基于NLWLDPSO的压力容器关键部位的结构优化  

Structural Optimization of Key Parts of Pressure Vessel Based on NLWLDPSO

作　　者：张思维 唐宇峰 李文杰 曹睿 Zhang Siwei;Tang Yufeng;Li Wenjie;Cao Rui(College of Mechanical Engineering,Sichuan University of Science&Engineering,Zigong,Sichuan 643000,China;Key Laboratory of Higher Education of Sichuan Province for Enterprise Informationalization and Internet of Things,Zigong,Sichuan 643000,China)

机构地区：[1]四川轻化工大学机械工程学院,四川自贡643000 [2]企业信息化与物联网测控技术四川省高校重点实验室,四川自贡643000

出　　处：《化工设备与管道》2025年第1期28-37,共10页Process Equipment & Piping

基　　金：企业信息化与物联网测控技术四川省高校重点实验室开放基金(2023WYJ04);重大危险源四川省重点实验室开放课题(KFKT2021-01)。

摘　　要：针对传统粒子群算法在进行压力容器结构优化时易出现陷入局部最优、求解速度偏慢的问题,提出了一种非线性权重与学习因子递减粒子群算法。首先,通过对惯性权重、位置更新公式及学习因子进行改进,提出了一种新的非线性递减粒子群算法;其次,分别以单变量及多变量非线性变化函数为例,验证了方法的可靠性及优势;最后,基于本文方法对某压力容器关键部位进行了结构优化,并且将优化结果与线性递减粒子群算法(LDWPSO、PSO)和非线性递减粒子群算法(FDIWPSO、NLDIWPSO、NLDWPSO)的优化结果进行对比。结果表明:采用本文方法结构优化后,相比原始结构节省了5%的材料,且与几种线性及非线性递减粒子群算法相比精度更高,迭代次数及用时更少,对于压力容器结构优化的效果更佳。A nonlinear particle swarm optimization algorithm with decreasing weight and learning factor was proposed to solve the problem that the traditional particle swarm optimization algorithm was prone to fall into local optimal and slow solving speed when optimizing the structure of pressure vessel.Firstly,a new nonlinear decreasing particle swarm optimization algorithm was proposed by improving inertia weight,position updating formula and learning factor.Secondly,the reliability and advantages of the method are verified by taking univariate and multivariable nonlinear variation functions as examples respectively.Finally,the structure optimization of the key parts of a pressure vessel is carried out based on the proposed method,and the optimization results are compared with those of linear decreasing particle swarm optimization algorithms(LDWPSO,PSO)and nonlinear decreasing particle swarm optimization algorithms(FDIWPSO,NLDIWPSO,NLDWPSO).The results show that:compared with the original structure,5%material is saved after structural optimization by the proposed method,and compared with several linear and nonlinear decreasing particle swarm optimization algorithms,the accuracy is higher,the number of iterations and time is less,and the effect of structural optimization of pressure vessel is better.

关 键 词：非线性递减粒子群算法 单变量 多变量 结构优化 

分 类 号：TP391.9[自动化与计算机技术—计算机应用技术] TP18[自动化与计算机技术—计算机科学与技术]