Multi-agent genetic algorithm with controllable mutation probability utilizing back propagation neural network for global optimization of trajectory design-Reference-Cited by-同舟云学术

Multi-agent genetic algorithm with controllable mutation probability utilizing back propagation neural network for global optimization of trajectory design

Published:2018-03-21 Issue:1 Volume:51 Page:120-139
ISSN:0305-215X
Container-title:Engineering Optimization
language:en
Short-container-title:Engineering Optimization

Author:

Zuo Mingcheng¹,Dai Guangming¹²,Peng Lei¹²

Affiliation:

1. School of Computer Science, China University of Geosciences, Wuhan, PR China

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

Funder

National Natural Science Foundation of China

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.2018.1443083

Reference43 articles.

1. Autonomous Planning of Multigravity-Assist Trajectories with Deep Space Maneuvers Using a Differential Evolution Approach

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

3. Biscani, F., D. Izzo, and C. H. Yam. 2010. “A Global Optimisation Toolbox for Massively Parallel Engineering Optimisation.” arXiv:1004.3824. https://www.researchgate.net/publication/45913344_A_Global_Optimisation_Toolbox_for_Massively_Parallel_Engineering_Optimisation.

4. Self-Adapting Control Parameters in Differential Evolution: A Comparative Study on Numerical Benchmark Problems

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