基于遗传算法的度约束最小生成树问题

Keke Liu, Zhenxiang Chen, A. Abraham, Wenjie Cao, Shan Jing
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引用次数: 8

摘要

随着计算机网络技术的迅猛发展,组播技术已成为Internet研究的热点。组播路由算法的主要目标是在给定网络中寻找最小代价的组播树,也称为斯坦纳树问题,是一个经典的np完全问题。通过对每个节点的程度约束来衡量每个节点的组播能力,并讨论了在程度约束情况下的组播问题,这在通信网络中具有重要意义。在信息传输的复制过程中限制各节点的容量,可以提高网络的速度,在实时服务中具有重要意义。本文研究了基于遗传算法的约束组播路由算法。这个想法是为了模拟达尔文的生物进化理论。同时,我们改进了生成随机树的方法,用两个变量的组合来替换变量。一方面提高了生成随机树的效率,另一方面可以更灵活地控制不同变异的突变。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Degree-constrained minimum spanning tree problem using genetic algorithm
Computer network technology has been growing explosively and the multicast technology has become a hot Internet research topic. The main goal of multicast routing algorithm is seeking a minimum cost multicast tree in a given network, also known as the Steiner tree problem, which is a classical NP-Complete problem. We measure the multicast capability of each node through the degree-constraint for each node and discuss the problem of multicast in the case of degree-constraint, which has an important significance in the communication network. Limiting the capacity of each node during the replication process of information transmission can improve the speed of the network, which has an important significance in real-time service. In this paper, we solve constrained multicast routing algorithm based on genetic algorithm. The idea is to simulate the Darwinian theory of biological evolution. At the same time, we improve the generating random tree and replace the variation by the combination of the two variations. On one hand, we improve the efficiency of generating random tree and on the other hand, we can control the mutation of different variations in a more flexible manner.
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