Engineering Commons^™

Comet: Constrained Optimization Of Multiple-Dimensions For Efficient Trajectories, Michael Curt Conrad

Master's Theses

The paper describes the background and concepts behind a master’s thesis platform known as COMET (Constrained Optimization of Multiple-dimensions for Efficient Trajectories) created for mission designers to determine and evaluate suitable interplanetary trajectories. This includes an examination of the improvements to the global optimization algorithm, Differential Evolution, through a cascading search space pruning method and decomposition of optimization parameters. Results are compared to those produced by the European Space Agency’s Advanced Concept Team’s Multiple Gravity Assist Program. It was found that while discrepancies in the calculation of ΔV’s for flyby maneuvers exist between the two programs, COMET showed a noticeable …

Optimization Of A Low Reynold's Number 2-D Inflatable Airfoil Section, Todd A. Johansen

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

A stand-alone genetic algorithm (GA) and an surrogate-based optimization (SBO) combined with a GA were compared for accuracy and performance. Comparisons took place using the Ackley Function and Rastrigin's Function, two functions with multiple local maxima and minima that could cause problems for more traditional optimization methods, such as a gradient-based method. The GA and SBO with GA were applied to the functions through a fortran interface and it was found that the SBO could use the same number of function evaluations as the GA and achieve at least 5 orders of magnitude greater accuracy through the use of surrogate …

Space Trajectories Optimization Using Variable-Chromosome-Length Genetic Algorithms, Ahmed H. Gad

Dissertations, Master's Theses and Master's Reports - Open

The problem of optimal design of a multi-gravity-assist space trajectories, with free number of deep space maneuvers (MGADSM) poses multi-modal cost functions. In the general form of the problem, the number of design variables is solution dependent. To handle global optimization problems where the number of design variables varies from one solution to another, two novel genetic-based techniques are introduced: hidden genes genetic algorithm (HGGA) and dynamic-size multiple population genetic algorithm (DSMPGA).

In HGGA, a fixed length for the design variables is assigned for all solutions. Independent variables of each solution are divided into effective and ineffective (hidden) genes. Hidden …