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A Multi QoS Genetic-based Adaptive Routing in Wireless Mesh Networks with Pareto Solutions

机译:具有Pareto解决方案的无线Mesh网络中基于多QoS遗传算法的自适应路由

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Wireless Mesh Networks(WMN) is an active research topic for wireless networks designers and researchers. Routing has been studied in the last two decades in the field of optimization due to various applications in WMN. In this paper, Adaptive Genetic Algorithm (AGA) for identifying the shortest path in WMN satisfying multi- QoS measure is introduced. The proposed algorithm is adaptive in the sense that it uses various selection methods during the reproduction process and the one with the best multi- QoS measure is adopted in that generation. The multi-objective QoS measure defined as the combination of the minimum number of hops, minimum delay, and maximum bandwidth. The multi-objective optimization has been formulated and solved using weighted sum approach with Pareto optimal solution techniques. The simulation experiments have been carried out in MATLAB environment with a wireless network modeled as weighted graph of fifty nodes and node coverage equals to 200 meter, and the outcomes demonstrated that the proposed AGA performs well and finds the shortest route of the WMN proficiently, rapidly, and adapts to the dynamic nature of the wireless network and satisfying all of the constraints and objective measures imposed on the networks.
机译:无线网状网络(WMN)是无线网络设计人员和研究人员的活跃研究主题。由于WMN中的各种应用,最近二十年来,已经在优化领域研究了路由。本文介绍了一种自适应遗传算法(AGA),用于识别WMN中满足多QoS措施的最短路径。从某种意义上说,所提出的算法是自适应的,因为它在再现过程中使用了多种选择方法,并且在该代中采用了具有最佳多QoS措施的一种。多目标QoS度量定义为最小跳数,最小延迟和最大带宽的组合。使用帕累托最优解技术,采用加权和的方法制定并解决了多目标优化问题。仿真实验是在MATLAB环境下进行的,无线网络建模为50个节点的加权图,节点覆盖范围等于200米,结果表明所提出的AGA性能良好,能够快速,有效地找到WMN的最短路径,并适应无线网络的动态特性,并满足对网络施加的所有约束和客观措施。

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