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Truss optimization with natural frequency constraints using generalized normal distribution optimization

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Abstract

The newly proposed Generalized Normal Distribution Optimization (GNDO) algorithm is used to design the truss structures with optimal weight. All trusses optimized have frequency constraints, which make them very challenging optimization problems. A large number of locally optimal solutions and non-convexity of search space make them difficult to solve, therefore, they are suitable for testing the performance of optimization algorithm. This work investigates whether the proposed algorithm is capable of coping with such problems. To evaluate the GNDO algorithm, three benchmark truss optimization problems are considered with frequency constraints. Numerical data show GNDO’s reliability, stability, and efficiency for structural optimization problems than other meta-heuristic algorithms. We thoroughly analyse and investigate the performance of GNDO in this engineering area for the first time in the literature.

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Authors and Affiliations

  1. Iran University of Science and Technology, Tehran, Iran

    Nima Khodadadi

  2. Centre for Artificial Intelligence Research and Optimization, Torrens University Australia, Sydney, Australia

    Seyedali Mirjalili

  3. YFL (Yonsei Frontier Lab), Yonsei University, Seoul, South Korea

    Seyedali Mirjalili

Authors
  1. Nima Khodadadi
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  2. Seyedali Mirjalili
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Correspondence to Seyedali Mirjalili.

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Khodadadi, N., Mirjalili, S. Truss optimization with natural frequency constraints using generalized normal distribution optimization. Appl Intell 52, 10384–10397 (2022). https://doi.org/10.1007/s10489-021-03051-5

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  • DOI: https://doi.org/10.1007/s10489-021-03051-5

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