Session Start Date

8-24-2012

Session End Date

8-25-2012

Keywords and Phrases

portal frame, cold-formed steel, optimization, genetic algorithm

Abstract

The design optimization of cold-formed steel portal frame buildings is considered in this paper. The real-coded genetic algorithm (GA) optimizer proposed considers both building’s topology (i.e. frame spacing and pitch) and cross-sectional sizes of the main structural members as the decision variables that are optimized. Previous GAs in the literature were characterized by poor convergence including slow progress that usually results in excessive computation times and/or frequent failure to achieve an optimal or near-optimal solution. This is the main issue addressed in this paper. In an effort to improve the performance of the conventional GA, a niching strategy is presented that is an effective means of enhancing the dissimilarity of the solutions in each generation of the GA. Through a benchmark example, it is shown that the efficient GA proposed generates the optimal solution more consistently with three times faster of the computation time in comparison to the conventional GA.

Department(s)

Civil, Architectural and Environmental Engineering

Research Center/Lab(s)

Wei-Wen Yu Center for Cold-Formed Steel Structures

Meeting Name

21st International Specialty Conference on Cold-Formed Steel Structures

Publisher

Missouri University of Science and Technology

Publication Date

8-24-2012

Document Version

Final Version

Rights

© 2012 Missouri University of Science and Technology, All rights reserved.

Document Type

Article - Conference proceedings

File Type

text

Language

English

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An Efficient Genetic Algorithm for the Design Optimization of Cold-formed Steel Portal Frame Buildings

The design optimization of cold-formed steel portal frame buildings is considered in this paper. The real-coded genetic algorithm (GA) optimizer proposed considers both building’s topology (i.e. frame spacing and pitch) and cross-sectional sizes of the main structural members as the decision variables that are optimized. Previous GAs in the literature were characterized by poor convergence including slow progress that usually results in excessive computation times and/or frequent failure to achieve an optimal or near-optimal solution. This is the main issue addressed in this paper. In an effort to improve the performance of the conventional GA, a niching strategy is presented that is an effective means of enhancing the dissimilarity of the solutions in each generation of the GA. Through a benchmark example, it is shown that the efficient GA proposed generates the optimal solution more consistently with three times faster of the computation time in comparison to the conventional GA.