results under different sample numbers are not the same, and the fluctuation range of the optimal design parameters is related to the influence of the design parameters on the optimization objectives. Compared with the prototype, the optimized impeller increases the radial velocity of the impeller outlet, reduces the flow loss in the volute, and increases the diffusion capacity, which improves the volume flow rate, and efficiency of the range hood system under multiple working conditions.

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A Kriging-Assisted Multi-Objective Constrained Global Optimization Method for Expensive Black-Box Functions

Mathematics ◽

10.3390/math9020149 ◽

2021 ◽

Vol 9 (2) ◽

pp. 149

Author(s):

Yaohui Li ◽

Jingfang Shen ◽

Ziliang Cai ◽

Yizhong Wu ◽

Shuting Wang

Keyword(s):

Global Optimization ◽

Pareto Frontier ◽

Kriging Model ◽

Optimization Method ◽

Function Evaluation ◽

Sampling Point ◽

Constrained Global Optimization ◽

Multi Objective ◽

Sample Data ◽

Sampling Points

The kriging optimization method that can only obtain one sampling point per cycle has encountered a bottleneck in practical engineering applications. How to find a suitable optimization method to generate multiple sampling points at a time while improving the accuracy of convergence and reducing the number of expensive evaluations has been a wide concern. For this reason, a kriging-assisted multi-objective constrained global optimization (KMCGO) method has been proposed. The sample data obtained from the expensive function evaluation is first used to construct or update the kriging model in each cycle. Then, kriging-based estimated target, RMSE (root mean square error), and feasibility probability are used to form three objectives, which are optimized to generate the Pareto frontier set through multi-objective optimization. Finally, the sample data from the Pareto frontier set is further screened to obtain more promising and valuable sampling points. The test results of five benchmark functions, four design problems, and a fuel economy simulation optimization prove the effectiveness of the proposed algorithm.

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