Improving Shared Awareness and QoS Factors in AntNet Algorithm Using Fuzzy Reinforcement and Traffic Sensing

Pooia Lalbakhsh, Bahram Zaeri, M. Fesharaki
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引用次数: 8

Abstract

The paper Describes a novel method to introduce new concepts in functional and conceptual dimensions of routing algorithms in swarm-based communication networks.The method uses a fuzzy reinforcement factor in the learning phase of the system and a dynamic traffic monitor to analyze and control the changing network conditions.The combination of the mentioned approaches not only improves the routing process, it also introduces new ideas to face some of the swarm challenges such as dynamism and uncertainty by fuzzy capabilities. Our strategy is simulated on AntNet routing algorithm to produce the performance evaluation results. The proposed strategy is compared with the Standard AntNet to analyze instantaneous/average throughput and packet delay together with the network awareness capability. The presented results demonstrate the improved performance of our strategy against the standard algorithm.
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利用模糊增强和流量感知改进AntNet算法中的共享感知和QoS因素
本文介绍了一种在基于群的通信网络中引入路由算法功能维度和概念维度新概念的新方法。该方法利用系统学习阶段的模糊强化因子和动态交通监控来分析和控制不断变化的网络状况。上述方法的结合不仅改进了路由过程,而且引入了新的思路来应对模糊能力带来的动态性和不确定性等群体挑战。在蚁网路由算法上进行了仿真,得出了性能评价结果。将该策略与标准蚁网进行比较,分析其瞬时/平均吞吐量、数据包延迟以及网络感知能力。给出的结果表明,我们的策略相对于标准算法的性能有所提高。
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