基于遗传算法优化的QoS组播路由算法

QoS组播路由技术作为网络多媒体信息传输的一种核心技术,下一代网络中的QoS组播路由的优化算法已经成为目前研究的一个热点。针对QoS组播的网络模型,提出一种适用于下一代网络的组播路由算法能够更好的满足下一代网络的QoS组播需求显得尤为重要。本文讨论了遗传算法优化的QoS组播路由算法,最后探讨了下一代网络QoS组播路由算法的研究方向。     

改进遗传算法在QoS组播路由选择中的研究

QoS组播路由的选择问题本质是从源节点至目的节点间找到满足QoS要求的传输路径,尽管现在有许多文献利用遗传算法对组播路由问题做了研究,但其组播路由费用都比较大,执行时间过长,并且编码难度都比较大,优化效率低。针对这些问题,文中提出了一种改进的遗传算法,该算法综合考虑了带宽约束、时延约束、时延抖动约束、丢包率约束以及路由费用等度量指标,在传统遗传算法的基础上,设计了一种基于应用层业务的自适度函数,改进了染色体编码方式,并且设计了自适应交叉函数与自适应变异函数等多种优化函数。通过仿真结果表明,该改进的遗传算法很大程度上降低了QoS组播路由的费用和执行时间,并且能快速地找到最优QoS组播路径。     

带受体编辑的克隆选择组播路由算法

 DE Castro提出的克隆选择算法（CSA）中,抗体的多样性主要通过高频变异来实现,而实际 生物免疫系统中还有一个重要的操作是受体编辑.受此启发,提出了带受体编辑的改进克隆 选择算法.该算法利用未成熟优良子群体提供的优良基因片断,根据路径代价最小化和延时 要求对抗体进行两次受体编辑.这样,在无需求解备选路径集的情况下,直接运用该改进算 法可快速寻到最优解.在时延受限组播路由的仿真实验中表明：该算法比一般CSA算法和遗传 （GA）算法的搜索效率更高,算法复杂度更低.     

基于遗传算法的QoS组播路由算法的改进

QoS组播路由技术在网络技术发展中占有很重要的地位,是一种关键的技术。在分析网络模型的基础上,建立了组播问题的数学模型。遗传算法是一种经典的寻优算法,应用广泛,提出了改进的算法。并且结合改进Dijkstra算法,使算法能很好地体现实际组播路由问题的特点。试验表明,这种新的算法可靠性更高,适应了当今网络性能优化的需要。     

QoS组播路由的启发式遗传算法

本文首先提出带宽延时约束、费用最小QoS组播路由模型,然后提出解决QoS组播路由问题的一种启发式遗传算法,该算法有以下特点:(1)预处理机制;(2)树结构编码;(3)启发式交叉策略;(4)指导性变异过程.最后通过仿真实验证明该算法快速有效.     

基于QoS的动态组播路由算法

在分析了网络中基于QoS的组播路由问题的基础上,本文提出了一种新的动态算法,并进行了实验和分析,文中构造的路由方案成功地解决了当网络中存在多个组播及组播节点动态变化情况下的QoS路由选择问题,此方案不仅保证了带宽,端到端延时和延时抖动,优化了路由树的代价,而且有效地控制了算法的复杂性并可适用于大规模的网络中。     

基于遗传算法的时延受限代价最小组播路由选择方法

目前多媒体业务的涌现对网络提出了更高的要求。希望既能满足实时性，又能够高效地利用网络资源。本文提出了一种基于遗传算法的组播路由选择方法，该方法在满足时延限制条件的基础上寻找代价最小的组播树。实验表明，该算法收敛速度快，可靠性高，能够满足多媒体网络对实时性的要求。     

基于蚁群算法和遗传算法融合的QoS组播路由问题求解

包含延迟、延迟抖动、带宽、丢包率和最小花费等约束条件在内的服务质量(QoS)组播路由问题,是一个NP完备问题,传统方法很难求得全局最优解.本文将遗传算法和蚁群算法融合,提出了用遗传蚁群算法(GAACS)求解QoS组播路由问题的解决方案.仿真实验表明,采用新算法比遗传算法具有更好的性能.     

遗传组播路由算法

本文探讨了基于遗传算法的组播路由算法.遗传算法是一种全局寻优技术,适合于在复杂而庞大的搜索空间中寻找最优解,它原理简单,易于并行,广泛用于许多NP难度求解的领域.仿真表明,该算法稳定、灵活,操作简单.     

基于遗传_蚁群算法的多QoS约束组播路由优化算法

提出了一种将遗传算法与蚁群算法融合的新算法,用以满足多QoS约束的组播路由优化。算法首先利用遗传算法生成若干组优化解,将其转换成蚁群算法的信息素初值,然后利用蚁群算法来求取满足QoS约束的最优解。仿真结果表明此算法是有效的,其性能优于文献[6]中算法。     

Multi-constraints QoS routing optimization based on improved immune clonal shuffled frog leaping algorithm

Aiming at the multi-constraint routing problem,a mathematical model was designed,and an improved immune clonal shuffled frog leaping algorithm (IICSFLA) was proposed,which combined immune operator with traditional SFLA.Under the constraints of bandwidth,delay,packet loss rate,delay jitter and energy cost,total energy cost from the source node to the terminal node was computed.The proposed algorithm was used to find an optimal route with minimum energy cost.In the simulation,the performance of IICSFLA with adaptive genetic algorithm and adaptive ant colony optimization algorithm was compared.Experimental results show that IICSFLA solves the problem of multi-constraints QoS unicast routing optimization.The proposed algorithm avoids local optimum and effectively reduces energy loss of data on the transmission path in comparison with adaptive genetic algorithm and adaptive ant colony optimization algorithm.     

一种多约束QoS多播路由算法

提出了带宽时延约束、代价最小的QoS多播路由模型,并提出了一种启发式算法求解该问题,分析了算法的复杂度。仿真试验证明,该算法是稳定有效的。它能够在满足两种约束的情况下,使多播树的代价优化。     

Ad Hoc网络中一种基于QoS的分布式多播路由算法

通过分析Ad Hoe网络的特点及基于QoS的多播路由问题，提出了一种新的分布式多播路由算法。实验和分析的结果表明，文中构造的路由方案成功地解决了Ad Hoe网中基于QoS的多播路由问题。当Ad Hoe网络的拓扑结构改变不太快时，本文提出的路由方案不仅满足了实时业务对网络带宽和端到端延时的要求，优化了路由树的代价，而且有效地控制了算法的复杂性并可适用于大规模的网络中。     

QoS multicast routing scheme using QGA in IP/DWDM networks

This article studies multi-constraints least-cost multicast routing problem in internet protocol over dense wavelength division multiplexing （IP/DWDM） networks. To address this problem, an individual-difference-based quantum genetic algorithm （IDQGA） is proposed. This algorithm considers individual differences among chromosomes by introducing an adaptive rotation angle step determination scheme and a grouping-based quantum mutation operation. Simulations are conducted over network topologies. The results indicate that compared with other heuristic algorithms, IDQGA has better optimal performance on solving quality of service （QoS） multicast routing problem in IP/DWDM networks and is characterized by strong robustness, high success ratio and excellent capability on global searching.     

求解带度约束多播路由问题的启发式遗传算法

为了能够有效求解带有度约束的多播路由模型，融合启发式算法与遗传算法，利用染色体作为启发信息，设计了一种求解该模型的混合遗传算法。该算法不但避免了直接对树形数据结构编码所带来的困难，而且具有快速收敛的特点和全局寻优的能力。最后，大量的数字仿真从实践上支持了该算法的有效性。     

基于蚂蚁算法的时延受限分布式多播路由研究

本文探讨了在高速包交换计算机网络中,具有端到端时延限制的多播路由问题。提出了一种新颖的基于蚂蚁算法的多播路由优化算法,该算法是完全分布式的。仿真实验表明,用该算法产生的多播路由树的费用比已存在的主要算法更好,并且适应于多播成员数的变化。     

QoS multicast routing in cognitive radio ad hoc networks

In this paper, we discussed the issues of QoS multicast routing in cognitive radio ad hoc networks. The problem of our concern was: given a cognitive radio ad hoc network and a QoS multicast request, how to find a multicast tree so that the total bandwidth consumption of the multicast is minimized while the QoS requirements are met. We proposed two methods to solve it. One is a two‐phase method. In this method, we first employed a minimal spanning tree‐based algorithm to construct a multicast tree and then proposed a slot assignment algorithm to assign timeslots to the tree links such that the bandwidth consumption of the tree is minimized. The other is an integrated method that considers the multicast routing together with the slot assignment. Extensive simulations were conducted to show the performance of our proposed methods. Copyright © 2011 John Wiley & Sons, Ltd.     

Tree-based differential evolution algorithm for QoS multicast routing

Differential evolution(DE)algorithm has attracted more and more attention due to its fast optimization performance and good stability.When DE algorithm is applied into multi-constrained multicast routing optimization problem,a common solution to such problem is to merge the paths into a tree after finding paths from the source node to each destination node.This method maybe obtains the better result,but it can consume a lot of computational time.To solve the problem,a tree-based DE algorithm is introduced in this paper.The central operations of the algorithm are realized with tree structure.This method saves the time of finding paths and integrating them to construct a multicast tree.The experiments show that the proposed algorithm can achieve higher success rate than several common algorithms with much smaller running time for different networks.