基于混合蚁群算法的对地观测卫星调度

2009 International Conference on Artificial Intelligence and Computational Intelligence Pub Date : 2009-11-07 DOI:10.1109/AICI.2009.87

Haibo Wang, Minqiang Xu, Rixin Wang, Yuqing Li

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引用次数: 22

摘要

为了解决现有蚁群优化算法在处理多卫星调度问题时容易陷入局部最优的缺点，提出了一种混合蚁群优化算法。该方法将蚁群算法作为全局搜索算法。根据蚁群优化算法的特点，提出了一种自适应记忆算法，并将其用于混合蚁群优化算法中解空间的局部搜索。混合算法可以提高求解质量。算例表明该算法是可行的。此外，与蚁群算法相比，混合算法具有更好的全局寻优能力。

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Scheduling Earth Observing Satellites with Hybrid Ant Colony Optimization Algorithm

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.

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2009 International Conference on Artificial Intelligence and Computational Intelligence

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