多目标的Internet路由优化控制算法

研究通过优化链路权值以控制网络路由来实施流量工程.以网络拥塞最小化和时延最小化为流量工程目标,建立了多目标的全局路由优化数学模型.求解该问题是NP困难的,提出一种混沌群搜索优化算法进行求解.算法采用群局部搜索,利用混沌变量产生一组分布好的初始解,并在邻域搜索进程中应用扩展贪心思想,提高了算法的全局搜索能力.仿真结果表明所提算法能够有效减少由于流量分布不平衡造成的网络拥塞,同时限制长路径,提高了网络性能.     

考虑功率限制的WDM光网有效设计

提出了一种基于禁忌搜索技术的启发式算法有效波长与路由分配(RWA-TS-P)来解决考虑功率限制的WDM光网的优化设计。该算法建立在局部搜索贪婪算法RWA-greedy之上,引入了功率验证过程来保证建立光路的功率有效性。通过环网和网状网的设计实例验证了算法的性能。数值结果表明,该算法能够在保证网络中建立的所有光路功率有效性的前提下最优地配置网络资源,同时具有可以控制的计算复杂性。     

基于表面自适应的定向贪婪选路

针对无线传感器网络中基于位置的路由算法中存在的重复搜索和冗余计算问题,提出一种基于表面自适应的定向贪婪路由算法（DGAFR）。该算法充分发挥贪婪转发、表面路由转发和定向选路的优势,依据局部区域节点的状态信息进行整个网络的路由选择。理论上分析证明DGAFR算法具备渐近最优性;仿真结果表明,相比于GPSR和GOAFR,该算法降低了大量额外的通信和计算开销,更适于大型的传感器网络。     

一种行进启发式节省能量的地理位置路由

文章首先介绍了基于地理位置的路由协议和它的优势,提出一种行进启发式节省能量的协议,基于贪婪算法和一种能量算法,采取提前绕洞机制,合理有效地解决了路由空洞问题。缩短了路由的路径长度,使得网络中节点消耗的能量尽可能平均,从而进一步延长了传感器网络的生命周期。     

基于动态人工鱼群优化的WSN分簇算法

将群智能优化算法引入无线传感器网络分簇路由协议的设计能有效地节约节点能量和提高分簇效率.针对基本人工鱼群算法在运算速度方面的不足,提出了一种基于动态人工鱼群优化的无线传感器网络分簇算法,算法为了同时具有较好的全局搜索和局部寻优能力,更快地得到最优分簇结果,在一次迭代进化中除了考虑人工鱼的觅食行为、聚群行为和追尾行为的寻...     

一种传感器网络的分布式多播路由优化算法

传感器感知的信息需要通过网络传送给感兴趣目标节点,传统网络中的多播技术往往能耗高、实时性不够理想,不利于在传感器网络中使用。针对 WSN中节点对网络拓扑未知,该文先将多播路由问题演化为最优多播路径问题,通过启发式算法求解分布式最优路径,并通过一种基于贪婪思想的裁剪合并策略优化多播路由树,直至整个网络得到最优路径,最后并结合了节点区域集中以及无线多播特性,提出了 DCast 路由算法。最后通过仿真实验与uCast, SenCast等经典的传感器网络的多播路由算法仿真比较,可以得出其算法在时延性以及能耗等方面性能有优势。     

基于粒子群优化的直觉模糊核匹配追踪算法

针对现有直觉模糊核匹配追踪算法采用贪婪算法搜索最优基函数而导致学习时间过长的问题,汲取了粒子群优化算法全局搜索能力强、收敛速度快的优势对最优基函数的搜索过程进行优化,提出了一种基于粒子群优化的直觉模糊核匹配追踪算法,并将该算法应用于时效性要求更高的空天目标识别领域.实验结果表明,与传统方法相比,本文方法在识别率相当的情况下有效缩短一次匹配追踪时间,计算效率明显提高,且所得模型具有稀疏性好,泛化能力高等优点,特别适用于兼顾识别率和实时性的应用领域.     

基于分级结构的优化QoS路由算法

Ad hoc(自组织)网络中包含延迟、延迟抖动、带宽和丢包率等约束条件在内的QoS(服务质量)路由问题,是一个NP完全问题,传统的平面QoS蚂蚁路由算法难以解决提高算法全局搜索能力和加快收敛速度之间的矛盾。针对以上问题,提出了HQAC(分级QoS蚁群)算法,在分级的基础上对蚁群算法的路由搜寻过程进行了改进,同时对信息素更新公式进行了优化。仿真结果表明,与传统的QoS路由算法相比,HQAC算法在搜索全局最优解,尤其是收敛速度等性能上有了很大的提高。     

禁忌粒子群算法在几何约束求解中的应用

约束问题可以转化为优化问题,针对粒子群优化算法在算法的后期易陷入局部最优的缺点,提出TPSO(禁忌粒子群优化算法),在算法的前期采用粒子群算法快速产生全局最优解信息素的初始分布,后期引入禁忌搜索算法,记录已经达到的局部最优解,在下一次搜索中,不再或者有选择地搜索这些点,从而跳出局部最优点,并且在搜索过程中允许接受劣解,充分利用禁忌搜索的记忆能力及较强的爬山能力,大大提高了获得全局最优解的概率.该算法综合了粒子群优化算法的快速性,随机性和全局收敛性以及禁忌搜索局部寻优的能力.在确保全局收敛性的基础上,能够快速搜索到高质量的优化解.该方法用于几何约束求解的性能明显高于标准粒子群算法,算法具有良好的优化性能和时间性能.     

基于分级结构的优化QoS路由算法北大核心

Ad hoc(自组织)网络中包含延迟、延迟抖动、带宽和丢包率等约束条件在内的QoS(服务质量)路由问题,是一个NP完全问题,传统的平面QoS蚂蚁路由算法难以解决提高算法全局搜索能力和加快收敛速度之间的矛盾。针对以上问题,提出了HQAC(分级QoS蚁群)算法,在分级的基础上对蚁群算法的路由搜寻过程进行了改进,同时对信息素更新公式进行了优化。仿真结果表明,与传统的QoS路由算法相比,HQAC算法在搜索全局最优解,尤其是收敛速度等性能上有了很大的提高。     

Binary monkey algorithm for approximate packing non-congruent circles in a rectangular container

A Packing problem consists in the best arrangement of several objects inside a bounded area named as the container. This arrangement must fulfill with technological constraints, for example, objects should not be overlapping. Some packing models for circular objects are typically formulated as non-convex optimization problems; where the continuous variables are the coordinates of the objects, so they are limited to not finding optimal solutions. Due to the combinatorial nature in the arrangement of such objects, heuristic methods are being used extensively which combine methods of global search and methods of local exhaustive search of local minima or their approximations. In this paper, we will address the packing problem for non-congruent (different size) circles with the binary version of the monkey algorithm which incorporates a cooperation process and a greedy strategy. We use a rectangular grid for covering the container. Every node in the grid represent potential positions for a circle. In this sense, binary monkey algorithm for the knapsack problem, can be used to solve de 0–1 approximate packing problem for non-congruet circles. The binary monkey problem uses two additional processes of the original monkey algorithm, these two processes are a greedy process and a cooperation processes.

     

A model for virtual network embedding using Artificial Bee Colony

In a network virtualization environment, a significant research problem is that of virtual network embedding. As the network virtualization system is distributed in nature, an effective solution on how to optimally embed a dynamically generated virtual network request on the substrate networks that are owned and managed by multiple infrastructure providers needs proper attention. The problem is computationally hard, and therefore, many approaches, implying heuristics/meta‐heuristics, have been applied for the same. A meta‐heuristic, Artificial Bee Colony algorithm is getting popular due to its robustness toward complex problem solving. A novel approach based on Artificial Bee Colony to address the dynamic virtual network embedding problem in a multiple infrastructure provider scenario is proposed in this work. Bee population is initialized by using a greedy heuristic in which the number of substrate networks together with virtual network requests constructs a bee. Generated solution, in the population, is improvised by using greedy selection that explores a local search method adopted by the bees. In greedy selection, the new candidate source is memorized by the bee if its fitness is better than the fitness of the existing source. The performance study of the proposed model is done by simulation over various metrics such as embedding cost, embedding time, and acceptance ratio. A comparative study is conducted with other nature‐inspired virtual network embedding algorithms on these metrics. The findings affirm that the proposed virtual network embedding approach performs well and produces better results.     

Application of Rollout Strategy to Test Points Selection for Integer-Coded Fault Wise Table

Test points selection for integer-coded fault wise table is a discrete optimization problem. The global minimum set of test points can only be guaranteed by an exhaustive search which is eompurationally expensive. In this paper, this problem is formulated as a heuristic depth-first graph search problem at first. The graph node expanding method and rules are given. Then, rollout strategies are applied, which can be combined with the heuristic graph search algorithms, in a computationally more efficient manner than the optimal strategies, to obtain solutions superior to those using the greedy heuristic algorithms. The proposed rollout-based test points selection algorithm is illustrated and tested using an analog circuit and a set of simulated integer-coded fault wise tables. Computa- tional results are shown, which suggest that the rollout strategy policies are significantly better than other strategies.     

Bayesian network structure learning algorithm based on hybrid binary salp swarm-differential evolution algorithm

Aiming at the disadvantages of Bayesian network structure learned by heuristic algorithms,which were trapping in local minimums and having low search efficiency,a method of learning Bayesian network structure based on hybrid binary slap swarm-differential evolution algorithm was proposed.An adaptive scale factor was used to balance local and global search in the swarm grouping stage.The improved mutation operator and crossover operator were taken into salp search strategy and differential search strategy respectively to renew different subswarms in the update stage.Two-point mutation operator was adopted to improve the swarm’s diversity in the stage of merging of subswarms.The convergence analysis of the proposed algorithm demonstrates that best structure can be found through the iterative search of population.Experimental results show that the convergence accuracy and efficiency of the proposed algorithm are improved compared with other algorithms.     

一种结合模拟退火和贪心策略的社团识别算法

为了提高复杂网络社团识别的精度和速度,文中结合模拟退火和贪心策略识别社团结构的优势,提出一种新的社团识别算法。该算法利用贪心策略引导模拟退火搜索最优解过程中单个结点的无规则盲目移动,消除了大量无效移动,在搜索到全局最优解的情况下,将搜索时间大幅缩减。实验表明,SAGA具有强大的搜索能力和较快的模拟退火执行速度,可获得较高的模块度,达到较为准确的社团分割,且具有一定的应用价值。     

A Novel Routing Protocol for Underwater Wireless Sensor Network Using Pareto Uninformed and Heuristic Search Techniques

In underwater communication, establishing a communication link between the sensor in the sea bed and the surface sinks is a daunting task. Further, the data has to be transmitted with minimum delay and maximum reliability. Therefore, the present study proposes a biobjective routing protocol for underwater wireless sensor networks. The existing protocols are reviewed and it is found that the traditional depth based and vector-based routing protocols are not able to tackle these conflicting objectives and hence suffer transmission failures with high delay. A biobjective optimization of delay and reliability of routes is proposed to obtain pareto-optimal routes employing uninformed search technique and a modified greedy best first search heuristic. Through simulation experiments, it is found that the biobjective protocol performs better than depth based, delay based and reliability-based routing protocols. However, since the biobjective routing problem in underwater wireless sensor networks is known to be NP-hard and dynamic in nature, the computational effort of uninformed search in yielding the exact solutions increases as the network size increases. The modified greedy best first search heuristic is employed to yield sub-optimal routes with less computational effort without compromising on the quality of the solutions and hence suitable for larger networks.

     

一类0-1背包问题的启发式网络策略模型与算法

针对遗传算法(GA)易陷入局部最优解、搜索精度低等缺点,提出了网络启发式策略的遗传算法(NSHGA),并将其成功地应用于0-1背包问题的求解。该算法采用网络节点关联策略,使算法具有良好的全局寻优能力。同时引入网络节点矩阵优化,利用其精细的局部遍历搜索性能,使算法具有较高地搜索精度。实例仿真结果表明,NSHGA算法可有效避免基本GA算法的早熟收敛,且具有寻优能力强、搜索精度高等特点。此外,与基本遗传算法仿真相比,可明显提高0-1背包问题求解的精度。     

一种基于禁忌搜索的全局最优化模糊聚类算法

模糊C均值（FCM）算法是一种基于贪心思想的迭代算法,算法沿迭代序列收敛到一个极小值,但存在搜索能力弱、易陷入局部最优的缺点.本文提出了一种基于禁忌搜索的模糊聚类算法,该算法在一个解的邻域内使用禁忌搜索,并采用了基于FCM局部收敛性质的长期表禁忌策略,保证在不断移动搜索起点的同时避免重复搜索;其次使用混沌优化思想与动态步长策略来提升算法的全局搜索能力,以达到获取全局最优解的目的.实验结果表明,改进算法极大地提高了聚类准确率,并具有良好的稳定性,与群智算法和遗传算法的优化相比也具有一定的优势.