KR-20260067305-A - METHOD AND SYSTEM FOR COMBINATORIAL OPTIMIZATION

KR20260067305AKR 20260067305 AKR20260067305 AKR 20260067305AKR-20260067305-A

Abstract

The present invention relates to a combinatorial optimization method and system. More specifically, the present invention relates to a combinatorial optimization method and system that performs parameter optimization through a combination of a hybrid evolutionary algorithm (EA) and Bayesian optimization (BO).

Inventors

어문정
임태윤
오연주
서민국
임우형

Assignees

주식회사 LG 경영개발원

Dates

Publication Date: 20260512
Application Date: 20250908
Priority Date: 20241105

Claims (20)

In a computerized method including the following, A step of specifying input values related to parameters to be optimized; A step of performing a search for a plurality of candidate solutions generated according to the input value using a pre-configured first optimization algorithm; A step of generating result data based on the search results for the plurality of candidate solutions; A step of training an artificial intelligence model using the above-mentioned generated result data; and A combination optimization method characterized by performing optimization on the optimization target parameter based on the above-mentioned learned artificial intelligence model and a pre-set second optimization algorithm.
In paragraph 1, The above input value is, It includes at least one of the number of candidate solutions including the above-mentioned optimization target parameters, the number of the above-mentioned optimization target parameters included in the candidate solutions, and the search range of the above-mentioned optimization target parameters. The step of performing the above search is, A step of repeatedly performing the search using the first optimization algorithm based on a preset number of iterations for the first optimization algorithm, and In the step of generating the above result data, A combination optimization method characterized by generating result data based on the search results repeatedly performed according to the number of repetitions set above.
In paragraph 2, In the step of performing the above search, The search using the first optimization algorithm is performed N times, corresponding to the number of repetitions N set above, and In the step of generating the above result data, A combination optimization method characterized by generating result data based on the search results performed N times.
In paragraph 2, In the step of performing the above search, Using the above first optimization algorithm, the plurality of candidate solutions are generated according to the above input value, and In order to select an optimal candidate solution satisfying a preset criterion from the plurality of candidate solutions, a search is performed for each of the plurality of candidate solutions, and In the step of generating the above result data, A combination optimization method characterized by generating result data based on the search results for each of the plurality of candidate solutions.
In paragraph 4, In the step of generating the above plurality of candidate solutions, A combination optimization method characterized by generating a plurality of candidate solutions based on the search range of the optimization target parameters included in the input values.
In paragraph 4, In the step of performing the above search, An evaluation is performed for each of the above-mentioned plurality of candidate solutions, and an evaluation result for each of the above-mentioned plurality of candidate solutions is produced. Based on the evaluation results for each of the plurality of candidate solutions, the optimal candidate solution among the plurality of candidate solutions that satisfies the preset criteria is selected, and The above result data is, A combination optimization method characterized by including at least one of the plurality of candidate solutions and an evaluation result for each of the plurality of candidate solutions.
In paragraph 6, In the step of performing the above search, Using a preset computational technique, at least some of the above multiple candidate solutions are updated, and A combination optimization method characterized by performing a search on the updated candidate solutions to select an optimal candidate solution that satisfies the previously set criteria from the updated candidate solutions.
In Paragraph 7, An evaluation of the above-mentioned updated candidate solution is performed to produce an evaluation result for the above-mentioned updated candidate solution, and Based on the evaluation results of the above-mentioned updated candidate solutions, the optimal candidate solution among the above-mentioned updated candidate solutions that satisfies the above-mentioned preset criteria is selected, and The above result data is, A combination optimization method characterized by further including at least one of the above-mentioned updated candidate solution and the evaluation result for the above-mentioned updated candidate solution.
In paragraph 1, The step of performing the above search is, A step of searching for at least one optimal candidate solution for each iteration based on a preset number of iterations for the first optimization algorithm, and A combinatorial optimization method characterized in that the above optimal candidate solution is included in the above result data and used for training the above artificial intelligence model.
In Paragraph 9, In the above result data, A combination optimization method characterized by including at least one of the optimal candidate solution, an evaluation result for the optimal candidate solution, and a search range for the optimal candidate solution.
In paragraph 1, To train the artificial intelligence model, the method further includes the step of storing the result data in a specified repository. In the step of performing the above optimization, A combination optimization method characterized by performing optimization on a search space defined based on the above-mentioned optimization target parameters.
In Paragraph 11, In the step of training the above artificial intelligence model, To perform optimization on the above-mentioned optimization target parameters, the artificial intelligence model is trained using the result data stored in the above-mentioned specified repository, and In the step of performing the above optimization, A combinatorial optimization method characterized by performing optimization on the search space based on the above-mentioned learned artificial intelligence model.
In Paragraph 11, In the step of performing the above optimization, A combination optimization method characterized by performing optimization on the target parameter through at least one parameter combination specified from the search space using the above-mentioned learned artificial intelligence model and a specific function.
In Paragraph 13, The above-mentioned trained artificial intelligence model is, A combination optimization method characterized by predicting at least one of a predicted value and a predicted variance for at least one combination of parameters.
In Paragraph 13, In the step of performing the above optimization, Update the learned artificial intelligence model based on the parameter combination specified from the above search space, and A combination optimization method characterized by performing optimization on the optimization target parameter based on the update to the learned artificial intelligence model.
In paragraph 1, In the step of performing the above optimization, A combinatorial optimization method characterized by performing optimization on the target parameter based on the updated artificial intelligence model and the second optimization algorithm to produce an optimal solution for the target parameter.
In paragraph 1, The method further includes the step of estimating a prior distribution for the optimization target parameter using a pre-established probability distribution estimation technique, and In the step of training the above artificial intelligence model, A combination optimization method characterized by training the artificial intelligence model using at least one of the prior distribution for the optimization target parameter and the result data.
In Paragraph 17, The above optimization target parameters include a plurality of parameters having different characteristics, and In the step of estimating the prior distribution mentioned above, Using the above-mentioned probability distribution estimation technique, the prior distribution for each of the above-mentioned plurality of parameters is estimated, and In the step of training the above artificial intelligence model, A combination optimization method characterized by training the artificial intelligence model using at least one of the prior distribution for each of the plurality of parameters and the result data.
In a system comprising memory configured to store executable instructions and one or more processors configured to perform operations by executing one or more instructions, The above system is, Specify input values related to the parameters to be optimized, and Using a pre-configured first optimization algorithm, a search for a plurality of candidate solutions generated according to the input value is performed, and Result data is generated based on the search results for the above multiple candidate solutions, and Using the result data generated above, an artificial intelligence model is trained, and A combination optimization system characterized by performing optimization on the optimization target parameter based on the above-mentioned learned artificial intelligence model and a pre-set second optimization algorithm.
A program that is executed by one or more processes in an electronic device and stored on a computer-readable recording medium, The above program is, A step of specifying input values related to parameters to be optimized; A step of performing a search for a plurality of candidate solutions generated according to the input value using a pre-configured first optimization algorithm; A step of generating result data based on the search results for the plurality of candidate solutions; A step of training an artificial intelligence model using the above-mentioned generated result data; and A program stored on a computer-readable recording medium characterized by including instructions for performing a step of optimizing the optimization target parameter based on the above-mentioned learned artificial intelligence model and a pre-set second optimization algorithm.

Description

Method and System for Combinational Optimization The present invention relates to a combinatorial optimization method and system. More specifically, the present invention relates to a combinatorial optimization method and system that performs parameter optimization through a combination of a hybrid evolutionary algorithm (EA) and Bayesian optimization (BO). Optimization problems aim to find the best solution under given conditions. Such optimization problems play a crucial role in various industries and research fields, including manufacturing, telecommunications, finance, and artificial intelligence (AI). In particular, efficiently exploring complex and multidimensional parameter spaces is essential in diverse areas, such as manufacturing processes, parameter tuning for machine learning models, and performance enhancement for image processing systems. In this regard, various industrial sectors frequently face the challenge of optimally adjusting numerous setting variables (or parameters) under complex conditions. For example, in visual inspection systems used in manufacturing processes, multiple variables such as lighting intensity, camera position, resolution, and image processing speed interact with each other, and inspection accuracy and processing speed vary significantly depending on their combination. In such an environment, testing every possible combination of variables is inefficient in terms of time and cost, and the resources required to find the optimal combination increase, especially as the number of variables grows. Furthermore, since even minute changes to some variables can significantly impact overall performance, techniques are needed to efficiently explore and precisely tune from the initial setup phase. In this regard, various algorithms to solve optimization problems are being actively researched recently. However, conventional algorithms may have excellent global search capabilities but are vulnerable to local optimization, or they may experience performance degradation when initial data is insufficient, even if precise local search is possible. As discussed above, in fields requiring the consideration of interactions among multiple parameters (e.g., visual inspection systems), it is often difficult to quickly derive the optimal solution using only a single algorithm. Therefore, there is a need for an optimization method that overcomes the limitations of conventional algorithms and ensures both initial search speed and the accuracy of the final solution. FIG. 1 is a conceptual diagram illustrating a combination optimization system according to the present invention. FIGS. 2, FIGS. 3 and FIGS. 4 are flowcharts illustrating a combination optimization method according to the present invention. FIGS. 5 and FIGS. 6 are formulas related to the combination optimization method according to the present invention. FIG. 7 shows an example of an algorithm of a combination optimization method according to the present invention. Hereinafter, embodiments disclosed in this specification will be described in detail with reference to the attached drawings. Identical or similar components are assigned the same reference number regardless of the drawing symbols, and redundant descriptions thereof will be omitted. The suffixes "module" and "part" used for components in the following description are assigned or used interchangeably solely for the ease of drafting the specification and do not have distinct meanings or roles in themselves. Furthermore, in describing the embodiments disclosed in this specification, if it is determined that a detailed description of related prior art could obscure the essence of the embodiments disclosed in this specification, such detailed description will be omitted. Additionally, the attached drawings are intended only to facilitate understanding of the embodiments disclosed in this specification; the technical concept disclosed in this specification is not limited by the attached drawings, and it should be understood that they include all modifications, equivalents, and substitutions that fall within the spirit and technical scope of the present invention. Terms including ordinal numbers, such as first, second, etc., may be used to describe various components, but said components are not limited by said terms. These terms are used solely for the purpose of distinguishing one component from another. When it is stated that one component is "connected" or "connected" to another component, it should be understood that while it may be directly connected or connected to that other component, there may also be other components in between. On the other hand, when it is stated that one component is "directly connected" or "directly connected" to another component, it should be understood that there are no other components in between. A singular expression includes a plural expression unless the context clearly indicates otherwise. In this application, terms such as “comprising” or