Biogeography-Based Optimization Algorithm for Designing of Planar Steel Frames

Year 2016, Volume: 4 Issue: Special Issue-1, 53 - 57, 26.12.2016

OSMAN Tunca SERDAR Çarbaş

https://doi.org/10.18201/ijisae.266128

Cited By: 3

Abstract

The optimization can be defined as a solution of problem under specific
conditions to achieve a specific purpose. Optimization strategies commonly used
for solving of various problems and have gained great importance in recent
years especially in engineering. 
Evolving optimization methods over the years has many varieties such as
shape optimization, topology optimization, size optimization etc. The latest
trend of optimization methods is metaheuristics which are more useful with easy
applicable to complex problems regarding to traditional optimization methods.
So that metaheuristics have supplanted the traditional methods particularly in
engineering by the time. In this study, a planar steel frame which is designed
according to the requirements comprised by AISC-LRFD (American Institute of
Steel Construction-Load and Resistance Factor Design) has been optimized by aid
of biogeography-based optimization (BBO) algorithm.

Keywords

Planar Steel Frames, Optimum Design, Stochastic Search Techniques, Biogeography-Based Optimization, Metaheuristics

References

• [11] I. Boussaid, A. Chatterjee, P. Siarry, and M. A. Nacer, ‘’Biogeography-based optimization for constrained optimization problems,’’ Comput Oper Res, vol. 39, no. 12, pp. 3293–3304, 2012.
• [12] S. Rajasomashekar and P. Aravindhababu, ‘’Biogeography based optimization technique for best compromise solution of economic emission dispatch,’’ Swarm Evol Comput, vol. 7, pp.47–57, 2012.
• [13] S. Kima, J. Byeonb, H. Yuc, and H. Liud, ‘’Biogeography-based optimization for optimal job scheduling in cloud computing,’’ Appl Math Comput, vol.247, pp. 266–280, 2014.
• [14] J. S. Wang, N. N. Shen, X. D. Ren, and G. N. Liu, “RBF neural network soft-sensor modeling of rotary kiln pellet quality indices optimized by biogeography-based optimization algorithm,” Journal of Chemical Engineering of Japan, vol. 48, no. 1, pp. 7–15, 2015.
• [15] P. K. Roy, S. P. Ghoshal, and S. S. Thakur, “Optimal reactive power dispatch considering flexible ac transmission system devices using biogeography-based optimization,” Electric Power Components and Systems, vol. 39, no. 8, pp. 733–750, 2011.
• [16] İ. Aydogdu, ‘’Cost optimization of reinforced concrete cantilever retaining walls under seismic loading using a biogeography-based optimization algorithm with Levy flights,’’ Eng Optimiz, doi: 10.1080/0305215X.2016.1191837, 2016
• [17] S. Çarbaş, “Optimum structural design of spatial steel frames via biogeography-based optimization,” Neural Computing and Applications, doi:10.1007/s00521-015-2167-6. 2016
• [18] İ. Aydoğdu, and A. Akin, “Biogeography based co2 and cost optimization of RC cantilever retaining walls.” in 17th International Conference on Structural Engineering, 2015, paper 1480, p. 5.
• [19] Load and Resistance Factor Design (LRFD), Vol. 1, Structural Members Specifications Codes. 3rd edn. American Institute of Steel Construction (AISC), Chicago, Illinois, 2001.
• [20] M. P. Saka and E. Dogan, "Design optimization of moment resisting steel frames using a cuckoo search algorithm", in Proc of The Eleventh International Conference on Computational Structures Technology, 2012, paper 71.