Coevolutionary Algorithm Applied to Skip Reentry Trajectory Optimization Design

Abstract:

This paper proposed a coevolutionary algorithm combining improved particle swarm optimization algorithm with differential evolution method and its application was provided. Adaptive position escapable mechanism is introduced in the particle swarm optimization to improve the diversity of population and guarantee to achieve the global optima. The differential algorithm is employed in a cooperative manner to maintain the characteristic of fast convergence speed in the later convergence phase. The coevolutionary algorithm is then applied to skip trajectory optimization design for crew exploration vehicle with low-lift-to-drag and several comparative cases are conducted, Results show that coevolutionary algorithm is quite effective in finding the global optimal solution with great accuracy.

• Periodical: Applied Mechanics and Materials (Volumes 427-429)
• Pages: 1424-1431
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.427-429.1424
• Online since: September 2013
• Authors: Feng Bo Wang*, Chang Hong Dong
• Keywords: Coevolutionary Algorithm, Differential Evolution, Particle Swarm Optimization (PSO), Skip Reentry, Trajectory Optimization

References

[1] K., Graichen, and N., Petit, Constructive Methods for Initialization and Handling Mixed State-Input Constraints in Optimal Control, Journal of Guidance, Control, and Dynamics, Vol. 31, No. 5, 2008, pp.1334-1343.

DOI: 10.2514/1.33870

[2] N., Yokoyama, and S., Suzuki, Modified Genetic Algorithm for Constrained Trajectory Optimization, Journal of Guidance, Control, and Dynamics, Vol. 28, No. 1, 2005, pp.139-144.

DOI: 10.2514/1.3042

[3] K., Premalatha, and A. M., Natarajan, Hybrid PSO and GA for Global Maximization, International Journal of Open Problems in Computer Science and Mathematics, Vol. 2, No. 4, 2009, pp.597-608.

[4] M., Pontani, and B. A., Conway, Particle Swarm Optimization Applied to Space Trajectories, Journal of Guidance, Control, and Dynamics, Vol. 33, No. 5, 2010, pp.1429-1441.

DOI: 10.2514/1.48475

[5] R., Eberhart, and J., Kennedy, A New Optimizer Using Particle Swarm Theory, Proceedings of the Sixth International Symposium on Micromachine and Human Science, Nagoya, Japan, 1995, pp.39-43.

DOI: 10.1109/mhs.1995.494215

[6] J., Kennedy, and R., Eberhart, Particle Swarm Optimization, Proceedings of the IEEE International Conference on Neural Networks, Inst. of Electrical and Electronics Engineers, Piscataway, NJ, 1995, p.1942-(1948).

[7] M., Pontani, and B. A., Conway, Particle Swarm Optimization Applied to Space Trajectories, Journal of Guidance, Control, and Dynamics, Vol. 33, No. 5, 2010, pp.1429-1441.

DOI: 10.2514/1.48475

[8] A., Rahimi, K. D., Kumar, and H., Alighanbari, Particle Swarm Optimization Applied to Spacecraft Reentry Trajectory, International Journal of Open Problems in Computer Science and Mathematics, Vol. 2, No. 4, 2009, pp.597-608.

[9] K., Price, R., Storn, and J., Lampinen, Differential Evolution: A Practical Approach to Global Optimization, Springer, 2005, ISBN 3-540-20950-6.

[10] A. K., Qin, and P. N., Suganthan, Self-adaptive differential evolution algorithm for numerical optimization, in: Proceedings of the 2005 IEEE Congress on Evolutionary Computation, vol. 2, 2005, pp.1785-1791.

DOI: 10.1109/cec.2005.1554904

[11] R. Storn, and K. Price, Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces, Journal of Global Optimization, Vol. 11, No. 4, 1997, pp.341-359.

[12] C. W., Brunner, P., Lu, Skip Entry Trajectory Planning and Guidance, Iowa: Iowa State University.