基于遗传算法的不确定流量鲁棒网络设计方法

Brenda Diaz-Baez, D. Pinto, C. V. Lücken
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引用次数: 4

摘要

如果网络设计能够在一定的边界和物理网络条件下处理任何流量需求,那么它就是健壮的。鲁棒网络设计是一个日益重要的复杂问题,通常情况下,唯一可用的信息是网络链路的流量边界。本文提出了一种遗传算法来设计具有最优链路容量的鲁棒网络,该网络考虑了可分为k个子路由的不确定流量的稳定路由。使用软管模型来处理不确定流量,该模型为每个网络节点施加最大输入/输出流量。对于一组具有不同路由数的实例的实验结果表明,相对于不可分路由(k = 1),稳定路由更方便。然而,k值的增加意味着可行解的数量的增加,因此,k与算法得到的解的质量之间存在权衡关系。
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Robust network design under uncertain taffic an approach based on Genetic Algorithm
A network design is robust if it is able to deal with any traffic requirement under certain bounds and physical network conditions. The robust network design is a complex problem of growing importance where, in general, the only information available are traffic bounds of the network links. This work proposes a Genetic Algorithm to design robust networks with optimal capacity of links considering a stable routing with uncertain traffic that can be divided in k sub-routes. The uncertain traffic is handled by usign the hose model which imposes a maximum input/output traffic for each network node. Experimental results for a set of instances with different number of routes show the convenience of a stable routing against a non-divisible routing (k = 1). However, an increasing of the k value implies an increasing of the number of viable solutions, thus, a trade-off relation between k and the quality of solutions obtained by the proposed algorithm was detected.
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