Next Article in Journal
Comparison and Interpretation Methods for Predictive Control of Mechanics
Previous Article in Journal
The Inapproximability of k-DominatingSet for Parameterized AC 0 Circuits
Open AccessArticle

A GA-SA Hybrid Planning Algorithm Combined with Improved Clustering for LEO Observation Satellite Missions

1
School of Aeronautics and Astronautics, Shanghai Jiao Tong University, Shanghai 200240, China
2
School of Aerospace, Mechanical and Mechatronic Engineering, University of Sydney, NSW 2006, Australia
*
Author to whom correspondence should be addressed.
Algorithms 2019, 12(11), 231; https://doi.org/10.3390/a12110231
Received: 9 October 2019 / Revised: 30 October 2019 / Accepted: 1 November 2019 / Published: 4 November 2019
This paper presents a space mission planning tool, which was developed for LEO (Low Earth Orbit) observation satellites. The tool is focused on a two-phase planning strategy with clustering preprocessing and mission planning, where an improved clustering algorithm is applied, and a hybrid algorithm that combines the genetic algorithm with the simulated annealing algorithm (GA–SA) is given and discussed. Experimental simulation studies demonstrate that the GA–SA algorithm with the improved clique partition algorithm based on the graph theory model exhibits higher fitness value and better optimization performance and reliability than the GA or SA algorithms alone. View Full-Text
Keywords: Earth observation satellite; Task clustering; Mission planning; GA-SA hybrid algorithm Earth observation satellite; Task clustering; Mission planning; GA-SA hybrid algorithm
Show Figures

Figure 1

MDPI and ACS Style

Long, X.; Wu, S.; Wu, X.; Huang, Y.; Mu, Z. A GA-SA Hybrid Planning Algorithm Combined with Improved Clustering for LEO Observation Satellite Missions. Algorithms 2019, 12, 231.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop