Keywords: steel space frames, firefly algorithm, metaheuristic techniques, optimum structural design, load and resistance factor design, combinatorial optimization.

Optimum design of steel frames is a complicated process because the designer has to consider large numbers of nonlinear constraints which are imposed by the steel design codes while also dealing with discrete design variables. Obtaining the optimum solution to discrete programming problems was never easy until the emergence of metaheuristic techniques. Metaheuristic algorithms try to find the solution of optimization problems by using certain tactics that are generally inspired from nature. They explore the design space by following these rules in order to determine the optimal or near optimal solutions [1,2,3,4,5,6,7]. These techniques are usually named after the natural phenomena theysimulate. Genetic algorithms, evolutionary strategies, simulating annealing, tabu search, ant colony optimization, particle swarm optimization, artificial bee colony, harmony search and firefly algorithms are some of the metaheuristic techniques that are used to develop structural optimization algorithms. One of the recently developed metaheuristic techniques is the firefly algorithm. This method is based on the idealised behaviour of the flashing characteristics of fireflies. In this paper, the optimum design problem of steel space frames is formulated according to the provisions of LRFD-AISC [8]. The firefly optimization technique is used to obtain the solution to the design problem. There are two frames that are selected in order to test the performance of the firefly algorithm for structural design problems. These are four-storey, 132 member and eight-storey, 1024-member space frames. The results obtained from the firefly algorithm are compared to the solutions from the dynamic harmony search and ant colony optimization algorithms that are used for large scale problems and have been shown to perform efficiently in the literature. At the end of these comparisons, the firefly algorithm performs better than the dynamic harmony search and ant colony optimization algorithm.

1

Z.G.A. Kochenberger, F. Glover, "Handbook of Meta-Heuristics", Kluwer Academic Publishers, 2003.

2

L.N. De Castro, F.J. Von Zuben, "Recent Developments in Biologically Inspired Computing", Idea Group Publishing, USA, 2005.

3

J. Dreo, A. Petrowski, P. Siarry, E. Taillard, "Meta-Heuristics for Hard Optimization", Springer-Verlag, Berlin, Heidelberg, 2006.

4

T.F. Gonzales, "Handbook of Approximation Algorithms and Metaheuristics", Chapman & Halls/CRC, 2007. doi:10.1201/9781420010749

5

X.-S. Yang, "Nature-Inspired Metaheuristic Algorithms", Luniver Press, 2008.

6

X.-S. Yang, "Engineering Optimization: An Introduction with Metaheuristic Applications", John Wiley, 2010.

7

S. Luke, "Essentials of Metaheuristics", 2010. http://cs.gmu.edu/ sean/book/metaheuristics

8

AISC, "Load and Resistance Factor Design, Volume 1, Structural Members Specifications Codes", Third edition, American Institute of Steel Construction, 2001.

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