@inproceedings{d066c08def65499da2d71fa0864d04f3,
title = "Test optimization selection based on HGPSO Algorithm",
abstract = "In order to solve the test optimization problem in aviation equipment test selection, a hybrid genetic particle swarm optimization (HGPSO) algorithm was improved on the basis of discrete particle swarm optimization (DPSO) algorithm. In the particle swarm optimization (PSO) algorithm, the cross and mutation operation of genetic algorithm is used to replace the updated formula of particle velocity and position. The method of crossover is that particles cross individual extremum and population extremum respectively, and the variation is linearly decreasing, so that particles can easily jump out of the local optimal solution and find the optimal solution. The simulation results show that the method works even better, the result of optimization is satisfied the requirements of testability of the system, which provide effective guidance for the selection of test optimization of complex systems.",
keywords = "DPSO algorithm, HGPSO algorithm, Test optimization",
author = "Xiaofeng Lv and Deyun Zhou and Fuqiang Li",
note = "Publisher Copyright: {\textcopyright} 2023 ACM.; 7th International Conference on Control Engineering and Artificial Intelligence, CCEAI 2023 ; Conference date: 28-01-2023 Through 30-01-2023",
year = "2023",
month = jan,
day = "28",
doi = "10.1145/3580219.3580240",
language = "英语",
series = "ACM International Conference Proceeding Series",
publisher = "Association for Computing Machinery",
pages = "113--117",
editor = "Dan Zhang and Yong Yue",
booktitle = "Proceedings - 2023 7th International Conference on Control Engineering and Artificial Intelligence, CCEAI 2023",
}