Design of planar steel frames using Teaching–Learning Based Optimization

This paper presents a design procedure employing a Teaching–Learning Based Optimization (TLBO) technique for discrete optimization of planar steel frames. TLBO is a nature-inspired search method that has been developed recently. It simulates the social interaction between the teacher and the learners in a class, which is summarized as teaching–learning process. The design algorithm aims to obtain minimum weight frames subjected to strength and displacement requirements imposed by the American Institute for Steel Construction (AISC) Load and Resistance Factor Design (LRFD). Designs are obtained selecting appropriate W-shaped sections from a standard set of steel sections specified by the AISC. Several frame examples from the literature are examined to verify the suitability of the design procedure and to demonstrate the effectiveness and robustness of the TLBO creating of an optimal design for frame structures. The results of the TLBO are compared to those of the genetic algorithm (GA), the ant colony optimization (ACO), the harmony search (HS) and the improved ant colony optimization (IACO) and they shows that TLBO is a powerful search and applicable optimization method for the problem of engineering design applications.

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► TLBO has simple numerical structure.
► TLBO is independent on a number of parameters to define the algorithm’s performance.
► TLBO algorithm is accurate, effective and robust.
► TLBO has great potential for solving constrained discrete problems.