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An Evolutionary Approach to Solve Network Route Optimization Problem

机译:解决网络路由优化问题的进化方法

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In all the type of physical networks, optimization is one of the important factors in the minimization of the cost. This paper presents an approach to connect the distributed network locations in the form of single network tree using the concept of genetic algorithm in such a way that total covered distance should be minimum and the connectivity of the node in the given preference should also be maintained. Connectivity of the node can be defined as DCMST problem which comes under the category of NP problems. Inclusion of additional constraints makes it NP hard problem. Approximation algorithms have no optimal solutions so far that is why metaheuristic approach Genetic Algorithm is applied and satisfactory results have been found. Current research has considered the size or network from 10 nodes to 500 nodes of network with fully connected. Population range and generation range has been considered from 10 to 5000 with the genetic operators with the uniform crossover and mutation insertion. This work has shown that population variation with the medium range and minimum number of generation can produce the better results in the comparison of extremely large size population or maximum number of generations. Optimization of the network is a real life worldwide useful problem and it has many applications from the data communication, VLSI design to logistic transition.
机译:在所有类型的物理网络中,优化是最小化成本的重要因素之一。本文提出了一种使用遗传算法的概念连接单个网络树形式的分布式网络位置的方法,即总覆盖距离应最小,并且还应保持给定优先级下节点的连接性。节点的连接性可以定义为DCMST问题,该问题属于NP问题。包含其他约束使其成为NP难题。到目前为止,逼近算法还没有最优解,这就是为什么应用元启发式遗传算法并获得令人满意的结果的原因。当前的研究已经考虑了从10个节点到500个完全连接的网络节点的规模或网络。考虑到具有统一的交叉和突变插入的遗传算子,种群范围和世代范围被认为是10到5000。这项工作表明,在比较极大型种群或最大世代数时,中等范围和最小世代数的种群变化可以产生更好的结果。网络优化是现实生活中在世界范围内有用的问题,它具有从数据通信,VLSI设计到物流过渡的许多应用。

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