基于粒子群算法的无人机航迹规划简

文中提出了一种基于粒子群算法的无人机航迹规划算法。为了减少规划的难度,通过“最小威胁曲面”概念的引入,将“最小威胁曲面”进行水平投影,从而将三维航迹规划问题转换成为二维航迹规划问题进行处理;然后利用动态粒子群算法在水平投影面里进行航迹搜索;最后给出了仿真验证,通过仿真结果可以看出,本文介绍的航迹规划方法的有效性。     

采用粒子群优化算法的无人机实时航迹规划

战场环境是动态变化的,很难预先获得全局精确的威胁信息,因此需要无人机具备一定的实时航迹规划能力.采用连续型粒子群优化(PSO)算法进行无人机参考航迹的实时规划,以最大转弯半径、步进、最短距离和回避威胁作为适应度函数的评价指标,得到代表最优航路的离散点.对算法进行了相应的仿真,结果表明该方法费时短,占用内存少,可以满足在线实时航迹规划的要求.     

基于改进粒子群优化算法的无人机实时航迹规划

在无人机航迹规划中,通过改变惯性权重和采用自适应粒子群编码方式,以最大转弯半径、步进、最短距离和回避威胁作为航迹的评价指标,将约束条件、地形地貌及威胁信息引入适应度函数等方法,对粒子群优化算法进行改进,解决了粒子群算法在寻优过程中易陷入局部最优的问题.仿真结果表明,该方法可实现在线实时航迹规划.     

量子粒子群算法在地磁匹配航迹规划中的应用

针对基本粒子群算法在飞行器地磁匹配航迹规划中容易陷入局部收敛的问题,借鉴粒子群算法和量子进化算法,将量子粒子群算法应用在基于地磁匹配的航迹规划中。结合飞行器的性能约束和地磁匹配自身特点,设计了一种适用于地磁匹配航迹规划的评价函数作为适应度函数。仿真结果表明,量子粒子群算法具有较快的收敛速度且改善了最优解,验证了量子粒子群算法应用于地磁匹配航迹规划的有效可行性。     

基于改进球面向量粒子群优化的UAV航迹规划

针对无人机在复杂环境下受到多种威胁时的航迹规划问题,提出一种改进的基于球面向量的粒子群优化算法(ISPSO)。利用融合压缩因子和异步变化学习因子的ISPSO算法,通过粒子位置和速度同无人机转角和爬升角的对应关系,高效地搜索无人机的构形空间,找到成本函数最小的最优路径。为了评估ISPSO的性能,从真实的数字高程模型地图中生成2个基准场景,仿真结果表明,该算法优于基于球面向量的粒子群算法。     

采用病毒粒子群优化算法的飞行器航迹规划

针对飞行器航迹规划问题, 对基本粒子群算法进行了改进, 提出一种基于病毒粒子群优化算法的飞行器航迹规划方法。该方法结合生物病毒进化系统理论, 在基本粒子群算法中引入病毒种群, 通过执行反向代换和转导算子两种操作, 利用病毒的水平感染和垂直传播能力来维持主群体粒子种群和病毒种群之间的信息交换, 保证了航迹规划中粒子个体的多样性, 提高了算法的局部搜索能力, 解决了基本粒子群优化算法容易使粒子陷入局部最优、收敛速度慢的问题。仿真实验结果表明, 在相同的约束条件下, 所提出的方法能够更快更有效地生成满足要求的航迹。     

基于改进粒子群算法的无人机三维航迹规划

针对传统粒子群(Particle Swarm Optimization, PSO)算法在航迹规划的过程中需要根据无人机性能建立约束条件和易陷入局部最优值的缺点,提出了一种结合天牛须(Beetle Antennae Search, BAS)算法的球坐标PSO算法。该改进算法直接利用球坐标系对无人机的航向角和俯仰角进行约束,并且通过BAS算法避免PSO算法陷入局部最优值。根据数字高程地图建立仿真环境,综合考虑航迹长度、平滑度和危险性等因素构建目标函数。仿真结果表明,改进后的算法与其他PSO算法相比,规划的三维航迹质量更高,能够很好地适应无人机在各种环境下的飞行要求。     

基于Voronoi图和离散粒子群优化的无人机航迹规划

航迹规划是根据任务目标规划出满足约束条件的飞行轨迹,是实现无人机突防攻击的关键技术.由于无人机航迹规划的复杂性,提出了一种无人机分层航迹规划的方法,该方法首先由Voronoi图生成初始航路,然后考虑各约束条件,赋予各航路相应的权值,最终应用离散型粒子群算法搜索出满意的规划解.仿真结果表明,该方法规划效率高,占用内存少,...     

基于自适应变异粒子群算法的无人机航迹规划

提出一种基于自适应粒子群算法的航迹规划方法.航迹规划是低空突防过程中的关键技术,目的是得到一条既安全可靠又全局代价最优的三维航迹.针对基本粒子群优化算法容易陷入局部极值、进化后期的收敛速度慢和精度低等缺点,采用自适应粒子群优化算法,仿真结果表明该方法能够快速有效地完成规划任务,获得满意的三维航迹.     

采用改进粒子群算法的无人机协同航迹规划

为合理解决多无人机协同航迹规划问题,提出了两种解决方法。针对提出的空间直接法,将多无人机三维协同航迹规划问题简化为具有时间约束的二维航迹规划,并对传统协同变量和协同函数进行新的构建,最后结合一种改进的粒子群算法作了进一步的仿真实验,并取得了比较理想的结果。     

A linear precoding strategy based on particle swarm optimization in multicell cooperative transmission

An optimal linear precoding scheme based on Particle Swarm Optimization (PSO), which aims to maximize the system capacity of the cooperative transmission in the downlink channel, is proposed for a multicell multiuser single input single output system. With such a scheme, the optimal precoding vector could be easily searched for each user according to a simplified objective function. Simulation results show that the proposed scheme can obtain larger average spectrum efficiency and a better Bit Error Rate (BER) performance than Zero Forcing (ZF) and Minimum Mean Square Error (MMSE) algorithm.     

粒子群优化算法在海杂波参数优化中的应用

以具有对数正态分布和高斯谱特性的海杂波产生为例,根据零记忆非线性变换法（ZMNL）的原理,将海杂波的产生转化为参数优化问题,并用粒子群优化算法（PSO）进行求解,最后还将仿真结果与遗传算法进行了比较。讨论了用PSO进行参数优化的具体实现过程,并找到了较优的滤波器系数,得到满意的杂波谱特性。仿真结果表明,该方法完全可应用到海杂波的产生方法中。     

Secure steganography based on binary particle swarm optimization

The objective of steganography is to hide message securely in cover objects for secret communication. How to design a secure steganographic algorithm is still major challenge in this research field. In this letter, developing secure steganography is formulated as solving a constrained IP (Integer Programming) problem, which takes the relative entropy of cover and stego distributions as the objective function. Furthermore, a novel method is introduced based on BPSO (Binary Particle Swarm Optimization) for achieving the optimal solution of this programming problem. Experimental results show that the proposed method can achieve excellent performance on preserving neighboring co-occurrence features for JPEG steganography.     

基于改进K-means聚类和量子粒子群算法的多航迹规划

针对在复杂环境下需要通过多航迹规划以实现武器协同的问题,利用排挤机制产生Kmeans聚类的初始聚类中心,并将改进K-means聚类与量子粒子群算法(QPSO)相结合应用于无人机的三维多航迹规划。改进算法解决了K-means聚类易陷入局部最优、聚类准确率低的问题。根据产生的初始聚类中心,将粒子划分成多个子种群,利用QPSO算法对每个子种群进行优化,使得每个子种群可以产生一条可行航迹。仿真分析证明了改进算法可以有效保证子种群之间的多样性,生成较为分散的多条可行航迹。     

An optimal spectrum‐balancing algorithm for digital subscriber lines based on particle swarm optimization

This paper presents a new algorithm for optimal spectrum balancing in modern digital subscriber line (DSL) systems using particle swarm optimization (PSO). In DSL, crosstalk is one of the major performance bottlenecks, therefore various dynamic spectrum management algorithms have been proposed to reduce excess crosstalks among users by dynamically optimizing transmission power spectra. In fact, the objective function in the spectrum optimization problem is always nonconcave. PSO is a new evolution algorithm based on the movement and intelligence of swarms looking for the most fertile feeding location, which can solve discontinuous, nonconvex and nonlinear problems efficiently. The proposed algorithm optimizes the weighted rate sum. These weights allow the system operator to place differing qualities of service or importance levels on each user, which makes it possible for the system to avoid the selfish‐optimum. We can show that the proposed algorithm converges to the global optimal solutions. Simulation results demonstrate that our algorithm can guarantee fast convergence within a few iterations and solve the nonconvex optimization problems efficiently. Copyright © 2008 John Wiley & Sons, Ltd.     

无人机三维路径规划的粒子群混合算法

路径规划是无人机控制过程中的重要环节之一,现有基于粒子群等算法的传统路径规划方法存在容易陷入局部最优等问题,无法适应现实场景中复杂环境及高搜索速度的要求。针对已有方法的缺陷,提出了一种无人机路径规划的高性能细菌觅食-遗传-粒子群混合算法,以传统粒子群优化算法为基础,引入细菌觅食算法及遗传算法思想,提高算法计算速度与能力,同时考虑实际场景中无人机的运行约束,进一步提高了方法的可用性。最后,利用仿真实验验证了所提方法的有效性,并通过与传统方法对比证明了所提方法在运行时间、规划航程等方面的优越性。     

基于离网格结合粒子群算法的稀疏阵列综合

针对大型稀疏阵列未知期望方向图的综合问题, 提出了一种基于离网格结合粒子群算法的方法综合稀布阵列单元位置以使增益最大化.该方法在粒子群算法的基础上, 再次在网格单元中进行梯度寻优, 能快速找到增益最大时对应的阵元位置且无需已知期望方向图.该算法对稀疏阵列综合效果良好, 增益提升显著且扫描过程中栅瓣抑制良好.通过对综合结果进行HFSS全波仿真对比, 数值仿真结果与综合结果基本一致, 证明了该方法的正确性.     

基于离散粒子群算法的CDMA多用户检测方法

研究了离散粒子群算法，并将其应用于CDMA多用户检测问题，提出一种基于离散粒子群优化算法的CDMA多用户检测的方法。该方法应用一种新的选择和分区搜索的策略，改进搜索的质量和效率。分析以及实验仿真表明该方法具有计算复杂度低且可以得到较好误码率性能的特点，为寻求新的多用户检测方法提供了思路。     

Optimized localization of target nodes using single mobile anchor node in wireless sensor network

In Wireless Sensor Networks (WSNs), main challenges which restrict the performance are data computation, lifetime, routing, task scheduling, security, organization and localization. Recently, numerous Computational Intelligence (CI) based potential solutions for above mentioned challenges have been proposed to fulfill the desired level of performance in WSNs. Use of CI gives autonomous and strong solutions to ascertain precise node location (2D/3D) with least hardware necessity (position finding device, i.e., GPS empowered gadget). Localization of target nodes in static scenario can be done more precisely. However, in case of mobility, determining accurate position of each node in network is a challenging problem. In this paper, a novel idea of localizing target nodes with moving single anchor node is proposed using CI based application of Particle Swarm Optimization (PSO) and H-Best Particle Swarm Optimization (HPSO). The moving anchor node is following the Hilbert trajectory. Proposed algorithms are actualized for range-based, distributed, non-collaborative and isotropic WSNs. Only single moving anchor node is used as a reference node to localize the target nodes in the entire network. In proposed algorithms, problem of Line of Sight (LoS) is minimized due to projection of virtual anchor nodes.     

Research on optimizing the merging results of multiple independent retrieval systems by a discrete particle swarm optimization

The result merging for multiple Independent Resource Retrieval Systems (IRRSs), which is a key component in developing a meta-search engine, is a difficult problem that still not effectively solved. Most of the existing result merging methods, usually suffered a great influence from the usefulness weight of different IRRS results and overlap rate among them. In this paper, we proposed a scheme that being capable of coalescing and optimizing a group of existing multi-sources-retrieval merging results effectively by Discrete Particle Swarm Optimization (DPSO). The experimental results show that the DPSO, not only can overall outperform all the other result merging algorithms it employed, but also has better adaptability in application for unnecessarily taking into account different IRRS’s usefulness weight and their overlap rate with respect to a concrete query. Compared to other result merging algorithms it employed, the DPSO’s recognition precision can increase nearly 24.6%, while the precision standard deviation for different queries can decrease about 68.3%.