Continuous space optimized artificial ant colony for real- time typhoon eye tracking
Continuous space optimized artificial ant colony for real- time typhoon eye tracking
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DOI：
10.1109/icsmc.2007.4414231
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发表时间：
2007-10
期刊：
影响因子：
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通讯作者：
Q. P. Zhang;L. L. Lai-L.;Hui Wei
中科院分区：
文献类型：
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作者：
Q. P. Zhang;L. L. Lai-L.;Hui Wei
对于实时台风眼追踪，人工蚁群（AAC）方法已被证明对于高效且有效地识别蛇形轮廓模型边界是有价值的，该模型是为模拟真实的不清晰台风眼的漩涡形状而建立的。卫星数字摄影技术使获取实时气象信息成为可能；通过构建解空间和启发式信息，可以智能地追踪不清晰台风眼的轮廓。然而，实际情况和气象现象非常复杂，仅使用离散能量参数作为启发式信息来引导蚂蚁的智能导向是不够可靠的。在本文中，引入连续空间多核函数来优化启发式信息。为了给能量汇聚过程提供更多实际因素，将给出相应的高斯参数计算方法。相比之下，对于要解决的相同复杂问题，可以减少迭代次数，这证明所提出的优化能够提高原始解决方案的实用性和有效性。
For real-time typhoon eye tracking, artificial ant colony (AAC) methodology has been proved valuable for the efficient & effective identification of snake contour model boundary, which was built to simulate the real unclear typhoon eye whirly shape. While satellite digital photograph technology make it possible to capture real-time meteorological information; by means of constructing solution space and heuristic information, the contour of non-clear typhoon eye can be tracked intelligently. However, the practical conditions and meteorological phenomena are very complicated, only using discrete energy parameters as the heuristic information to lead the intelligent directing of ants are not reliable enough. In this paper, continuous space multi-kernel functions are introduced to optimize the heuristic information. In order to supply more practical factors for the energy converging procedure, corresponding Gaussian parameters calculation method will be given. In comparison, the iteration numbers can be decreased concerning same complexity of the problem to be solved, which proves that proposed optimization could provide the improvement on the practicability and effectiveness of original solutions.