求解带时延约束组播路由问题的启发式遗传算法

组播是一点到多点的信息传送方式。在视频会议等实时性要求较高的组播应用中，保证信息的传输时延尽可能小非常重要。有时延约束的组播路由问题已被证明是NP－Complete问题。研究带时延约束的组播路由问题，给出了一种遗传算法以求解该问题，算法采用了适合问题特点的遗传算子，能够较好地解决时延受限组播路由问题。     

基于遗传算法的带时延约束组播路由算法

探讨了带时延约束组播路由优化算法,选用时延约束信息产生备选路径集并编码,给出了在该编码方式下使用不同进化阶段应用不同变异概率思想的改进遗传算法.仿真试验结果表明,该算法是可行有效的.     

基于量子粒子群算法的时延受限组播路由问题研究

对具有延时约束的最小代价的组播路由问题进行研究,提出一种收敛速度快、全局性能好、不易陷入局部最优的智能迭代算法-量子粒子群算法来实现该问题的求解.该算法采用整数编码方式,将路由优化问题转化成准连续优化,并采用惩罚函数处理约束条件.最后通过具体算例,对该算法进行了仿真验证,结果表明,在求解延时约束的组播路由问题时,量子粒子群算法要优于遗传算法、克隆算法,从而验证了该算法的可行性和有效性.     

基于遗传算法的时延受限组播路由优化

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，并利用模式定理说明利用遗传算法解决时限受限的组播路由的难易。实验比较表明，该算法操作简单、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。     

基于动态变异遗传算法的组播路由算法

具有时延约束的组播路由问题已被证明是NP-完全问题。论文提出了一种基于动态变异遗传算法的组播路由算法,用来解决带时延约束的组播路由问题。通过计算机仿真分析和与同类算法的比较,此算法收敛速度快,不易陷入早熟,具有很强的鲁棒性和实用性。     

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.     

时延约束动态不重组组播路由优化

针对时延约束的组播路由问题,提出了一种动态不重组组播路由算法NDMADC。算法将DGA和Floyd最短路径优化算法相结合,确保节点在满足时延约束的前提下动态选择到组播树有最小代价的路径加入组播会话。由于采用贪心算法思想,NDMADC算法保证了节点加入组播树时不需要组播树重组。仿真表明,该算法能正确地构造出满足时延约束的组播树,具有较低的代价和计算复杂度。     

一种有时延约束的动态组播路由算法

贪婪算法是一种动态组播路由算法,该算法获得的通信树时延较大。文章在改进的多标号算法基础上,提出了一种有时延约束的动态组播路由算法,可以获得满足时延约束的通信树,仿真显示该算法可以获得问题的次优解。     

并行量子遗传算法在QoS组播路由中的应用

随着网络通信技术的发展和Internet的普及,性能出色的组播路由越来越重要。著名的组播路由Steiner树问题是NP完全问题,应采用启发式方法求解。文中在常规量子遗传算法中引入并行进化模型,提出了一种解决多约束QoS组播路由优化问题的算法。在满足带宽、时延约束条件下寻找代价最小的组播树,并合理安排节点负荷,减少通信开销。仿真实验结果表明本算法搜索速度快、全局寻优能力强,性能和效率优于常规量子遗传算法。     

一种满足时延和时延差约束的组播路由算法

针对时延和时延差约束的组播路由优化问题,提出一种最优代价组播路由算法。基于Dijkstra最短路径树算法,通过指示函数调整新加入节点的优先级,利用局部信息构建低代价组播树,使其能较好地平衡组播树代价、时延和时延差之间的关系。仿真实验结果表明,该算法能正确构造出满足时延和时延差约束的组播树,同时具有时间复杂度低、求解成功率高等综合性能。     

一种求解QoS约束组播路由问题的遗传算法*

针对多维QoS约束的组播路由问题,提出了一种基于遗传算法的解决方案QCMRA-GA(GA of QoS Constraints Multicast Routing Algorithm).该算法对经典遗传算法的三大算子进行了重新设计,有效地克服了遗传算法的早熟现象.对染色体进行Prufer树型编码,可以避免回路的产生,并根据编码特性,进行基于叶子节点和Steiner节点的解空间压缩,提高了算法的收敛速度.实验表明QCMRA-GA的正确性和效率性.     

新的群播路由算法

考虑了费用非对称通信网络上的群播路由问题。首先提出了一种满足带宽约束接近最小成本的启发式算法NEW1-GM算法。该算法以FMPH算法(FastMinimumPathCostHeuristicAlgorithm)为基础,可以有效地降低成本。数值实验表明,这种算法是有效的,且所获得解的总费用几乎总是小于或等于由GTM算法所获得的解的总费用。然后,提出了一种满足延迟约束的DFMPH算法(Delay-ConstrainedFastMinimumPathCostHeuristicAlgorithm)。最后,在NEW1-GM算法和DFMPH算法的基础上提出了一种不仅满足延迟和带宽要求且接近最小成本的启发式算法DGM1算法。NEW1-GM算法和DGM1算法的时间复杂度均与GTM算法相同,为O(p3n2)。     

An Adaptive Bumble Bees Mating Optimization algorithm

The finding of the suitable parameters of an evolutionary algorithm, as the Bumble Bees Mating Optimization (BBMO) algorithm, is one of the most challenging tasks that a researcher has to deal with. One of the most common used ways to solve the problem is the trial and error procedure. In the recent few years, a number of adaptive versions of every evolutionary and nature inspired algorithm have been presented in order to avoid the use of a predefined set of parameters for all instances of the studied problem. In this paper, an adaptive version of the BBMO algorithm is proposed, where initially random values are given to each one of the parameters and, then, these parameters are adapted during the optimization process. The proposed Adaptive BBMO algorithm is used for the solution of the Multicast Routing Problem (MRP). As we would like to prove that the proposed algorithm is suitable for solving different kinds of combinatorial optimization problems we test the algorithm, also, in the Probabilistic Traveling Salesman Problem (PTSP) and in the Hierarchical Permutation Flowshop Scheduling Problem (HPFSP). Finally, the algorithm is tested in four classic benchmark functions for global optimization problems (Rosenbrock, Sphere, Rastrigin and Griewank) in order to prove the generality of the procedure. A number of benchmark instances for all problems are tested using the proposed algorithm in order to prove its effectiveness.     

Lagrangian relaxation for maximum service in multicast routing with QoS constraints

In this paper, we propose a new variant of the Multicast Routing Problem called Maximum Service in Multicast Routing with Quality of Service constraints applied in the context of vehicular ad hoc networks, for which data must be sent from a root node to a set of terminal nodes. The use of all nodes is not mandatory and each connection between the root and a terminal aims to satisfy the quality of service according to the limits established for each metric. The objective is to maximize the number of serviced terminals according to the network's quality of service metrics. We present an integer programming formulation and four Lagrangian relaxations, to obtain good primal and dual bounds. We also develop a local search applied during the resolution of the Lagrangian relaxations. These methodologies were subjected to computational experiments with a set of 40 instances generated with characteristics of vehicular ad hoc networks. Statistical analyses were performed to compare the performance between methodologies, where the model achieved optimal values for 29 instances, and the Lagrangian relaxations rendered competitive bounds, especially for large instances.     

时延及时延抖动限制的最小代价多播路由策略

满足多种服务质量请求的多播路由问题是目前多播通信中的重要课题之一。该文作者在研究受端到端时延及时延抖动限制的多播路由问题的过程中，发现当前许多算法所普遍使用的两个最佳链路选择函数并不能完全体现路由的动态过程，同时它们还存在一定的缺陷。而正是由于这种缺陷，在某些情况下通过这两个最佳链路选择函数所得到的结果树可能不包含所有的目标节点，文中称这种情况为“多播不可达”。针对上述问题，该文提出了“多播可达”的假设条件以及一个新的最佳链路选择函数，并在此基础上提出了一个满足时延及时延抖动双重限制的最小代价多播树的建立算法（DDVBMRA）以及一种动态重组多播组目标节点的方法。仿真结果表明本算法具有很好的延抖动及代价性能。     

基于梯森分割的城市物流配送路线优化选择

物流配送车辆路径优化问题是一个典型的NP难题,也是近年来物流研究中的一个热点。文章利用先分组再排路线的思想,把城市零售商品物流配送车辆路径优化问题分解成一个分派问题和一个类似旅行商问题(TravelingSalesm an Problem,TSP)。应用空间分析中的梯森分割(Thiessen Tessellation)理论解决分派问题,同时改进用于求解TSP问题的插队算法,将其应用于对车辆巡回路线寻优问题的求解,最后,对此算法进行了应用举例。     

A lower bound for the Node,Edge, and Arc Routing Problem

The Node, Edge, and Arc Routing Problem (NEARP) was defined by Prins and Bouchenoua in 2004, although similar problems have been studied before. This problem, also called the Mixed Capacitated General Routing Problem (MCGRP), generalizes the classical Capacitated Vehicle Routing Problem (CVRP), the Capacitated Arc Routing Problem (CARP), and the General Routing Problem. It captures important aspects of real-life routing problems that were not adequately modeled in previous Vehicle Routing Problem (VRP) variants. The authors also proposed a memetic algorithm procedure and defined a set of test instances called the CBMix benchmark. The NEARP definition and investigation contribute to the development of rich VRPs. In this paper we present the first lower bound procedure for the NEARP. It is a further development of lower bounds for the CARP. We also define two novel sets of test instances to complement the CBMix benchmark. The first is based on well-known CARP instances; the second consists of real life cases of newspaper delivery routing. We provide numerical results in the form of lower and best known upper bounds for all instances of all three benchmarks. For three of the instances, the gap between the upper and lower bound is closed. The average gap is 25.1%. As the lower bound procedure is based on a high quality lower bound procedure for the CARP, and there has been limited work on approximate solution methods for the NEARP, we suspect that a main reason for the rather large gaps is the quality of the upper bound. This fact, and the high industrial relevance of the NEARP, should motivate more research on approximate and exact methods for this important problem.     

An adaptive local search algorithm for vehicle routing problem with simultaneous and mixed pickups and deliveries

The Vehicle Routing Problem with Simultaneous Pickup and Delivery (VRPSPD) is an extension to the classical Vehicle Routing Problem (VRP), where customers may both receive and send goods simultaneously. The Vehicle Routing Problem with Mixed Pickup and Delivery (VRPMPD) differs from the VRPSPD in that the customers may have either pickup or delivery demand. However, the solution approaches proposed for the VRPSPD can be directly applied to the VRPMPD. In this study, an adaptive local search solution approach is developed for both the VRPSPD and the VRPMPD, which hybridizes a Simulated Annealing inspired algorithm with Variable Neighborhood Descent. The algorithm uses an adaptive threshold function that makes the algorithm self-tuning. The proposed approach is tested on well-known VRPSPD and VRPMPD benchmark instances derived from the literature. The computational results indicate that the proposed algorithm is effective in solving the problems in reasonable computation time.     

A hybrid clonal selection algorithm for the location routing problem with stochastic demands

In this paper, a new formulation of the Location Routing Problem with Stochastic Demands is presented. The problem is treated as a two phase problem where in the first phase it is determined which depots will be opened and which customers will be assigned to them while in the second phase, for each of the open depots a Vehicle Routing Problem with Stochastic Demands is solved. For the solution of the problem a Hybrid Clonal Selection Algorithm is applied, where, in the two basic phases of the Clonal Selection Algorithm, a Variable Neighborhood Search algorithm and an Iterated Local Search algorithm respectively have been utilized. As there are no benchmark instances in the literature for this form of the problem, a number of new test instances have been created based on instances of the Capacitated Location Routing Problem. The algorithm is compared with both other variants of the Clonal Selection Algorithm and other evolutionary algorithms.     

位置关联的延迟容忍网络路由算法

提出一种位置关联的延迟容忍网络路由(LRR)算法。引入位置信息,将动态的节点间路由转化为静止的位置间路由,利用节点访问位置的概率信息计算并选择中继位置,无需获取全局的节点间概率信息。实验结果表明,与现有的延迟容忍网络路由算法相比,LRR算法具有较高的传输成功率和较小的平均传输延迟。