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Truss Optimisation - Dissertation Example

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Table of Contents Table of Contents 1 1.Introduction 2 1.1. Background 2 1.2.Literature Review 3 1.3. Objective 4 2. Theory 4 2.1. Structural optimization 4 2.2. The GA Principle 6 2.3. Truss Structures 7 3. Structural Optimization Using Genetic Algorithms 8 4…
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Download file to see previous pages Ghasemi et al. (1999) have revealed the appropriateness of the genetic algorithms to deal with the large trusses that have numerous indefinite variables. This study shows that how an algorithm of our design can be employed to match this previous study. The paper will hugely concentrate over the application of genetic algorithm to trusses developed under indefinite conditions (Ganzerli et al., 2003). 1.1. Background Galileo Galilei has been recognized as the first scientist by the Coello Coello et al. (1994), who studied optimization of structures over the bending of beams in his work. Over the period of time, this subject has developed and become an area of engineering, in itself, which is known as the structural optimization. For the last few decades, the rising interest towards this area has been because of the availability of powerful and cheap computers as well as due to the rapid progress in the analytic and optimization methods for the structures (Soh and Yang, 1998). The optimization of the weight of the structures is of great importance to many fields of engineering. It might be linked to cost optimization, in some aspects, as it clearly tends towards an optimal usage of the materials. The weight optimized structures, in civil engineering, are very convenient as the construction as well as the transportation work in, relation to the build-up, is simple. The engagement of the least possible share of the load capacity by the structure itself is another benefit of developing a structure with its weight being optimized. Also, in the aircraft and car industries, the structural optimization is highly important since a lighter structure leads to a better fuel efficiency. The use of genetic algorithms is an efficient optimization technique. GA is a form of evolutionary programming (Alander, 1999) and most likely known as the best optimization technique of the present time (Ashlock, 2006). It provokes the evolutionary principle of survival of the fittest through aggregating the optimum solutions to a problem in numerous generations in order to augment the outcome gradually. The elementary population of solutions is constructed on the random basis and then along with the evolution, the best solutions are aggregated in each generation until they converged in to an optimal solution (Gold Berg, 1989). 1.2. Literature Review Over the previous two decades, the genetic algorithms have been used in search for an optimal design solution for trusses that has been explained in numerous scientific reports. However the optimization in the majority of these studies does not relate to shape, size and topology simultaneously. In general, the topology of the truss is fixed that means the inner connectivity of the members is constant (Ravindran and Ragsdell, 2006). The most frequently used method to deal with the optimization of the truss topology is the ground structure method that has been used by Hajela & Lee (1995) and Deb & Gulati (2000) in their work. An extremely connected ground structure having numerous nodes and elements, in the ground structure method, is gradually minimized until just the basic required elements are left (Ohsaki, 2005). The emphasis has been over the development of a highly efficient genetic algorithm, in some of the recent studies on truss optimization with GA, which determines an optimal solution through the least possible number of calculations such as the adaptive approach given by Togan & Daloglu (2006) and the directed mutation ...Download file to see next pagesRead More
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