一种自适应人工蜂群算法求解U型顺序相依拆卸线平衡问题

产品拆卸过程中零部件之间会相互干扰影响任务作业时间,基于该情形构建了多目标U型SDDLBP优化模型,并提出一种自适应ABC算法。所提算法设计了自适应动态邻域搜索方法,以提高局部开发能力;采用了轮盘赌与锦标赛法结合的分段选择法,以有效评价并选择蜜源进行深度开发;建立了基于当前最优解的变异操作,以提高全局探索能力快速跳出局部最优。最后,通过算例测试和实例分析验证算法的高效性。     

A coefficient identification problem for a mathematical model related to ductal carcinoma in situ

We consider a coefficient identification problem for a mathematical model with free boundary related to ductal carcinoma in situ (DCIS). This inverse problem aims to determine the nutrient consumption rate from additional measurement data at a boundary point. We first obtain a global‐in‐time uniqueness of our inverse problem. Then based on the optimization method, we present a regularization algorithm to recover the nutrient consumption rate. Finally, our numerical experiment shows the effectiveness of the proposed numerical method.     

古巴比伦人求算术平方根的探究

分析了古巴比伦人求算术平方根的算法，证明了其正确性。然后将其算法推广到求n次算术根，并证明了其正确性。     

Modified two-grid method for solving coupled Navier-Stokes/Darcy model based on Newton iteration

A new decoupled two-gird algorithm with the Newton iteration is proposed for solving the coupled Navier-Stokes/Darcy model which describes a fluid flow filtrating through porous media. Moreover the err...     

vdW-TSSCDS—An automated and global procedure for the computation of stationary points on intermolecular potential energy surfaces

We present a generalization of the transition state search using chemical dynamics simulations (TSSCDS) methodology (discussed in a previous study) which allows the topographical characterization of intermolecular potential energy surfaces (IPES) for non-covalently bound complexes (vdW-TSSCDS). Starting from a single random input geometry, we show that vdW-TSSCDS is able to globally and automatically locate stationary points of an IPES, even in limiting cases such as extremely flat regions or nontrivial topologies (eg, bifurcation points). The basic idea is the expression of the connectivity matrix in block structure, where diagonal blocks correspond to the isolated fragments and off-diagonal blocks provide the intermolecular connectivity. To this end, we introduce a new definition of bound or not, in a non-covalent sense, utilizing an extra set of van der Waals distances, which encompasses all kinds of non-covalent distances. To discuss the use of the vdW-TSSCDS method, we present a series of 2-body van der Waals systems, namely, Ar-Benzene (3D), N₂-Benzene (6D) and H₂O-Benzene (9D). Finally, we further illustrate its capabilities by presenting some applications for n-body problems (n > 2), (H₂O)₂-Benzene (12D) and (H₂O)₃-Benzene (21D), as well as to a reactive, fully-flexible, system (Benzene-NO₂)⁺ (39D) in which the simultaneous breaking/formation of both covalent and non-covalent interactions takes place.     

Sparse Multicategory Generalized Distance Weighted Discrimination in Ultra-High Dimensions

Distance weighted discrimination (DWD) is an appealing classification method that is capable of overcoming data piling problems in high-dimensional settings. Especially when various sparsity structures are assumed in these settings, variable selection in multicategory classification poses great challenges. In this paper, we propose a multicategory generalized DWD (MgDWD) method that maintains intrinsic variable group structures during selection using a sparse group lasso penalty. Theoretically, we derive minimizer uniqueness for the penalized MgDWD loss function and consistency properties for the proposed classifier. We further develop an efficient algorithm based on the proximal operator to solve the optimization problem. The performance of MgDWD is evaluated using finite sample simulations and miRNA data from an HIV study.     

基于改进遗传算法的自适应越区切换方案

针对列车通信在越区切换过程中切换触发率和切换成功率均较低的问题, 提出一种基于改进遗传算法的自适应联合判决切换算法. 首先, 以遗传算法为基础, 在高速特性下对切换判决过程中的相关参数进行优化, 针对列车的不同运行速度动态选择出满足快速切换需求的迟滞容限值； 其次, 引入满意通信概率（SCP), 当列车在小区之间移动时, SCP值达不到要求的将不能接入下一个小区, 以保障通信质量的优良. 仿真结果表明, 与传统切换方案相比, 该方案可实现提高切换触发率和切换成功率的目标, 具有较高的满意通信概率.     

Learning extended tree augmented naive structures

This work proposes an extended version of the well-known tree-augmented naive Bayes (TAN) classifier where the structure learning step is performed without requiring features to be connected to the class. Based on a modification of Edmonds' algorithm, our structure learning procedure explores a superset of the structures that are considered by TAN, yet achieves global optimality of the learning score function in a very efficient way (quadratic in the number of features, the same complexity as learning TANs). We enhance our procedure with a new score function that only takes into account arcs that are relevant to predict the class, as well as an optimization over the equivalent sample size during learning. These ideas may be useful for structure learning of Bayesian networks in general. A range of experiments shows that we obtain models with better prediction accuracy than naive Bayes and TAN, and comparable to the accuracy of the state-of-the-art classifier averaged one-dependence estimator (AODE). We release our implementation of ETAN so that it can be easily installed and run within Weka.