Structured-chromosome evolutionary algorithms for variable-size autonomous interplanetary trajectory planning optimization

Document Type

Article

Publication Date

1-1-2015

Abstract

Copyright © 2015 by the American Institute of Aeronautics and Astronautics, Inc. All rights reserved. In interplanetary trajectory optimization, events such as planetary gravitational-assist maneuvers (swingbys) and deep-space maneuvers can be added/removed from the trajectory plan to reduce the cost or the flight time. This renders the number of design variables in the optimization problem variable. Global optimization methods that optimize this type of multimodal objective function can only handle problems with a fixed number of design variables. This paper presents the structured-chromosome evolutionary algorithm framework that is developed to handle variable-size design space optimization problems. In this framework, a solution (chromosome) is represented by a hierarchical data structure where the genes in the chromosome are classified as dependent and nondependent genes. This structure provides the capability to apply genetic operations between solutions of different lengths, and thus to automatically determine the number ofswingbys, the planetstoswingby, launch and arrival dates, and the number of deep-space maneuvers, as well as their locations, magnitudes, and directions, in an optimal sense. This new method is applied to several interplanetary trajectory design problems. Results show that solutions obtained using this tool match known solutions for some complex problems. A comparison between genetic algorithms and differential evolution in the structured-chromosome framework is presented.

Publication Title

Journal of Aerospace Information Systems

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