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Multi-target test optimization method based on series-parallel genetic algorithm

A genetic algorithm and multi-objective technology, applied in the field of multi-objective test optimization based on serial-parallel genetic algorithm, can solve problems such as inability to provide multiple choices, single optimization results, and strong subjectivity of weight factors

Active Publication Date: 2018-05-29
UNIV OF ELECTRONICS SCI & TECH OF CHINA
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Problems solved by technology

[0010] There are two problems with this approach: (1) the weight factor is highly subjective, and it is often not easy for the designer to choose; (2) the optimization result is single, and multiple choices cannot be provided

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  • Multi-target test optimization method based on series-parallel genetic algorithm
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  • Multi-target test optimization method based on series-parallel genetic algorithm

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Embodiment Construction

[0028] Specific embodiments of the present invention will be described below in conjunction with the accompanying drawings, so that those skilled in the art can better understand the present invention. It should be noted that in the following description, when detailed descriptions of known functions and designs may dilute the main content of the present invention, these descriptions will be omitted here.

[0029] figure 1 It is a specific implementation flow chart of the multi-objective testing optimization method based on the serial-parallel genetic algorithm of the present invention. Such as figure 1 As shown, the concrete steps of the multi-objective testing optimal method based on serial-parallel genetic algorithm of the present invention include:

[0030] S101: Determine the preferred objectives and constraints of the test:

[0031] Determine the optimal optimization objectives and constraint conditions for the test of several electronic systems according to the needs...

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Abstract

The invention discloses a multi-target test optimization method based on a series-parallel genetic algorithm. An optimized target and a constraint condition for test optimization of a plurality of electronic systems are determined based on needs; a genetic algorithm is performed by multiple times, wherein during the genetic algorithm performing process, a new population is obtained each time, individuals meeting the constraint condition are screened out and the screened individuals are added into an elite solution set, the number of dominated times of the individuals is obtained, and fitness values are calculated by using different ways by determining whether the individuals in the population belong to the elite solution set; optimal solution sets obtained by performing the genetic algorithm by multiple times are combined to form an individual in an initial population; the genetic algorithm is performed again to obtain an optimal solution set, wherein each individual is a testing optimization plan. According to the invention, on the basis of Pareto Optimality, the inventor designs a series-parallel genetic algorithm to obtain multiple kinds of test optimization plans meeting multiple targets, so that several kinds of test optimization plan alternatives are provided for the decision maker and different solutions are provided on different occasions.

Description

technical field [0001] The invention belongs to the technical field of electronic system fault diagnosis, and more specifically relates to a multi-objective test optimization method based on a series-parallel genetic algorithm. Background technique [0002] 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 or engineering fields, such as testability indicators that satisfy the constraints at the same time or even tend to be better. [0003] For the above test optimization problem that considers multiple testability indicators at the same time, it can be regarded as a multi-objective optimization problem. The multi-objective optimization problem is to discuss how to find the optimal solution that satisfies multiple obj...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06N3/12
CPCG06N3/126
Inventor 杨成林陈芳
Owner UNIV OF ELECTRONICS SCI & TECH OF CHINA
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