Celestial Mechanics and Dynamical Astronomy

, 105:211

Cooperative evolutionary algorithm for space trajectory optimization

Original Article

DOI: 10.1007/s10569-009-9223-4

Cite this article as:
Rosa Sentinella, M. & Casalino, L. Celest Mech Dyn Astr (2009) 105: 211. doi:10.1007/s10569-009-9223-4


A hybrid evolutionary algorithm which synergistically exploits differential evolution, genetic algorithms and particle swarm optimization, has been developed and applied to spacecraft trajectory optimization. The cooperative procedure runs the three basic algorithms in parallel, while letting the best individuals migrate to the other populations at prescribed intervals. Rendezvous problems and round-trip Earth–Mars missions have been considered. The results show that the hybrid algorithm has better performance compared to the basic algorithms that are employed. In particular, for the rendezvous problem, a 100% efficiency can be obtained both by differential evolution and the genetic algorithm only when particular strategies and parameter settings are adopted. On the other hand, the hybrid algorithm always attains the global optimum, even though nonoptimal strategies and parameter settings are adopted. Also the number of function evaluations, which must be performed to attain the optimum, is reduced when the hybrid algorithm is used. In the case of Earth–Mars missions, the hybrid algorithm is successfully employed to determine mission opportunities in a large search space.


Trajectory optimizationEvolutionary algorithmsMission planningHybrid algorithmEarth–Mars round-trip mission

Copyright information

© Springer Science+Business Media B.V. 2009

Authors and Affiliations

  1. 1.Diparimento di EnergeticaPolitecnico di TorinoTorinoItaly