基于改进粒子群算法的儿童防护系统优化

doi:10.16265/j.cnki.issn1003-3033.2023.01.0895

中国安全科学学报 ›› 2023, Vol. 33 ›› Issue (1): 122-129.doi: 10.16265/j.cnki.issn1003-3033.2023.01.0895

基于改进粒子群算法的儿童防护系统优化

王婉秋¹(), 马明辉¹, 钱宇彬¹, 孔容敏²

¹ 上海工程技术大学机械与汽车工程学院,上海 201620
² 郑州科技学院车辆与交通工程学院,河南郑州 450064

收稿日期:2022-08-11 修回日期:2022-11-02 出版日期:2023-01-28 发布日期:2023-07-28
作者简介:
王婉秋 (1976—),女,贵州遵义人,博士,讲师,主要从事道路交通安全等方面的研究。E-mail: tutu_net@163.com。
马明辉,副教授
钱宇彬, 副教授
基金资助:
国家自然科学基金-青年科学基金资助(71801149)

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

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

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

摘要/Abstract

摘要：

为保障儿童乘车安全,采用改进多目标粒子群算法优化儿童约束防护系统。首先,开展40%偏置碰撞的台车试验,验证儿童座椅台车试验仿真模型的有效性,建立儿童安全气囊模型;然后,建立防护系统参数与儿童头部、胸部损伤指标的二阶响应曲面模型,融合遗传算法的交叉变异和精英保留策略,提出改进的多目标粒子群算法,并验证改进算法的有效性;最后,利用多目标模糊优选决策算法获得系统设计参数的最优值,结合台车试验仿真模型,验证优化模型及算法的有效性。结果表明:模型的最优值兼顾对儿童头部和胸部的防护;遗传算法和粒子群算法的融合算法,可提高模型的收敛速度。

关键词: 儿童约束防护系统, 多目标粒子群算法, 交叉和变异, 精英保留策略, 模糊优选决策

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

王婉秋, 马明辉, 钱宇彬, 孔容敏. 基于改进粒子群算法的儿童防护系统优化[J]. 中国安全科学学报, 2023, 33(1): 122-129.

WANG Wanqiu, MA Minghui, QIAN Yubin, KONG Rongmin. Optimization of child protection system based on improved particle swarm optimization algorithm[J]. China Safety Science Journal, 2023, 33(1): 122-129.

图/表 10

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表3

算法优化值与仿真值结果对比

分组	L/mm	$μ$	P/mm	S/ $m m 2$	F	$a H$ /(m·s^-2)	$a T$ /(m·s^-2)
算法优化值	16.326 2	0.276 9	330.000 2	25.035 0	0.800 0	399.322 1	370.574 1
仿真值	16.326 2	0.276 9	330.000 2	25.035 0	0.800 0	412.643 1	392.672 4

表3

参考文献 16

[1]	HU Jingwen, WU Jun, MATTHEW P, et al. Rear seat restraint system optimization for older children in frontal crashes[J]. Traffic Injury Prevention, 2013, 14(6): 614-622. doi: 10.1080/15389588.2012.743123 pmid: 23859119
[2]	PARK W, HONG S. A study on the modeling technique for the passenger out-of-position simulation[C]. SAE World Congress,2005:DOI:10.4271/2005-01-1298. doi: 10.4271/2005-01-1298
[3]	曹立波, 胡渊, 颜凌波, 等. 基于E-NCAP 的6 岁儿童乘员损伤防护研究[J]. 汽车工程, 2017, 39(2):170-180.
	CAO Libo, HU Yuan, YAN Lingbo, et al. A study on injury protection of 6 years old child occupant based on E-NCAP[J]. Automotive Engineering, 2017, 39(2):174-180.
[4]	葛如海, 陈珣, 张学荣, 等. 校车儿童安全气囊安全性仿真分析[J]. 中国安全科学学报, 2015, 25(3):9-15.
	GE Ruhai, CHEN Xun, ZHANG Xuerong, et al. Research on safety of school bus airbag for child occupant[J]. China Safety Science Journal, 2015, 25(3):9-15.
[5]	余修武, 谢晓永, 梁北孔, 等. 基于粒子群优化和巷道分区的深井WSN定位算法[J]. 中国安全科学学报, 2019, 29(2):166-171.
	YU Xiuwu, XIE Xiaoyong, LIANG Beikong, et al. A deep mine WSN localization algorithm based on both particle swarm optimization and tunnel partition[J]. China Safety Science Journal, 2019, 29(2):166-171.
[6]	林勇, 肖骅, 张立, 等. 震灾应急物资分配-运输优化模型及算法研究[J], 中国安全科学学报, 2020, 30(10):171-178. doi: 10.16265/j.cnki.issn 1003-3033.2020.10.024
	LIN Yong, XIAO Hua, ZHANG Li, et al. Optimization model and algorithm of emergency supplies allocation-transportation in earthquake disasters[J]. China Safety Science Journal, 2020, 30(10):171-178. doi: 10.16265/j.cnki.issn 1003-3033.2020.10.024
[7]	郑金华, 蒋浩, 邝达. 用擂台赛法则构造多目标Pareto最优解集的方法[J]. 软件学报, 2007, 18(6):1287-1297.
	ZHENG Jinhua, JIANG Hao, KUANG Da. An approach of constructing multi-objective pareto optimal solutions using arena's principle[J]. Journal of Software, 2007, 18(6):1287-1297. doi: 10.1360/jos181287
[8]	李明辉, 孟宪坤. 采用轮盘赌法的造纸企业生产调度排产模型的优化设计[J]. 陕西科技大学学报:自然科学版, 2012, 30(2):44-48.
	LI Minghui, MENG Xiankun. Optimization design of paper-making enterprises production scheduling model using roulette law[J]. Journal of Shaanxi University of Science and Technology:Natural Science Edition, 2012, 30(2):44-48.
[9]	张进峰, 何芸枫, 吴小红, 等. 库区水上应急救助设施选址多目标优化模型[J]. 中国安全科学学报, 2018, 28(4):175-181. doi: 10.16265/j.cnki.issn1003-3033.2018.04.030
	ZHANG Jinfeng, HE Yunfeng, WU Xiaohong, et al. Multi-objective optimization model for water emergency rescue facilities location in reservoir area[J]. China Safety Science Journal, 2018, 28(4):175-181. doi: 10.16265/j.cnki.issn1003-3033.2018.04.030
[10]	屠传运, 陈韬伟, 余益民, 等. 膜系统下的一种多目标优化算法[J]. 智能系统学报, 2017, 12(5):678-683.
	TU Chuanyun, CHEN Taowei, YU Yimin, et al. Multi-objective optimization algorithm based on membrane system[J]. CAAI Transactions on Intelligent Systems, 2017, 12(5):678-683.
[11]	胡成玉, 余果, 代立国, 等. 知识驱动的高维多目标优化算法研究[J]. 华中科技大学学报:自然科学版, 2020, 48(6):19-25.
	HU Chengyu, YU Guo, DAI Liguo, et al. Research on knowledge-driven many-objective optimization algorithm[J]. Journal of Huazhong University of Science and Technology:Nature Science Edition, 2020, 48(6):19-25.
[12]	DEB K, GOYAL M. A combined genetic adaptive search (Gene AS) for engineering design[J]. Computer Science and Informatics, 1996, 26(4):30-45.
[13]	DEB K, AGRAWAL R B. Simulated binary crossover for continuous search space[J]. Complex Systems, 1995, 9(3):115-148.
[14]	张哲, 王会利, 石磊, 等. 桥梁方案多层多目标模糊优选模型及其应用[J]. 哈尔滨工业大学学报, 2006, 38(9): 1567-1571.
	ZHANG Zhe, WANG Huili, SHI Lei, et al. Application of multi-levels and multi-objectives fuzzy optimization model of bridge type selection[J]. Journal of Harbin Institute of Technology, 2006, 38(9): 1567-1571.
[15]	贾富淳, 孟宪皆, 雷雨龙. 基于多目标遗传算法的二自由度动力吸振器优化设计[J]. 吉林大学学报:工学版, 2019, 49(6):1969-1976.
	JIA Fuchun, MENG Xianjie, LEI Yulong. Optimal design of two degrees of freedom dynamic vibration absorber based on multi-objective genetic algorithm[J]. Journal of Jilin University: Engineering and Technology Edition, 2019, 49(6):1969-1976.
[16]	王婉秋, 陈雨人, 钱宇彬. 双车道公路弯道驾驶人模糊优选决策模型与实验验证[J]. 中南大学学报:自然科学版, 2015, 46(1):332-341.
	WANG Wanqiu, CHEN Yuren, QIAN Yubin. Driver's fuzzy optimal decision-making models in curves of two-lane highway and experimental validation[J]. Journal of Central South University:Science and Technology, 2015, 46(1):332-341.

评价指标	试验值	仿真值	误差/%
a_H/(m·s^-2)	599.98	604.23	0.71
a_T/(m·s^-2)	575.21	560.34	2.59

评价指标	未添加安全气囊	添加安全气囊
a_H/(m·s^-2)	604.23	563.27
a_T/(m·s^-2)	560.34	535.88

基于改进粒子群算法的儿童防护系统优化

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

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