Open AccessTest configuration optimization method based on NSGA2-MOPSO algorithm
Author(s) -
Yun Lin
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1754/1/012186
Subject(s) - test functions for optimization , particle swarm optimization , mathematical optimization , selection (genetic algorithm) , multi swarm optimization , algorithm , computer science , optimization problem , testability , meta optimization , test (biology) , optimization algorithm , process (computing) , mathematics , engineering , reliability engineering , artificial intelligence , paleontology , biology , operating system
Test configuration optimization is an important part in the process of testability design. In order to be close to the reality, this paper chooses to carry out the test optimization selection under the condition of unreliable test. Under this condition, Test configuration optimization is essentially a multi-objective optimization problem. Therefore, this paper establishes a mathematical model of the problem with test cost, test quantity and false alarm rate as optimization objectives. In this paper, the multi-objective particle swarm optimization algorithm and NSGA2 algorithm are comprehensively analyzed. According to the advantages and disadvantages of the two algorithms, NSGA2-MOPSO algorithm is proposed to solve the test optimization selection problem. The results show that the NSGA2-MOPSO algorithm has good performance and high practicability.