Statistically Enhanced Evolutionary Algorithm for Aerospace Design Applications

This paper describes an approach to developing a robust real-coded genetic algorithm (GA) that is implemented for three representative Aerospace Applications, a propeller optimization, a solid rocket motor missile system optimization, and a liquid rocket engine powered missile system optimization. The propeller design and optimization analysis using Bernstein polynomials to develop the geometries is optimized for a cruise condition. The full propeller design optimization problem requires a GA that can overcome abundant local maxima and find a real solution as there are a multitude of parameters for the GA to adjust. The GA makes use of demes (subpopulations) to avoid local maxima. The development of this genetic algorithm uses a tournament style parent selection to obtain the four fittest members of a given subpopulation. The results for the GA applied to the two different genres of aerospace analysis are provided here. The propeller geometry is optimized for thrust and efficiency, and the missile applications are optimized for range and apogee. The number of subpopulations used to generate the main population is varied for both applications to provide insight into their assistance to the GA model.