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Scheduling Earth Observing Satellites with Hybrid Ant Colony Optimization Algorithm

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4 Author(s)
Haibo Wang ; Deep Space Exploration Res. Center, Harbin Inst. of Technol., Harbin, China ; Xu, Minqiang ; Rixin Wang ; Yuqing Li

In order to solve the disadvantage of current ant colony optimization algorithm (ACO) which easily plunged into local optimal in dealing with multi-satellite scheduling problem (MuSSP), a hybrid ant colony optimization algorithm (HACO) is proposed. In this method, the ACO algorithm is served as a global search algorithm. According to the characteristics of the MuSSP, an adaptive memory algorithms is presented, which is used as the local search on the solution space in the hybrid ant colony optimization algorithm. The hybrid algorithm can improve the solution's quality for MuSSP. Several cases showed that the HACO algorithm is feasibility. In addition, compared with ACO, the hybrid algorithm demonstrates that the global optimization ability is better.

Published in:

Artificial Intelligence and Computational Intelligence, 2009. AICI '09. International Conference on  (Volume:2 )

Date of Conference:

7-8 Nov. 2009