基于局部填充函数的混合优化算法的改进

填充函数算法是求解全局优化问题的常用算法,其应用效果依赖于如何合理地选择算法参数。为了方便地选择参数,该文提出了局部填充函数的概念,讨论了基于局部填充函数的混合优化算法的改进策略。对于给定的参数,混合优化算法寻找一个包含极小点的区域,使得所构造的函数在该区域上满足局部填充函数的定义,从而利用局部填充函数的性质简化寻优过程,减少优化过程中参数调整的次数和难度,提高算法的效率和稳定性。此外,针对填充函数算法研究中简单盆存在性问题,该文给出了一个实例,说明二次连续可微的函数在一定条件下其孤立极小点附近可以不存在简单盆。     

可数强仿S-闭空间

给出了可数强仿S-闭空间的概念，讨论了该空间的一些性质及其半正则化，同时给出它了的一个极不连通的定理．     

单向变异S-粗集的概率特征

利用单向变异S-粗集的结构,给出元素迁移的概率特征:属性集α的下近似概率特征,属性集α的上近似概率特征,利用这些结果,给出单向变异S-粗集的概率特征,提出单向变异S-粗集的随机结构与随机定理.单向变异S-粗集的结构是单向变异S-粗集的随机结构的特例,单向变异S-粗集的随机结构是单向变异S-粗集结构的一般形式.     

P systems based multi-objective optimization algorithm

Based on P systems, this paper proposes a new multi-objective optimization algorithm (PMOA). Similar to P systems, PMOA has a cell-like structure. The structure is dynamic and its membranes merge and divide at different stages. The key rule of a membrane is the communication rule which is derived from P systems. Mutation rules are important for the algorithm, which has different ranges of mutation in different membranes. The cooperation of the two rules contributes to the diversity of the population, the conquest of the muhimodality of objective function and the convergence of algorithm. Moreover, the unique structure divides the whole population into several sub populations, which decreases the computational complexity. Almost a dozen popular algorithms are compared using several test problems. Simulation results illustrate that the PMOA has the best performance. Its solutions are closer to the true Pareto-optimal front     

端部脱砂压裂充填防砂设计及其在涩北气田的应用

介绍了端部脱砂压裂充填防砂机理，考虑压裂增产比和挡砂效果，研究了压裂充填防砂砾石尺寸的选择思路和方法。以实现端部脱砂从而产生短而宽的充填裂缝为目的，对脱砂时间、脱砂浓度、加砂时间、加砂浓度以及施工泵注程序等压裂防砂施工参数进行了优化设计。该套防砂工艺及设计方法在涩北气田应用13井次，防砂增产成功率为85％，防砂后产量平均增幅41．9％，防砂效果良好，达到了防砂与增产的目的。     

一种基于H.264的自适应快速模式选择算法

提出了一种基于H.264的快速模式选择算法.该算法首先进行Skip模式的判断.如果Skip模式不适合当前宏块,则继续利用宏块的空间同质性和时间静态性进行其他帧间模式的筛选.然后根据帧间模式选择的结果判断是否继续进行帧内预测.算法设计过程中,充分考虑Quan-tize Parameter对模式选择的影响,从而可以适用于不同码率的环境.实验证明,该算法在高低码率下都能在保证图像质量的同时大幅度提升编码速度.     

E-凸类函数及其在最优化中的应用

在已有研究基础上，对几类E-凸函数进行了研究，得出了它们的一些新性质，并研究了它们在最优化问题中的应用，推广了以前的部分结论，完善了这几类非凸函数．     

Stability Analysis of Stochastic Swarm Systems

In this paper,we present a model of stochastic swarm system and prove the stability of this kind of systems. We establish the stable aggregating behavior for the group using a coordination control scheme. This individual-based control scheme is a combination of attractive and repulsive interactions among the individuals in the group,which ensures the cohesion of the group and collision avoidance among the individuals. The dynamics of each individual depends on the relative positions between the individuals and the influences of the random disturbances. Under the influences of the noises,this position-based control strategy still generates the stable aggregating behavior harmoniously for the group and the self-organized swarm pattern is formed.     

KDS-CM： A Cache Mechanism Based on Top-K Data Source for Deep Web Query

Caching is an important technique to enhance the efficiency of query processing. Unfortunately, traditional caching mechanisms are not efficient for deep Web because of storage space and dynamic maintenance limitations. In this paper, we present on providing a cache mechanism based on Top-K data source （KDS-CM） instead of result records for deep Web query. By integrating techniques from IR and Top-K, a data reorganization strategy is presented to model KDS-CM. Also some measures about cache management and optimization are proposed to improve the performances of cache effectively. Experimental results show the benefits of KDS-CM in execution cost and dynamic maintenance when compared with various alternate strategies.     

Convergence of Recursive Identification for ARMAX Process with Increasing Variances

The autoregressive moving average exogenous (ARMAX) model is commonly adopted for describing linear stochastic systems driven by colored noise. The model is a finite mixture with the ARMA component and external inputs. In this paper we focus on a parameter estimate of the ARMAX model. Classical modeling methods are usually based on the assumption that the driven noise in the moving average (MA) part has bounded variances, while in the model considered here the variances of noise may increase by a power of log n. The plant parameters are identified by the recursive stochastic gradient algorithm. The diminishing excitation technique and some results of martingale difference theory are adopted in order to prove the convergence of the identification. Finally, some simulations are given to show the reliability of the theoretical results.