Genetic algorithms applied to the solution of hybrid optimal control problems in astrodynamics

Genetic algorithms applied to the solution of hybrid optimal control problems in astrodynamics

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Article ID: iaor200971248
Country: Netherlands
Volume: 44
Issue: 4
Start Page Number: 493
End Page Number: 508
Publication Date: Aug 2009
Journal: Journal of Global Optimization
Authors: ,
Keywords: heuristics: genetic algorithms
Abstract:

Many space mission planning problems may be formulated as hybrid optimal control problems, i.e. problems that include both continuous-valued variables and categorical (binary) variables. There may be thousands to millions of possible solutions; a current practice is to pre-prune the categorical state space to limit the number of possible missions to a number that may be evaluated via total enumeration. Of course this risks pruning away the optimal solution. The method developed here avoids the need for pre-pruning by incorporating a new solution approach using nested genetic algorithms; an outer-loop genetic algorithm that optimizes the categorical variable sequence and an inner-loop genetic algorithm that can use either a shape-based approximation or a Lambert problem solver to quickly locate near-optimal solutions and return the cost to the outer-loop genetic algorithm. This solution technique is tested on three asteroid tour missions of increasing complexity and is shown to yield near-optimal, and possibly optimal, missions in many fewer evaluations than total enumeration would require.

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