Skip to main content
SpringerLink
Log in

Frame structural sizing and topological optimization via a parallel implementation of a modified particle Swarm algorithm

  • Structural Engineering
  • Published:
KSCE Journal of Civil Engineering Aims and scope

Abstract

As a comparatively new developed stochastic method — Particle Swarm Optimization (PSO), it is widely applied to various kinds of optimization problems especially of nonlinear, non-differentiable or non-concave types. In this paper, a Parallel Modified Guaranteed Converged Particle Swarm algorithm (PMGCPSO) is proposed, which is inspired by the Guaranteed Converged Particle Swarm algorithm (GCPSO) proposed by von den Bergh. Details in the algorithm implementation and properties are presented and, an analytical benchmark test and structural sizing and topological test cases are used to evaluate the performance of the proposed PSO variant, PMGCPSO exhibited competitive performance due to improved global searching ability and its corresponding parallel model indicates nice parallel efficiency.

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

Similar content being viewed by others

References

  • Alatas, B., Akin, E., and Ozer, A. B. (2009). “Chaos embedded particle swarm optimization algorithms.” Chaos, Solitons & Fractals, Vol. 40, No. 4, pp. 1715–1734.

    Article  MathSciNet  MATH  Google Scholar 

  • Angeline, P. J. (1998). “Evolutionary optimization versus particle swarm optimization: Philosophy and performance differences.” Evolutionary Programming VII, Springer, Berlin, Heidelberg, Germany, pp. 601–610.

    Chapter  Google Scholar 

  • Bochenek, B. and Foryś, P. (2006). “Structural optimization for post-buckling behavior using particle swarms.” Structural and Multidisciplinary Optimization, Vol. 32, No. 6, pp. 521–531.

    Article  Google Scholar 

  • Chen, M. R., Li, X., Zhang, X., and Lu, Y. Z. (2010). “A novel particle swarm optimizer hybridized with extremal optimization.” Journal of Applied Soft Computing, Vol. 10,Issue 2, pp. 367–373.

    Article  Google Scholar 

  • Chen, M. R., Lu, Y. Z., and Yang, G. (2006). “Population-based extremal optimization with adaptive Lévy mutation for constrained optimization.” Proc. of International Conference on Computational Intelligence and Security, CIS 2006, Guangzhou, China, pp. 258–261.

    Google Scholar 

  • Clerc, M. (1999). “The swarm and the queen: Towards a deterministic and adaptive particle swarm optimization.” Proc. of the Congress on Evolutionary Computation, IEEE, Vol. 3, pp. 1951–1957.

    Google Scholar 

  • Clerc, M. and Kennedy, J. (2002). “The particle swarm: Explosion, stability and convergence in a multi-dimensional complex space.” Transactions on Evolutionary Computation, IEEE, Vol. 16, No.1, pp. 58–73.

    Article  Google Scholar 

  • Eberhart, R. and Kennedy, J. (1995). “A new optimizer using particle swarm theory.” Proc. of the sixth International Symposium on Micro Machine and Human Science, Nagoya, Japan, Vol. 1, pp. 39–43.

    Article  Google Scholar 

  • Fiacco, A. V. and McCormick, G. P. (1990). Nonlinear programming: Sequential unconstrained minimization techniques, Classics in Applied Mathematics, No. 4, Society for Industrial Mathematics.

    Book  MATH  Google Scholar 

  • Fourie, P. C. and Groenwold, A. (2002). “The particle swarm optimization algorithm in size and shape optimization.” Structural and Multidisciplinary Optimization, Vol. 23, No. 4, pp. 259–267.

    Article  Google Scholar 

  • Hansen, N. and Kern, S. (2004). “Evaluating the CMA Evolution Strategy on Multimodal Test Functions.” Proc. of Eighth International Conference on Parallel Problem Solving from Nature PPSN VIII, Springer, Berlin, Heidelberg, Germany, pp. 282–291.

    Chapter  Google Scholar 

  • Kaoa, Y. T. and Zahara, E. (2008). “A hybrid genetic algorithm and particle swarm optimization for multimodal functions.” Applied Soft Computing, Vol. 8, No. 2, pp. 849–857.

    Article  Google Scholar 

  • Khan, M. R. (1984). “Optimality criterion techniques applied to frames having general cross-sectional relationships.” A/AA Journal, Vol. 22, No. 5, pp. 669–676.

    MATH  Google Scholar 

  • Krink, T. and Løvbjerg, M. (2002). “The lifecycle model: Combining particle swarm optimisation, genetic algorithms and hillclimbers.” Proc. of Parallel Problem Solving from Nature VII, Springer, Berlin, Heidelberg, Germany, pp. 621–630.

    Chapter  Google Scholar 

  • Levitin, G., Hu, X. H., and Dai, Y. S. (2007). “Particle swarm optimization in Reliability Engineering.” Studies in Computational Intelligence, Vol. 40, pp. 83–112.

    Article  Google Scholar 

  • Løvberg, M., Rasmussen, T. and Krink, T. (2001). “Hybrid Particle Swarm Optimiser with Breeding and Subpopulation.” Proc. of the Third Genetic and Evolutionary Computation Conference, San Francisco, USA, Vol. 2001, pp. 469–476.

    Google Scholar 

  • Pedersen, M. E. H. and Chipperfield, A. J. (2010). “Simplifying Particle Swarm Optimization.” Applied Soft Computing, Vol. 10, No. 2, pp. 618–628.

    Article  Google Scholar 

  • Poli, R. (2007). An analysis of publications on particle swarm optimization applications, Tech. Rep., CSM-469, Department of Computing and Electronic Systems, University of Essex, Colchester, Essex, UK.

    Google Scholar 

  • Prez, R.E. and Behdinan, K. (2007). “Particle swarm approach for structural design optimization.” Computer & Structures, Vol. 85, Nos. 19–20, pp. 1579–1588.

    Article  Google Scholar 

  • Pulido, G. T. and Coello C. A. C. (2004). “A constraint-handling mechanism for particle swarm optimization.” Evolutionary Computation, IEEE, Vol. 2, pp. 1396–1403.

    Google Scholar 

  • Rechenberg, R. (1973). Evolutionsstrategie: Optimierung technischer syseme nach prinzipien der biologischen evolution, Frommann-Holzboog, Stuttgart, Germany.

    Google Scholar 

  • Sadek, E. A. (1992). “Optimization of structures having general crosssectional relationships using an optimality criterion method.” Computers & Structures, Vol. 43, No. 5, pp. 959–969.

    Article  Google Scholar 

  • Schutte, J. F., Reinbolt, J. A., Fregly, B. J., Haftka, R. T., and George, A. D. (2004). “Parallel Global Optimization with the Particle Swarm Algorithm.” Int. J. Numer. Meth. Engrg., Vol. 61,Issue 13, pp. 2296–2315.

    Article  MATH  Google Scholar 

  • Shelokar, P., Siarry, P., Jayaraman, V., and Kulkarni, B. (2007). “Particle swarm and ant colony algorithms hybridized for improved continuous optimization.” Applied Mathematics and Computation, Vol. 188, No. 1, pp. 129–142.

    Article  MathSciNet  MATH  Google Scholar 

  • Shi, X. H., Liang, Y. C., Lee, H. P., Lu, C., and Wang, L. M. (2005). “An improved GA and a novel PSOGA-based hybrid algorithm.” Information Processing Letters, Vol. 93, No. 5, pp. 255–261.

    Article  MathSciNet  MATH  Google Scholar 

  • Svanberg, K. (1984). “On local and global minima in structural optimization.” New Directions in Optimum Structural Design, Wiley, New York, pp. 327–341.

    Google Scholar 

  • Venter, G. and Sobieszczanski-Sobieski, J. (2004). “Multidisciplinary optimization of a transport aircraft wing using particle swarm optimization.” Struct. Multidiscip. Optim., Vol. 26, Nos. 1–2, pp. 121–31.

    Article  Google Scholar 

  • Von den Bergh, Fan. (2001). An analysis of particle swarm optimizers, PhD Thesis, University of Pretoria, Pretoria, South Africa.

    Google Scholar 

  • Wang, Q. and Arora, J. S. (2006). “Alternative equations for structural optimization: An evaluation using frames.” Structural Engineering, Vol. 132,Issue 12, pp. 1880–1889.

    Article  Google Scholar 

  • Wolpert, D. H. and Marceady, W. G. (1997). “No free lunch theorems for optimization.” IEEE Transactions on Evolutionary Computation, Vol. 1, No. 1, pp. 67–82.

    Article  Google Scholar 

  • Xie, X., Zhang, W., and Yang, W. (2002). “A dissipative particle swarm optimization.” Proc. of the 2002 Congress on Evolutionary Computation, Honolulu, Hawaii, USA, pp. 1456–1461.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Dept. of Building Engineering, Tongji University, Shanghai, 200092, China

    Bin Yang, Qilin Zhang & Zhihao Zhou

  2. Dept. of Civil Engineering and Geodesy, Technical University of Munich, Munich, Germany

    Kai-Uwe Bletzinger

Authors
  1. Bin Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kai-Uwe Bletzinger
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Qilin Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Zhihao Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Bin Yang.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Yang, B., Bletzinger, KU., Zhang, Q. et al. Frame structural sizing and topological optimization via a parallel implementation of a modified particle Swarm algorithm. KSCE J Civ Eng 17, 1359–1370 (2013). https://doi.org/10.1007/s12205-013-0001-1

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12205-013-0001-1

Keywords

Search

Navigation