CN110689160A - Parameter configuration optimization method and device for large-scale complex system - Google Patents
Parameter configuration optimization method and device for large-scale complex system Download PDFInfo
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Abstract
A parameter configuration optimization method and device for a large-scale complex system comprise the following steps: step S1, training a low-fidelity Gaussian process regression model according to the low-fidelity experimental observation data; s2, acquiring a high fidelity experimental evaluation point according to a prediction curve output by the low fidelity Gaussian process regression model; step S3, training a high-fidelity Gaussian process regression model according to the high-fidelity experimental data; s4, fusing the prediction output of the low-fidelity Gaussian process regression model and the prediction output of the high-fidelity Gaussian process regression model, determining a next high-fidelity experiment evaluation point S5, and returning to the step S3 to iterate if the high-fidelity experiment times are less than a preset value; otherwise, selecting an optimal point from the high fidelity experimental evaluation points and outputting. The method adopts a Bayesian model fusion mechanism to fully utilize low-fidelity experimental data, and obtains optimal system configuration parameter values with less experimental cost.
Description
Technical Field
The invention belongs to the technical field of data optimization, and particularly relates to a parameter configuration optimization method and device for a large-scale complex system.
Background
In many fields such as industrial production, academic research, meteorological prediction and the like, a large-scale complex system exists, and the operation state of the system depends on the configuration condition of system parameters. The large-scale complex system parameter configuration optimization process has the characteristics of more parameters to be optimized, high evaluation experiment cost, no analytic form of an optimization objective function and no available gradient information in the optimization process, and great challenges are brought to the search of optimal system configuration parameter values. The commonly used non-gradient optimization method comprises manual parameter adjustment and a heuristic optimization searching method based on evolutionary computation, if enough good parameter configuration values need to be obtained, multiple high-fidelity experiments need to be carried out, and because each experiment is expensive, the actually allowed high-fidelity experiment times are limited, the parameter configuration values output by the methods are far from the optimum.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide a parameter configuration optimization method and device for a large-scale complex system.
The invention provides a parameter configuration optimization method for a large-scale complex system, which comprises the following steps:
step S1, training a low-fidelity Gaussian process regression model according to the low-fidelity experimental observation data;
s2, acquiring a high fidelity experimental evaluation point according to a prediction curve output by the low fidelity Gaussian process regression model;
step S3, training a high-fidelity Gaussian process regression model according to the high-fidelity experimental data;
s4, fusing the prediction output of the low-fidelity Gaussian process regression model and the prediction output of the high-fidelity Gaussian process regression model, and determining the next high-fidelity experiment evaluation point;
s5, if the high fidelity experiment times are less than the preset value, returning to the S3 for iteration; otherwise, selecting an optimal point from the high fidelity experimental evaluation points and outputting.
As a further technical scheme of the invention, the low-fidelity Gaussian process regression model stores and encapsulates low-fidelity experimental data information.
Further, in step S2, the low-fidelity gaussian process regression model predicts the experimental response at any experimental evaluation point, and selects a high-fidelity experimental evaluation point according to the predicted value.
Furthermore, an optimal search model is adopted for selecting the high fidelity experimental evaluation points, and the objective function of the optimal search model is determined according to the predicted value of the low fidelity Gaussian process regression model.
Further, in step S4, the prediction output of the low-fidelity gaussian process regression model and the prediction output of the high-fidelity gaussian process regression model are weighted and fused by a dynamic weighting mechanism based on bayesian.
Furthermore, the fused weighting coefficient is determined by the model prediction value and the actual response value.
The invention also provides a parameter configuration optimization device for the large-scale complex system, which is characterized by comprising a low-fidelity experimental data storage device, a low-fidelity Gaussian process regression prediction device, a high-fidelity data acquisition device, a high-fidelity Gaussian process regression prediction device, a prediction fusion device and an optimal decision device;
the low-fidelity experimental data storage device is used for storing terminal equipment of low-fidelity experimental observation data;
the low-fidelity Gaussian process regression prediction device is used for analyzing and processing low-fidelity experimental observation data and determining the parameter values of a low-fidelity Gaussian process regression model;
high fidelity data acquisition device. The device is used for collecting and storing high fidelity experimental evaluation data;
the high-fidelity Gaussian process regression prediction device is used for analyzing and processing high-fidelity experimental observation data and determining high-fidelity Gaussian process regression model parameter values;
and the prediction fusion device is used for performing weighted fusion on the low-fidelity Gaussian process regression prediction and the high-fidelity Gaussian process regression prediction and giving a fusion prediction response value to any experimental evaluation point.
And the optimal decision device is used for selecting the optimal next iteration experiment evaluation point according to the fusion prediction response value given by the prediction fusion device.
Further, the low fidelity gaussian process regression prediction device outputs the initial high fidelity experimental evaluation points.
Furthermore, the prediction fusion device and the optimal decision device adopt a dynamic weighting mechanism based on Bayes to perform weighted fusion on the prediction output of the low-fidelity Gaussian process regression model and the prediction output of the high-fidelity Gaussian process regression model.
Furthermore, the fused weighting coefficient is determined by the model prediction value and the actual response value.
The method of the invention adopts a Bayesian model fusion mechanism to fully utilize low-fidelity experimental data and obtains the optimal system configuration parameter value with less experimental cost.
Drawings
FIG. 1 is a schematic flow diagram of the process of the present invention;
FIG. 2 is a block diagram of the apparatus of the present invention.
Detailed Description
Referring to fig. 1, the present embodiment provides a method for optimizing parameter configuration for a large-scale complex system, including the following steps:
step S1, training a low-fidelity Gaussian process regression model according to the low-fidelity experimental observation data;
s2, acquiring a high fidelity experimental evaluation point according to a prediction curve output by the low fidelity Gaussian process regression model;
step S3, training a high-fidelity Gaussian process regression model according to the high-fidelity experimental data;
s4, fusing the prediction output of the low-fidelity Gaussian process regression model and the prediction output of the high-fidelity Gaussian process regression model, and determining the next high-fidelity experiment evaluation point;
s5, if the high fidelity experiment times are less than the preset value, returning to the S3 for iteration; otherwise, selecting an optimal point from the high fidelity experimental evaluation points and outputting.
And storing and packaging the low-fidelity experimental data information by the low-fidelity Gaussian process regression model.
In step S2, the low-fidelity gaussian process regression model predicts the experimental response at any experimental evaluation point, and selects a high-fidelity experimental evaluation point according to the predicted value.
And selecting the high fidelity experimental evaluation points by adopting an optimal search model, wherein the target function of the optimal search model is determined according to the predicted value of the low fidelity Gaussian process regression model.
In step S4, the prediction output of the low-fidelity gaussian process regression model and the prediction output of the high-fidelity gaussian process regression model are weighted and fused by a bayesian-based dynamic weighting mechanism.
The fused weighting coefficient is determined by the model predicted value and the actual response value.
As shown in fig. 2, a parameter configuration optimization device for a large-scale complex system is characterized by comprising a low-fidelity experimental data storage device, a low-fidelity gaussian process regression prediction device, a high-fidelity data acquisition device, a high-fidelity gaussian process regression prediction device, a prediction fusion device and an optimal decision device;
the low-fidelity experimental data storage device is used for storing terminal equipment of low-fidelity experimental observation data;
the low-fidelity Gaussian process regression prediction device is used for analyzing and processing low-fidelity experimental observation data and determining the parameter values of a low-fidelity Gaussian process regression model;
high fidelity data acquisition device. The device is used for collecting and storing high fidelity experimental evaluation data;
the high-fidelity Gaussian process regression prediction device is used for analyzing and processing high-fidelity experimental observation data and determining high-fidelity Gaussian process regression model parameter values;
and the prediction fusion device is used for performing weighted fusion on the low-fidelity Gaussian process regression prediction and the high-fidelity Gaussian process regression prediction and giving a fusion prediction response value to any experimental evaluation point.
And the optimal decision device is used for selecting the optimal next iteration experiment evaluation point according to the fusion prediction response value given by the prediction fusion device.
The low fidelity gaussian process regression prediction device outputs the initial high fidelity experimental evaluation points.
And the prediction fusion device and the optimal decision device adopt a dynamic weighting mechanism based on Bayes to perform weighted fusion on the prediction output of the low-fidelity Gaussian process regression model and the prediction output of the high-fidelity Gaussian process regression model.
The fused weighting coefficient is determined by the model predicted value and the actual response value.
The method and the device have the characteristics of more parameters to be optimized, high evaluation experiment cost, no analytic form of an optimization objective function and no available gradient information in the optimization process, fully utilize low-fidelity experiment data by adopting a Bayesian model fusion mechanism, and obtain the optimal system configuration parameter value with less experiment cost.
The foregoing illustrates and describes the principles, general features, and advantages of the present invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, which are intended to further illustrate the principles of the invention, and that various changes and modifications may be made without departing from the spirit and scope of the invention, which is intended to be protected by the appended claims. The scope of the invention is defined by the claims and their equivalents.
Claims (10)
1. A parameter configuration optimization method for a large-scale complex system is characterized by comprising the following steps of S1, training a low-fidelity Gaussian process regression model according to low-fidelity experimental observation data;
s2, acquiring a high fidelity experimental evaluation point according to a prediction curve output by the low fidelity Gaussian process regression model;
step S3, training a high-fidelity Gaussian process regression model according to the high-fidelity experimental data;
s4, fusing the prediction output of the low-fidelity Gaussian process regression model and the prediction output of the high-fidelity Gaussian process regression model, and determining the next high-fidelity experiment evaluation point;
s5, if the high fidelity experiment times are less than the preset value, returning to the S3 for iteration; otherwise, selecting an optimal point from the high fidelity experimental evaluation points and outputting.
2. The method of claim 1, wherein the low-fidelity Gaussian process regression model stores and encapsulates low-fidelity experimental data information.
3. The method for optimizing parameter configuration for large-scale complex systems according to claim 1, wherein in step S2, the low-fidelity gaussian process regression model predicts experimental responses at any experimental evaluation point, and selects a high-fidelity experimental evaluation point according to the predicted value.
4. The method according to claim 3, wherein the selection of the high fidelity experimental evaluation point adopts an optimal search model, and an objective function of the optimal search model is determined according to a predicted value of a low fidelity Gaussian process regression model.
5. The method for optimizing parameter configuration for large-scale complex system as claimed in claim 1, wherein in step S4, the prediction output of the low-fidelity gaussian process regression model and the prediction output of the high-fidelity gaussian process regression model are weighted and fused by a bayesian-based dynamic weighting mechanism.
6. The method according to claim 5, wherein the fused weighting coefficients are determined by model prediction values and actual response values.
7. A parameter configuration optimization device for a large-scale complex system is characterized by comprising a low-fidelity experimental data storage device, a low-fidelity Gaussian process regression prediction device, a high-fidelity data acquisition device, a high-fidelity Gaussian process regression prediction device, a prediction fusion device and an optimal decision device;
the low-fidelity experimental data storage device is used for storing terminal equipment of low-fidelity experimental observation data;
the low-fidelity Gaussian process regression prediction device is used for analyzing and processing low-fidelity experimental observation data and determining a low-fidelity Gaussian process regression model parameter value;
the high-fidelity data acquisition device is used for acquiring and storing high-fidelity experimental evaluation data;
the high-fidelity Gaussian process regression prediction device is used for analyzing and processing high-fidelity experimental observation data and determining high-fidelity Gaussian process regression model parameter values;
the prediction fusion device is used for performing weighted fusion on the low-fidelity Gaussian process regression prediction and the high-fidelity Gaussian process regression prediction and giving a fusion prediction response value to any experimental evaluation point;
and the optimal decision device is used for selecting an optimal next iteration experiment evaluation point according to the fusion prediction response value given by the prediction fusion device.
8. The apparatus of claim 7, wherein the low fidelity gaussian process regression prediction means outputs an initial high fidelity experimental evaluation point.
9. The device for optimizing parameter configuration for large-scale complex systems according to claim 7, wherein the prediction fusion device and the optimal decision device perform weighted fusion on the prediction output of the low-fidelity gaussian process regression model and the prediction output of the high-fidelity gaussian process regression model by using a bayesian-based dynamic weighting mechanism.
10. The apparatus according to claim 7, wherein the fused weighting coefficients are determined by the model predicted values and the actual response values.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN113254472A (en) * | 2021-06-17 | 2021-08-13 | 浙江大华技术股份有限公司 | Parameter configuration method, device, equipment and readable storage medium |
| WO2022009010A1 (en) * | 2020-07-08 | 2022-01-13 | International Business Machines Corporation | Model fidelity monitoring and regeneration for manufacturing process decision support |
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| CN106971240A (en) * | 2017-03-16 | 2017-07-21 | 河海大学 | The short-term load forecasting method that a kind of variables choice is returned with Gaussian process |
| CN109214605A (en) * | 2018-11-12 | 2019-01-15 | 国网山东省电力公司电力科学研究院 | Power-system short-term Load Probability prediction technique, apparatus and system |
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| CN106971240A (en) * | 2017-03-16 | 2017-07-21 | 河海大学 | The short-term load forecasting method that a kind of variables choice is returned with Gaussian process |
| CN109214605A (en) * | 2018-11-12 | 2019-01-15 | 国网山东省电力公司电力科学研究院 | Power-system short-term Load Probability prediction technique, apparatus and system |
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| WO2022009010A1 (en) * | 2020-07-08 | 2022-01-13 | International Business Machines Corporation | Model fidelity monitoring and regeneration for manufacturing process decision support |
| GB2612502A (en) * | 2020-07-08 | 2023-05-03 | Ibm | Model fidelity monitoring and regeneration for manufacturing process decision support |
| CN113254472A (en) * | 2021-06-17 | 2021-08-13 | 浙江大华技术股份有限公司 | Parameter configuration method, device, equipment and readable storage medium |
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