| This thesis promises to provide a panoramic view of optimization in general before zooming into a specific artificial intelligence technique in optimization. Detailed information on optimization techniques commonly used in mechanical engineering is first provided to ensure a clear understanding of the thesis. Moreover, the thesis highlights the differences and similarities, advantages and disadvantages of these techniques. After a brief study of the techniques entailed in optimization, an artificial intelligence algorithm, namely genetic algorithm, was selected, developed, improved and later applied to a wide variety of mechanical engineering problems. Ample examples from various fields of engineering are provided to illustrate the versatility of genetic algorithms.; The major focus of this thesis is therefore the application of genetic algorithms to solve a broad range of engineering problems. The viability of the genetic algorithm (GA) as an optimization tool for mechanical engineering applications is assessed and discussed. Comparison between GA generated results and results found in the literature are presented when possible to underscore the power of GA to solve problems. Moreover, the disadvantages and advantages of the genetic algorithms are discussed based on the results obtained. The mechanical engineering applications studied include conceptual aircraft design, design of truss structures under various constraints and loading conditions, and armour design using established penetration analytical models.; Results show that the genetic algorithm developed is capable of handling a wide range of problems, is an efficient cost effective tool, and often provides superior results when compared to other optimization methods found in the literature. (Abstract shortened by UMI.)... |
