Skip to main content
SpringerLink
Log in

Comprehensive learning phasor particle swarm optimization of structures under limited natural frequency conditions

限定固有频率约束下基于全面学习和相量粒子群的结构优化

  • Research Paper
  • Published:
Acta Mechanica Sinica Aims and scope Submit manuscript

Abstract

The paper presents the combined phasor particle swarm optimization and comprehensive learning method for the optimal design of space truss structures under the constraints addressing limited natural frequency. The proposed approach enhances the performance of the standard particle swarm optimization approach by incorporating the efficient phasor theory in mathematics with comprehensive learning strategy. Within the optimization process, a so-called phase angle adopts the periodic sine and cosine functions to model the key parameters defining velocities that learn from a sole medium of exemplar’s velocity selected among previous best positions of all particles. The scheme not only enables the fast learning of swarm particles, but also computes the safe and optimal size distribution of structural members under applied forces and natural frequency conditions at modest computing resources. Various design benchmarks on practical-scale (three-dimensional space) engineering applications have been successfully solved by the proposed design method. These illustrate the accuracy and robustness of the algorithm as compared with various state-of-the-art metaheuristic approaches recently developed.

摘要

本文提出了全面学习和相量粒子群相结合的方法用于解决限定固有频率约束下的空间结构优化问题. 通过将相量理论和全面 学习策略相结合以提高粒子群算法的效率. 在优化的过程中, 一个所谓的相角采用了周期正弦和余弦函数给定义速度的关键参数建模 使得速度可以从所有粒子前一步的最佳位置中选取. 这个方法不但实现了粒子们可以快速学习并且在少量的计算中进行了在作用力 和固有频率约束下结构杆件的安全性评估和优化尺寸分布. 提出的优化设计方法成功解决了一些工程应用结构的基准设计问题, 这证 明了本文方法相对于最新元启发方法的准确性和稳定性.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

References

  1. A. Kaveh, and S. Talatahari, Geometry and topology optimization of geodesic domes using charged system search, Struct. Multidisc. Optim. 43, 215 (2011).

    Article  Google Scholar 

  2. C. Millan-Paramo, and J. E. Abdalla Filho, Size and shape optimization of truss structures with natural frequency constraints using modified simulated annealing algorithm, Arab. J. Sci. Eng. 45, 3511 (2020).

    Article  Google Scholar 

  3. R. Grandhi, Structural optimization with frequency constraints—a review, AIAA J. 31, 2296 (1993).

    Article  MATH  Google Scholar 

  4. L. Bellagamba, and T. Y. Yang, Minimum-mass truss structures with constraints on fundamental natural frequency, AIAA J. 19, 1452 (1981).

    Article  Google Scholar 

  5. R. Sedaghati, A. Suleman, and B. Tabarrok, Structural optimization with frequency constraints using the finite element force method, AIAA J. 40, 382 (2002).

    Article  Google Scholar 

  6. L. Wei, M. Zhao, G. Wu, and G. Meng, Truss optimization on shape and sizing with frequency constraints based on genetic algorithm, Comput. Mech. 35, 361 (2005).

    Article  MATH  Google Scholar 

  7. A. Kaveh, and A. Zolghadr, Optimal design of cyclically symmetric trusses with frequency constraints using cyclical parthenogenesis algorithm, Adv. Struct. Eng. 21, 739 (2017).

    Article  Google Scholar 

  8. A. Kaveh, and V. R. Mahdavi, Colliding-bodies optimization for truss optimization with multiple frequency constraints, J. Comput. Civ. Eng. 29, (2015).

  9. O. K. Erol, and I. Eksin, A new optimization method: Big bang-big crunch, Adv. Eng. Software 37, 106 (2006).

    Article  Google Scholar 

  10. Z. W. Geem, J. H. Kim, and G. V. Loganathan, A new heuristic optimization algorithm: harmony search, Simulation 76, 60 (2001).

    Article  Google Scholar 

  11. A. Kaveh, and M. Ilchi Ghazaan, Vibrating particles system algorithm for truss optimization with multiple natural frequency constraints, Acta Mech. 228, 307 (2017).

    Article  MathSciNet  Google Scholar 

  12. N. Khodadadi, and S. Mirjalili, Truss optimization with natural frequency constraints using generalized normal distribution optimization, Appl. Intell. 52, 10384 (2022).

    Article  Google Scholar 

  13. A. Kaveh, K. B. Hamedani, and M. Kamalinejad, An enhanced forensic-based investigation algorithm and its application to optimal design of frequency-constrained dome structures, Comput. Struct. 256, 106643 (2021).

    Article  Google Scholar 

  14. S. O. Degertekin, G. Yalcin Bayar, and L. Lamberti, Parameter free Jaya algorithm for truss sizing-layout optimization under natural frequency constraints, Comput. Struct. 245, 106461 (2021).

    Article  Google Scholar 

  15. J. Kennedy, and R. Eberhart, in Particle swarm optimization: Proceedings of International Conference on Neural Networks, 1995.

  16. H. M. Gomes, Truss optimization with dynamic constraints using a particle swarm algorithm, Expert Syst. Appl. 38, 957 (2011).

    Article  Google Scholar 

  17. A. Kaveh, and A. Zolghadr, Democratic PSO for truss layout and size optimization with frequency constraints, Comput. Struct. 130, 10 (2014).

    Article  Google Scholar 

  18. A. Kaveh, and M. Ilchi Ghazaan, Hybridized optimization algorithms for design of trusses with multiple natural frequency constraints, Adv. Eng. Software 79, 137 (2015).

    Article  Google Scholar 

  19. R. Mendes, J. Kennedy, and J. Neves, The fully informed particle swarm: Simpler, maybe better, IEEE Trans. Evol. Computat. 8, 204 (2004).

    Article  Google Scholar 

  20. F. vandenBergh, and A. P. Engelbrecht, A cooperative approach to particle swarm optimization, IEEE Trans. Evol. Computat. 8, 225 (2004).

    Article  Google Scholar 

  21. J. J. Liang, A. K. Qin, P. N. Suganthan, and S. Baskar, Comprehensive learning particle swarm optimizer for global optimization of multimodal functions, IEEE Trans. Evol. Computat. 10, 281 (2006).

    Article  Google Scholar 

  22. C. Li, S. Yang, and T. T. Nguyen, A self-learning particle swarm optimizer for global optimization problems, IEEE Trans. Syst. Man Cybern. B 42, 627 (2012).

    Article  Google Scholar 

  23. D. Tang, Y. Cai, J. Zhao, and Y. Xue, A quantum-behaved particle swarm optimization with memetic algorithm and memory for continuous non-linear large scale problems, Inf. Sci. 289, 162 (2014).

    Article  Google Scholar 

  24. R. Kar, D. Mandal, S. Mondal, and S. P. Ghoshal, Craziness based particle swarm optimization algorithm for FIR band stop filter design, Swarm Evol. Comput. 7, 58 (2012).

    Article  Google Scholar 

  25. Z. H. Zhan, J. Zhang, Y. Li, and H. S. H. Chung, Adaptive particle swarm optimization, IEEE Trans. Syst. Man Cybern. B 39, 1362 (2009).

    Article  Google Scholar 

  26. T. H. Van, S. Tangaramvong, S. Limkatanyu, and H. N. Xuan, Two-phase ESO and comprehensive learning PSO method for structural optimization with discrete steel sections, Adv. Eng. Software 167, 103102 (2022).

    Article  Google Scholar 

  27. T. H. Van, S. Tangaramvong, S. Muong, and P. T. Van, Combined Gaussian local search and enhanced comprehensive learning PSO algorithm for size and shape optimization of truss structures, Buildings 12, 1976 (2022).

    Article  Google Scholar 

  28. S. Tangaramvong, F. Tin-Loi, and W. Gao, Optimal retrofit of moment resisting frames using braces accounting for geometric nonlinearity and serviceability conditions, Eng. Struct. 80, 189 (2014).

    Article  Google Scholar 

  29. S. Tangaramvong, and F. Tin-Loi, Optimal performance-based rehabilitation of steel frames using braces, J. Struct. Eng. 141, 04015015 (2015).

    Article  Google Scholar 

  30. M. Ghasemi, E. Akbari, A. Rahimnejad, S. E. Razavi, S. Ghavidel, and L. Li, Phasor particle swarm optimization: a simple and efficient variant of PSO, Soft Comput. 23, 9701 (2019).

    Article  Google Scholar 

  31. S. O. Degertekin, and M. S. Hayalioglu, Sizing truss structures using teaching-learning-based optimization, Comput. Struct. 119, 177 (2013).

    Article  Google Scholar 

  32. Y. Shi, and R. Eberhart, in A modified particle swarm optimizer: Proceedings of the IEEE Conference on Evolutionary Computation, Anchorage, 1998.

  33. J. J. Liang, A. K. Qin, P. M. Suganthan, and S. Baskar, in Particle swarm optimization algorithms with novel learning strategies: Proceedings of 2004 IEEE International Conference on Systems, Man and Cybernetics (IEEE Cat. No.04CH37583), The Hague, 2004.

  34. A. Kaveh, and A. Zolghadr, Truss optimization with natural frequency constraints using a hybridized CSS-BBBC algorithm with trap recognition capability, Comput. Struct. 102–103, 14 (2012).

    Article  Google Scholar 

  35. A. Kaveh, and V. R. Mahdavi, Optimal design of structures with multiple natural frequency constraints using a hybridized BB-BC/Quasi-Newton algorithm, Per. Pol. Civil Eng. 57, 27 (2013).

    Article  Google Scholar 

  36. A. Kaveh, and M. Ilchi Ghazaan, Optimal design of dome truss structures with dynamic frequency constraints, Struct. Multidisc. Optim. 53, 605 (2016).

    Article  MathSciNet  Google Scholar 

  37. J. P. G. Carvalho, A. C. C. Lemonge, É. C. R. Carvalho, P. H. Hallak, and H. S. Bernardino, Truss optimization with multiple frequency constraints and automatic member grouping, Struct. Multidisc. Optim. 57, 547 (2018).

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by Thailand Science Research and Innovation Fund Chulalongkorn University (Grant No. IND66210025) and Chulalongkorn University under Ratchadaphiseksomphot Endowment Fund.

Author information

Authors and Affiliations

  1. Center of Excellence in Applied Mechanics and Structures, Department of Civil Engineering, Chulalongkorn University, Bangkok, 10330, Thailand

    Ei Cho Pyone, Sawekchai Tangaramvong, Thu Huynh Van & Linh Van Hong Bui

  2. Division of Computational Mathematics and Engineering, Institute for Computational Science, Ton Duc Thang University, Ho Chi Minh City, Viet Nam

    Linh Van Hong Bui

  3. Centre of Infrastructure Engineering and Safety, School of Civil and Environmental Engineer, The University of New South Wales, Sydney, NSW, 2000, Australia

    Wei Gao  (高伟)

Authors
  1. Ei Cho Pyone
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sawekchai Tangaramvong
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Thu Huynh Van
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Linh Van Hong Bui
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Wei Gao  (高伟)
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Data curation, Formal analysis, Investigation, Writing – review & editing. Sawekchai Tangaramvong: Conceptualization, Funding acquisition, Methodology, Resources, Supervision, Validation, Writing – original draft. Thu Huynh Van: Software, Validation, Writing – review & editing. Linh Van Hong Bui: Visualization. Wei Gao: Writing – review & editing.

Corresponding author

Correspondence to Sawekchai Tangaramvong.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Pyone, E.C., Tangaramvong, S., Van, T.H. et al. Comprehensive learning phasor particle swarm optimization of structures under limited natural frequency conditions. Acta Mech. Sin. 39, 722386 (2023). https://doi.org/10.1007/s10409-023-22386-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10409-023-22386-x

Keywords

Search

Navigation