High‐precision formation control of nonlinear multi‐agent systems with switching topologies: A learning approach

Arbitrary high precision is considered one of the most desirable control objectives in the relative formation for many networked industrial applications, such as flying spacecrafts and mobile robots. The main purpose of this paper is to present design guidelines of applying the iterative schemes to develop distributed formation algorithms in order to achieve this control objective. If certain conditions are met, then the control input signals can be learned by the developed algorithms to accomplish the desired formations with arbitrary high precision. The systems under consideration are a class of multi‐agent systems under directed networks with switching topologies. The agents have discrete‐time affine nonlinear dynamics, but their state functions do not need to be identical. It is shown that the learning processes resulting from the relative output formation of multi‐agent systems can converge exponentially fast with the increase of the iteration number. In particular, this work induces a distributed algorithm that can simultaneously achieve the desired relative output formation between agents and regulate the movement of multi‐agent formations as desired along the time axis. The illustrative numerical simulations are finally performed to demonstrate the effectiveness and performance of the proposed distributed formation algorithms. Copyright © 2014 John Wiley & Sons, Ltd.     

Switching adaptive learning control for nonlinearly parameterized systems with disturbance of unknown periods

In this paper, a logic‐based switching adaptive learning control mechanism is proposed for a class of nonlinearly parameterized systems with disturbance of unknown periods. The switching algorithms include two parts: one is to stabilize the nonlinearly parameterized uncertainties, the other is to learn the periodic bounded disturbance. An adaptive control method with fully saturated periodic adaptation law is presented to take advantage of the periodic and bounded property of the disturbance. It is shown that under the proposed control designs, the asymptotic convergence is ensured irrespective of initial conditions with all the signals in the closed‐loop system bounded. An illustrative example is given to show the validity of the switching adaptive learning control. Copyright © 2014 John Wiley & Sons, Ltd.     

面向生态的陕西省渭河流域水资源合理配置

结合陕西省渭河流域的水资源开发利用现状,对其水资源优化管理进行研究。通过分析流域社会经济发展的特点及生态环境形势,以定额法为基础对2020年流域的生活、生产和生态需水进行了预测。根据系统概化图和配水目标,综合考虑水资源配置中的生态指标、社会指标和经济指标,建立了面向生态的水资源合理配置模型。将混沌优化算法与遗传算法相结合对模型进行求解,结果表明:陕西省渭河流域2020年75%保证率下的缺水率为18.1%。     

Overview of Information Security Using Genetic Algorithm and Chaos

ABSTRACT

Security, integrity, nonrepudiation, confidentiality, and authentication services are the most important factors in information security. Genetic algorithms (GAs) are a class of optimization algorithms. Many problems can be solved using genetic algorithms through modeling a simplified version of genetic processes. The application of a genetic algorithm to the field of cryptology is unique. Few works exist on this topic. In this article, an attempt has been made to give an overview of genetic algorithm-based cryptography and to propose a new approach to GA with pseudorandom sequence to encrypt data stream. The feature of such an approach includes high data security and high feasibility for easy integration with commercial multimedia transmission applications. The experimental results of the proposed technique confirm that high throughput rate needed for real time data protection is achieved.     

NetCluster: A clustering-based framework to analyze internet passive measurements data

Internet measured data collected via passive measurement are analyzed to obtain localization information on nodes by clustering (i.e., grouping together) nodes that exhibit similar network path properties. Since traditional clustering algorithms fail to correctly identify clusters of homogeneous nodes, we propose the NetCluster novel framework, suited to analyze Internet measurement datasets. We show that the proposed framework correctly analyzes synthetically generated traces. Finally, we apply it to real traces collected at the access link of Politecnico di Torino campus LAN and discuss the network characteristics as seen at the vantage point.     

A novel artificial bee colony algorithm with Powell's method

Artificial bee colony (ABC) algorithm is a relatively new optimization technique which has been shown to be competitive to other population-based algorithms. However, there is still an insufficiency in ABC regarding its solution search equation, which is good at exploration but poor at exploitation. To address this concerning issue, we first propose a modified search equation which is applied to generate a candidate solution in the onlookers phase to improve the search ability of ABC. Further, we use the Powell's method as a local search tool to enhance the exploitation of the algorithm. The new algorithm is tested on 22 unconstrained benchmark functions and 13 constrained benchmark functions, and are compared with some other ABCs and several state-of-the-art algorithms. The comparisons show that the proposed algorithm offers the highest solution quality, fastest global convergence, and strongest robustness among all the contenders on almost all test functions.     

Parameter estimation for five- and seven-parameter photovoltaic electrical models using evolutionary algorithms

Equivalent electric circuit modeling of PV devices is widely used to predict PV electrical performance. The first task in using the model to calculate the electrical characteristics of a PV device is to find the model parameters which represent the PV device. In the present work, parameter estimation for the model parameter using various evolutionary algorithms is presented and compared. The constraint set on the estimation process is that only the data directly available in module datasheets can be used for estimating the parameters. The electrical model accuracy using the estimated parameters is then compared to several electrical models reported in literature for various PV cell technologies.     

Spectroscopic Multicomponent Analysis Using Multi-objective Optimization for Variable Selection

The multiple determination tasks of chemical properties are a classical problem in analytical chemistry. The major problem is concerned in to find the best subset of variables that better represents the compounds. These variables are obtained by a spectrophotometer device. This device measures hundreds of correlated variables related with physicocbemical properties and that can be used to estimate the component of interest. The problem is the selection of a subset of informative and uncorrelated variables that help the minimization of prediction error. Classical algorithms select a subset of variables for each compound considered. In this work we propose the use of the SPEA-II （strength Pareto evolutionary algorithm II）. We would like to show that the variable selection algorithm can selected just one subset used for multiple determinations using multiple linear regressions. For the case study is used wheat data obtained by NIR （near-infrared spectroscopy） spectrometry where the objective is the determination of a variable subgroup with information about E protein content （%）, test weight （Kg/HI）, WKT （wheat kernel texture） （%） and farinograph water absorption （%）. The results of traditional techniques of multivariate calibration as the SPA （successive projections algorithm）, PLS （partial least square） and mono-objective genetic algorithm are presents for comparisons. For NIR spectral analysis of protein concentration on wheat, the number of variables selected from 775 spectral variables was reduced for just 10 in the SPEA-II algorithm. The prediction error decreased from 0.2 in the classical methods to 0.09 in proposed approach, a reduction of 37%. The model using variables selected by SPEA-II had better prediction performance than classical algorithms and full-spectrum partial least-squares.     

新虚拟场景模型下智能漫游算法设计与实现①

基于对相关智能预测算法的分析,发现现有算法并未考虑到虚拟场景节点间存在的潜在关联关系,降低了算法的预测准确率。针对此问题,在一种新的立方体虚拟场景模型的基础上,提出了一种智能漫游算法,通过为每个节点建立一个以当前节点为根结点的完全二叉树结构,利用节点间潜在的关联关系判断下一步可能被操作的对象,对预测对象提前进行绘制并调入内存,有效弥补了相关算法存在的缺陷,从而提高了虚拟场景漫游的效率和实时性,算法具有较高的准确性。     

一种基于改进遗传算法的面向路径测试用例自动生成方法

介绍了基于改进遗传算法测试用例生成的核心算法,针对软件测试用例生成问题的特点对传统遗传算法的遗传算子进行了改进,实验获得了较好的效果。