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A guided evolution strategy for discrete sizing optimization of space steel frames

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Abstract

In this paper, a new design-driven hybrid optimization algorithm called guided evolution strategy (GES) is proposed for a reliable and rapid optimum design of space steel frames. The rationale behind the proposed GES algorithm is to improve convergence characteristics of the evolution strategies (ESs) optimization method by guiding search process according to the satisfaction/violation of strength constraints in a previous design. This is referred to as guided mutation, which is introduced as an auxiliary tool to a stochastic mutation scheme for accelerating the convergence speed of the optimization algorithm. The efficiency of the GES algorithm is investigated and quantified using design examples where sizing optimization of two space steel frames are achieved under strength and displacement constraints imposed according to ANSI/AISC 360-10 (Specification for structural steel buildings, ANSI/AISC 360-10, Illinois, 2010) and ASCE/SEI 7-10 (Minimum design loads for buildings and other structures, ASCE/SEI 7-10, Reston, 2010) design specifications. The solutions produced to these design examples with the GES algorithm are compared to those of some selected metaheuristic search techniques in terms of accuracy of the obtained solutions as well as speed of convergence to the optimum designs. It is shown that the GES algorithm has improved search abilities with respect to other employed techniques.

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Acknowledgements

The authors would like to thank Dr. Hasan Eser from Middle East Technical University for providing the finite element models of the steel frames used in this study.

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Authors and Affiliations

  1. Department of Civil Engineering, Middle East Technical University, Ankara, Turkey

    Aytaç Korucu & Oğuzhan Hasançebi

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  1. Aytaç Korucu
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  2. Oğuzhan Hasançebi
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Correspondence to Oğuzhan Hasançebi.

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Korucu, A., Hasançebi, O. A guided evolution strategy for discrete sizing optimization of space steel frames. Struct Multidisc Optim 66, 183 (2023). https://doi.org/10.1007/s00158-023-03640-7

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