Autonomous interplanetary trajectory planning using structured-chromosome evolutionary algorithms
A main challenge in designing interplanetary trajectories is the fact that the number of design variables varies among different solutions. Global optimization methods that optimize this type of multi-modal objective functions can only handle problems with a fixed number of design variables. This paper presents the Structured-Chromosome Evolutionary Algorithm (SCEA) framework which was 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 non-dependent genes. This structure provides the capability to apply genetic operations between solutions of different lengths and thus to automatically determine the number of swing-bys, the planets to swing-by, 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. The results presented in this paper show that solutions obtained using this tool match known solutions for complex case studies. In addition, a comparative study of genetic algorithms and differential evolution for the same set of problems is contributed. © 2012 by the American Institute of Aeronautics and Astronautics, Inc.
AIAA/AAS Astrodynamics Specialist Conference 2012
Autonomous interplanetary trajectory planning using structured-chromosome evolutionary algorithms.
AIAA/AAS Astrodynamics Specialist Conference 2012.
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