基于改进的蜂群算法求解专有路径保护设计优化问题

针对光网络故障恢复资源利用的优化问题,采用改进的蜂群算法（IABC）来求解专有路径保护设计优化问题。由于采蜜机理的蜂群算法全局寻优能力较弱,引入禁忌表机制,增强算法搜索全局最优解的能力,并改进蜂群算法的交叉算子,增强算法的收敛速度。通过实验仿真。结果表明与传统的ABC算法相比,IABC能算法大大地提高计算效率,针对较复杂网络资源优化的NP问题提供有效的可行性实施方法。     

Layout Optimization for Greenhouse WSN Based on Path Loss Analysis

When wireless sensor networks (WSN) are deployed in the vegetable greenhouse with dynamic connectivity and interference environment, it is necessary to increase the node transmit power to ensure the communication quality, which leads to serious network interference. To offset the negative impact, the transmit power of other nodes must also be increased. The result is that the network becomes worse and worse, and node energy is wasted a lot. Taking into account the irregular connection range in the cucumber greenhouse WSN, we measured the transmission characteristics of wireless signals under the 2.4 Ghz operating frequency. For improving network layout in the greenhouse, a semi-empirical prediction model of signal loss is then studied based on the measured data. Compared with other models, the average relative error of this semi-empirical signal loss model is only 2.3%. Finally, by combining the improved network topology algorithm and tabu search, this paper studies a greenhouse WSN layout that can reduce path loss, save energy, and ensure communication quality. Given the limitation of node-degree constraint in traditional network layout algorithms, the improved algorithm applies the forwarding constraint to balance network energy consumption and constructs asymmetric network communication links. Experimental results show that this research can realize the energy consumption optimization of WSN layout in the greenhouse.     

Heuristic collision-free path planning for an autonomous platform

In this paper, a heuristic and learning, algorithmic scheme for collision-free navigation is presented. This scheme determines an optimum collision-free navigation path of an autonomous platform by using a trial and error process, past navigation knowledge and current information extracted from the generated surrounding environment.     

ZigBee网络路径均衡算法在供水系统的仿真研究

研究城市供水系统的高效路径均衡算法。供水系统根据供水请求调控供水路径,在早晚城市用水高峰期供水请求突增,待均衡的供水路径任务骤增加大了均衡计算量,传统方法只选用较少的高层节点作为均衡节点,高层节点因超额的工作量不能承担起均衡任务的突增,造成网络数据传输路径均衡的效率较低。为了解决这一难题,提出基于开销能量的供水系统ZigBee网络路径均衡算法。通过建立供水系统各节点能量消耗代价开销模型,在模型中根据开销能量将路径均衡控制的任务分派到网络的各个节点中共同分担完成路径均衡的任务,避免了只利用少数高层节点均衡而带来的均衡效率不高的问题。仿真结果表明,改进方法能够高效完成供水系统路径的均衡控制,取得了满意的结果。     

改进蚁群算法的无线传感器网络路径优化

研究无线传感器网络路径优化问题,针对无线传感器网络(WSN)路径优化问题,在分析了遗传算法和蚁群算法各自优缺点的基础上,通过把蚁群算法作为WSN路径优化的主框架,采用遗传算的选择、交叉和变异算子提高蚁群算法搜索速度,提出一种改进蚁群算法的WSN路径优化方法。仿真结果表明,改进蚁群算法有效地克服了基本蚁群算法的缺陷,提高了WSN路径优化效率和成功率,减少了能理消耗,有效延长了网络生存时间。     

基于解析法和遗传算法的机械手运动学逆解

研究优化机械手轨迹规划问题,机械手运动时要具有稳定性避障性能。针对平面3自由度冗余机械手优化控制问题,建立机械手的结构模型。提出用解析法和遗传算法相结合满足具有计算量小和适应性强的特点。在给定机械手末端执行器的运动轨迹,按着机械手冗余自由度,运动轨迹上每个点对应的关节角有无穷多个解。而通过算法可以找到一组最优的关节角,可得到优化机械手运动过程中柔顺性和避障点。仿真结果表明,该算法可以快速收敛到全局最优解,可用于计算冗余机械手运动学逆解,并可实现机器人的轨迹规划和避障优化控制。     

基于遗传算法的平流层飞艇路径规划设计

利用传统线性化方法--小扰动法将模型线性化,结合平流层飞艇的工作特性,把飞艇上升运动看成是纵向基准运动和横侧向扰动运动.然后采用最小值原理,根据飞艇的控制量和边界条件,设计和优化飞艇的上升轨迹,其性能目标函数是从初始点到目标点运动过程中能量消耗最小.利用遗传算法,在运动模型和控制量变化的共同约束作用下,求取目标函数的最小值.最后根据飞艇实际工作过程,对仿真结果分析表明,该方法具有实际应用价值.     

基于多条件约束的无线传感器网络QoS路径选择算法

多约束QoS路径选择是无线多媒体传感器网络亟待解决的关键问题,已被证明是NP全问题。分析了有线传感器网络使用的多项式时间和伪多项式时间启发式路径选择算法存在着计算复杂度高的特点,不适合无线多媒体传感器网。提出了一种基于改进的模拟退火的多约束QoS路径选择算法,选取控制参数T的衰减函数和控制参数T的终值Tf这两个重要参数进行优化,构造出更精细的冷却进度表。分析表明该算法是一种高效的路径选择算法,在不牺牲算法复杂度的情况下,能够提高最终解的质量。     

改进的Dijkstra算法在灾害决策系统中的应用

灾害决策系统中,核心安置点的选择对救灾工作效率影响重大;分析常用的最短路径搜索方法,选用Dijkstra算法并对其进行改进,计算选取到达最远安置点最近的安置点作为核心安置点。实验结果表明,该方法具有良好的实用性。     

A Modified Nonparametric Prewhitened Covariance Estimator

Abstract. This paper proposes a fully modified version of the spectral matrix estimator (and the long‐run variance estimator as a special case) proposed originally by Xiao and Linton [Journal of Time Series Analysis (2002) Vol. 23, pp. 215–250], and derives its asymptotic results. A striking feature of the modified spectral matrix estimator is to achieve the convergence rate of O(T ^?8/9) in the mean squared error (MSE), which is usually achieved under the fourth‐order spectral window. However, this estimator does not sacrifice the positive definiteness of the resulting estimate for the rate improvement; it is Hermitian and positive definite in finite samples by construction. The faster convergence rate is established by a multiplicative bias correction of the crude spectral estimator under the second‐order spectral window. The approximations to some sensible definitions of the MSE of the estimator and the bandwidths that minimize the asymptotic MSEs are also derived. Monte Carlo results indicate that for a wide variety of processes the modified spectral matrix estimator reduces the bias without inflating the variance and thus improves the MSE, compared with the crude, bias‐uncorrected estimator.