Sequential test optimization method based on multi-objective genetic programming algorithm
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A technology of multi-objective genetic and optimization method, applied in the field of sequential test optimization based on multi-objective genetic programming algorithm, can solve problems such as unsuitable systems
Inactive Publication Date: 2017-05-17
UNIV OF ELECTRONICS SCI & TECH OF CHINA
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However, this method only considers the test cost as an optimization goal, which cannot be applied to systems with multiple test indicators as the optimization goal, and further improvement is needed
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[0106] In order to better illustrate the technical effect of the present invention, a specific embodiment is used for simulation verification. Table 1 is the failure-test dependency matrix of the electronic system in this example, including failure probability and test cost.
[0107]
[0108] Table 1
[0109] The conditions for this experiment verification are as follows: CPU: Pentium G3250; operating system: Windows 7; programming language: JAVA. Genetic programming related parameters: population size: 100; evolution algebra: 100 crossover rate: 0.8; mutation rate 0.05. The test indicators used are test cost and test time, and the threshold of congestion distance δ share = 0.3.
[0110] The sequential test optimization is carried out by using the present invention, and 12 Pareto optimal solutions are obtained. Table 2 is a list of test costs and test time for the Pareto optimal solution in this embodiment.
[0111] testing time testing fee 3.303 12.48...
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Abstract
The invention discloses a sequential test optimization method based on a multi-objective genetic programming algorithm. The method comprises the steps of firstly initializing to obtain a fault diagnostic tree population, using the multi-objective genetic programming method to select, cross and mutate a fault diagnostic tree, during the iteration of each generation, grouping the fault diagnosis tree individuals, wherein, the adaptability of each individual is calculated by using grouping adaptability and a crowding distance; after multiple iterations, selecting non-domination individuals from a final generation population as a non-domination fault diagnosis tree of the sequential test of the system. The sequential test optimization method based on the multi-objective genetic programming algorithm can be used for acquiring a Pareto optimum solution of the fault diagnosis tree of the sequential test for which multiple test indexes can be used as optimized targets, the optimum solutions can be selected by testers to provide guidance for system testers.
Description
technical field [0001] The invention belongs to the technical field of electronic system fault diagnosis, and more specifically relates to a sequential test optimization method based on a multi-objective genetic programming algorithm. Background technique [0002] In electronic system fault diagnosis technology, the sequential testing problem is defined as a five-tuple problem (S, P, T, C, D). where, S={s 0 ,s 1 ,s 2 ,...,s M} represents a finite set of system fault states, where s 0 Indicates the state of the system without faults, s 1 to s M Indicates the state of different faults in the system. P={p 0 ,p 1 ,p 2 ,...,p M} is the prior failure probability vector of occurrence of each system state. Assuming that the system can only be in a certain fault state or no fault state, it is necessary to normalize the prior fault probability vector P. T={t 1 ,t 2 ,...,t N} is the N available test sets of the system. C={c 1 ,c 2 ,...,c N} represents the correspond...
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