A case learning-based differential evolution algorithm for global optimization of interplanetary trajectory design-Reference-Cited by-同舟云学术

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A case learning-based differential evolution algorithm for global optimization of interplanetary trajectory design

Published:2020-09 Issue: Volume:94 Page:106451
ISSN:1568-4946
Container-title:Applied Soft Computing
language:en
Short-container-title:Applied Soft Computing

Author:

Zuo Mingcheng,Dai Guangming^ORCID,Peng Lei,Wang Maocai^ORCID,Liu Zhengquan,Chen Changchun

Funder

Fundamental Research Funds for National Universities

China University of Geosciences

13th Five-year Pre-research Project of Civil Aerospace in China

National Key R&D Program of China

China Postdoctoral Science Foundation

Aeronautical Science Fund

Hubei Natural Science Foundation

Ministry of Education

Publisher

Elsevier BV

Subject

Software

Reference70 articles.

1. Search space pruning and global optimisation of multiple gravity assist spacecraft trajectories;Izzo;J. Global Optim.,2007

2. MGA trajectory planning with an ACO-inspired algorithm;Ceriotti;Acta Astronaut.,2010

3. Machine learning and evolutionary techniques in interplanetary trajectory design;Izzo,2019

4. Spacecraft trajectory optimization: A review of models, objectives, approaches and solutions;Shirazi;Prog. Aerosp. Sci.,2018

5. Multi-objective low-thrust interplanetary trajectory optimization based on generalized logarithmic spirals;Morante;J. Guid. Control Dyn.,2018

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