Multi-class teaching–learning-based optimization for truss design with frequency constraints

•A multi-class teaching–learning based optimization algorithm is proposed.•The two-stage algorithm improves the exploration and exploitation of TLBO.•Size and shape optimization under frequency constraints is addressed.•Comparative studies illustrate the superiority of the proposed algorithm.

The primary objective of this study is to introduce a multi-class teaching–learning-based optimization (MC-TLBO) technique for structural optimization with frequency constraints. Teaching–learning-based optimization (TLBO) is based on a simple and efficient algorithm with no intrinsic parameters controlling its performance. The multi-class approach proposed here increases the initial exploration capability of the optimization process resulting in a more efficient search. MC-TLBO extends the concept of the education process from a single classroom to a school with multiple parallel classes. The MC-TLBO algorithm employs a two-stage procedure: in the first stage, parallel classes explore the search space; in the second stage, the best solutions of the first stage form a super class to be the initial population for a modified TLBO. In order to examine the efficiency of the proposed methodology, the MC-TLBO algorithm is applied on various benchmark truss optimization problems with frequency constraints and the designs results are compared to the results of both a modified TLBO algorithm and other optimization methods.