遗传算法的一个改进及其在气动设计中的应用

提出一个概率型二值搜索思想，对标准遗传算法进行改进，改进是在原算法中增设一个概率型二值决策步。在该步中，首先通过计算种群中每个个体染色体（二值串）各分量的适应值，统计染色体分量所在位置（基因位）取值的历史表现，由此给每一个分量位赋予一个分值；然后，利用该分值以概率方式产生若个干新个体，并加入新代种群参与进化。由于分量位分值包含全局收敛信息，以此为基础产生的新个体可望具有良好素质，从而提高收敛速度，这在一个二维多峰函数极大值搜素问题中得到了验证。最后将所提出的方法应用于一离心压缩机扩压器叶片逆命题设计问题，与标准遗传算法求解过程的对比。

活用三步法 巧解漫画题

漫画题是近年来中考试题中出现频率较高的一种题型。本文分析了漫画题的三大特点：形式简单、立意新颖、答案灵活,并在此基础上提出了解答漫画题的三步法：“一品”是指细品漫画,感悟寓意;“二搜”是指联系教材,搜准搜全解题依据;“三答”是指结合题意,正确作答。

Theoretical Study of Double Cost Function Linear Quadratic Regulator(LQR)

Double cost function linear quadratic regulator (DLQR) is developed from LQR theory to solve an optimal control problem with a general nonlinear cost function. In addition to the traditional LQ cost function, another free form cost function was introduced to express the physical need plainly and optimize weights of LQ cost function using the search algorithms. As an instance, DLQR was applied in determining the control input in the front steering angle compensation control (FSAC) model for heavy duty vehicles. The brief simulations show that DLQR is powerful enough to specify the engineering requirements correctly and balance many factors effectively. The concept and applicable field of LQR are expanded by DLQR to optimize the system with a free form cost function.

施蛰存现代志怪中的“巫鬼”文化——“搜神”二记与《梅雨之夕》比较谈

作为一名热衷传统文化的现代作家，施蛰存一直在寻找中西合璧的合理路径。其小说集《梅雨之夕》不仅在意象、人物、时间、空间、情节等方面继承了六朝志怪资源，更从创作原则、人物形象、主旨立意、结构方式四个方面对其进行了现代草新：从突出内容到注重方法。施蛰存的现代志怪不仅为被新文化运动所排斥的巫鬼文化提供了重新进入新文学语境的合法性，也间接诠释着海派文化的驳杂包容特性。

A Fast and Memory-Efficient Approach to NDN Name Lookup

For name-based routing/switching in NDN, the key challenges are to manage large-scale forwarding Tables, to lookup long names of variable lengths, and to deal with frequent updates. Hashing associated with proper length-detecting is a straightforward yet efficient solution. Binary search strategy can reduce the number of required hash detecting in the worst case. However, to assure the searching path correct in such a schema, either backtrack searching or redundantly storing some prefixes is required, leading to performance or memory issues as a result. In this paper, we make a deep study on the binary search, and propose a novel mechanism to ensure correct searching path without neither additional backtrack costs nor redundant memory consumptions. Along any binary search path, a bloom filter is employed at each branching point to verify whether a said prefix is present, instead of storing that prefix here. By this means, we can gain significantly optimization on memory efficiency, at the cost of bloom checking before each detecting. Our evaluation experiments on both real-world and randomly synthesized data sets demonstrate our superiorities clearly

A novel PID controller tuning method based on optimization technique

An approach for parameter estimation of proportional-integral-derivative(PID) control system using a new nonlinear programming(NLP) algorithm was proposed.SQP/IIPM algorithm is a sequential quadratic programming(SQP) based algorithm that derives its search directions by solving quadratic programming(QP) subproblems via an infeasible interior point method(IIPM) and evaluates step length adaptively via a simple line search and/or a quadratic search algorithm depending on the termination of the IIPM solver.The task of tuning PI/PID parameters for the first-and second-order systems was modeled as constrained NLP problem. SQP/IIPM algorithm was applied to determining the optimum parameters for the PI/PID control systems.To assess the performance of the proposed method,a Matlab simulation of PID controller tuning was conducted to compare the proposed SQP/IIPM algorithm with the gain and phase margin(GPM) method and Ziegler-Nichols(ZN) method.The results reveal that,for both step and impulse response tests,the PI/PID controller using SQP/IIPM optimization algorithm consistently reduce rise time,settling-time and remarkably lower overshoot compared to GPM and ZN methods,and the proposed method improves the robustness and effectiveness of numerical optimization of PID control systems.

A Tabu Search Algorithm for Quadratic 0-1 Programming Problem

In this paper,quadratic 0-1 programming problem (I) is considered, in terms of its features quadratic 0-1 programming problem is solved by linear approxity heurstic algrothm and a developed tabu search ahgrothm .

Network coding resources optimization with transmission delay constraint in multicast networks

Minimizing network coding resources of multicast networks,such as the number of coding nodes or links,has been proved to be NP-hard,and taking propagation delay into account makes the problem more complicated. To resolve this optimal problem,an integer encoding routing-based genetic algorithm( REGA) is presented to map the optimization problem into a genetic algorithm( GA)framework. Moreover,to speed up the search process of the algorithm,an efficient local search procedure which can reduce the searching space size is designed for searching the feasible solution.Compared with the binary link state encoding representation genetic algorithm( BLSGA),the chromosome length of REGA is shorter and just depends on the number of sinks. Simulation results show the advantages of the algorithm in terms of getting the optimal solution and algorithmic convergence speed.

基于Robocup的比赛场地和障碍物识别

2014年7月,清华"Hephaestus"参加了在巴西举办的R obocup比赛,获得类人足球机器人Adult size组第三名。本文针对R obocup比赛的场地和障碍物识别,讨论并提出了一套较为完整的算法体系。比赛场地的识别:分片处理,宽搜以确定绿色连通域,边界的线性拟合,凸包处理。障碍物的识别:借助场地信息缩小搜索范围,二次宽搜,合并横向距离相近的障碍物。

Smoothing Newton-Like Method for the Solution of Nonlinear Systems of Equalities and Inequalities

In this paper,we present a smoothing Newton-like method for solving nonlinear systems of equalities and inequalities.By using the so-called max function,we transfer the inequalities into a system of semismooth equalities.Then a smoothing Newton-like method is proposed for solving the reformulated system,which only needs to solve one system of linear equations and to perform one line search at each iteration. The global and local quadratic convergence are studied under appropriate assumptions. Numerical examples show that the new approach is effective.

Research on Anti-collision Algorithm of Short Distance Data communication based on Binary-Tree Disassembly

This paper puts forward adaptive anti collision algorithm based on two fork tree decomposition. New search algorithm built on the basis of binary-tree algorithm, using the uniqueness of the label EPC, to estimate the distribution of label by slot allocation, the huge and complicated two fork tree is decomposed into several simple binary-tree by search the collision slots for binary-tree, so, it can simplifies the search process. The algorithm fully considers4 important performance parameters of the reader paging times, transmission delay, energy consumption and throughput label, the simulation results show that, the improved anti-collision algorithm is obviously improved performance than other two fork tree algorithm, it is more suitable for RFID anti-collision protocols.

A RECURSIVE QUADRATIC PROGRAMMING ALGORITHM THAT USES A NEW NONDIFFERENTIABLE PENALTY FUNCTIONS

In this papert a recursive quadratic programming algorithm is proposed andstudied.The line search functions used are Han's nondifferentiable penalty functionswith a second order penalty term. In order to avoid maratos effect,Fukushima's mixeddirection is used as the direction of line search.Finallyt we prove the global convergenceand the local second order convergence of the algorithm.

Nonlinear inversion for electrical resistivity tomography based on chaotic DE-BP algorithm

Nonlinear resistivity inversion requires efficient artificial neural network(ANN)model for better inversion results.An evolutionary BP neural network(BPNN)approach based on differential evolution(DE)algorithm was presented,which was able to improve global search ability for resistivity tomography 2-D nonlinear inversion.In the proposed method,Tent equation was applied to obtain automatic parameter settings in DE and the restricted parameter Fcrit was used to enhance the ability of converging to global optimum.An implementation of proposed DE-BPNN was given,the network had one hidden layer with 52 nodes and it was trained on 36 datasets and tested on another 4 synthetic datasets.Two abnormity models were used to verify the feasibility and effectiveness of the proposed method,the results show that the proposed DE-BP algorithm has better performance than BP,conventional DE-BP and other chaotic DE-BP methods in stability and accuracy,and higher imaging quality than least square inversion.

An MDL approach to efficiently discover communities in bipartite network

An minimum description length(MDL) criterion is proposed to choose a good partition for a bipartite network. A heuristic algorithm based on combination theory is presented to approach the optimal partition. As the heuristic algorithm automatically searches for the number of partitions, no user intervention is required. Finally, experiments are conducted on various datasets, and the results show that our method generates higher quality results than the state-of-art methods, cross-association and bipartite, recursively induced modules. Experiment results also show the good scalability of the proposed algorithm. The method is applied to traditional Chinese medicine(TCM) formula and Chinese herbal network whose community structure is not well known, and found that it detects significant and it is informative community division.

Found in Translation

Internet search giant Baidu won a second-level prize at the China 2015 National Science and Technology Awards for its technological advancement of machine translation in early January.

Model penicillin fermentation by least squares support vector machine with tuning based on amended harmony search

Penicillin fermentation is an important part of microbial fermentation. Due to the existence of error date in the independent variables and dependent variables of the penicillin fermentation sample data, the accuracy of the model of penicillin fermentation is affected. In this paper, an amended harmony search （AHS） algorithm is developed to adjust the hyper-parameters of least squares support vector machine （LS-SVM） in order to build penicillin fermentation process model with prediction accuracy. The AHS algorithm is investigated by unconstrained benchmark functions with different characteristics. Compared with other several optimization approaches, AHS demonstrates a better performance. Moreover, using the simulation data from the PenSim simulation platform to validate the effectiveness of the penicillin fermentation process modeling, experiment results show that the penicillin fermentation process modeling based on the tuned LS-SVM by AHS possesses robustness and generalization ability.