Grouping-comprehensive multi-objective evolutionary-based multi-task test optimization method

Abstract

The invention discloses a grouping-comprehensive multi-objective evolutionary-based multi-task test optimization method. The method comprises the steps of firstly adopting a multi-objective evolutionary algorithm for each task mode to obtain an elite individual set of test optimization, wherein individuals in the multi-objective evolutionary algorithm are test scheme selection vectors, evolutionary objectives are preset evolutionary objectives of a testability index in the task modes, and a constraint condition of each evolutionary objective is a constraint condition of the testability index in the corresponding task mode; secondly performing multi-task comprehensive test optimization by adopting the multi-objective evolutionary algorithm according to the elite individual set of each task mode, wherein elements of the individuals are serial numbers of the elite individuals of each task mode in the corresponding set; and according to the obtained elite individual set, obtaining multi-task test scheme selection vectors, namely, non-dominated solutions of the multi-task test optimization. By adopting the method, the non-dominated solutions of the multi-task test optimization under the multi-objective condition can be obtained more quickly, and a calculation result is more accurate.

Description

technical field [0001] The invention belongs to the technical field of system fault diagnosis, and more specifically relates to a multi-task test optimization method based on grouping-integrated multi-objective evolution. Background technique [0002] With the development of semiconductor integrated circuits in the direction of integration and miniaturization, electronic systems are becoming more and more complex, and it is becoming more and more inconvenient to set measuring points in the circuit. Due to the sharp reduction of measuring points, fault diagnosis is more difficult. The situation of inversion of development cost and maintenance cost often occurs, which increases the number of maintenance personnel, requires higher technical levels for them, and lengthens training time. 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...

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Problems solved by technology

Then, for the multi-task test optimization problem, if the overall optimization objective and the single-task optimization objective are simply integrated together, and the multi-objective evolutionary algorithm is used to solve the problem, there will be problems with too many objectives and too high dimensionality, which is harmful to the search solution. is unfavorable and requires adaptive improvement

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