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Shape Optimization using GA with Stress-based Crossover

By Cuimin Li, Tomoyuki Hiroyasu and Mitsunori Miki


In this paper, genetic algorithm with a stress-based crossover is improved to solve structural shape optimization problems. The design domain is well divided by finite element method. According to one initial topology, the boundary profile elements and the neighboring outside elements, which are design variables, are randomly set to “0 ” or “1 ” to generate the initial population. To keep the shape deforming gradually, a logical “OR ” operation is applied on each child structure and a “mask ” structure. Moreover, the material weight of child is adjusted dynamically. Three experiments were performed to verify the effectiveness of improved SX for structural shape optimization. Key Words

Topics: Improved Stress-based Crossover, Stress-based Crossover, Genetic Algorithm, Shape Optimization, Structure Optimization
Year: 2011
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