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Search-evasion path planning for submarines using the Artificial Bee Colony algorithm

机译:使用人造蜂菌落算法的潜艇的搜索逃放路径规划

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Submarine search-evasion path planning aims to acquire an evading route for a submarine so as to avoid the detection of hostile anti-submarine searchers such as helicopters, aircraft and surface ships. In this paper, we propose a numerical optimization model of search-evasion path planning for invading submarines. We use the Artificial Bee Colony (ABC) algorithm, which has been confirmed to be competitive compared to many other nature-inspired algorithms, to solve this numerical optimization problem. In this work, several search-evasion cases in the two-dimensional plane have been carefully studied, in which the anti-submarine vehicles are equipped with sensors with circular footprints that allow them to detect invading submarines within certain radii. An invading submarine is assumed to be able to acquire the real-time locations of all the anti-submarine searchers in the combat field. Our simulation results show the efficacy of our proposed dynamic route optimization model for the submarine search-evasion path planning mission.
机译:潜艇搜救路径规划旨在为潜艇获得逃避路线,以避免检测直升机,飞机和表面船等敌对防潜艇搜索者。在本文中,我们提出了一种用于入侵潜艇的搜索逃离路径规划的数值优化模型。我们使用人工蜂殖民地(ABC)算法,与许多其他自然启发算法相比,已经证实竞争力,以解决该数值优化问题。在这项工作中,已经仔细研究了二维平面中的几个搜索逃离案例,其中防潜艇车辆配备有具有圆形脚印的传感器,使它们能够检测某些半径内的入侵潜艇。假设一个入侵潜水艇能够获取战斗领域中所有防潜艇搜索者的实时位置。我们的仿真结果表明,我们提出的潜艇搜救路径规划任务的动态路线优化模型的功效。

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