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A Novel Genetic Algorithm for Topology Aggregation in Multidomain Optical Networks

机译:多域光网络拓扑聚集的新型遗传算法

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In a optical multidomain network, each domain needs to advertise its topology information to the other domains. Topology aggregation is an effective means to reduce the amount of the advertised topology information for each domain in large scale networks. The accuracy of aggregating the topology states is improved by using the approximation curves with limited amount of advertising topology information. Polyline approximation has the best performance for all approximation methods. Based on study of the polyline approximation methods and aimed at minimizing the approximation distortion, this paper proposes a genetic algorithm to resolve the polyline approximation curve, and addresses some key issues, including the coding, initial population production, fitness, crossover and mutation within such an algorithm. Simulations show that the approximation curve distortion of proposed algorithm is least comparing all other approximation curve methods.
机译:在光学多源网络中,每个域需要将其拓扑信息通告给其他域。拓扑聚合是减少大规模网络中每个域的广告拓扑信息量的有效手段。通过使用具有有限的广告拓扑信息信息的近似曲线来改善聚合拓扑状态的准确性。折线近似对所有近似方法具有最佳性能。基于对折线近似方法的研究,旨在最小化近似失真,提出了一种遗传算法来解决折线近似曲线,并解决一些关键问题,包括编码,初始群体生产,健身,交叉和突变一种算法。仿真表明,所提出的算法的近似曲线失真是比较所有其他近似曲线方法的最小比较。

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