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A teaching-learning-based optimization algorithm for reliability analysis with an adaptive penalty coefficient
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DOI：10.1016/j.istruc.2024.106695.png)
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Abstract
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The Hasofer-Lind-Rackwitz-Fiessler (HLRF) algorithm of the first order reliability method may fail in the presence of nonlinear problems. As a simple and efficient meta-heuristic strategy, the teaching-learning-based optimization (TLBO) algorithm accompanied with one of its variants, TLBO with triangular varying population sizes, is employed in reliability analysis to overcome the numerical difficulties that occurs in the HLRF algorithm or its modifications of the first order reliability method mainly falling into a state of instability such as periodic solutions and chaos. In the meantime, choice on the penalty coefficient in the equivalent unconstrained optimization problem of reliability analysis is discussed. A more appropriate scheme to adaptively select a sequence of the penalty coefficients in iterations is presented in terms of Karush-Kuhn-Tucker (KKT) conditions to ensure the equivalence of the original constrained optimization problem of the first order reliability method. Numerical experiments show that, compared with the manner of exponential growth might producing great errors in complex and nonlinear problems, the adaptive choice of penalty coefficients in light of KKT conditions results in a good efficiency with a satisfactory accuracy especially when TLBO with triangular varying population sizes is utilized to solve the equivalent optimization problem of reliability analysis.
Keywords:
Hasofer-Lind-Rackwitz-Fiessler algorithm
Teaching -learning -based optimization
Reliability analysis
Penalty coefficient
Karush-Kuhn-Tucker conditions
integral integral
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4.3
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1.3W
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2.7W
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