System-level testability design multi-objective optimization method based on improved PBI method
A multi-objective optimization and testing technology, applied in design optimization/simulation, genetic law, genetic model, etc., can solve the problems of slow search speed, high time complexity, long algorithm running time, etc.
Active Publication Date: 2021-06-01
UNIV OF ELECTRONICS SCI & TECH OF CHINA
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The NSGA-III algorithm is more typical and can find a more comprehensive non-dominated solution set. However, due to the high time complexity of the calculation of the dominance relationship and the slow convergence speed, the algorithm runs for a long time.
In the problem of slow search speed and high convergence algebra
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[0049] In order to better illustrate the technical solution of the present invention, the technical principle of the present invention is described first.
[0050] figure 1 is a schematic illustration of the PBI method. Such as figure 1 As shown, in the PBI (penalty-based boundary intersection, boundary intersection based on penalty function) method, W is the reference vector specified in advance, which is generally automatically generated according to the number specified by the user, such as dividing the two-dimensional space (a quadrant) into 5 parts , then 6 reference vectors are needed, and the angle between them is 90° / 5=18°. The multi-objective optimization based on this method is to distribute an objective function F(X) on each reference vector, and it is close to the coordinate origin (minimization problem). To measure whether an objective function (1) is close to the reference vector; (2) is close to the coordinate origin, available figure 1 shown d 1 and d 2 t...
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The invention discloses a system-level testability design multi-objective optimization method based on an improved PBI method, and the method comprises the steps: firstly initializing a group of uniformly distributed reference vectors, calculating a penalty factor for each reference vector, then carrying out the iterative search of an optimal influence factor vector based on a genetic algorithm, carrying out the progressive increase of the penalty factors in a search process, and carrying out the calculation of the optimal influence factor vector; and performing optimization by combining the objective function value and the improved PBI function value to obtain a new population, performing individual reselection operation on the new population, and deleting a dominated solution in a final generation population, thereby obtaining a Pareto optimal solution set of the influence factor vector. By adopting the method, the convergence effect and the uniformity of the Pareto optimal solution of the influence factor vector can be improved while the optimal solution is ensured, so that the influence factors are reasonably configured, and the purpose of testability optimization design is achieved.
Description
technical field [0001] The invention belongs to the technical field of equipment testability design optimization, and more specifically relates to a system-level testability design multi-objective optimization method based on an improved PBI method. Background technique [0002] In order to reduce the difficulty of maintaining equipment in the future, testability design should be considered in the initial stage of system design. Testability refers to the degree to which the state of a system can be accurately detected. In the problem of fault diagnosis for large-scale electronic equipment systems, how to choose a test plan so that the fault detection rate (FDR, fault diagnosis rate), false alarm rate (FAR, fault alarm rate) and test costs (time, economy, etc.) It is a problem that is constantly being explored in the academic and engineering fields that the index satisfies the constraints at the same time or even tends to be better. [0003] In the test optimization problem...
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IPC IPC(8): G06F30/27G06N3/12
CPCG06F30/27G06N3/126
Inventor 杨成林高亮亮鲜航
Owner UNIV OF ELECTRONICS SCI & TECH OF CHINA