基于特征点集GABC算法的WSN覆盖优化

针对人工蜂群算法利用网格点计算网络覆盖率会导致计算量大且容易陷入局部最优解的问题,提出一种基于特征点集的全局最优解人工蜂群算法优化无线传感器网络。首先将目标区域划分成有限个特征点,用传感器对特征点的覆盖来转化为对若干特征点的覆盖计算,减少求解覆盖率的计算量,进而描述整个网络的覆盖情况。然后在特征点集的基础上,将全局最优解人工蜂群算法成功应用在网络覆盖领域,并且重点对比标准人工蜂群算法和基于全局最优解人工蜂群算法在网络覆盖上的性能。仿真实验结果表明基于全局最优解人工蜂群算法优化节点覆盖后,覆盖率得到有效的提升且不易陷入局部最优解。     

求解GCP问题的ILSBR算法

图着色问题（GCP,Graph Coloring Problem）是经典的NP-Hard组合优化问题之一。长期以来,人们一直在寻求快速、高效的启发式算法,以便在合理的计算时间内解决大规模问题。由于对规模较大的问题,目前的启发式算法尚不能在较短的时间内给出高质量的解,因此提出了一种基于全局最优解和局部最优解关系的ILS算法（ILSBR）。该算法的基本原理是通过对GCP问题的局部最优解和全局最优解之间关系的分析,发现对局部最优解的简单的相交操作能以很高的概率得到全局最优解的部分解。利用这些部分解构造一种新的扰动策略（RLG重着色）,并将其应用到传统的ILS算法中。在DIMACS标准集中,典型实例上的实验结果表明,采用RBILS算法在求解质量不变的情况下,求解速度上与目前的已知算法相比有较大的改进。     

基于融合算法的测试优化选择问题研究

测试优化选择是个集覆盖问题,而启发式算法是求解集覆盖问题的有效方法。文中将遗传算法、BP神经网络和模拟退火算法进行融合,提出了一种融合算法,该算法充分利用遗传算法全局搜索能力强、BP神经网络训练能力强和模拟退火算法搜索速度快的优点,既避免陷入局部最优的现象,又提高了搜索的效率和精度。该算法已应用于求解测试优化问题。实例证明,该算法能够快速有效地求得测试优化问题的最优解。     

利用遗传算法解决非线性系统优化问题

处理非线性最优化问题的常规方法是采用启发式策略,但这些启发式算法多数只能得到局部最优解。本文结合最优化理论及遗传算法,提出了一种新的优化方法,并通过用例对此方法进行了验证。最终实现系统的全局最优解。     

连续空间优化问题的自适应蚁群系统算法

蚁群算法是进化计算中一种新型优化算法,其基本算法用于求解排序类型的组合优化问题本文提出一种用于连续空间优化问题求解的蚁群算法,采用了新的基于目标函数值的启发式信息素分配算法,以及搜索过程中最优解的筛选方法.根据目标函数来自适应调整蚂蚁的路径搜索行为,从而保证算法快速找到全局最优解.一个多极值点的连续优化问题求解实例证明了该方法的有效性     

车辆路径问题的水波算法

水波算法(Water Wave Optimization WWO)是郑宇军于2014年基于浅水波理论提出的一种新颖的元启发式算法,用于全局优化问题.通过水波的传播、折射和碎浪操作,可以用来导出在高维解决方案空间中搜索的有效机制.算法WWO的框架简单,易于实现,并且只需要少量的控制参数.本文应用WWO求解车辆路径问题(the Capacitated Vehicle Routing Problem CVRP),算法采用0-1矩阵编码方式,通过传播操作进行全局搜索,反射操作实现进化,碎浪操作防止陷入局部最优.利用构建的算法求解64个benchmark算例,求解的结果中有65%的算例获得已知最优解,有6个算例更新了已知最好解,验证水波算法求解车辆路径问题的可行性,为水波算法应用于其他优化问题提供参考.     

一种启发式算法和改进遗传混合算法在流水车间重调度中的应用

在解决以合同惩罚和存储成本最小化为优化目标的流水车间重调度问题时，提出了一种启发式算法和改进的遗传混合算法。传统的遗传算法是一种基于优胜劣汰的随机、自适应的优化算法。通过复制，交叉和变异，将问题解编码所表示的“染色体”群在逐代进化，最终收敛到最合适的群体，从而得到问题的最优或满意解。但缺点是求解结果依赖于初始值，且运行时间过长。因此对传统遗传算法做了相应的改进，考虑到启发式算法的快速性，为充分发挥俩种算法的优势，提出启发式算法和改进遗传混合算法。最后对性能进行分析；试验结果表明：该算法运行时间短，且在大规模数据集下，更易于靠近全局最优解。     

基于启发式变异的蚁群算法

提出一种基于启发式变异的蚁群算法,结合传统蚁群算法和遗传变异算法的优点,利用蚁群算法找到一条全局近优解,采用启发式变异进行路径优化,并将优化信息以信息素的方式传递给下一代,从而快速得到全局最优解。以旅行商问题为例进行仿真实验,结果表明该算法比其他同类算法具有更好的性能。     

求解约束优化问题的中心引力优化算法及其工程应用

为了平衡算法的全局探测能力和局部搜索能力,提出一种基于交叉与变异的中心引力优化算法用于求解约束优化问题。该算法首先利用佳点集方法构造初始种群以保证粒子的多样性。以一定概率随机选择粒子与当前最优粒子进行算术交叉操作,引导粒子向全局最优解靠拢。对当前最优粒子进行多样性变异以避免算法陷入局部最优。标准测试函数和工程优化应用问题的实验结果表明,新算法能有效求解不同的约束优化问题。     

冒泡择优遗传算法

为快速地寻求复杂多峰函数的全局极值点，提出一种冒泡择优遗传算法。它以冒泡的形式让每一代种群中的最优的个体参加列队竞争，以成为局部最优或全局最优。对于达到局部极值的个体，进行湮灭操作，重新进行进化与列队竞争。该算法能自动保持种群多样性且易于实现。实验结果表明，该算法对于求解多峰函数优化的问题十分有效，通常都能找到全部全局最优解。     

参数计算中核心化技术及其应用

在参数计算与复杂性理论中,一个参数问题是固定参数可解的问题当且仅当该问题是可核心化的.核心化技术是参数化算法设计中应用最为广泛、有效的技术,是参数理论中的一个研究热点.通过实例分析对比了最主要的4种核心化技术的基本思想、应用特点和方法,总结了核心化技术在cover类、packing类和cut类等几个重要领域中的应用成果,展望核心化技术的进一步研究方向并加以分析讨论,针对核心化新技术研究和某些热点问题,提出了可能采取的核心优化方法和思路.     

QoS multicast routing using a quantum-behaved particle swarm optimization algorithm

QoS multicast routing in networks is a very important research issue in networks and distributed systems. It is also a challenging and hard problem for high-performance networks of the next generation. Due to its NP-completeness, many heuristic methods have been employed to solve the problem. This paper proposes the modified quantum-behaved particle swarm optimization (QPSO) method for QoS multicast routing. In the proposed method, QoS multicast routing is converted into an integer programming problem with QoS constraints and is solved by the QPSO algorithm combined with loop deletion operation. The QPSO-based routing method, along with the routing algorithms based on particle swarm optimization (PSO) and genetic algorithm (GA), is tested on randomly generated network topologies for the purpose of performance evaluation. The simulation results show the efficiency of the proposed method on QoS the routing problem and its superiority to the methods based on PSO and GA.     

MTU configuration for real-time switched Ethernet networks

In this paper, we show that in real-time switched Ethernet networks reducing the Maximum Transmission Unit (MTU) size may cause an increase or decrease in the response time of messages. This contradicting behavior arises an optimization problem for configuring the MTU size. We formulate the optimization problem in the context of the multi-hop HaRTES architecture, which is a hard real-time Ethernet protocol. As part of the solution, we propose a search-based algorithm to achieve optimum solutions. We modify the algorithm by presenting two techniques to reduce the search space. Then, we propose a heuristic algorithm with a pseudo-polynomial time complexity based on the search-based algorithm. We perform several experiments, and we show that the proposed heuristic results in an improvement regarding messages response times, compared with configuring the MTU to the maximum or minimum values. Moreover, we show in small network configurations that the heuristic performs as good as the search-based algorithm in many cases.     

无线多跳网络中吞吐量优化的多播路由算法研究*

现有网络中提高多播吞吐量的算法通常是以提高链路速率为目的,但单纯地提高链路速率而忽略多播树的度也限制了多播吞吐量的提高。主要研究了多跳无线网络中多播吞吐量最优化问题,深入分析了无线多跳网络特点,并在综合考虑链路速率和多播树度对多播吞吐量影响的基础上,提出了应用于节点发射功率相同环境下的UUP_MTOA算法和应用于节点发射功率不同环境下的UNP_MTOA算法。通过仿真实验与同类近似最优化算法相比,UUP_MTOA算法和UNP_MTOA算法能够获得更高的吞吐量,更适应于多跳无线网络环境。     

一种集成最后一公里的四方物流网络设计问题启发式算法

最后一公里分销网络可以帮助企业达成高响应性的供应链管理目标,集成最后一公里四方物流网络设计问题成为网络设计的一个重要研究方向.解决该问题需要对分销中心的位置,三方物流的选择、分配以及其车辆路径规划进行决策.在满足车辆路径规划、流守恒等约束条件下,以最小化网络构建费用为目标建立混合整数规划模型.由于该问题的NP-难特性,...     

Cluster Editing: Kernelization Based on Edge Cuts

Kernelization algorithms for the cluster editing problem have been a popular topic in the recent research in parameterized computation. Most kernelization algorithms for the problem are based on the concept of critical cliques. In this paper, we present new observations and new techniques for the study of kernelization algorithms for the cluster editing problem. Our techniques are based on the study of the relationship between cluster editing and graph edge-cuts. As an application, we present a simple algorithm that constructs a 2k-vertex kernel for the integral-weighted version of the cluster editing problem. Our result matches the best kernel bound for the unweighted version of the cluster editing problem, and significantly improves the previous best kernel bound for the weighted version of the problem. For the more general real-weighted version of the problem, our techniques lead to a simple kernelization algorithm that constructs a kernel of at most 4k vertices.     

基于遗传算法的可扩展应用层组播树构建

在应用层组播中,为降低节点的路径延时,通常采用遗传算法和启发式算法来减小组播树直径的方法,但在组播树具有大规模节点数时,遗传算法收敛时间长,而采用启发式算法难以在有约束条件下达到全局最优.本文在具有超节点的双层应用层组播模型基础上,提出了利用遗传算法构建出度受限最小带权路径延时生成树（MWPL-DC-ST）的生成算法GA-MWPL-DC-ST,利用该算法可在超节点上对双层组播树进行分布式构建,从而将求最优解问题的巨大计算量分担到多个超节点上.算法中的初始化、杂交和变异阶段采用启发式算法,对变异参数进行适应性调整,加快了算法的收敛速度.仿真试验表明,本文提出的双层应用层组播模型和GA-MWPL-DC-ST算法能得到比启发式算法更优的解,与采用单层模型的遗传算法相比较,显著降低了算法收敛时间,解决了遗传算法构建有大规模节点数的应用层组播树的可扩展性问题.     

Joint routing and flow rate optimization in multi-rate ad hoc networks

Typical radios in ad hoc networks can support multi-rate transmissions. However, traditional routing protocols do not use this feature well in multi-rate ad hoc networks and therefore, the network performance and resource utilization are not optimized. Some algorithms have been proposed to take advantage of the multi-rate transmission scheme, but their performance is not optimized either. In this paper, we show that a cross-layer optimization based approach can significantly improve the performance of multi-rate ad hoc networks over existing routing algorithms. For this, we consider link interference and propose joint routing and flow rate optimization for optimal performance in multi-rate ad hoc networks, i.e., a Cross-layer Optimization based Model for Multi-rate Ad hoc Networks (COMMAN). Considering the characteristics of multi-rate ad hoc networks, we design and implement a distributed heuristic of this centralized model. It is shown that the distributed heuristic algorithm can approximate the performance of COMMAN closely.     

面向异构通信卫星的地球站组网规划方法

经过多年的建设发展,卫星通信已经形成了多系列卫星并存、相互支撑、相互补充的通信系统.然而,由于各卫星系统建设时间跨度大,通信技术手段存在差异,如何高效利用异构的卫星通信资源成为了一个实际难题.为此,文中首次根据卫星与地球站的可互通条件构建了系统模型,并将其归结为线性约束下离散变量的非线性优化问题,优化目标是异构卫星波束...     

Joint Algorithm of Message Fragmentation and No-Wait Scheduling for Time-Sensitive Networks

Time-sensitive networks(TSNs)support not only traditional best-effort communications but also deterministic communications,which send each packet at a deterministic time so that the data transmissions of networked control systems can be precisely scheduled to guarantee hard real-time constraints.No-wait scheduling is suitable for such TSNs and generates the schedules of deterministic communications with the minimal network resources so that all of the remaining resources can be used to improve the throughput of best-effort communications.However,due to inappropriate message fragmentation,the realtime performance of no-wait scheduling algorithms is reduced.Therefore,in this paper,joint algorithms of message fragmentation and no-wait scheduling are proposed.First,a specification for the joint problem based on optimization modulo theories is proposed so that off-the-shelf solvers can be used to find optimal solutions.Second,to improve the scalability of our algorithm,the worst-case delay of messages is analyzed,and then,based on the analysis,a heuristic algorithm is proposed to construct low-delay schedules.Finally,we conduct extensive test cases to evaluate our proposed algorithms.The evaluation results indicate that,compared to existing algorithms,the proposed joint algorithm improves schedulability by up to 50%.