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Multi-class teaching-learning-based optimization for truss design with frequency constraints
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DOI：10.1016/j.engstruct.2015.10.039.png)
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Abstract
En 中文
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. (C) 2015 Elsevier Ltd. All rights reserved.
Keywords:
Optimization
Teaching-learning-based optimization (TLBO)
Truss structures
Shape and size optimization
Frequency constraints
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