Skip to main content

Advertisement

SpringerLink
Log in

Cooperative evolutionary algorithm for space trajectory optimization

  • Original Article
  • Published:
Celestial Mechanics and Dynamical Astronomy Aims and scope Submit manuscript

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  • Bessette C., Spencer D.: Optimal space trajectory design: a heuristic-based approach. Adv. Astronaut. Sci. 124, 1611–1628 (2006)

    Google Scholar 

  • Biesbroek, R.: Study of genetic algorithm settings for trajectory optimisation. In: Paper Presented at the 54th International Astronautical Congress, Bremen, Germany, IAF-03-A.P.30, Sept.–Oct. (2003)

  • Biesbroek, R.: A comparison of differential evolution method with genetic algorithms for orbit optimisation. In: Paper Presented at the 57th International Astronautical Congress, Valencia, Spain, IAF-06-C1.4.02, Oct. (2006)

  • Bramlette M.: Initialization, mutation, and selection methods in genetic algorithms for function optimization. In: Belew, R.K., Booker, L.B.(eds) Proceedings of the Fourth International Conference on Genetic Algorithms, pp. 100–107. Morgan Kaufmann, San Mateo, CA (1991)

    Google Scholar 

  • Casalino L., Colasurdo G., Pastrone D.: Mission opportunities for human exploration of Mars. Planet. Space. Sci. 46(11/12), 1613–1622 (1998)

    Article  ADS  Google Scholar 

  • Colasurdo, G., Pastrone, D.: Indirect optimization method for impulsive transfer. In: Paper Presented at the AIAA/AAS Astrodynamics Conference, Scottsdale, AZ, AIAA 94-3762 (1994)

  • Conway B.A., Chilan C.M., Wall B.J.: Evolutionary principles applied to mission planning problems. celest. Mech. Dyn. Astron. 97(2), 73–86 (2007)

    Article  MATH  MathSciNet  ADS  Google Scholar 

  • Crain T., Bishop R., Fowler W., Rock K.: Interplanetary flyby mission optimization using a hybrid global-local search method. J. Spacecr. Rockets. 37(4), 468–474 (2000)

    Article  ADS  Google Scholar 

  • Dachwald B., Wie B.: Solar sail kinetic energy impactor trajectory optimization for an asteroid-deflection mission. J. Spacecr. Rockets. 44(4), 755–764 (2007)

    Article  ADS  Google Scholar 

  • Di Lizia, P., Radice, G.: Advanced global optimization tools for mission analysis and design. Final report of ESA Ariadna ITT AO4532/ 18139/04/NL/MV, Call 03/4101, ESA (2004)

  • Eberhart, R., Kennedy, J.: A new optimizer using particle swarm theory. In: Sixth International Synposium on Micro Machine and Human Science, pp. 39–43. IEEE, Piscataway, NJ, (1995)

  • Gage P., Braun R., Kroo I.: Interplanetary trajectory optimization using a genetic algorithm. J. Astron. Sci. 43(1), 59–76 (1995)

    Google Scholar 

  • Goldberg D., Deb K.: A comparison of selection schemes used in genetic algorithms. In: Rawlins, G.(eds) Foundations of Genetic Algorithms, vol. 1, pp. 450–457. Morgan Kaufmann, San Francisco, CA (1991)

    Google Scholar 

  • Goldberg D.: Genetic Algorithms in Engineering Design. Wiley, New York, NY (1997)

    Google Scholar 

  • Hartman J., Coverstone-Carroll V., Williams S.: Optimal interplanetary spacecraft trajectories via pareto genetic algorithm. J. Astron. Sci. 46(3), 267–282 (1998)

    Google Scholar 

  • Holland J.: Adaption in Natural and Artificial Systems. University of Michigan Press, Ann Arbor, MI (1975)

    MATH  Google Scholar 

  • Izzo D., Becerra V., Myatt D., Nasuto S., Bishop J.: Search space pruning and global optimization of multiple gravity assist spacecraft trajectories. J. Glob. Optim. 38(2), 283–296 (2007)

    Article  MATH  MathSciNet  Google Scholar 

  • Kennedy, J., Eberhart, R., Particle swarm optimisation. In: Proceedings of the IEEE International Conference on Neural Networks, pp. 1942–1948. IEEE, Piscataway, NJ, (1995)

  • Mitchell M.: Introduction to Genetic Algorithms. MIT Press, Ann Arbor, MI (1996)

    Google Scholar 

  • Myatt, D., Becerra, V., Nasuto, S., Bishop, J.: Advanced global optimization tools for mission analysis and design. Final Report of ESA Ariadna ITT AO4532/18138/04/NL/MV, Call 03/4101, ESA (2004)

  • Olds A., Kluever C., Cupples M.: Interplanetary mission design using differential evolution. J. Spacecr. Rockets. 44(5), 1060–1070 (2007)

    Article  ADS  Google Scholar 

  • Prussing J.E., Chiu J.-H.: Optimal multiple-impulse time-fixed rendezvous between circular orbits. J. Guid. Control Dyn. 9(1), 17–22 (1986)

    Article  MATH  ADS  Google Scholar 

  • Rauwolf G., Coverstone-Carroll V.: Near-optimal low-thrust orbit transfers generated by a genetic algorithm. J. Spacecr. Rockets. 33(6), 859–862 (1996)

    Article  ADS  Google Scholar 

  • Rauwolf G., Coverstone-Carroll V.: Near-optimal low-thrust trajectories via micro-genetic algorithms. J. Guid. Control Dyn. 20(1), 196–198 (1997)

    Article  Google Scholar 

  • Rosa Sentinella, M.: Comparison and integrated use of differential evolution and genetic algorithms for space trajectory optimisation. In: Proceedings of the 2007 IEEE Congress on Evolutionary Computation, pp. 973–978. IEEE Press, Singapore, (2007)

  • Rosa Sentinella, M.: Development of new procedures and hybrid algorithms for space trajectories optimisation. Ph.D. thesis, Politecnico di Torino, Turin, Italy (2008)

  • Rosa Sentinella, M., Casalino, L.: Genetic algorithm and indirect method coupling for low-thrust trajectory optimization. In: Paper Presented at the 42nd AIAA/ASME/SAE/ASEE Joint Propulsion Conference, Sacramento, CA, Paper AIAA 06-4468, June (2006)

  • Rosa Sentinella M., Casalino L.: Hybrid evolutionary algorithm for the optimization of interplanetary trajectories. J. Spacecr. Rockets. 46(2), 365–372 (2009)

    Article  ADS  Google Scholar 

  • Storn, R.: On the Usage of differential evolution for function optimization. In: 1996 Biennial Conference of the North American Fuzzy Information Processing Society, pp. 519–523. NAFIPS, Berkeley, (1996)

  • Storn, R., Price, K.: Differential evolution—a simple and efficient adaptive scheme for global optization over continuos spaces. ICSI TR-95-012, ICSI (1995)

  • Tomassini M.: A survey of genetic algorithm. In: Stauffer, D.(eds) Annual Reviews of Computational Physics, vol. III, pp. 87–118. World Scientific, Singapore (1995)

    Google Scholar 

  • Trelea I.C.: The particle swarm optimization algorithm: convergence analysis and parameter selection. Inf. Process. Lett. 85(6), 317–325 (2003)

    Article  MATH  MathSciNet  Google Scholar 

  • Vasile M., De Pascale P.: Preliminary design of multiple gravity-assist trajectories. J. Spacecr. Rockets. 43(4), 794–805 (2006)

    Article  ADS  Google Scholar 

  • Vinko, T., Izzo, D., Bombardelli, C.: Benchmarking different global optimisation techniques for preliminary space trajectory design. In: Paper Presented at the 58th International Astronautical Congress, Hyderabad, India, IAC-07-A1.3.01, Oct. (2007)

  • Walberg G.: How shall we go to Mars? A review of mission scenarios. J. Spacecr. Rockets. 30(2), 129–139 (1993)

    Article  ADS  Google Scholar 

  • Whitley D., Rana S., Heckendorn R.B.: Exploiting separability in search: the island model genetic algorithm. J. Comput. Inf. Technol. 7(1), 33–47 (1999) (special issue on evolutionary computing)

    Google Scholar 

  • Woo B., Coverstone-Carroll V., Cupples M.: Low-thrust trajectory optimization procedure for gravity-assist, outer-planet missions. J. Spacecr. Rockets. 43(1), 121–129 (2006)

    Article  ADS  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Diparimento di Energetica, Politecnico di Torino, Corso Duca degli Abruzzi, 24, 10129, Torino, Italy

    Matteo Rosa Sentinella & Lorenzo Casalino

Authors
  1. Matteo Rosa Sentinella
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Lorenzo Casalino
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Lorenzo Casalino.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Rosa Sentinella, M., Casalino, L. Cooperative evolutionary algorithm for space trajectory optimization. Celest Mech Dyn Astr 105, 211–227 (2009). https://doi.org/10.1007/s10569-009-9223-4

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10569-009-9223-4

Keywords

Search

Navigation