Augmented Lagrangian teaching–learning-based optimization for structural design

Abstract

Stochastic optimization methods have been widely employed to find solutions to structural design optimization problems in the past two decades, especially for truss structures. The primary aim of this study is to introduce a design optimization method combining an augmented Lagrangian function and teaching-learning-based optimization for truss and nontruss structural design optimization. The augmented Lagrangian function serves as a constraint-handling tool in the proposed method and converts a constrained optimization problem into an unconstrained one. On the other hand, teaching-learning-based optimization is employed to resolve the transformed, unconstrained optimization problems. Since the proper values of the Lagrangian multipliers and penalty factors are unknown in advance, the proposed method is implemented in an iterative way to avoid the issue of selecting them, i.e. the Lagrangian multipliers and penalty factors are automatically updated according to the violation level of all constraints. To examine the performance of the proposed method, it is applied on a group of benchmark truss optimization problems and a group of nontruss optimization problems of aircraft wing structures. The computational results obtained by the proposed method are compared to the results produced by both other version of teaching-learning-based optimization and stochastic optimization methods.

Multidisciplinary Collaborative Design and Optimization of Turbine Rotors Considering Aleatory and Interval Mixed Uncertainty under a SORA Framework

“…Epistemic uncertainty can be called aleatory uncertainty [9][10][11][12]. Multidisciplinary design optimization (MDO) with mixed aleatory and interval uncertainties must be studied to ensure feasibility [13][14][15][16][17].…”