ANNA: Advanced neural network algorithm for optimization of structures

被引:7
作者
Khodadadi, Nima [1 ]
Talatahari, Siamak [2 ,3 ]
Gandomi, Amir H. [2 ,4 ,5 ]
机构
[1] Univ Miami, Coral Gables, FL USA
[2] Univ Technol Sydney, Fac Engn & IT, Ultimo, NSW, Australia
[3] Univ Tabriz, Departmen Civil Engn, Tabriz, Iran
[4] Obuda Univ, Univ Res & Innovat Ctr EKIK, Budapest, Hungary
[5] Univ Technol Sydney, Fac Engn & IT, Ultimo, NSW 2007, Australia
关键词
Advanced Neural network algorithm (ANNA); Artificial neural networks (ANN); Structural Optimization; Optimal design; Truss structures; OPTIMAL-DESIGN; TRUSSES;
D O I
10.1680/jstbu.22.00083
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
The purpose of this study is to develop an advanced neural network algorithm (ANNA) as a new optimization for the optimal design of truss structures. The central concept of the algorithm is based on biological nervous structures and artificial neural networks. The performance of the proposed method is explored in engineering design problems. Two efficient methods for improving the standard Neural Network Algorithm (NNA) are regarded here. The first one is an enhanced initialization mechanism based on opposite-based learning. The second one is on using a few tunable parameters to provide proper exploration and exploitation abilities for the algorithm that causes finding better solutions while the required structural analyses are reduced. The new algorithm's performance is investigated by using five well-known restricted benchmarks to assess its efficiency concerning the latest optimization techniques. The outcome of the examples demonstrates that the upgraded version of the algorithm has increased efficacy and robustness in comparison to the original version of the algorithm and to some other methods.
引用
收藏
页码:529 / 551
页数:23
相关论文
共 58 条
  • [1] Classification of Monkeypox Images Based on Transfer Learning and the Al-Biruni Earth Radius Optimization Algorithm
    Abdelhamid, Abdelaziz A.
    El-Kenawy, El-Sayed M.
    Khodadadi, Nima
    Mirjalili, Seyedali
    Khafaga, Doaa Sami
    Alharbi, Amal H.
    Ibrahim, Abdelhameed
    Eid, Marwa M.
    Saber, Mohamed
    [J]. MATHEMATICS, 2022, 10 (19)
  • [2] Mountain Gazelle Optimizer: A new Nature-inspired Metaheuristic Algorithm for Global Optimization Problems
    Abdollahzadeh, Benyamin
    Gharehchopogh, Farhad Soleimanian
    Khodadadi, Nima
    Mirjalili, Seyedali
    [J]. ADVANCES IN ENGINEERING SOFTWARE, 2022, 174
  • [3] Abualigah L., 2022, Integrating Meta-Heuristics and Machine Learning for Real-World Optimization Problems, P481, DOI [DOI 10.1007/978-3-030-99079-4_19, 10.1007/978-3-030-99079-4_19]
  • [4] CONCURRENT GENETIC ALGORITHMS FOR OPTIMIZATION OF LARGE STRUCTURE
    ADELI, H
    CHENG, NT
    [J]. JOURNAL OF AEROSPACE ENGINEERING, 1994, 7 (03) : 276 - 296
  • [5] Al-Tashi Q, 2022, Handbook of Moth-Flame Optimization Algorithm, P11
  • [6] Alhussan A.A., 2023, Comput. Syst. Sci. Eng., V45, P1917, DOI [10.32604/csse.2023.032497, DOI 10.32604/CSSE.2023.032497]
  • [7] Large scale economic dispatch of power systems using oppositional invasive weed optimization
    Barisal, A. K.
    Prusty, R. C.
    [J]. APPLIED SOFT COMPUTING, 2015, 29 : 122 - 137
  • [8] Design of space trusses using big bang-big crunch optimization
    Camp, Charles V.
    [J]. JOURNAL OF STRUCTURAL ENGINEERING-ASCE, 2007, 133 (07): : 999 - 1008
  • [9] Design of space trusses using ant colony optimization
    Camp, CV
    Bichon, BJ
    [J]. JOURNAL OF STRUCTURAL ENGINEERING, 2004, 130 (05) : 741 - 751
  • [10] Cavalieri S., 1996, WCNN'96. World Congress on Neural Networks. International Neural Network Society 1996 Annual Meeting, P559