Exponential quality function for community detection in complex networks

One of the most popular topics in analyzing complex networks is the detection of its community structure. In this paper, we introduce a new criterion for community detection, called the E‐quality function. The quality of an individual community is defined as a difference between its benefit and its cost, where both are exponential functions of the number of internal edges and the number of external edges, respectively. The obtained optimization problem, maximization of the E‐quality function over all possible partitions of a network, is solved by the variable neighborhood search (VNS)‐based heuristic. Comparison of the new criterion and modularity is performed on the usual test instances from the literature. Experimental results obtained both on artificial and real networks show that the proposed E‐quality function allows detection of the communities existing in the network.     

一种快速鲁棒核空间图形模糊聚类分割算法

针对现有鲁棒图形模糊聚类算法难以满足强噪声干扰下大幅面图像快速分割的需要，提出一种快速鲁棒核空间图形模糊聚类分割算法。该算法将欧氏空间样本通过核函数映射至高维空间；采用待分割图像中像素邻域的灰度和空间等信息构建线性加权滤波图像，对其进行鲁棒核空间图形模糊聚类；并引入当前聚类像素与其邻域像素均值所对应的二维直方图信息，获得鲁棒核空间图形模糊聚类快速迭代表达式。对大幅面图像添加高斯和椒盐噪声进行分割测试，实验结果表明:本文算法相比基于图形模糊聚类等分割算法的分割性能、抗噪鲁棒性和实时性有了显著提高。     

应用于人脸图像识别的邻域保持极限学习机

极限学习机广泛应用于人脸识别领域。传统的极限学习机算法因在少量标签样本上进行训练，容易发生学习过程不充分问题，同时在学习过程中往往忽略了样本内在的几何结构，影响其对人脸识别的分类能力。受流形学习思想的启发，提出一种邻域保持极限学习机算法。该算法保持数据最本质的结构和同类数据的判别信息，利用最小化类内散度矩阵来提高极限学习机整体的分类性能。通过人脸数据集上的多次实验结果表明，该算法的人脸识别准确率高于其他算法，更能有效地进行分类识别。     

A four-echelon supply chain network design with shortage: Mathematical modeling and solution methods

Often, companies deliberately fulfill demands with delay since they can benefit from reducing transportation and setup costs. This paper aims at designing a four-echelon supply chain structure including multiple suppliers, multiple producers, multiple distributors and multiple customers. The objectives are to minimize the total operating costs of all the supply chain elements and to maximize the reliability of the system. A number of transportation systems with different reliability rates are considered. The paper mathematically formulates the problem as a mixed integer linear programming model. In order to solve the large-sized instances of the problem, the paper proposes a novel heuristic algorithm called Comparative Particle Swarm Optimization. This algorithm employs a mechanism in order to compare the generated solutions and to prevent from generating worse solutions. The results of different numerical experiments endorse the effectiveness of the proposed heuristic.     

解决无等待流水线调度问题的变邻域搜索算法

提出了解决无等待流水线调度问题的变邻域搜索调度算法。采用基于自然数编码的工件序列表达问题的解，采用多重Insert移动邻域和多重Swap移动邻域作为变邻域搜索的两种邻域结构。多重移动有利于算法向包含较优解的区域搜索，因而有较高的求解质量和效率。仿真实验证明了变邻域搜索算法的有效性。     

Rack shape and energy efficient operations in automated storage and retrieval systems

Energy efficiency has become a primary goal to be pursued for sustainable logistics. In automated storage and retrieval systems this leads to revise the traditional control policies aimed at picking time minimisation and to pay more attention to rack configuration, which has been not a research concern from the time-based perspective. Proper models for energy calculation should be developed by introducing new factors neglected in time analysis, such as the weight of unit loads and the differentiation of shifts along the horizontal and vertical axis as regard energy requirements, due to different contribution of gravity, inertia and friction. In this study, a classification of racks based on system height is proposed in order to select the proper crane specifications needed to compute the torque to be overcome by motors to serve a given location within a rack. An overall optimisation model based on Constraint Programming hybridised with Large Neighborhood Search is developed, allowing the joint application of the best control policies for storage assignment and sequencing both for time and energy-based optimisation, as well as the introduction of multiple weight unit loads and energy recovery. Simulations analysis is performed in order to assess the impact of the rack shape on energy saving. Results show how, regardless the demand curve and the optimisation objective, the best performances in terms of energy efficiency are reached by the intermediate height rack shapes, while the lower ones outperform when considering travel time performance.     

求解第Ⅰ类装配线平衡问题的离散粒子群优化算法

为求解具有NP难性质的第Ⅰ类装配线平衡问题,提出一类离散粒子群优化算法。该算法中所发展的排列数编码方法使得粒子解码后总满足装配作业间先后关系约束。针对排列数编码特点,提出一种基于位置交叉算子的粒子位置更新机制,确保了更新后粒子仍为排列数。为增强该算法的全局寻优能力,将简化变邻域搜索算法嵌入该算法中,对群体最佳粒子的邻域进行局部搜索,从而构建一种混合粒子群优化算法。通过将该算法和混合粒子群优化算法用于一系列测试算例并与遗传算法结果比较,验证了算法的有效性。计算结果对比表明,离散粒子群算法引入简化变邻域搜索可明显增强全局寻优能力,就综合解的质量和计算效率而言,混合粒子群优化算法优于现有遗传算法。     

宜居城市建设中旧住宅区更新宜居评价体系构建

城市旧住宅区的更新是宜居城市建设的重要组成部分。通过构建旧住宅区更新宜居评价体系,可定量化标示更新的必要性,并为更新决策和更新规划设计提供直接的依据。研究通过对马斯洛需要层次理论、道萨迪亚斯人类聚居学说和我国的人居环境科学研究成果进行梳理和借鉴,并参考层次分析法,构建了包括4层共85项指标的城市旧住宅区更新宜居评价体系。体系构建遵循了可操作性、理论先进性、目标导向型、客观真实性及灵活性等原则,形成了目标导向性、清晰的层次性和启发引导性三个特点。     

基于不变矩的掌纹识别方法

特征提取是掌纹识别研究中最基本的问题之一,对于掌纹图像识别而言,提取有效的分类特征是完成掌纹识别任务的一个关键问题。掌纹特征提取的本质就是把原始的高维掌纹图像样本数据投影到一个更有利于掌纹分类的低维特征子空间中。利用不变矩,对掌纹图像进行特征提取,采用最近邻分类器进行掌纹识别,在公共掌纹数据库上的实验结果表明,该方法是可行的。