Enhancing the search ability of a hybrid LSHADE for global optimization of interplanetary trajectory design-Reference-Cited by-同舟云学术

Enhancing the search ability of a hybrid LSHADE for global optimization of interplanetary trajectory design

Published:2022-01-11 Issue:4 Volume:55 Page:632-649
ISSN:0305-215X
Container-title:Engineering Optimization
language:en
Short-container-title:Engineering Optimization

Author:

Tang Zhe¹,Peng Lei¹²,Dai Guangming¹²,Wang Panpan¹,Zhao Yuwei¹,Yang Haozhe¹,Pu Zhuoying¹,Zuo Mingcheng³

Affiliation:

1. School of Computer Science, China University of Geosciences, Wuhan, P.R. China

2. Hubei Key Laboratory of Intelligent Geo-Information Processing, China University of Geosciences, Wuhan, P.R. China

3. Artificial Intelligence Research Institute, China University of Mining and Technology, Xuzhou, P.R. China

Funder

Civil Aerospace Technology Research Project

Open Research Project of the Hubei Key Laboratory of Intelligent Geo-Information Processing

China Postdoctoral Science Foundation

Publisher

Informa UK Limited

Subject

Applied Mathematics,Industrial and Manufacturing Engineering,Management Science and Operations Research,Control and Optimization,Computer Science Applications

Link

https://www.tandfonline.com/doi/pdf/10.1080/0305215X.2021.2019250

Reference49 articles.

1. KEEL: a software tool to assess evolutionary algorithms for data mining problems

2. Ampatzis, Christos, and Dario Izzo. 2009. “Machine Learning Techniques for Approximation of Objective Functions in Trajectory Optimisation.” In Proceedings of the 21st International Joint Conference on Artificial Intelligence (IJCAI) 2009, Workshop on Artificial Intelligence in Space, 1–6. Palo Alto, CA: IJCAI Organization. https://www.researchgate.net/publication/233721625_Machine_Learning_Techniques_for_Approximation_of_Objective_Functions_in_Trajectory_Optimisation.

3. Ensemble sinusoidal differential covariance matrix adaptation with Euclidean neighborhood for solving CEC2017 benchmark problems

4. An ensemble sinusoidal parameter adaptation incorporated with L-SHADE for solving CEC2014 benchmark problems

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