Date

2005

Author

Saraç, Yavuz

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Difficult gradient calculations, converging to a local optimum without exploring the design space adequately, too much dependency on the starting solution, lacking capabilities to treat discrete and mixed design variables are the main drawbacks of conventional optimization techniques. So evolutionary optimization methods received significant interest amongst researchers in the optimization area. Genetic algorithms (GAs) and simulated annealing (SA) are the main representatives of evolutionary optimization methods. These techniques emerged as powerful and modern strategies to efficiently deal with the difficulties encountered in conventional techniques, and therefore rightly attracted a substantial interest and popularity. The underlying concepts of these techniques and thus their algorithmic models have been devised by establishing between the optimization task and events occurring in nature. While, Darwin̕s survival of the fittest theory is mimicked by GAs, annealing process of physical systems are employed to SA. On the other hand, dome structures are among the most preferred types of structures for large unobstructed areas. Domes have been of a special interest in the sense that they enclose a maximum amount of space with a minimum surface. This feature provides economy in terms of consumption of constructional materials. So merging these two concepts make it possible to obtain optimum designs of dome structures. This thesis is concerned with the use of GAs and SA in optimum structural design of dome structures, which range from some relatively simple problems to the problems of increased complexity. In this thesis, firstly both techniques are investigated in terms of their practicality and applicability to the problems of interest. Then numerous test problems taken from real life conditions are studied for comparing the success of the proposed GA and SA techniques

Subject Keywords

General Engineering.

URI

http://etd.lib.metu.edu.tr/upload/12606793/index.pdf
https://hdl.handle.net/11511/15600

Collections

Graduate School of Natural and Applied Sciences, Thesis