Coevolutionary Algorithm Applied to Skip Reentry Trajectory Optimization Design
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 a...
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Published in | Applied Mechanics and Materials Vol. 427-429; no. Mechanical Engineering, Industrial Electronics and Information Technology Applications in Industry; pp. 1424 - 1431 |
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Main Authors | , |
Format | Journal Article |
Language | English |
Published |
Zurich
Trans Tech Publications Ltd
01.09.2013
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Subjects | |
Online Access | Get full text |
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Summary: | 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. |
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Bibliography: | Selected, peer reviewed papers from the 2013 2nd International Conference on Mechanical Engineering, Industrial Electronics and Informatization (MEIEI 2013), September 14-15, 2013, Chongqing, China ObjectType-Article-2 SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 23 |
ISBN: | 9783037858905 3037858907 |
ISSN: | 1660-9336 1662-7482 1662-7482 |
DOI: | 10.4028/www.scientific.net/AMM.427-429.1424 |