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Mathematical Problems in Engineering
Volume 2014, Article ID 823659, 13 pages
http://dx.doi.org/10.1155/2014/823659
Research Article

Asteroid Rendezvous Mission Design Using Multiobjective Particle Swarm Optimization

1College of Aerospace Science and Engineering, National University of Defense Technology, Changsha 410073, China
2Center for National Security and Strategy Studies, National University of Defense Technology, Changsha 410073, China

Received 18 October 2013; Revised 26 January 2014; Accepted 13 February 2014; Published 31 March 2014

Academic Editor: Kui Fu Chen

Copyright © 2014 Ya-zhong Luo and Li-ni Zhou. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

A new preliminary trajectory design method for asteroid rendezvous mission using multiobjective optimization techniques is proposed. This method can overcome the disadvantages of the widely employed Pork-Chop method. The multiobjective integrated launch window and multi-impulse transfer trajectory design model is formulated, which employes minimum-fuel cost and minimum-time transfer as two objective functions. The multiobjective particle swarm optimization (MOPSO) is employed to locate the Pareto solution. The optimization results of two different asteroid mission designs show that the proposed approach can effectively and efficiently demonstrate the relations among the mission characteristic parameters such as launch time, transfer time, propellant cost, and number of maneuvers, which will provide very useful reference for practical asteroid mission design. Compared with the PCP method, the proposed approach is demonstrated to be able to provide much more easily used results, obtain better propellant-optimal solutions, and have much better efficiency. The MOPSO shows a very competitive performance with respect to the NSGA-II and the SPEA-II; besides a proposed boundary constraint optimization strategy is testified to be able to improve its performance.