基于遗传算法的模糊关系方程求解

针对模糊关系方程的求解问题,即模糊综合评判逆问题,提出了一种基于遗传算法的求解方法.算法能有效地找出模糊关系方程的全局近似最优解,并且与模糊关系合成算子的具体形式无关,有良好的鲁棒性和自适应能力.仿真结果表明,此方法是一种有效、实用的模糊关系方程求解方法.     

基于QoS的语义Web服务组合优化算法

在面向服务的环境下,存在众多可满足服务请求者要求的服务组合方案,如何从中选出最优或近似最优方案是Web服务组合优化要解决的问题.设计出一个不必探索整个解空间的优化算法——SBG算法,为服务请求者提供最优选择方案.实验结果表明,这种方法可有效提高选择方案的求解效率.     

相关对数阴影信道条件下基于贪婪的最优感知节点集合选择算法

为了在资源使用效率与感知性能之间进行有效折中,通过分析感知节点集合的虚警概率上限,提出了一种基于贪婪的最优感知节点集合的自适应选择算法.理论分析结果与仿真结果均表明该算法可以快速地得到最优感知节点集合的近似最优解,并且该近似最优解与最优解之间的差别不大,但是其复杂度却大大降低,具有很好的实用性.     

SVDD参数优化的有限穷举—局部遗传算法及故障检测应用

针对支持向量数据描述(SVDD)训练过程中的参数优化问题,提出了一种有限穷举—局部遗传算法.首先,在分别分析参数C和σ对SVDD分类性能不同影响的基础上,得到参数σ是影响分类性能主因的结论.然后针对σ的优化问题,通过穷举有限个整数解并比较其分类性能来确定近似最优解,在近似最优解的领域内用遗传算法进行局部搜索,最终得到精确的优化参数.仿真实验及电路故障检测应用结果表明:算法有效避免了参数搜索的盲目性,能以更短的时耗逼近最优解.     

基于有界增长图的无线传感器网络虚拟骨干形成算法

提出了基于有界增长图的虚拟骨干近似形成算法(VBF).算法采用网络划分机制构建极大独立集,使用染色过程形成簇图;以2分离集合子集递归计算(1 ε)近似局部最小支配集,合并局部最优解构造全局最优解:然后调整簇头传输范围直接以全局最优解形成最小近似连通支配集,无须加入网关节点,降低计算开销.构造的连通支配集具有常量扩展因子和常量度,并且算法运行时节点仅需直接邻域信息.理论分析和仿真比较证明了算法的正确性和有效性.     

基于LCS的特征树最大相似性匹配网页去噪算法

提出了一种基于LCS的特征树最大相似性匹配网页去噪算法.通过将目标网页和相似网页转化为特征树,并将特征树映射为一个特征节点序列,利用LCS算法能获得最长子序列全局最优解的特点,找出两棵特征树之间的不同节点作为候选集,并对候选集进行聚集评分找出网页重要内容块.给出了算法的原型系统,并对每一个模块的实现做了详尽的描述.     

用混合遗传算法求解两类多边形近似问题

多边形近似是一种重要的曲线描述方法.研究用遗传算法求解平面数字曲线的多边形近似碰到的两个主要问题是不可行解难以处理和基本遗传算法局部搜索能力差.针对这两个问题,本文提出了一种组合拆分与合并技术的混合遗传算法(SMGA).它将两种经典算法-拆分技术与合并技术引入到对染色体的修复过程.采用这种方法,一个不可行解不仅能得到快速的修复,而且还能被推进到解空间中一个局部较优的位置.它的另外一个优点是:不同于已有的遗传算法,只能解决一类多边形近似问题,SMGA是一种能求解两类多边形近似问题的通用算法.实验结果表明:本文提出的算法比其他同类算法性能更优越.     

一种用于前向分组数据业务的新调度算法

该文通过分析前向分组数据业务中应用的各种调度算法的性能,指出调度算法的吞吐量和公平性之间必然存在着折衷关系.在满足给定的公平性要求的情况下,吞吐量累积分布函数曲线越靠近公平性准则,吞吐量越高.基于这个认识,该文提出一种新的调度算法,它可以通过调节参数来改变吞吐量和公平性曲线的关系.而且,从吞吐量和公平性曲线折衷的角度出发,可以比较容易地找出一种近似最优解.     

基于蚁群优化算法的多QoS选播路由算法研究

提出一种基于蚁群优化算法的多QoS选播路由算法.算法设计了合理的路径评价方法,从平衡网络负载的角度出发,寻找适当的选播路由路径.通过对迭代最优解路径上的信息素的调整和算法重启的策略,算法有效地避免了陷入局部最优的缺陷,扩大了算法的搜索范围.实验结果表明,该算法能找到满足QoS约束的全局最优解,并在资源预留的基础上较好地满足用户对于带宽、时延的要求.     

WSN中结合双层编码和JPSO的多约束Steiner树算法

聚合树是无线传感器网络(WSN)中的一种典型的数据聚合技术。针对多目标约束的Steiner树问题(MCSTP),提出一种基于双层编码机制(TE)和跳跃粒子群优化(JPSO)的启发式算法构建最优树结构。首先,选择总能耗、网络寿命、收敛时间和通信干扰作为优化约束目标。然后,根据提出的双层编码方案对生成树的解进行编码,同时利用跳跃粒子群优化算法寻找帕累托最优解。最后,利用提出的混合适应度函数找出近似最优树结构。实验结果表明,JPSO-TE方法可以产生近似最优的树结构,具有高效性和可行性。     

Reconstruction algorithm based on multi-reference frames hypothesis optimization for compressive sensing

In multi-hypothesis based distributed compressed video sensing systems,the quality of the multi-hypothesis set has important influence on the reconstruction performance of decoder.However,the acquiring of the hypothesis set has not been concerned in existing works.A reconstruction algorithm based on multi-reference frames hypothesis optimization (MRHO) was proposed.This algorithm expanded the selection of hypothesis vectors by increasing the number of reference frames.The quality of the prediction set was improved by hypotheses optimization selection under the same size with the original hypothesis set.Simulation results show that the proposed MRHO algorithm effectively improves the reconstructed quality of the distributed compressed video sensing scheme.     

基于改进遗传算法的油田配电网无功优化

遗传算法是一种模拟生物进化过程的优化算法,可用来求解包含离散化变量的复杂优化问题。将遗传算法应用于油田配电网无功优化,并对常规遗传算法的编码方式、遗传算子以及终止判据等方面进行改进,提高了计算效率和全局寻优能力。通过对油田配电网的分析和计算,结果表明该改进遗传算法应用于油田无功优化是合理可行的,其优化效果优于传统遗传算法。     

Interference management for spectral coexistence in a heterogeneous satellite network

With the advent of the fifth generation of mobile radio communication by 2020, there will be many challenges such as increasing service demand with low delay in providing billions of end users called the satellite mobile users. It is expected that terrestrial communication systems will be faced with a dense network having many small cells anywhere and anytime. Therefore, there are some remote regions in the world where terrestrial systems cannot provide any services to end users. Furthermore, because of lack of spectral resources, it is very important that the spectrum is shared between satellite systems and terrestrial equipment by a suitable solution to interference management. In this paper, a heterogeneous satellite network that includes low earth orbit (LEO) satellite constellation and terrestrial equipment is proposed to provide low delay services. In this type of structure, interference management based on transmission power control between LEO satellite systems and mobile users is very important for obtaining high throughput. Moreover, in order to mitigate interference, transmission power control is shown based on noncooperative Stackelberg game under many subgames through pricing‐based algorithm and convex optimization method. Finally, the simulation results show that the performance of this study's system model will be improved through the proposed algorithm.     

Improved satellite resource allocation algorithm based on DRL and MOP

In view of the multi-objective optimization (MOP) problem of sequential decision-making for resource allocations in multi-beam satellite systems,a deep reinforcement learning(DRL) based DRL-MOP algorithm was proposed to improve the system performance and user satisfaction degree.With considering the normalized weighted sum of spectrum efficiency,energy efficiency,and satisfaction index as the optimization goal,the dynamically changing system environments and user arrival model were built by the proposed algorithm,and the optimization of the accumulative performance in satellite systems based on DRL and MOP was realized.Simulation results show that the proposed algorithm can solve the MOP problem with rapid convergence ability and low complexity,and it is obviously superior to other algorithms in terms of system performance and user satisfaction optimization.     

基于鲶鱼粒子群优化算法的认知无线电频谱分配

目前亟待解决如何获得认知无线电系统效益最大化问题,而求解最优频谱分配方法是一项关键技术,针对传统粒子群(PSO)算法收敛速度慢、易陷入局部最优解等缺陷,提出一种基于鲶鱼粒子群算法(CE-PSO)的认知无线电频谱分配方法。首先建立认知无线电频谱分配优化的数学模型,然后以用户取得的效益最大化为优化目标,引入"鲶鱼效应",保持粒子群的多样性,通过粒子间信息交流找到空闲频谱最优分配方案,最后采用仿真实验测试CE-PSO算法的有效性。结果表明,CE-PSO算法克服了PSO算法的缺陷,可以快速、准确地寻找到最优频谱分配方案,更好地实现系统效益的最大化,可以满足认知无线电系统的应用需求。     

New method of traffic flow forecasting based on quantum particle swarm optimization strategy for intelligent transportation system

Traffic flow forecasting is one of the essential means to realize smart cities and smart transportation. The accurate and effective prediction will provide an important basis for decision‐making in smart transportation systems. This paper proposes a new method of traffic flow forecasting based on quantum particle swarm optimization (QPSO) strategy for intelligent transportation system (ITS). We establish a corresponding model based on the characteristics of the traffic flow data. The genetic simulated annealing algorithm is applied to the quantum particle swarm algorithm to obtain the optimized initial cluster center, and is applied to the parameter optimization of the radial basis neural network prediction model. The function approximation of radial basis neural network is used to obtain the required data. In addition, in order to compare the performance of the algorithms, a comparison study with other related algorithms such as QPSO radial basis function (QPSO‐RBF) is also performed. Simulation results show that compared with other algorithms, the proposed algorithm can reduce prediction errors and get better and more stable prediction results.     

一种新的自适应增益盲优化算法提高自由空间光通信性能

由于大气湍流的影响,光束在大气中传输时产生 严重畸变,造成自由空间光(FSO)通信链路 性能恶化。为了提高系 统性能,构建了基于盲优化自适应光学的FSO通信系统,提出了一种新的自适应 增益随机并行梯度下降(SPGD)优化 算法。数值模拟结果表明,本文算法对大气湍流引起的波前误差进行实时补偿,能 够达到 很好的校正效果,比固定增益优化算法具有更好的收敛速度和精度。     

基于机器学习的网络投诉预测分析

减少网络相关的投诉一直是运营商的重点工作之一。目前,网络投诉用户预警方案多以网优工程师经验为主导,准确率和效率都较低。本文通过对历史网络投诉用户数据进行全面深入的分析,基于XGboost算法建立投诉用户特征模型,实现了对网络投诉用户的预测。该方法预测准确率较高,与其他网优系统对接后能够定位用户质差原因,使网络部门能够提前进行网络优化,提升用户满意度。     

基于遗传算法的非监督层次分类方法研究

快速准确地确定单个样本的所属类别以及总体样本类别数是解决非监督模式识别的前提,然而它们的确定通常是非常困难的.通过研究基于遗传算法的相似性度量最优分类算法以及最优分类数确定算法,提高非监督识别的准确性,并将所研究的算法应用到飞机识别当中.实验结果表明,本算法可以进行最优分类及分类数的确定.