基于虚拟阵列变换的相干源测向方法

针对传统空间谱测向方法对相干信号源测向失效的实际问题,提出了一种基于任意形状平面阵列的测向方法。该方法在建立了相干信号源数学模型的基础上,对虚拟阵列变换思想进行了分析,给出了采用虚拟阵列变换原理和MUSIC算法实现了对相干信号源DOA的估计,提高了测向性能。经仿真试验和理论分析,验证了算法的可行性,并分析了该算法的适用局限性。     

Charged system search for optimal design of frame structures

The charged system search (CSS) algorithm is utilized for design of frame structures. The algorithm is inspired by the laws in physics. The CSS utilizes a number of charged particles which influence each other based on their fitness values and their separation distances considering the governing law of Coulomb. A comparison between the characteristics of the CSS algorithm and other well-known meta-heuristics is performed to indicate their similarities and differences. Some benchmark frame examples are optimized with the CSS algorithm. Comparison of the results of CSS with some other meta-heuristic algorithms shows the robustness of the new algorithm.     

Genetic algorithm based multi-objective reliability optimization in interval environment

In most of the real world design or decision making problems involving reliability optimization, there are simultaneous optimization of multiple objectives such as the maximization of system reliability and the minimization of system cost, weight and volume. In this paper, our goal is to solve the constrained multi-objective reliability optimization problem of a system with interval valued reliability of each component by maximizing the system reliability and minimizing the system cost under several constraints. For this purpose, four different multi-objective optimization problems have been formulated with the help of interval mathematics and our newly proposed order relations of interval valued numbers. Then these optimization problems have been solved by advanced genetic algorithm and the concept of Pareto optimality. Finally, to illustrate and also to compare the results, a numerical example has been solved.     

Centralized and distributed spectrum channel assignment in cognitive wireless networks: A Harmony Search approach

This paper gravitates on the spectrum channel allocation problem where each compounding node of a cognitive radio network is assigned a frequency channel for transmission over a given outgoing link, based on optimizing an overall network performance metric dependant on the level of interference among nearby nodes. In this context, genetically inspired algorithms have been extensively used so far for solving this optimization problem in a computationally efficient manner. This work extends previous preliminary research carried out by the authors on the application of the heuristic Harmony Search (HS) algorithm to this scenario by presenting further results and derivations on both HS-based centralized and distributed spectrum allocation techniques. Among such advances, a novel adaptive island-like distributed allocation procedure is presented, which dramatically decreases the transmission rate required for exchanging control traffic among nodes at a quantifiable yet negligible performance penalty. Extensive simulation results executed over networks of increasing size verify, on one hand, that our proposed technique achieves near-optimum spectral channel assignments at a low computational complexity. On the other hand, the obtained results assess that HS vastly outperforms genetically inspired allocation algorithms for the set of simulated scenarios. Finally, the proposed adaptive distributed allocation approach is shown to attain a control traffic bandwidth saving of more than 90% with respect to the naive implementation of a HS-based island allocation procedure.     

Necessary and sufficient conditions for the existence of a positive definite solution of a nonlinear matrix equation

Some necessary and sufficient conditions for the existence of a positive definite solution of the nonlinear matrix equation X+A*X ^?α A=Q (0<α≤1) are given. By the way an iterative method is presented. Furthermore, the convergence and error estimation of the iterative algorithm are derived. The illustrative numerical examples due to Peng are worked out.     

基因算法结合类神经网络优化注塑成型参数

以熔融温度、射出时间、保压压力、保压时间、冷却时间等5个制程参数作为控制因子,利用Moldflow仿真软件对导光板模型进行模流分析,应用田口法搭配倒传递类神经网路Super PCNeuom 5.0程序,建立导光板总翘曲值、体积顶出收缩值、缩痕指数质量预测模型,再应用MATLAB基因演算程序来搜寻在控制参数水平范围内局部最佳解参数组合,使塑料产品的质量提升.     

Orderly algorithms for graph generation

The existence of orderly analogues of graph generators proposed by Heap and Farrell is established. The modifications to these algorithms supply practical methods enabling one to generate exhaustive lists of graphs and locally restricted graphs; moreover, the difficulty involved in ensuring that no duplications occur in the list is greatly reduced.     

基于蜂窝网络拓扑的无线传感网定位算法

针对无线传感网中DV-Hop定位算法过于依赖信标节点数量以及定位精度低的问题,提出基于蜂窝网络拓扑的定位算法(LABCNT)。通过对网络中节点的有向筛选,选取满足特定条件的节点,构造一个蜂窝的网络拓扑结构,得到网络上节点的相对位置;根据拓扑结构中的第二个信标节点确定网络上节点的绝对位置;将已定位的节点升级为协作节点,采用最小二乘算法对域内其它未知节点进行定位。仿真结果表明,与DH-RLS、IDVH-LA、IDVH-HCHEC算法相比,LABCNT算法的定位精度较高,所需信标节点数量最少。     

Measurement method and pipe wall misalignment adjustment algorithm of the pipe butting machine

This paper presents a runout measurement method and a novel finite grouping method to predict and optimize the rotational angle and translational displacement of butting pipes to minimize pipe wall misalignment (PWM). This study develops a method to minimize the PWM of the pipes excluding the positions of welding seams. In this method, the measurement data are divided into finite groups and the criteria are created to identify the positions of welding seams and eliminate the effect of the welding seams. Finally, the rotational angle and translational displacement of the butting pipes are optimized to minimize the PWM. A butting machine is designed to implement this method. The machine is benchmarked by a standard smooth pipe to minimize system errors. Three butting experiments have been performed with welded pipes of diameter 406 mm. The comparison shows that the computation results agree with the experimental results very well. The maximum PWMs in three experiments are less than 1.87 mm, which satisfies the butting requirements, that is, a PWM of less than 2.0 mm. Then, the uncertainties of the measurement results are discussed.     

Krill herd algorithm for optimal location of distributed generator in radial distribution system

Distributed generator (DG) is recognized as a viable solution for controlling line losses, bus voltage, voltage stability, etc. and represents a new era for distribution systems. This paper focuses on developing an approach for placement of DG in order to minimize the active power loss and energy loss of distribution lines while maintaining bus voltage and voltage stability index within specified limits of a given power system. The optimization is carried out on the basis of optimal location and optimal size of DG. This paper developed a new, efficient and novel krill herd algorithm (KHA) method for solving the optimal DG allocation problem of distribution networks. To test the feasibility and effectiveness, the proposed KH algorithm is tested on standard 33-bus, 69-bus and 118-bus radial distribution networks. The simulation results indicate that installing DG in the optimal location can significantly reduce the power loss of distributed power system. Moreover, the numerical results, compared with other stochastic search algorithms like genetic algorithm (GA), particle swarm optimization (PSO), combined GA and PSO (GA/PSO) and loss sensitivity factor simulated annealing (LSFSA), show that KHA could find better quality solutions.