Show/Hide Menu
Hide/Show Apps
Logout
Türkçe
Türkçe
Search
Search
Login
Login
OpenMETU
OpenMETU
About
About
Open Science Policy
Open Science Policy
Open Access Guideline
Open Access Guideline
Postgraduate Thesis Guideline
Postgraduate Thesis Guideline
Communities & Collections
Communities & Collections
Help
Help
Frequently Asked Questions
Frequently Asked Questions
Guides
Guides
Thesis submission
Thesis submission
MS without thesis term project submission
MS without thesis term project submission
Publication submission with DOI
Publication submission with DOI
Publication submission
Publication submission
Supporting Information
Supporting Information
General Information
General Information
Copyright, Embargo and License
Copyright, Embargo and License
Contact us
Contact us
Improving the big bang-big crunch algorithm for optimum design of steel frames
Date
2012-01-01
Author
Hasançebi, Oğuzhan
Metadata
Show full item record
Item Usage Stats
274
views
0
downloads
Cite This
This paper presents an improved version of the big bang-big crunch (BB-BC) algorithm namely exponential BB-BC algorithm (EBB-BC) for optimum design of steel frames according to ASD-AISC provisions. It is shown that the standard version of the algorithm sometimes is unable to provide reasonable solutions for problems from discrete design optimization of steel frames. Therefore, by investigating the shortcomings of the BB-BC algorithm, it is aimed to enhance the algorithm for solving complicated steel frame optimization problems. In order to evaluate the performance of the proposed algorithm, the optimization results attained using the EBB-BC algorithm are compared to those of other well known metaheuristics. The numerical results demonstrate the efficiency and robustness of the proposed algorithm in practical design optimization of steel frames.
Subject Keywords
Big bang-big crunch algorithm
,
Discrete optimization
,
Metaheuristics
,
Practical design
,
Steel frames
URI
https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84893868381&origin=inward
https://hdl.handle.net/11511/79757
https://www.scopus.com/record/display.uri?eid=2-s2.0-84893868381&origin=resultslist
Conference Name
8th International Conference on Engineering Computational Technology, ECT
Collections
Department of Civil Engineering, Conference / Seminar
Suggestions
OpenMETU
Core
Structural Optimization Using Big Bang-Big Crunch Algorithm: A Review
Kazemzadeh Azad, Sina; Kazemzadeh Azad, Saeid; Hasançebi, Oğuzhan (2016-06-01)
The big bang-big crunch (BB-BC) algorithm is a popular metaheuristic optimization technique proposed based on one of the theories for the evolution of the universe. The algorithm utilizes a two-phase search mechanism: big-bang phase and big-crunch phase. In the big-bang phase the concept of energy dissipation is considered to produce disorder and randomness in the candidate population while in the big-crunch phase the randomly created solutions are shrunk into a single point in the design space. In recent y...
Rigorous optimizations of three dimensional antenna arrays using full wave simulations
Onol, Can; Gokce, Ozer; Boyacı, Huseyın; Ergül, Özgür Salih (null; 2015-07-09)
We present optimizations of three-dimensional antenna arrays using heuristic techniques coupled with the multilevel fast multipole algorithm (MLFMA). Without resorting to any periodicity and infinity assumptions, antenna arrays are modeled with surface integral equations and simulated via MLFMA, which also enables the analysis of arrays with non-identical elements. Genetic algorithms and particle swarm optimization methods are employed on the complex data produced by MLFMA in phasor domain to find optimal s...
Optimizations of Patch Antenna Arrays Using Genetic Algorithms Supported by the Multilevel Fast Multipole Algorithm
Onol, Can; Ergül, Özgür Salih (2014-12-01)
We present optimizations of patch antenna arrays using genetic algorithms and highly accurate full-wave solutions of the corresponding radiation problems with the multilevel fast multipole algorithm (MLFMA). Arrays of finite extent are analyzed by using MLFMA, which accounts for all mutual couplings between array elements efficiently and accurately. Using the superposition principle, the number of solutions required for the optimization of an array is reduced to the number of array elements, without resorti...
Improving Computational Efficiency of Particle Swarm Optimization for Optimal Structural Design
Hasançebi, Oğuzhan (2013-06-01)
This paper attempts to improve the computational efficiency of the well known particle swarm optimization (PSO) algorithm for tackling discrete sizing optimization problems of steel frame structures. It is generally known that, in structural design optimization applications, PSO entails enormously time-consuming structural analyses to locate an optimum solution. Hence, in the present study it is attempted to lessen the computational effort of the algorithm, using the so called upper bound strategy (UBS), wh...
On the reduction of Gaussian inverse Wishart mixtures
Granström, Karl; Orguner, Umut (2012-09-12)
This paper presents an algorithm for reduction of Gaussian inverse Wishart mixtures. Sums of an arbitrary number of mixture components are approximated with single components by analytically minimizing the Kullback-Leibler divergence. The Kullback-Leibler difference is used as a criterion for deciding whether or not two components should be merged, and a simple reduction algorithm is given. The reduction algorithm is tested in simulation examples in both one and two dimensions. The results presented in the ...
Citation Formats
IEEE
ACM
APA
CHICAGO
MLA
BibTeX
O. Hasançebi, “Improving the big bang-big crunch algorithm for optimum design of steel frames,” Dubrovnik, Hırvatistan, 2012, vol. 100, Accessed: 00, 2021. [Online]. Available: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84893868381&origin=inward.
