Improved proportional topology optimization algorithm for solving minimum compliance problem

被引:18
|
作者
Wang, Hui [1 ,2 ]
Cheng, Wenming [1 ,2 ]
Du, Run [1 ,2 ]
Wang, Shubiao [1 ,2 ]
Wang, Yupu [1 ,2 ]
机构
[1] Southwest Jiaotong Univ, Sch Mech Engn, Chengdu 610031, Peoples R China
[2] Technol & Equipment Rail Transit Operat & Mainten, Chengdu 610031, Peoples R China
基金
中国国家自然科学基金;
关键词
Topology optimization; Minimum compliance problem; Improved proportional topology optimization algorithm; LEVEL SET METHOD; CONTINUUM STRUCTURES; STRESS; FILTERS; CODE;
D O I
10.1007/s00158-020-02504-8
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
The paper proposes four improved proportional topology optimization (IPTO) algorithms which are called IPTO_A, IPTO_B, IPTO_C, and IPTO_D, respectively. The purposes of this work are to solve the minimum compliance optimization problem, avoid the problems of numerical derivation and sensitivity calculation involved in the process of obtaining sensitivity information, and overcome the deficiencies in the original proportional topology optimization (PTO) algorithm. Inspired by the PTO algorithm and ant colony algorithm, combining the advantages of the filtering techniques, the new algorithms are designed by using the compliance proportion filter and the new density variable increment update scheme and modifying the update way of the density variable in the inner and main loops. To verify the effectiveness of the new algorithms, the minimum compliance optimization problem for the MBB beam is introduced and used here. The results show that the new algorithms (IPTO_A, IPTO_B, IPTO_C, and IPTO_D) have some advantages in terms of certain performance aspects and that IPTO_A is the best among the new algorithms in terms of overall performance. Furthermore, compared with PTO and Top88, IPTO_A has some advantages such as improving the objective value and the convergence speed, obtaining the optimized structure without redundancy, and suppressing gray-scale elements. Besides, IPTO_A also possesses the advantage of strong robustness over PTO.
引用
收藏
页码:475 / 493
页数:19
相关论文
共 50 条
  • [1] Improved proportional topology optimization algorithm for solving minimum compliance problem
    Hui Wang
    Wenming Cheng
    Run Du
    Shubiao Wang
    Yupu Wang
    Structural and Multidisciplinary Optimization, 2020, 62 : 475 - 493
  • [2] Improved proportional topology optimization algorithm for minimum volume problem with stress constraints
    Cheng, Wenming
    Wang, Hui
    Zhang, Min
    Du, Run
    ENGINEERING COMPUTATIONS, 2021, 38 (01) : 392 - 412
  • [3] An enhanced proportional topology optimization method with new density filtering weight function for the minimum compliance problem
    Li, Wang
    Cui, Mingtao
    Wang, Xiaobo
    Gao, Mengjiao
    MECHANICS BASED DESIGN OF STRUCTURES AND MACHINES, 2025, 53 (04) : 2666 - 2695
  • [5] Proportional Topology Optimization: A New Non-Sensitivity Method for Solving Stress Constrained and Minimum Compliance Problems and Its Implementation in MATLAB
    Biyikli, Emre
    To, Albert C.
    PLOS ONE, 2015, 10 (12):
  • [6] Isogeometric gradient-free proportional topology optimization (IGA-PTO) for compliance problem
    Vo, Duy
    Nguyen, Minh Ngoc
    Bui, Tinh Quoc
    Suttakul, Pana
    Rungamornrat, Jaroon
    INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN ENGINEERING, 2023, 124 (19) : 4275 - 4310
  • [7] The improved initialization method of genetic algorithm for solving the optimization problem
    Kang, Rae-Goo
    Jung, Chai-Yeoung
    NEURAL INFORMATION PROCESSING, PT 3, PROCEEDINGS, 2006, 4234 : 789 - 796
  • [8] An Improved Ant Colony Optimization Algorithm for Solving the TSP Problem
    Du, Zhanwei
    Yang, Yongjian
    Sun, Yongxiong
    Zhang, Chijun
    Li, Tuanliang
    ADVANCED MECHANICAL ENGINEERING, PTS 1 AND 2, 2010, 26-28 : 620 - 624
  • [9] Solving VRPTW Problem Based on Improved Drosophila Optimization Algorithm
    Biao, Pang
    Qiu, Zheng Ru
    Jie, Luo Sheng
    2022 6TH INTERNATIONAL SYMPOSIUM ON COMPUTER SCIENCE AND INTELLIGENT CONTROL, ISCSIC, 2022, : 261 - 264
  • [10] An improved social spider optimization algorithm based on rough sets for solving minimum number attribute reduction problem
    Mohamed Abd El Aziz
    Aboul Ella Hassanien
    Neural Computing and Applications, 2018, 30 : 2441 - 2452