Accession Number : AD1029846


Title :   Unscented Sampling Techniques For Evolutionary Computation With Applications To Astrodynamic Optimization


Descriptive Note : Technical Report,21 Sep 2013,21 Sep 2016


Corporate Author : NAVAL POSTGRADUATE SCHOOL MONTEREY CA MONTEREY United States


Personal Author(s) : McGrath,Christopher B


Full Text : https://apps.dtic.mil/dtic/tr/fulltext/u2/1029846.pdf


Report Date : 01 Sep 2016


Pagination or Media Count : 417


Abstract : This dissertation investigates several innovative approaches to evolutionary optimization that are relevant to numerous applications in astronautical engineering. The challenges and shortfalls associated with evolutionary algorithms are translated into three overarching goals that directly motivate the research and innovations of this dissertation. The first goal is to investigate and employ techniques that enable evolutionary algorithms to effectively handle constraints in a way that allows for feasible solutions to constrained optimization problems. The second goal is to improve computation times and efficiencies associated with evolutionary algorithms. The last goal is to enhance the evolutionary algorithms robustness and ability to consistently find accurate solutions within a finite number of iterations. Novel techniques involving the application of unscented sampling, parallel computation, and various forms of exact penalty functions are developed and applied to both genetic algorithms and evolution strategies to achieve these goals. The results of this research offer a promising new set of modified evolutionary algorithms that outperform state-of-the-art techniques on a number of challenging multimodal optimization problems. In addition, these new methods are shown to be very effective in solving a minimum-propellant lunar lander optimal control problem, representing a class of problems that are historically difficult to solve using evolutionary algorithms.


Descriptors :   parallel computing , evolutionary algorithms , computational science , operations research , control systems , optimization


Subject Categories : Computer Programming and Software
      Operations Research


Distribution Statement : APPROVED FOR PUBLIC RELEASE