Saliency-directed color image segmentation using modified particle swarm optimization

Color image segmentation, an ill-posed problem, can be treated as a process of dividing a color image into some constituent regions and each region is homogeneous. In this study, a saliency-directed color image segmentation approach using “simple” modified particle swarm optimization (PSO) is proposed, in which both low-level features and high-level image semantics extracted from each color image are employed. To extract high-level image semantics from each color image, the visual attention saliency map for each color image is generated by three (color, intensity, and orientation) feature maps, which is used to guide region merging using “simple” modified PSO and a hybrid fitness function for color image segmentation. The proposed approach contains four stages, namely, color quantization, feature extraction, small region elimination, and region merging using “simple” modified PSO. Based on the experimental results obtained in this study, as compared with four comparison approaches, the proposed approach usually provides the better color image segmentation results.     

带惯性权重的粒子群优化算法性能仿真

粒子群优化算法是一种随机搜索算法,并能以较大概率收敛到全局最优,微粒群算法中关键参数的选择方法对算法特性有显著影响.文中针对微粒群算法中的加速常数、惯性权重、取值范围、种群规模的设置对算法基本性能的影响进行了分析.实验结果证明:选择适合的参数设置水平,能够获得稳健和高效的优化效果.     

Fuzzy particle swarm algorithm for function optimization

在传统粒子群算法的基础上运用模糊规则表加入了新的扰动因子,提出了一种新的算法--模糊粒子群算法。算法结合了模糊控制器中输入输出的模糊化处理和粒子群寻优的特点,为实际问题提供了新的解决手段。将模糊粒子群算法应用于函数优化的问题上,通过多组实例数据进行测试,验证表明了本算法具有良好的有效性和鲁棒性。     

An analog/digital pre-distorter using particle swarm optimization for RF power amplifiers

In this paper, a novel pre-distorter is presented using the particle swarm optimization (PSO) for an RF power amplifier linearization has been presented. We used the PSO in order to design of an efficient pre-distorter for the linearization of the output of an RF power amplifier by using the output data of the proposed power amplifier. The PSO is implemented to estimate and optimize the coefficient parameters of the work function in the proposed pre-distorter block diagram. The proposed method using PSO is most efficient because this approach is independent of the output of the power amplifier. The proposed method has been simulated with two-tone input signal and output power spectrum has been compared, where the obtained adjacent channel leakage ration (ACLR) is better than 50 dBc for both channels. Therefore, a quite significant improvement in linearity is achieved.     

改进的多目标粒子群算法优化设计及应用

针对粒子群算法存在易陷入局部最优点的缺点,提出了一种改进的带变异算子的多目标粒子群优化算法。采用非支配排序策略和动态加权法选择最优粒子,引导种群飞行,提高帕累托(Pareto)最优解的多样性。与其他优化算法相比,该算法易于实现并且计算速度更快。通过计算Pareto前沿最优解设计最佳多层电磁吸收体,在吸收体的厚度与反射系数之间取得最佳折衷。通过对反射系数函数与吸收体厚度函数测试验证,该算法能够在保持优化解多样性的同时具有较好的收敛性。     

基于混合粒子群算法求解TSP问题

遗传算法是研究TSP问题中最为广泛的一种算法,它具有全局搜索的能力。而粒子群算法收敛速度较快,但容易造成局部最优的情况。本文基于遗传算法的交叉变异设计了混合粒子群算法,通过对TSP问题求解分析,证实该方法提高了标准粒子群的搜索能力,获得了较高的收敛速度和近似最优解。     

基于VB和Access的温度采集系统的设计与实现

针对函数优化问题,提出了一种基于离差平方和法的粒子群优化算法。该算法用混沌序列初始化粒子的位置和速度,选择好于粒子群优化算法产生的粒子位置。通过离差平方和法进行聚类,利用分类方式来更新粒子的速度。最后将算法应用到3个典型的函数优化问题中,数值结果比较表明,提高了算法搜索能力,全局最优解的精度和收敛速度。     

基于粒子群ISODATA电梯交通模式识别

将粒子群算法与迭代自组织数据分析算法（ISODATA）结合,提出了一种基于粒子群的ISODATA算法。仿真实验表明,利用该算法可以对电梯交通模式进行准确、实时的识别。     

基于车流量和粒子群算法的无线接入点布局

市车载网环境下车辆的高速移动以及街道障碍物阻挡等原因,导致VANETs分割现象严重,以至于车载网不能正常通信,因此许多研究提出通过引入无线接入点（AP）来增强车载网通信的可能性.本文就是针对城市环境的VANETs的AP布局问题的研究,在基于车流量和粒子群算法的基础上提出的解决方案,并给出了相应的仿真,仿真结果表明该算法能在保证覆盖率的情况下实现AP的优化布局,同时在寻优过程中具有较快的收敛速度和较好的收敛性.     

Robust loop shaping-fuzzy gain scheduling control of a servo-pneumatic system using particle swarm optimization approach

In this paper, a new technique called robust loop shaping-fuzzy gain scheduled control (RLS-FGS) is proposed to design an effective nonlinear controller for a long stroke pneumatic servo system. In our technique, a nonlinear dynamic model of a long stroke pneumatic servo plant is identified by the fuzzy identification method and is used as the plant for our design. The structure of local controllers is selected as PID control which is proven by many research works that this type of control has many advantages such as simple structure, well understanding, and high performance. The proposed technique uses particle swarm optimization (PSO) to find the optimal local controllers which maximize the average stability margin. In addition, performance weighting function which is normally difficult to obtain is automatically determined by PSO. By the proposed technique, the RLS-FGS controller can be designed, and the structure of local controllers is still not complicated. As seen in the simulation and experimental results, our proposed technique is better than the classical gain scheduled PID controller tuned by pole placement and the conventional fuzzy PID controller tuned by ISE method in terms of robust performance.     

粒子群优化算法在企业财务危机预警中的应用

文章选取企业盈利能力作为企业财务预警指标,运用粒子群优化算法,对样本进行指标分析,得出企业财务预警模型的分析结论,对企业经营决策起到重要的参考和指导性作用。     

Saliency-directed color image interpolation using artificial neural network and particle swarm optimization

In this study, a saliency-directed color image interpolation approach using artificial neural network (ANN) and particle swarm optimization (PSO) is proposed. First, a high-quality saliency map of a color image to be interpolated is generated by a modified block-based visual attention model in an effective manner. Then, based on the saliency map, bilinear interpolation and ANN-PSO interpolation are employed for non-saliency (non-ROI) and saliency (ROI) blocks, respectively, to obtain the final color interpolation results. In the proposed ANN-PSO interpolation scheme, ANN is used to determine the orientation of each 5 × 5 image pattern (block), whereas PSO is employed to determine the weights in 5 × 5 interpolation filtering masks. The proposed approach is applicable to image interpolation with arbitrary magnification factors (MFs). Based on the experimental results obtained in this study, the color interpolation results by the proposed approach are better than those by five comparison approaches.     

粒子群算法的可视化在MATLAB下的实现

粒子群算法是一种新型的仿生优化算法,由于收敛速度快、参数设置少、实现简单,在很多领域得到应用。以标准粒子群算法为例,首先描述了粒子群算法的工作原理,然后给出了该算法在MATLAB中实现的详细步骤,并实现了二、三维粒子的可视化。可视化实现不但能够使粒子群算法更容易理解,同时也能更深入的理解粒子群算法的运行原理,为进一步的深入研究、改进及推广应用提供了理论与实践基础。     

粒子群优化算法用于阵列天线方向图综合设计

粒子群优化算法是基于一群粒子的智能运动而产生的一类随机进化算法,其优点是算法非常利于理解和应用.本文介绍了粒子群算法的原理和流程,研究了如何将这种方法运用于天线阵的方向图综合上,给出了PSO算法在综合阵列方向图的应用实例,表明粒子群算法在天线阵列综合中具有广泛的应用前景.     

微粒群算法局部模型邻域研究

详细介绍了微粒群算法局部最好模型几种邻域结构的特点,分析了引入元胞自动机邻居模型的优越性,并通过冯-诺依曼型邻域、摩尔型邻域、扩展的摩尔型邻域以及环形结构4种邻域结构在函数优化中性能的对比,指出了虽然针对不同的函数应该选用不同的邻域,但存在一些好的邻域结构,它们往往更适合比较广泛范围函数的优化。     

一种基于粒子群优化算法的混沌控制方法

提出了一种基于粒子群优化算法的神经网络控制混沌的方法。用粒子群优化算法来训练神经网络,利用训练好的神经网络作为混沌控制器,使混沌系统产生预期的运动。该方法无需了解被控系统的确切的动力学机制,实现方法简单。并对Logistic和Hénon映射进行了仿真,结果表明该方法能将系统控制到预定的轨道。因此,该方法能对混沌系统实施有效的控制,且能够应用于动力学模型未知而仅获得实验数据的情况。     

基于粒子群优化算法的复杂船舶电力系统脆性研究

针对复杂船舶电力系统的研究,提出一种复杂系统脆性分析的方法。建立了复杂船舶电力系统脆性的赋权图模型,以图的边描述子系统之间的脆性联系,边的权值描述子系统之间的脆性联系程度,并且定义了系统最大崩溃路径,提出了一种求解系统最大崩溃路径的粒子群优化算法。为了进一步说明问题,又以Matlab仿真为例验证了新算法,并对算法中参数的选取进行了讨论。     

粒子群算法优化的智能机器人无线充电方法

目前机器人传统直插式充电方式存有布线复杂、灵活性差,且不适用于易燃、易爆等特殊环境等问题,无线充电技术的应用有利于促进机器人智能化方向发展。为提高机器人无线充电效率,设计了一种基于无线通信技术、定位技术、无线充电技术的机器人智能无线充电系统;为提高粒子群算法全局寻优能力,引入鸽群优化算法地图、指南针算子和地标算子,提出了一种粒子群-鸽群融合定位算法对机器人充电位置进行定位优化;实验数据表明,设计算法具有较高的定位精度,较快的收敛速度,有利于机器人获取最佳充电位置,提高充电效率。     

分组扰动的粒子群优化算法在车辆调度中的应用

车辆调度是一个复杂的系统,具有多目标控制、高度非线性、时变等特征。基于粒子群算法,本文引入了分组扰动的思想,将其应用于高铁施工现场混凝土预拌车调度方案优化问题中。通过对一个实例的仿真研究表明,该算法可以极大限度地同时满足混凝土拌合站和工地的利益,能够很好地解决车辆调度优化问题。     

基于多目标粒子群算法的小型化槽螺旋天线研究

设计了一种小型化的槽螺旋天线。首先在普通的槽螺旋天线臂上开槽,再在天线最外圈的天线臂上加载6个等距分布的贴片电阻,基于多目标粒子群算法对开槽位置和贴片电阻阻值进行优化,使得天线的工作频率向低频偏移,同时保证具有良好的圆极化辐射特性。仿真结果表明:在0.44~18.00 GHz频段内回波损耗小于–10 d B,1.8~18.0 GHz频段内轴比小于3dB。与普通的螺旋天线相比,在天线口径不变的情况下,极大地改善了天线的低频端的辐射特性。