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首页> 外文期刊>Journal of network and computer applications >Ant colony algorithms in MANETs: A review
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Ant colony algorithms in MANETs: A review

机译：MANET中的蚁群算法：回顾

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摘要

Mobile ad-hoc networks (MANETs) consist of special kind of wireless mobile nodes which form a temporary network without using any infrastructure or centralized administration. MANETs can be used in wide range of future applications as they have the capability to establish networks at anytime, anywhere without aid of any established infrastructure. It is a challenging task to find most efficient routing due to the changing topology and the dynamic behavior of the nodes in MANET. It has been found that ant colony optimization (ACO) algorithms can give better results as they are having characterization of Swarm Intelligence (SI) which is highly suitable for finding the adaptive routing for such type of volatile network. ACO algorithms are inspired by a foraging behavior of group of ants which are able to find optimal path based upon some defined metric which is evaluated during the motion of ants. ACO routing algorithms use simple agents called artificial ants which establish optimum paths between source and destination that communicate indirectly with each other by means of stigmergy. Keeping in view of the above, in this paper we provide a taxonomy of various ant colony algorithms with advantages and disadvantages of each others with respect to various metrics.
机译：移动自组织网络（MANET）由特殊类型的无线移动节点组成，这些节点形成临时网络，而无需使用任何基础结构或集中式管理。由于MANET能够在任何时间，任何地点建立网络，而无需任何已建立的基础设施，因此可以在未来的广泛应用中使用。由于MANET中不断变化的拓扑和节点的动态行为，找到最有效的路由是一项艰巨的任务。已经发现，由于蚁群优化（ACO）算法具有群智能（SI）的特征，因此可以提供更好的结果，该算法非常适合为此类易失性网络找到自适应路由。 ACO算法的灵感来自蚂蚁群的觅食行为，这些行为能够基于在蚂蚁运动过程中评估的某些定义指标来找到最佳路径。 ACO路由算法使用称为人工蚂蚁的简单代理，这些代理在源和目标之间建立了最佳路径，这些路径通过静息能量相互间接通信。鉴于上述情况，在本文中，我们提供了各种蚁群算法的分类法，这些蚁群算法在各种指标方面各有优缺点。

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