"The field of structural optimization has gained much academic interest in the recent decades. Different streams of optimization methods have been applied to this problem including analytical methods, optimality criteria-based method and gradient-based methods. During the recent decade, there has been a growing interest among researchers to apply stochastic population-based methods, the so-called meta-heuristics, to this class of optimization problems. The motivation is the robustness and capability of meta-heuristics to avoid local minima. On the downside, their required evaluation budget grows fast when the number of design variables is increased, which limits the complexity of problems to which they can be applied. Furthermore, majority of these methods are tailored to optimize only the cross-sectional areas of the members, the potential saving from which is highly limited. At the same time, several factors have diminished practitioners' interests in the academic research on this topic, including simplicity of conventional test problems compared to real structures, variety of design constraints in practice and the complexity of evaluation of the total cost. This dissertation aims at addressing some of the most critical shortcomings in the available truss optimization methods, both from academic and practical perspectives. It proposes a novel bi-level method for simultaneous optimization of topology, shape and size of truss structures. In the upper level, a specialized evolution strategy (ES) is proposed which follows the principles of contemporary evolution strategies (ESs), although the formulation is modified to handle mixed- variable highly constrained truss optimization problems. The concept of fully stressed design is employed in the lower level as an efficient method for resizing the sampled solution in the upper level. The concept of fully stressed design is also utilized to define a specialized penalty term based on the estimated required increase in the structural weight such that all constraints are satisfied. The proposed method, called fully stressed design evolution strategy (FSD-ES), is developed in four stages. It is tested on complicated problems, some of which are developed in this dissertation, as an attempt to reduce the gap between complexity of test problems and real structures. Empirical evaluation and comparison with the best available methods in the literature reveal superiority of FSD-ES, which intensifies for more complicated problems. Aside from academically interesting features of FSD-ES, it addresses some of the practicing engineers' critiques on applicability of truss optimization methods. FSD-ES can handle large-scale truss optimization problems with more than a thousand design parameters, in a reasonable amount of CPU time. Our numerical results demonstrate that the optimized design can hardly be guessed by engineering intuition, which demonstrates superiority of such design optimization methods. Besides, the amount of material saving is potentially huge, especially for more complicated problems, which justifies simulation cost of the design problem. FSD-ES does not require any user-dependent parameter tuning and the code is ready to use for an arbitrary truss design problem within the domain of the code."--Pages ii-iii.
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