Evolutionary Constrained Optimization for a Jupiter Capture

This investigation considers the optimization of multiple gravity assist capture trajectories in the Jupiter system combining the well known Differential Evolution algorithm with different classes of constraint handling techniques. The trajectories are designed to reach a desired target orbit around Jupiter with minimum fuel consumption while satisfying mission design constraints on maximum thrust level, maximum time of flight and minimum closest distance to the planet. The advanced constraints handling techniques are compared for different set of constraints on the challenging mission design problem. For each method the trade off between performance, efficiency and the structure of the feasible space is analyzed in light of the results obtained.

[1]  Slawomir J. Nasuto,et al.  Search space pruning and global optimisation of multiple gravity assist spacecraft trajectories , 2007, J. Glob. Optim..

[2]  Sanyou Zeng,et al.  Advances in Computation and Intelligence, Second International Symposium, ISICA 2007, Wuhan, China, September 21-23, 2007, Proceedings , 2007, ISICA.

[3]  Ángel Fernando Kuri Morales,et al.  A UNIVERSAL ECLECTIC GENETIC ALGORITHM FOR CONSTRAINED OPTIMIZATION , 2022 .

[4]  Dario Izzo,et al.  Search for a grand tour of the jupiter galilean moons , 2013, GECCO '13.

[5]  Stefano Campagnola Jupiter Magnetospheric Orbiter trajectory design : reaching high inclination in the Jovian system , 2011 .

[6]  R. Storn,et al.  Differential Evolution - A simple and efficient adaptive scheme for global optimization over continuous spaces , 2004 .

[7]  Carlos Artemio Coello-Coello,et al.  Theoretical and numerical constraint-handling techniques used with evolutionary algorithms: a survey of the state of the art , 2002 .

[8]  Bruce A. Conway,et al.  Automated Mission Planning via Evolutionary Algorithms , 2012 .

[9]  Nikhil Padhye,et al.  Interplanetary trajectory optimization with swing-bys using evolutionary multi-objective optimization , 2007, GECCO '08.

[10]  Rainer Storn,et al.  Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces , 1997, J. Glob. Optim..

[11]  Janez Brest,et al.  Self-Adapting Control Parameters in Differential Evolution: A Comparative Study on Numerical Benchmark Problems , 2006, IEEE Transactions on Evolutionary Computation.

[12]  Carlos A. Coello Coello,et al.  Use of a self-adaptive penalty approach for engineering optimization problems , 2000 .

[13]  Jongsoo Lee,et al.  Constrained genetic search via schema adaptation: An immune network solution , 1996 .

[14]  Ossama Abdelkhalik,et al.  Dynamic-Size Multiple Populations Genetic Algorithm for Multigravity-Assist Trajectory Optimization , 2012 .

[15]  Dario Izzo,et al.  Spacecraft Trajectory Optimization: Global Optimization and Space Pruning for Spacecraft Trajectory Design , 2010 .

[16]  D. Izzo,et al.  Global Optimisation Heuristics and Test Problems for Preliminary Spacecraft Trajectory Design , 2009 .