An Efficient Cuckoo Search Algorithm for System-Level Fault Diagnosis

We propose a new efficient algorithm named Cuckoo search fault diagnosis (CSFD) to solve system-level fault diagnosis problem.KMP algorithm is proposed for initialization based on the K-means partition algorithm;a fitness function is designed according to the equation constraints satisfied by the test model;the binary mapping method is advanced by optimizing existing binary mapping algorithm.Experiments show that KMP algorithm significantly reduces the disparity between the initial solution and the actual solution,and CSFD algorithm improves the efficiency and correctness significantly compared with existing typical swarm intelligence diagnosis algorithm.     

基于APF的二元WSN运动目标跟踪算法

针对传感节点计算、通信及资源受限的特点,引入二元WSN模型,提出了一种基于辅助粒子滤波(APF)的集中式算法,以实现运动目标的实时跟踪。由于每个二元传感器只对目标是否进入其感知区域做出反应(向数据融合中心报告0或1),粒子滤波算法的复杂运算集中在融合中心完成,因此节点结构简单、通信代价低廉,有助于延长监测网络的生存周期。仿真实验结果表明,该算法对随机部署和规则部署的两种方案,均具有良好的跟踪性能,能满足一般机动目标实时跟踪的应用要求。     

求解多目标优化问题的GTSPA混合算法

为了有效求解多目标优化问题,文中结合遗传算法、禁忌搜索算法以及粒子群算法的优势,设计了一种新型的混合算法GTSPA,并对所设计的GTSPA混合算法的总体步骤进行了描述.通过从解的质量、算法的收敛性以及算法的复杂度这几方面对算法进行分析,可以发现:GTSPA混合算法是以较大概率收敛于全局最优的,具有很好的收敛性,并且运算相当快、计算复杂度并不高,具有很好的优势.     

基于模拟退火的自适应离散型布谷鸟算法求解旅行商问题

提出了一种基于模拟退火的自适应离散型布谷鸟算法求解旅行商问题.该算法在布谷鸟搜索算法原理的基础上,构造了旅行商问题的路径求解策略.由于算法的局限性,随着算法的调整和迭代次数的增加,容易破坏已形成的路径,从而使得算法通用性不强.针对这一局限性,本文提出了一种自适应局部调整算子和全局随机扰动策略.采用简单的2-opt算子作为局部优化算子加快算法收敛速度,引入模拟退火机制防止算法陷入局部最优.采用标准TSPLIB多组数据进行测试,并与有代表性的优化算法进行结果比较.实验结果证明了该算法在精度和稳定性方面的优势.     

Q-MOHRA: QoS Assured Multi-objective Hybrid Routing Algorithm for Heterogeneous WSN

Quality of Service (QoS) assurance in Wireless Sensor Network (WSN) is a tough task, and it is more exciting due to the scarcity of resources. The requirement of different WSN applications running over has different constraints. In QoS, routing protocol the network has to balance the traffic. This paper presents a novel heuristic routing algorithm known as QoS assured Multi-objective Hybrid Routing Algorithm (Q-MOHRA) for Heterogeneous WSN. Q-MOHRA takes into account the link (energy, hop count, link quality indicator etc.) and path (jitter) metrics for optimal path selection. The performance of Q-MOHRA is evaluated through intensive simulation and equated with Simple Hybrid Routing Protocol (SHRP) and Dynamic Multi-objective Routing Algorithm (DyMORA). The metrics such as average energy consumption, residual energy, packet delivery ratio, jitter, and normalized routing load are used for comparison. The performance of Q-MOHRA has been observed to outclass SHRP and DyMORA. It improves the packet delivery ratio by 24.31% as compared to SHRP and 11.86% as compared to DyMORA. Q-MOHRA outperforms DyMORA in terms of average energy consumption by a factor of 8.27%.     

基于链表的图像连通区域提取算法

提出一种基于链表存储的连通区域提取算法。该算法结合段标记法和区域生长法的特点,以目标段为对象,采用链表存储结构。介绍了该算法的基本原理、实现和优化,分析了算法的实验结果,提出了算法的优化方法。实验表明:算法具有较高的效率,对任意形状的连通区域均有良好的鲁棒性,可有效应用于工业在线检测。     

一个有效的快速并行检索算法

本文给出了一种在多处理器环境下对有序序列进行快速并行检索的方法,详细讨论了有关算法及其数据结构.并给出了该检索系统对硬件结构的要求.     

改进布谷鸟算法在协同干扰资源分配中的应用

针对敌方雷达网的多干扰机协同干扰资源分配问题,提出了一种基于二维整数编码的改进布谷鸟(ICS)算法。首先,根据不同干扰样式在不同恒虚警检测器中的干扰效果差异,从压制概率公式出发,构建目标函数;然后,根据分配原则建立干扰资源分配模型;最后,利用ICS算法对模型寻优求解,并给出具体求解步骤。仿真结果表明:ICS算法收敛速度快,全局寻优能力强,能很好地解决雷达干扰资源协同优化分配问题。     

基于相位谱的红外小目标搜索算法研究

针对红外小目标搜索阶段中图像背景稀疏的特点,提出了一种利用图像相位谱计算显著图和定位目标的新方法。与传统的利用海天线和海岸线对目标进行搜索的方法相比,本文算法的计算复杂度大大降低,并弥补了其受气温影响而不能准确定位海天线和海岸线的不足;与利用Itti模型的方法相比,该算法克服了其不能有效分离目标和背景的缺点。阐述了利用离散傅里叶变换(Discrete Fourier Transform,DFT)和离散余弦变换(Discrete Cosine Transform,DCT)计算相位谱的两种方法及它们之间的一致性。构建了利用图像相位谱计算显著图的数学模型,明确了模型参数选择的作用和意义。通过理论和实例证明了本文算法对于稀疏背景下小目标定位的可行性和高效性。     

Self-Adaptive Discrete Cuckoo Search Algorithm for the Service Routing Problem with Time Windows and Stochastic Service Time

Making house calls is very crucial to deal with the competitive pressures of the service business and to improve service quality. We design a model called service routing problem with time windows and stochastic service time(SRPTW-SST) that is based on vehicle routing problem with time windows. A self-adaptive discrete cuckoo search algorithm with genetic mechanism(sDCSGM) is proposed for the model SRPTW-SST. Moreover,we design a selection mechanism to improve the logicality of the algorithm bas...     

A Novel Method for Identifying Recursive Systematic Convolutional Encoders Based on the Cuckoo Search Algorithm

The existing methods for identifying recursive systematic convolutional encoders with high robustness require to test all the candidate generator matrixes in the search space exhaustively.With the increase of the codeword length and constraint length,the search space expands exponentially,and thus it limits the application of these methods in practice.To overcome the limitation,a novel identification method,which gets rid of exhaustive test,is proposed based on the cuckoo search algorithm by usi...     

Novel Fuzzy Based Crow Search Optimization Algorithm for Secure Node-to-Node Data Transmission in WSN

Wireless Personal Communications - This paper studies the performance of device-to-device (D2D)-enabled cellular network with D2D users reusing downlink resources of cellular links. The cellular...     

一种基于二分搜索法的平面螺旋电感的快速优化技术

针对平面螺旋电感的物理模型和Jenei等提出的闭式电感公式,提出了一种基于二分搜索法在工艺参数和工作频率确定的条件下快速优化电感版图参数的技术.实验证明Jenei公式的计算速度比Greenhouse方法提高了大约两个数量级并且能够提供相当的精度.对Jenei公式进一步数学分析证明在工艺参数和工作频率确定的条件下可以用二分搜索法快速地找出满足电感值和其他约束条件的所有版图参数,并从中选出Q值最大的一组.实验证明该法能够提供足够的精度和较快的优化速度.     

一种基于二分搜索法的平面螺旋电感的快速优化技术

针对平面螺旋电感的物理模型和Jenei等提出的闭式电感公式,提出了一种基于二分搜索法在工艺参数和工作频率确定的条件下快速优化电感版图参数的技术.实验证明Jenei公式的计算速度比Greenhouse方法提高了大约两个数量级并且能够提供相当的精度.对Jenei公式进一步数学分析证明在工艺参数和工作频率确定的条件下可以用二分搜索法快速地找出满足电感值和其他约束条件的所有版图参数,并从中选出Q值最大的一组.实验证明该法能够提供足够的精度和较快的优化速度     

An Automatic Functional Coverage for Digital Systems Through a Binary Particle Swarm Optimization Algorithm with a Reinitialization Mechanism

At present, functional verification represents the most expensive part of the digital systems design. Moreover, different problems such as: clock synchronization, code compatibility, simulation automation, new design methodologies, proper use of coverage metrics, among others represent challenges in this area. The automated test vector generation is involved in these problems. In this work, an automated functional test sequences generation for digital systems based on the use of coverage models and a binary Particle Swarm Optimization algorithm with a reinitialization mechanism (BPSOr) is described. Also, a comparison with other meta-heuristic algorithms such as: Genetic algorithms (GA) and pseudo-random generation is presented using different fitness functions, coverage models and devices under verification. The main strategy is based on the combination of the simulation and meta-heuristic algorithms to test the device behavior through the generation of test vector sequences. According to the results, the proposed test generation method represents a good alternative to increase the functional coverage during the automated functional verification of block-level digital systems verification.     

Hybrid Cuckoo Search with Salp Swarm Optimization for Spectral and Energy Efficiency Maximization in NOMA System

Non-Orthogonal Multiple Access (NOMA) holds the efficiency of enabling 5G communication. Due to the faster emergence of smart devices and their correlated applications, there is a huge demand for data traffic to increase the data rate. As a result, these raising demands of the users and the restricted spectrum will minimize the energy and spectral efficiency of the wireless network. There are two major measures like spectral and energy efficiency in Fifth-Generation (5G) communication models technically analyzed in this paper. The main intent of this paper is to develop a hybrid meta-heuristic algorithm for maximizing the spectral and energy efficiency of NOMA, thus avoiding low latency communication. The proposed model integrates two well-performing meta-heuristic algorithms like Salp Swarm optimization algorithm (SSA) and Cuckoo Search Algorithm (CS) for attaining the energy and spectral efficiency maximization in NOMA. The proposed hybrid meta-heuristic algorithm called Cuckoo Levy-based SSA (CL-SSA) is developed to optimize the parameters like beamforming vectors and time allocation ratio at the base station and relay. As the conventional optimization algorithm spectral efficiency of the system reaches maximum. The mean of the proposed CL-SSA for spectral efficiency is 23%, 2.8%, 54%, 32%, and 10% increased than Cuckoo Search (CS), Salp Swarm optimization (SSA), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Fire Fly algorithm (FF), respectively. The experimental result shows that the proposed CL-SSA maximizes the spectral efficiency and energy efficiency than conventional techniques like Cuckoo Search (CS), Salp Swarm optimization (SSA), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Fire Fly algorithm (FF) in developed NOMA model.

     

一种超宽带雷达多人体目标跟踪算法

提出了一种超宽带雷达对多个运动人体距离跟踪的方法。该方法基于人体多散射点的回波模型,利用CLEAN算法提取人体多个散射点的量测信息,结合最近邻域和联合概率数据互联算法,提出了一种简易最近邻联合概率数据互联算法,以解决多个人体目标轨迹交叉时数据关联的问题。通过对实测数据的处理,证明了该算法能够对轨迹交叉的人体目标进行有效跟踪。     

基于可变拟阵搜索算法构造码率为1/p的二进制系统准循环码

该文针对拟阵搜索算法复杂度高以及局部拟阵搜索算法无法搜索到全部最优码的问题,通过研究拟阵搜索算法,提出可变拟阵搜索算法,并用于搜索准循环码。该算法通过减少重复搜索从而降低运算复杂度;基于该算法构造码率为1/p的二进制系统准循环码,随着整数p的变化,生成矩阵减少或者增加一个循环矩阵,产生码率均为1/p的最优码。通过实验得到两个最小距离比现有最优码更大的准循环码,表明算法的可行性和优越性。     

考虑信息延迟的无人机分布式协同搜索算法

多无人机协同搜索是多无人机协同控制的重要内容之一,无人机协同搜索的两大问题是通讯延迟和分布式的计算环境。各无人机既要提高搜索效率又要避免碰撞。提出了一种以分布式模型预测算法为基础的搜索算法。通过引入合适的缓冲周期,安排不同的无人机分别对未来不同的时间范围进行规划。不同无人机的搜索规划在时间上不再耦合,从而解决了多机分布式计算和通讯延迟的问题。该方法的目的是实现真实环境下的分布式协同控制。最后用仿真算例验证了算法的可行性。