粒子群优化算法

系统地介绍了粒子群优化算法,归纳了其发展过程中的各种改进如惯性权重、收敛因子、跟踪并优化动态目标等模型。阐述了算法在目标函数优化、神经网络训练、模糊控制系统等基本领域的应用并给出其在工程领域的应用进展,最后,对粒子群优化算法的研究和应用进行了总结和展望,指出其在计算机辅助工艺规划领域的应用前景。     

Frankenstein's PSO: A Composite Particle Swarm Optimization Algorithm

During the last decade, many variants of the original particle swarm optimization (PSO) algorithm have been proposed. In many cases, the difference between two variants can be seen as an algorithmic component being present in one variant but not in the other. In the first part of the paper, we present the results and insights obtained from a detailed empirical study of several PSO variants from a component difference point of view. In the second part of the paper, we propose a new PSO algorithm that combines a number of algorithmic components that showed distinct advantages in the experimental study concerning optimization speed and reliability. We call this composite algorithm Frankenstein's PSO in an analogy to the popular character of Mary Shelley's novel. Frankenstein's PSO performance evaluation shows that by integrating components in novel ways effective optimizers can be designed.     

基于多点力学的粒子群算法改进

针对于粒子群算法（PSO）易早熟、参数相关性强以及高维数据难以优化的问题。通过引入多点力学计算和理论建模推导,提出了一个新的粒子速度的计算方法。通过理论推导和实验证明对理论结果进行仿真和测试,对于常见的测试函数,新粒子群算法较标准粒子群算法有良好的适应性和准确性的改进。     

一种基于粒子群的流识别

当今的网络黑客对网络的攻击十分频繁,因此日常的网络流量中常常夹杂着某些恶意流,这些流是由黑客发送的报文。为了有效应对来自网络的各种攻击,安全技术的研究人员必须对网络中的各种流进行识别。当今对流的识别算法是多种多样的,粒子群算法是一种智能性属性匹配算法。该算法已在多个领域进行应用,因此本文结合已有的网络技术和已有的网络知识,提出了一种基于粒子群的流识别算法。     

Critical Configurations for 1-View in Projections from ℙ<Superscript>k</Superscript> → ℙ<Superscript>2</Superscript>

In this paper we describe, from a theoretical point of view, critical configurations for the projective reconstruction of a set of points, for a single view, i.e. for calibration of a camera, in the case of projections from ℙ^k to ℙ² for k ≥ 4. We give first a general result describing these critical loci in ℙ^k, which, if irreducible, are algebraic varieties of dimension k−2 and degree 3. If k=4 they can be either a smooth ruled surface or a cone and if k = 5 they can be a smooth three dimensional variety, ruled in planes, or a cone. If k≥ 6, the variety is always a cone, the vertex of which has dimension at least k − 6. The reducible cases are studied in Appendix A. These results are then applied to determine explicitly the critical loci for the projections from ℙ^k which arise from the dynamic scenes in ℙ³ considered in [13]. Marina Bertolini is currently Associate Professor of Geometry at the Department of Mathematics at the Università degli Studi di Milano, Italy. Her main field of research is Complex Projective Algebraic Geometry, with particular interest for the classification of projective varieties and for the geometry of Grassmann varieties. On these topics M. Bertolini has published more than twenty reviewed papers on national and international journals. She has been for some years now interested also in applications of Algebraic Geometry to Computer Vision problems. Cristina Turrini is Associate Professor of Geometry at the Department of Mathematics of Università degli Studi di Milano, Italy. Her main research interest is Complex Projective Algebraic Geometry: subvarieties of Grassmannians, special varieties, automorphisms, classification. In the last two years she has started to work on applications of Algebraic Geometry to problems of Computer Vision. She is author or co-author of about thirty reviewed papers. She is also involved in popularization of Mathematics, and on this subject she is co-editor of some books.     

Auth<Superscript>2</Superscript>(nPA)

Für viele heutige IT-Dienste w?re eine st?rkere Authentifizierung als die mit Nutzername und Passwort wünschenswert. Insbesondere für sicherheitskritische Prozesse, wie das Zurücksetzen von Passw?rtern, sind bestehende L?sungen oft unbefriedigend. Der neue Personalausweis (nPA) stellt mit seiner eID-Funktion die M?glichkeit für eine starke Authentisierung bereit. Jedoch stehen dem erreichbaren Sicherheitsgewinn nicht unerhebliche Kosten gegenüber, die pro Dienstanbieter aufgebracht werden müssen, um die eID-Funktion zu nutzen. Wir zeigen mit dem Konzept Auth²(nPA), wie eine Zweifaktorauthentisierung mit dem nPA von mehreren Einrichtungen gemeinsam genutzt werden kann. Um für diesen Dienst ein Berechtigungszertifikat zu erhalten, realisiert das Auth²(nPA)-Konzept ein vergleichbar hohes Datenschutzniveau wie die generische eID-L?sung mit getrennten Dienstanbietern.     

A personalized e-course composition based on a genetic algorithm with forcing legality in an adaptive learning system

This paper proposes a personalized e-course composition based on a genetic algorithm with forcing legality (called GA^?) in adaptive learning systems, which efficiently and accurately finds appropriate e-learning materials in the database for individual learners. The forcing legality operation not only reduces the search space size and increases search efficiency but also is more explicit in finding the best e-course composition in a legal solution space. In serial experiments, the forcing legality operation is applied in Chu et al.'s the particle swarm optimization (called PSO^?) and Dheeban et al.'s the improved particle swarm optimization (called RPSO^?) to show the forcing legality can speed up the computational time and reduce the computational complexity of algorithm. Furthermore, GA^? regardless of the number of students or the number of materials in the database, to compose a personalized e-course within a limited time is much more efficient and accurate than PSO^? and RPSO^?. For the experiment increasing the number of students to 1200, the average improvement ratios of errors (learning concept error, materials difficulty error, learning time error), fitness value, stability, and execution time are above 96%, 79%, 90%, and 10%, respectively. For the experiment increasing the number of materials to 500 and the execution time set to the shortest execution time of RPSO^?, the average improvement ratios of errors (learning concept error, materials difficulty error, learning time error), fitness value, and stability are above 97%, 51%, and 80%, respectively. Therefore, GA^? is able to enhance the quality of personalized e-course compositions in adaptive learning environments.     

基于K-均值聚类的免疫粒子群优化算法

在解决复杂函数优化问题时,免疫粒子群优化算法在疫苗提取操作中,通常把最佳粒子作为有效特征信息,没有从根本上解决"趋同性"问题,影响算法的收敛速度和收敛精度.针对该问题,提出了一种基于K-均值聚类的免疫粒子群优化算法(KIPSO),将K-均值聚类算法用于免疫疫苗的提取,确定最高平均适应值的聚类中心及其最大邻域,得到符合具有最优个体特征的疫苗集,并以自适应方式确定疫苗集进行疫苗更新,提高算法的收敛性.仿真结果表明,与免疫粒子群优化算法相比,该算法提高了收敛速度和收敛精度.     

粒子群优化算法的改进研究

本文针对粒子群优化算法(PSO)存在早熟收敛的问题,提出了一系列改进措施,分别将混沌理论、遗传算法和免疫算法应用到PSO算法中。计算机仿真实验表明:改进算法基本保持了PSO算法简单、易实现的特点,且能够有效避免算法的早熟收敛问题,具有很强的全局搜索能力。     

C<Superscript>2</Superscript> quartic spline surface interpolation

This paper discusses the problem of constructing C² quartic spline surface interpolation. Decreasing the continuity of the quartic spline to C² offers additional freedom degrees that can be used to adjust the precision and the shape of the interpolation surface. An approach to determining the freedom degrees is given, the continuity equations for constructing C² quartic spline curve are discussed, and a new method for constructing C² quartic spline surface is presented. The advantages of the new method are that the equations that the surface has to satisfy are strictly row diagonally dominant, and the discontinuous points of the surface are at the given data points. The constructed surface has the precision of quartic polynomial. The comparison of the interpolation precision of the new method with cubic and quartic spline methods is included.     

Cooperative Velocity Updating model based Particle Swarm Optimization

Particle swarm optimization (PSO) has shown its good performance in many optimization problems. This paper proposes a Cooperative Velocity Updating algorithm based Particle Swarm Optimization (CVUPSO), which is inspired by the competition and cooperation methods of different populations in natural swarm living, such as bees, ants, birds, fish, etc. In this algorithm, before an elite is introduced from other sub-swarms or a new particle is randomly born, the weak particle will be eliminated out of its sub-swarm. In each iteration process, every sub-swarm abandons a least potential particle. The CVUPSO recorded four special positions: pbest, lbest, gbest and lworst. The pbest represents the current particle’s best position while lbest represents the current sub swarm’s best position, and gbest is the best position among the whole swarm, lworst is the position of the particle with the worst performance. A new update method is adopted in CVUPSO, where the particles are more likely to follow lbest than follow gbest in the early stage of iteration, but opposite in the later stage. In this paper, two variants of CVUPSO are proposed, one variant is CVUPSO with Random inertia weight (for short CVUPSO-R), and another is CVUPSO with Exponential decline inertia weight (for short CVUPSO-E). By making comparative experiments on several widely used benchmark functions, analysis results show that the performance of these two improved variants are more promising than the recently developed PSO algorithms for searching multiple peak values of multiple objects optimization problem.     

双群体随机微粒群算法

以保证全局收敛的随机微粒群算法为基础,文章提出了一种双群体随机微粒群算法——DB-SPSO。该方法采用两个群体同时进化,一个群体在进化过程中所出现的停止微粒由另一群体的微粒来代替,并和此群体中其余的微粒一起继续进化。通过对此算法的参数适用范围及收敛率进行讨论,给出了此算法的适用范围。其仿真结果表明:对于单峰函数和多峰函数,此算法都能够取得较好的优化效果。     

E(FG)<Superscript>2</Superscript>: a new fixed-grid shape optimization method based on the element-free galerkin mesh-free analysis

We propose a shape optimization method over a fixed grid. Nodes at the intersection with the fixed grid lines track the domain’s boundary. These “floating” boundary nodes are the only ones that can move/appear/disappear in the optimization process. The element-free Galerkin (EFG) method, used for the analysis problem, provides a simple way to create these nodes. The fixed grid (FG) defines integration cells for EFG method. We project the physical domain onto the FG and numerical integration is performed over partially cut cells. The integration procedure converges quadratically. The performance of the method is shown with examples from shape optimization of thermal systems involving large shape changes between iterations. The method is applicable, without change, to shape optimization problems in elasticity, etc. and appears to eliminate non-differentiability of the objective noticed in finite element method (FEM)-based fictitious domain shape optimization methods. We give arguments to support this statement. A mathematical proof is needed.     

(Two-Dimensional)<Superscript>2</Superscript> whitening reconstruction for newborn recognition

Recently, various feature extraction techniques and its variations have been proposed for computer vision. However, most of these techniques are sensitive to the images acquired in uncontrolled environment. The illumination, expression and occlusion for face images result in random error entries in the 2days matrix representing the face. Techniques such as Principal Component Analysis (PCA) do not handle these entries explicitly. This paper proposes a (Two-dimensional)² whitening reconstruction (T²WR) pre-processing step to be coupled with the PCA algorithm. This combined method would process illumination & expression variations better than standalone PCA. This technique has been compared with state-of-the-art Two-dimensional whitening reconstruction (TWR) pre-processing method. The final results clearly indicate the reason for better performance of T²WR over TWR. The histograms plotted for both these algorithms show that T²WR makes for a smoother frequency distribution than TWR. The proposed method indicated increased recognition rate and accuracy with increasing number of training images; up to 93.82 % for 2 images, 94.76 % for 4 images and 97.42 % for 6 training images.     

多机并行作业车间调度的混合粒子群算法

本文通过对多目标多机作业车间调度的研究提出了一个改进的结合启发式规则的粒子群算法,采用了依机器和工件排序的编码规则,两段式的表达形式,带启发式规则的调整,并结合聚类分析思想的保留策略,对此类问题提出了一个新的解决方法,并在文中通过一个仿真案例进行求解。     

改进粒子群算法在无功优化中的应用

针对电力系统无功优化的特点,本文提出以有功网损最小为目标函数,以负荷节点电压质量和PV发电机节点无功出力为罚函数．以有功功率和无功功率为约束条件的数学模型,并应用改进的粒子群算法对无功优化问题进行求斛。该算法在权重系数和不活动粒子两方面进行改进,有效地解决了进化过程中陷入局部最优和搜索精度差的缺点。最后,将改进后的粒子群算法应用于IEEE14节电力系统进行无功优化算例分析,仿真结果验证了该算法解决电力系统无功优化问题的有效性和可行性。     

三段式粒子群算法在车辆调度问题中的应用

针对标准粒子群算法在解决车辆调度问题上的不足,提出了一种基于整数编码的粒子群优化策略。它依据粒子群算法中粒子进化的思想,给出了三段式保优方法,重新定义了粒子进化速度和位置更新的方式。结合Floyd算法对调度模型进行了仿真验证分析。结果表明,该策略具有较好的寻优能力。     

粒子群优化算法研究

粒子群优化算法是根据鸟群觅食过程中的迁徙和群集模型而提出的,用于解决优化问题的一类新兴的随机优化算法。本文首先介绍PSO算法的基本原理和工作机制;然后介绍粒子群优化算法的优化策略,包括提高收敛速度﹑算法离散化﹑提高总群多样性;最后对其将来的发展进行了展望。     

基于粒子群优化算法的系统可靠性优化

系统可靠性优化问题是典型的NP难题,建立了可靠性冗余优化模型,采用粒子群优化算法对其进行求解。通过对其它文献中仿真实例的计算和结果对比,表明了算法对求解可靠性优化问题的可行性和有效性。