China Safety Science Journal ›› 2023, Vol. 33 ›› Issue (1): 122-129.doi: 10.16265/j.cnki.issn1003-3033.2023.01.0895

• Safety engineering technology • Previous Articles     Next Articles

Optimization of child protection system based on improved particle swarm optimization algorithm

WANG Wanqiu1(), MA Minghui1, QIAN Yubin1, KONG Rongmin2   

  1. 1 School of Mechanical and Automotive Engineering, Shanghai University of Engineering Science, Shanghai 201620, China
    2 College of Vehicle and Traffic Engineering,Zhengzhou University of Science and Technology, Zhengzhou Henan 450064, China
  • Received:2022-08-11 Revised:2022-11-02 Online:2023-01-28 Published:2023-07-28

Abstract:

In order to ensure the safety of children in the car, the improved particle swarm optimization algorithm was used to optimize the child restraint protection system. Firstly, the validity of the simulation model of the child seat trolley test was verified based on the 40% offset crash trolley test, and the child airbag model was established as well. Then, the second order response surface models were established between the significant parameters of the protective system and indexes of head and chest injuries in children, combined with Crossover, mutation and elite reserved strategy of the genetic algorithm. The improved multi-objective particle swarm optimization algorithm was put forward. Meanwhile, the improved algorithm was verified. Finally, the multi-objective fuzzy optimization algorithm was used to obtain the optimal values of the system design parameters. Combined with the trolley simulation experiment model, the effectiveness of the simulation models and algorithm were verified. The results show that the protections of the child's head and chest are taken into account by optimal values of the models, and that convergence speeds of the models are improved by the fusion of the genetic algorithm and particle swarm optimization algorithm.

Key words: child restraint protection system, multi-objective particle swarm optimization, crossover and mutation operations, elite retention strategy, fuzzy optimization decision