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The design of many engineering systems can be a complex process, which means many possibilities and factors must be considered during problem formulation. The process of searching for a design that meets both performance and safety standards with reliable impact is the goal of structural optimization. Structural optimization is an approach whereby the structural design is subjected to being optimized in terms of weight while maintaining all design constraints such as stress, strain, and stability. Structural design optimization problems involve searching for the minimum of the stated objective function, usually the weight of the structure and constructability. Trusses are triangular frame works in which the members are subjected to essentially axial forces due to externally applied load at the joints only. Truss structures can be optimized by varying the structure’s size, shape, and topology. Although combining these three prototypes of optimization can ultimately a yield better result, the underlying mathematical model becomes complicated. Over the last decade, new optimization strategies based on metaheuristic algorithms have been devised to obtain the optimal design for structural systems. Harmony search (HS) is a metaheuristic algorithm proposed by Geem et al., inspired by the observation that the aim of music is to search for a perfect state of harmony. In this research, the implementation of HS algorithm has been applied to optimize the size of truss structures that results in the weight reduction of the truss members. The results obtained with HS were compared to those obtained from the original sizes before optimization, to verify the influence on the optimal design of truss structures subjected to stresses, deflections, vertical and lateral displacements, and buckling constrains. / Civil Engineering

<p> This thesis defends the use of genetic algorithms (GA) to solve the maximum number of repetitions in a binary string. Repetitions in strings have significant uses in many different fields, whether it is data-mining, pattern-matching, data compression or computational biology 14]. Main extended the definition of repetition, he realized that in some cases output could be reduced because of overlapping repetitions, that are simply rotations of one another [10]. As a result, he designed the notion of a run to capture the maximal leftmost repetition that is extended to the right as much as possible. Franek and Smyth independently computed the same number of maximum repetition for strings of length five to 35 using an exhaustive search method. Values greater than 35 were not computed because of the exponential increase in time required. Using GAs we are able to generate string with very large, if not the maximum, number of runs for any string length. The ability to generate strings with large runs is an advantage for learning more about the characteristics of these strings. </p> / Thesis / Master of Science (MSc)

<p> For 30 years the Lempel-Ziv factorization LZx of a string x = x[1..n] has been a fundamental data structure of string processing, especially valuable for string compression and for computing all the repetitions (runs) in x. When the Internet came in, a huge need for Lempel-Ziv factorization was created. Nowadays it has become a basic efficient data transmission format on the Internet.</p> <p> Traditionally the standard method for computing LZx was based on O(n)-time processing of the suffix tree STx of x. Ukkonen's algorithm constructs suffix tree online and so permits LZ to be built from subtrees of ST; this gives it an advantage, at least in terms of space, over the fast and compact version of McCreight's STCA [37] due to Kurtz [24]. In 2000 Abouelhoda, Kurtz & Ohlebusch proposed a O(n)-time Lempel-Ziv factorization algorithm based on an "enhanced" suffix array - that is, a suffix array SAx together with other supporting data structures.</p> <p> In this thesis we first examine some previous algorithms for computing Lempel-Ziv factorization. We then analyze the rationale of development and introduce a collection of new algorithms for computing LZ-factorization. By theoretical proof and experimental comparison based on running time and storage usage, we show that our new algorithms appear either in their theoretical behavior or in practice or both to be superior to those previously proposed. In the last chapter the conclusion of our new algorithms are given, and some open problems are pointed out for our future research.</p> / Thesis / Master of Science (MSc)