多跳光波网络拓扑设计的遗传算法

C. Gazen, C. Ersoy
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引用次数: 31

摘要

多跳光波网络是利用光纤大带宽的一种手段。在这些网络中,每个节点都有固定数量的发射器和接收器连接到共同的光学介质。多跳拓扑通过分配不同的波长给发送器和接收器对在逻辑上实现。通过使用可调谐激光器或接收器,可以在节点发生故障或流量负载发生变化时动态修改拓扑结构。逻辑多跳光波网络的可重构性要求找到最优拓扑和流分配。本文研究了遗传算法对这些逻辑拓扑的优化。遗传算法将拓扑图视为种群中的个体,并试图通过交配、变异和淘汰来找到最优的拓扑图。在求解过程中,采用带流偏差的最小跳路由来分配流,并评估拓扑的适应度。该算法在不同的参数集和流量矩阵类型下进行了测试,并与解空间中随机样本的直方图进行了比较。这些测试表明,遗传算法的解与现有的启发式算法的解相当,在某些情况下甚至优于启发式算法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Genetic algorithms for designing multihop lightwave network topologies

Multihop lightwave networks are a means of utilizing the large bandwidth of optical fibers. In these networks, each node has a fixed number of transmitters and receivers connected to a common optical medium. A multihop topology is implemented logically by assigning different wavelengths to pairs of transmitters and receivers. By using tunable lasers or receivers, it is possible to modify the topology dynamically when node failures occur or traffic loads change. The reconfigurability of logical multihop lightwave networks requires that optimal topologies and flow assignments be found. In this article, optimization of these logical topologies by genetic algorithms is investigated. The genetic algorithm takes topologies as individuals of its population, and tries to find optimal ones by mating, mutating and eliminating them. During the evolution of solutions, minimum hop routing with flow deviation is used to assign flows, and evaluate the fitness of topologies. The algorithm is tested with different sets of parameters and types of traffic matrices and the solutions are compared against histograms of random samples from the solution space. These tests show that the solutions found by the genetic algorithm are comparable with and in some cases better than those found by existing heuristic algorithms.

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