基于多种群协同进化免疫多目标优化算法的百叶窗优化研究

doi:10.3969/j.issn.1000-1093.2019.04.003

兵工学报 ›› 2019, Vol. 40 ›› Issue (4): 689-696.doi: 10.3969/j.issn.1000-1093.2019.04.003

基于多种群协同进化免疫多目标优化算法的百叶窗优化研究

骆清国¹，赵耀¹，桂勇¹，刘红彬¹，帅刚²

(1.陆军装甲兵学院车辆工程系，北京 100072； 2.中国人民解放军驻627厂军事代表室，湖南湘潭 411100)

收稿日期:2018-04-17 修回日期:2018-04-17 上线日期:2019-06-10
作者简介:骆清国(1965—)，男，教授，博士生导师。 E-mail: lqg_zgy@163.com
基金资助:
国防“十三五”预先研究项目（30105080101）

Research on Optimization of Louvered Fin with Hybrid Multi-objective Optimizations Based on EDA and AIS

LUO Qingguo¹, ZHAO Yao¹, GUI Yong¹, LIU Hongbin¹ , SHUAI Gang²

(1.Department of Vehicle Engineering, Army Academy of Armored Forces, Beijing 100072, China；2.Military Representative Office of the Chinese People's Liberation Army in No.627 Factory, Xiangtan 411100, Hunan, China)

Received:2018-04-17 Revised:2018-04-17 Online:2019-06-10

摘要/Abstract

摘要： 为降低空气流通阻力、增加空气流量以及获得最大的防护能力，对装甲车辆进气和排气百叶窗结构优化进行研究。建立装甲车辆进气和排气百叶窗数值计算模型，根据冷却系统工作特点搭建冷却风道半实物仿真模型，对比分析优化前的仿真结果和台架测试结果，并给出了优化目标函数表达式。为解决优化效率低、计算量大的问题，确定设计变量并建立设计变量与目标函数之间关系的椭圆基神经网络替代模型。通过分析目标函数随设计变量的变化规律，采用基于多种群协同进化免疫的多目标优化算法对模型进行优化，确定了最终解。利用自组织神经网络对样本点进行数据挖掘，找到了内在规律。研究结果表明，优化后的进气和排气百叶窗在提高防护能力的同时，提高了散热性能。

关键词: 装甲车辆, 动力舱, 百叶窗翅片, 多目标优化, 试验设计

Abstract: The structure optimization of intake and exhaust louvers of armored vehicle is studied to reduce air flow resistance and increase air flow. A numerical calculation model is established for the intake and exhaust louvers of armored vehicle. According to the operating characteristics of cooling system, a hardware-in-the-loop test-rig of air-cooling duct is set up. The simulated and test results of the duct before and after optimization are compared and analyzed, and the expression of optimization objective function is given. The design variables are determined. In order to solve the problem of low efficiency and large computation, an elliptic basis neural network agent model is established for the relationship between design variables and objective functions. The hybrid multi-objective optimization based on estimation of distribution algorithm (EDA) and artificial immune systems is used to find a final solution by analyzing the variation of objective function with the design variables. The inherent law of sample points is found by using self-organizing neural network for data mining. The research results show that the optimized inlet and exhaust louvers improve the performance of heat dissipation while improving the protection capability. Key

Key words: armoredvehicle, enginecompartment, louveredfin, multi-objectiveoptimization, testdesign

中图分类号:

TJ810.2

骆清国，赵耀，桂勇，刘红彬，帅刚. 基于多种群协同进化免疫多目标优化算法的百叶窗优化研究[J]. 兵工学报, 2019, 40(4): 689-696.

LUO Qingguo, ZHAO Yao, GUI Yong, LIU Hongbin , SHUAI Gang. Research on Optimization of Louvered Fin with Hybrid Multi-objective Optimizations Based on EDA and AIS[J]. Acta Armamentarii, 2019, 40(4): 689-696.

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基于多种群协同进化免疫多目标优化算法的百叶窗优化研究

Research on Optimization of Louvered Fin with Hybrid Multi-objective Optimizations Based on EDA and AIS

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