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Optimization of engineering systems using genetic algorithm enhanced computational techniques

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posted on 23.05.2021, 14:30 by Nicholas Farouk Ali
The field of optimization has been and continues to be an area of significant importance in the industry. From financial, industrial, social and any other sector conceivable, people are interested in improving the scheme of existing methodologies and products and/or in creating new ideas. Due to the growing need for humans to improve their lives and add efficiency to a system, optimization has been and still is an area of active research. Typically optimization methods seek to improve rather than create new ideas. However, the ability of optimization methods to mold new ideas should not be ruled out, since optimized solutions usually lead to new designs, which are in most cases unique. Combinatorial optimization is the term used to define the method of finding the best sequence or combination of variables or elements in a large complex system in order to attain a particular objective. 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.





Master of Applied Science


Mechanical and Industrial Engineering

Granting Institution

Ryerson University

LAC Thesis Type


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Mechanical and Industrial Engineering (Theses)