Title :
Scheduling Earth Observing Satellites with Hybrid Ant Colony Optimization Algorithm
Author :
Wang, Haibo ; Xu, Minqiang ; Wang, Rixin ; Li, Yuqing
Author_Institution :
Deep Space Exploration Res. Center, Harbin Inst. of Technol., Harbin, China
Abstract :
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.
Keywords :
artificial satellites; optimisation; search problems; Earth Observing Satellite; adaptive memory algorithm; global search algorithm; hybrid ant colony optimization; multisatellite scheduling problem; Ant colony optimization; Earth Observing System; Force control; Genetic algorithms; Processor scheduling; Satellite ground stations; Scheduling algorithm; Simulated annealing; Single machine scheduling; Solid state circuits; ACO; earth observation satellite; hybrid algorithm; scheduling;
Conference_Titel :
Artificial Intelligence and Computational Intelligence, 2009. AICI '09. International Conference on
Conference_Location :
Shanghai
Print_ISBN :
978-1-4244-3835-8
Electronic_ISBN :
978-0-7695-3816-7
DOI :
10.1109/AICI.2009.87
