Journal of the Architectural Institute of Korea (대한건축학회논문집)

Architectural Institute of Korea (대한건축학회)
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Multi-Objective Structural Optimization using Particle Swam Optimization

입자 군집 최적화기법을 이용한 다중목적함수 구조최적화

  • 이상진 (경상대학교 건축공학과) ;
  • 배정은 (경상대학교 구조연구실)
  • Received : 2020.08.05
  • Accepted : 2020.11.14
  • Published : 2020.11.30
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Abstract

Particle swam optimization (PSO) technique is introduced to produce the Pareto optimal solutions in structural optimization. Both cost and deflection of beam to be minimized are considered as the objective functions and the values of stress and deflection are adopted as constraints. The weighted sum method is introduced to transform the multi-objective function problem into the single objective function problem and new weight function is introduced. The penalty function method is used to enforce design constraints during optimization process. Two numerical examples are carried out to verify the capability of PSO in structural optimization with multi-objective functions. From numerical results, the present PSO is a very effective way of finding Pareto front. Finally, we provide the present numerical results as future reference solutions using PSO.

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References

  1. Bandarua, S., & Deb, K. (2016). Metaheuristic Techniques, in the Book of Decision Sciences: Theory and Practice, Edited by Raghu Nandan Sengupta, Aparna Gupta and Joydeep Dutta, CRC Press, Taylor & Francis Group, 693-750.
  2. Castro, R.E., & Barbosa, H. (2000). A genetic algorithm for multiobjective structural optimization, IV Simposio Mineiro de Mecanica Computacional, 219-226.
  3. Fourie, P.C., & Groenwold, A.A. (2002). The particle swarm optimization algorithm in size and shape optimization, Struct Multidisc Optim, 23, 259-267. https://doi.org/10.1007/s00158-002-0188-0
  4. Gomes, M. (2011). Truss optimization with dynamic constraints using a particle swarm algorithm, Expert Systems with Applications, 38, 957-968. https://doi.org/10.1016/j.eswa.2010.07.086
  5. Kennedy, J., & Eberhart, R. C. (1995). Particle swarm optimization. In Proceedings of the IEEE international conference on neural networks IV (pp. 1942-1948). Piscataway: IEEE.
  6. Li, Y., Peng, Y., & Zhou, S. (2013). Improved PSO algorithm for shape and sizing optimization of truss structure. Journal of Civil Engineering and Management, 19(4), 542-549. https://doi.org/10.3846/13923730.2013.786754
  7. Omidinasab, F., & Goodarzimehr, V. (2020). A Hybrid Particle Swarm Optimization and Genetic Algorithm for Truss Structures with Discrete Variables, J. Appl. Comput. Mech., 6(3), 593-604.
  8. Plevris, V. Batavanis, A., & Papadrakakis, M. (2011). Optimum design of steel structures with the particle swarm optimization method based on EC3, COMPDYN 2011 & 3rd ECCOMAS Thematic Conference on Computational Methods in Structural Dynamics and Earthquake Engineering Papadrakakis, M., Fragiadakis, M. & Plevris, V. (eds.) Corfu, Greece, 25-28. May 2011.
  9. Ray, T., & Liew, K.M. (2010). A Swarm Metaphor for Multiobjective Design Optimization, Engineering Optimization, 34(2) 141-153. https://doi.org/10.1080/03052150210915
  10. Ruben E. Perez, & Kamran Behdinan, (2007). Particle Swarm Optimization in Structural Design, Swarm Intelligence: Focus on Ant and Particle Swarm Optimization, Book edited by: Chan F.T.S. & Tiwari, M.K. 532-553. Itech Education and Publishing, Vienna Austria
  11. Vanderplaats, G.N. (1993). Thirty years of modern structural optimization, Advances in Engineering Software, 16(2), 81-88 https://doi.org/10.1016/0965-9978(93)90052-U
  12. Venkayya, V.B. (1978). Structural optimization: A review and some recommendations, International Journal for Numerical Methods in Engineering, 13(2), 203-228. https://doi.org/10.1002/nme.1620130202
  13. Yang, X. S., Karamanoglu, M., & He X. S. (2014). Flower Pollination Algorithm: A Novel Approach for Multiobjective Optimization, Engineering Optimization, 46(9), 1222-1237. https://doi.org/10.1080/0305215x.2013.832237
  14. Zain, M.Z.M., Kanesana, J., Chuaha, J.H., Dhanapal, S., & Kendall, G.(2018). A multi-objective particle swarm optimization algorithm based on dynamic boundary search for constrained optimization, Applied Soft Computing, 70, September. 680-700. https://doi.org/10.1016/j.asoc.2018.06.022