Eureka AIR delivers breakthrough ideas for toughest innovation challenges, trusted by R&D personnel around the world.

A multi-objective optimization method based on Gaussian process simultaneous mimo model

A multi-objective optimization and Gaussian process technology, applied in the field of multi-objective optimization design of Gaussian process simultaneous modeling combined with optimization algorithms, can solve the problem of insufficient utilization of system information, small sample size, and cross-correlation model fitting of category input variables or the prediction accuracy cannot be captured and other issues

Active Publication Date: 2016-07-06
GUANGXI UNIV
View PDF1 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0008] The purpose of the present invention is to propose a multi-objective optimization method based on the Gaussian process simultaneous MIMO (multiple inputs and multiple outputs) model, by virtue of the advantages of the Gaussian process simultaneous MIMO model, which has fewer model-related parameters, is easy to implement, and has a smaller sample size, to solve the optimization problem The calculation time is long, the system information is not fully utilized, the multi-objective proxy model cannot use the cross-correlation of the category input variables to improve the accuracy of the model's fitting or prediction of unknown points, and it cannot capture different types of correlations between the output terminals.

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • A multi-objective optimization method based on Gaussian process simultaneous mimo model
  • A multi-objective optimization method based on Gaussian process simultaneous mimo model
  • A multi-objective optimization method based on Gaussian process simultaneous mimo model

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0053] Numerical examples for solving multi-objective problems

[0054] A multi-objective optimization method based on a Gaussian process simultaneous MIMO model, including experimental design method sampling, standard Gaussian process simultaneous MIMO model and multi-objective optimization algorithm optimization, including the following steps:

[0055] Step 1. According to the numerical multi-objective problem given by the user, its mathematical description is as formula (9), and the experimental design is carried out through the Latin hypercube experimental design method, and 20 sample points are obtained by sampling in the variable x∈[0,1]. And substitute into the analytical formula f of formula (9) 1 , g(x 1 ,x 2 ), get the corresponding response value, and form the training data set of the model together with the sample set of variable x;

[0056] f 1 =x 1

[0057] f 2 =g(x 1 ,x 2 )h(x 1 ,x 2 )

[0058] g(x 1 ,x 2 )=11+x 2 2 -10cos(2πx 2 )(9)

[0059] ...

Embodiment 2

[0088] Application of Multi-objective Optimization Method Based on Gaussian Process Simultaneous MIMO Model in Sheet Metal Drawing

[0089] Using the multi-objective optimization method based on the Gaussian process simultaneous MIMO model to optimize the rear cover of an automobile axle housing, such as Figure 6 and Figure 7 As shown, the multi-objective optimization of deep drawing process parameters, the optimization process flow chart is as follows Figure 8 shown, including the following steps:

[0090] Step 1. Select the forming process parameters to be optimized according to the process documents formulated by the customer, and determine their value ranges as follows: 80≤BHF(kN)≤500, 275≤Bill diameter D(mm)≤325, 0.10≤Static friction coefficient μ between sheet metal and die 1 ≤0.15, 0.10≤Static friction coefficient μ of sheet metal and blank holder ring 2 ≤0.15, through the enhanced translation propagation Latin hypercube (ETPLHD) sampling method for experimental ...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

Abstract

Provided is a multi-objective optimization method based on a Gaussian process simultaneous MIMO model. According to the method, samples are obtained in an experiment design method, a mapping relation between design variables and responses to be studied is approximately set by utilizing the Gaussian process simultaneous MIMO model, multi-objective optimization is carried out on the Gaussian process simultaneous MIMO model in a Gaussian variation hybrid genetic algorithm, an ant colony algorithm and the like, then Pareto front related to design variable combinations is obtained, furthermore, distribution quality of solution sets in the Pareto front is judged, finally, one design variable combination in the Pareto front is selected according to specific demands for high-accuracy analysis and solution, and physical experiments are carried out when obtained results are satisfactory. According to the multi-objective optimization, experiment design, the high-accuracy analysis and solution, an agent model technology and an optimization algorithm are integrated and applied to the optimization design, therefore, time consumption and computation cost of the optimization design are reduced greatly, and work efficiency is greatly improved.

Description

technical field [0001] The invention relates to a multi-objective optimization method in which proxy model technology is combined with optimization algorithm, in particular to a multi-objective optimization design method in which Gaussian process simultaneous modeling is combined with optimization algorithm. Background technique [0002] The development of surrogate model technology provides an effective bridge for the introduction of optimization technology. The sample data is obtained through advanced experimental design methods, and the mapping relationship between design variables and the response to be investigated is approximated by using surrogate model technology. Finally, the optimization algorithm is used to solve the relationship. The model obtains the optimal combination of design variables. This method of integrating experimental design, high-precision analysis and solving, surrogate model technology, and optimization algorithms into optimal design can not only ...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
Patent Type & Authority Patents(China)
IPC IPC(8): G06F17/50
Inventor 夏薇杨欢廖小平龙凤英曹高翔
Owner GUANGXI UNIV
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Eureka Blog
Learn More
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic, Popular Technical Reports.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com