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Geometrically Nonlinear Analysis of Trusses Using Particle Swarm Optimization

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Recent Advances in Swarm Intelligence and Evolutionary Computation

Part of the book series: Studies in Computational Intelligence ((SCI,volume 585))

Abstract

Particle swarm optimization (PSO) algorithm is a heuristic optimization technique based on colony intelligence, developed through inspiration from social behaviors of bird flocks and fish schools. It is widely used in problems in which the optimal value of an objective function is searched. Geometrically nonlinear analysis of trusses is a problem of this kind. The deflected shape of the truss where potential energy value is minimal is known to correspond to the stable equilibrium position of the system analyzed. The objective of this study is to explore the success of PSO using this minimum total potential energy principle, in finding good solutions to geometrically nonlinear truss problems. For this purpose analyses are conducted on three structures, two plane trusses and a space truss. The results obtained show that in case of using 20 or more particles, PSO produces very good and robust solutions.

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Authors and Affiliations

  1. Department of Civil Engineering, Faculty of Engineering, Istanbul University, 34320, Istanbul, Turkey

    Rasim Temür

  2. Open and Distance Education Faculty, Istanbul University, 34452, Istanbul, Turkey

    Yusuf Sait Türkan

  3. Department of Civil Engineering, Faculty of Engineering, Bilecik Seyh Edebali University, 11210, Bilecik, Turkey

    Yusuf Cengiz Toklu

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  1. Rasim Temür
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  2. Yusuf Sait Türkan
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  3. Yusuf Cengiz Toklu
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Correspondence to Yusuf Cengiz Toklu .

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Editors and Affiliations

  1. School of Science and Technology, Middlesex University, London, United Kingdom

    Xin-She Yang

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Temür, R., Türkan, Y.S., Toklu, Y.C. (2015). Geometrically Nonlinear Analysis of Trusses Using Particle Swarm Optimization. In: Yang, XS. (eds) Recent Advances in Swarm Intelligence and Evolutionary Computation. Studies in Computational Intelligence, vol 585. Springer, Cham. https://doi.org/10.1007/978-3-319-13826-8_15

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  • DOI: https://doi.org/10.1007/978-3-319-13826-8_15

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-13825-1

  • Online ISBN: 978-3-319-13826-8

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