SIMULATED ANNEALING BASED POLYNOMIAL TIME QOS ROUTING ALGORITHM FOR MANETS

Multi-constrained Quality-of-Service (QoS) routing is a big challenge for Mobile Ad hoc Networks (MANETs) where the topology may change constantly. In this paper a novel QoS Routing Algorithm based on Simulated Annealing (SA_RA) is proposed. This algorithm first uses an energy function to translate multiple QoS weights into a single mixed metric and then seeks to find a feasible path by simulated annealing. The pa- per outlines simulated annealing algorithm and analyzes the problems met when we apply it to Qos Routing (QoSR) in MANETs. Theoretical analysis and experiment results demonstrate that the proposed method is an effective approximation algorithms showing better performance than the other pertinent algorithm in seeking the (approximate) optimal configuration within a period of polynomial time.

Ship Weather Routing Based on Hybrid Genetic Algorithm Under Complicated Sea Conditions

Considering the effects of increased economic globalization and global warming,developing methods for reducing shipping costs and greenhouse gas emissions in ocean transportation has become crucial.Owing to its key role in modern navigation technology,ship weather routing is the research focus of several scholars in this field.This study presents a hybrid genetic algorithm for the design of an optimal ship route for safe transoceanic navigation under complicated sea conditions.On the basis of the basic genetic algorithm,simulated annealing algorithm is introduced to enhance its local search ability and avoid premature convergence,with the ship’s voyage time and fuel consumption as optimization goals.Then,a mathematical model of ship weather routing is developed based on the grid system.A measure of fitness calibration is proposed,which can change the selection pressure of the algorithm as the population evolves.In addition,a hybrid crossover operator is proposed to enhance the ability to find the optimal solution and accelerate the convergence speed of the algorithm.Finally,a multi-population technique is applied to improve the robustness of the algorithm using different evolutionary strategies.

QoS multicast routing algorithm based on GA

The multicast routing problem with multiple QoS constraints in networks with uncertain parameters is discussed, and a network model that is suitable to research such QoS multicast routing problem is described. The QMRGA, a multicast routing policy for Internet, mobile network or other highperformance networks is mainly presented, which is based on the genetic algorithm(GA), and can provide QoSsensitive paths in a scalable and flexible way in the network environment with uncertain parameters. The QMRGA can also optimize the network resources such as bandwidth and delay, and can converge to the optimal or nearoptimal solution within few iterations, even for the network environment with uncertain parameters. The incremental rate of computational cost can be close to a polynomial and is less than exponential rate. The performance measures of the QMRGA are evaluated by using simulations. The results show that QMRGA provides an available approach to QoS multicast routing in network environment with uncertain parameters.

Multicast Routing Based on Hybrid Genetic Algorithm

A new multicast routing algorithm based on the hybrid genetic algorithm (HGA) is proposed. The coding pattern based on the number of routing paths is used. A fitness function that is computed easily and makes algorithm quickly convergent is proposed. A new approach that defines the HGA's parameters is provided. The simulation shows that the approach can increase largely the convergent ratio, and the fitting values of the parameters of this algorithm are different from that of the original algorithms. The optimal mutation probability of HGA equals 0.50 in HGA in the experiment, but that equals 0.07 in SGA. It has been concluded that the population size has a significant influence on the HGA's convergent ratio when it's mutation probability is bigger. The algorithm with a small population size has a high average convergent rate. The population size has little influence on HGA with the lower mutation probability.

QOS-BASED MULTICAST ROUTING OPTIMIZATION ALGORITHMS FOR INTERNET

Most of the multimedia applications require strict Quality-of-Service (QoS) guarantee during the communication between a single source and multiple destinations. The paper mainly presents a QoS Multicast Routing algorithms based on Genetic Algorithm (QMRGA). Simulation results demonstrate that the algorithm is capable of discovering a set of QoS-based near optimized, non-dominated multicast routes within a few iterations, even for the networks environment with uncertain parameters.

Two-stage evolutionary algorithm for dynamic multicast routing in mesh network

In order to share multimedia transmissions in mesh networks and optimize the utilization of network resources, this paper presents a Two-stage Evolutionary Algorithm (TEA), i.e., unicast routing evolution and multicast path composition, for dynamic multicast routing. The TEA uses a novel link-duplicate-degree encoding, which can encode a multicast path in the link-duplicate-degree and decode the path as a link vector easily. A dynamic algorithm for adding nodes to or removing nodes from a multicast group and a repairing algorithm are also covered in this paper. As the TEA is based on global evaluation, the quality of the multicast path remains stabilized without degradation when multicast members change over time. Therefore, it is not necessary to rearrange the multicast path during the life cycle of the multicast sessions. Simulation results show that the TEA is efficient and convergent.

基于遗传-蚁群优化算法的QoS组播路由算法设计

为了提高网络路由性能,提出并设计了一种基于遗传-蚁群优化算法的服务质量(quality of service,QoS)组播路由算法。首先,设计了自适应变频采集策略用于采集网络与节点信息,以此获得网络和节点的状态,为后续路由优化提供数据支持;其次,计算路径代价,将路径代价最小作为优化目标,建立QoS组播路由优化模型,并设置相关约束条件;最后,结合遗传算法和蚁群算法提出一种遗传-蚁群优化算法求解上述模型,输出最优路径,完成路由优化。实验结果表明,所提算法可有效降低路径长度与路径代价,提高搜索效率与路由请求成功率,优化后的路由时延抖动较小。

遗传算法与带权搜索融合的QoS组播路由算法

针对传统多约束路由选择算法计算负担重的问题,论文提出了一种基于遗传算法与带权宽度优先搜索融合的QoS组播路由算法.所提方法融合了遗传算法与带权宽度优先搜索方法,在分组丢包率、带宽、时延抖动、时延等QoS条件约束下,通过所提算法快速得到备选路径,并获取最优的组播路径.在仿真实验中,将所提算法与LDT进行了相比,实验结果表明,论文所提算法可以解决多约束条件下的QoS分组路由问题,并能够有效降低计算负载、减少算法执行时间.

下一代互联网中的智能QoS组播路由算法

将遗传算法、模拟退火算法、禁忌搜索算法这3种智能优化算法引入基于QoS的优化组播树计算中·首先建立了支持QoS组播的高速网络模型,在此基础上给出了组播算法问题的数学模型·分析了在组播算法问题中应用这3种智能算法所面临的关键问题及解决方案,然后给出了3种算法在费用寻优和运行时间性能方面的对比·仿真表明,这3种算法都有较高的性能,能够满足下一代互联网支持QoS组播的需求·

Ad Hoc网络中基于模拟退火-蚁群算法的QoS路由发现方法

针对Ad Hoc网络的动态网络环境和链路、节点性能限制等不利因素,提出了一种新的QoS路由发现方法———SAANT.该方法利用蚁群算法增加了发现可用QoS路由的概率,利用基于概率的路由转发策略来减少洪泛造成的网络开销,从而强化所提算法的全局搜索能力和自适应性,减小了洪泛对Ad Hoc网络性能的影响.所提方法还利用模拟退火算法调整路由发现算法的搜索方向,以弥补蚂蚁算法收敛速度上的弱点,减少了搜索过程中的停滞现象.在包投递成功率、平均包延迟和吞吐量等方面,通过仿真实验对SAANT、仅基于蚁群算法的QoS路由算法和传统的按需路由算法的方法进行了性能比较,结果表明,在Ad Hoc网络环境下,SAANT的收敛速度、移动性能和网络负载性能均表现出更好的适应性.

基于改进遗传算法的QoS路由优化

针对多约束QoS路由问题,将其转化为一个多约束赋权图最优路径问题,选择费用、带宽、时延和丢包率为参数。采用基于遗传算法的QoS路由选择算法,算法采用网络消耗和负载分布平衡为目标函数,目的在使用最小网络资源的基础上,使网络达到负载平衡,降低网络拥塞,降低丢包率,提高服务质量。仿真试验结果表明,该算法具有较好的性能。

基于蚁群遗传混合算法的QoS组播路由

具有延迟、延迟抖动、带宽、丢包率等服务质量约束的组播路由问题具有NP完全的复杂度。基于蚁群优化算法和遗传算法,提出解决QoS约束组播路由问题的混合算法。利用遗传算法和蚁群优化算法各自的优点,使用蚁群优化算法选择种群,遗传算法优化蚂蚁遍历所得到的解。仿真实验结果表明,该算法可满足各个约束条件,且全局寻优性能好,能够满足网络服务质量要求。

基于混合遗传算法的QoS多播路由算法

具有多QoS约束的多播路由问题具有NP完全的复杂度。基于延时、延时抖动、带宽、丢包率等QoS约束,描述了一种适应于研究QoS多播路由的网络模型,提出了基于遗传算法和禁忌搜索混合策略的具有多QoS约束的多播路由算法。该算法充分利用了遗传算法和禁忌搜索的优点,克服了遗传算法在求解多QoS约束多播路由问题中的爬山能力差以及不成熟收敛等问题。仿真实验结果表明,该算法为多QoS约束多播路由问题的求解提供了一种有效的新途径。

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.

融合小生境机制的QoS多播路由遗传模拟退火算法

针对通信网络中多重QoS约束条件下的多播路由计算,提出了一个基于模拟退火技术的改进遗传算法HGA-QoSR。该算法把模拟退火技术的局部寻优能力与遗传算法的全局寻优能力有机结合,并利用隔离小生境机制控制种群的独立进化,使演化过程中的种群保持生态多样性,以提高算法运行效率和解的质量。理论分析和仿真实验表明,与传统遗传算法相比较,该算法性能有显著改进。

基于遗传模拟退火算法的QoS组播路由算法

提出了一种基于遗传模拟退火算法的带宽、时延和时延抖动约束费用最小的组播路由选择方法,该方法针对遗传算法的局限性,采用基于备选路径集的整数队列编码机制,对适应度函数进行了调整,改进了交叉和变异操作,结合了模拟退火算法。实验表明,该算法能够有效地提高收敛速度、避免早熟收敛、满足多媒体网络对相应QoS的需求。

一种基于遗传算法的多约束QoS多播路由优化算法

随着Internet、移动网络和高性能网络的不断发展,在网络和不确定参数下具有多约束QoS多播路由优化技术已成为网络及分布式系统领域的一个重要研究课题,这也是下一代Internet和高性能网络的难题。它吸引了许多爱好者。论文研讨了具有多约束QoS多播路由问题,其中主要包含延迟、延迟抖动、带宽、分组丢失率等QoS约束,文中描述了一种适应于研究QoS多播路由的网络模型。论文在网络环境及不确定参数下,提出了一种在网络规模、可行性方面优化Internet、移动网络和其他高性能网络的基于遗传算法的多约束QoS多播路由优化算法(MQROGA)。MQROGA在网络环境及不确定参数下能够优化网络资源,如带宽、延迟、分组丢失率等得到一个最优解和次优解。仿真实验结果表明,MQROGA为QoS多播路由提供了一种新的有效途径。

基于遗传模拟退火算法的多约束QOS组播路由优化算法

组播路由问题在计算机网络中是著名的Steiner树问题,是NP完全问题。通过考虑组播通信服务质量需求与网络资源约束,研究了基于服务质量的组播路由选择算法问题,首次提出了一个基于遗传算法和模拟退火算法的多约束组播路由优化算法,该算法在满足带宽、延时、延时抖动及包丢失率约束条件下寻找代价最小的组播树。

Web服务组合QoS优化中的改进遗传算法

结合模拟退火算法与传统遗传算法,提出一种应用于Web服务组合质量优化的改进遗传算法。在选择算子和变异算子的筛选过程中引入模拟退火算法选择更优解的思想,并在算法选择和变异过程中通过设置过滤劣质基因的概率以及逐渐增加变异比率,保证算法种群的多样性。实验结果表明,与传统遗传算法、模拟退火算法、粒子群优化算法等相比,改进算法的收敛速度更快,并且获取的Web服务组合质量更高。