This paper proposes a self-organizing genetic algorithm (SOGA) with good global search properties and a high convergence speed. First, we introduce a new dominant selection operator that enhances the action of the dominant individuals, along with a cyclical mutation operator that periodically varies the mutation probability in accordance with evolution generation found in biological evolutionary processes. Next, the SOGA is constructed using the two operators mentioned above. The results of a nonlinear regression analysis demonstrate that the self-organizing genetic algorithm is able to avoid premature convergence with a higher convergence speed, and also indicate that it possesses self-organization properties. Finally, the new algorithm is used to optimize Proportional Integral Derivative (PID) controller parameters. Our simulation results indicate that a suitable set of PID parameters can be calculated by the proposed SOGA.

在分析谱聚类实现思路和已有算法基础上，对规范切判据，最小最大切判据和自动确定聚类数目的谱聚类典型算法进行了研究和应用，通过理论分析算法各自实现机理的联系与区别，讨论它们各自的聚类特点，并利用UCI （University of California。Irvine）机器学习数据集试验对比了三种算法的聚类效果。发现谱聚类算法实现数据聚类的有效性，以及参数及相似度度量对算法性能有很大影响，在此基础上提出了算法用于解决可建模为模式识别的工程问题的可行思路，为工程实践提供了借鉴。

尽管社会融合一直为社会学家、心理学家、政策分析家和政策制定者们所关注，但社会融合的定义还比较纷杂，其理论也缺乏一贯性，一般被视为多维度现象或多参数的潜变量。根据研究对象和目的的不同，将相关研究区分为实证研究和政策应用两个研究领域；依据关注层次的不同，将社会融合的研究归纳为个体层次、群体层次和整体层次等三个层次。以此为脉络，首先介绍了社会融合在社会学、社会心理学和政策研究中的概念；其次，对社会融合由来已久的“同化论”与“多元化”之间的争议进行了回顾，并评述了社会融合和社会网络理论之间的关系；再次，总结了社会融合的测量方法及其在实证研究和政策研究中的应用情况；最后指出社会融合时中国转型社会背景下相关研究的借鉴意义，并对其在中国的应用前景进行了讨论。

Nondominated Neighbor Immune Algorithm (NNIA) is proposed for multiobjective optimization by using a novel nondominated neighbor-based selection technique, an immune inspired operator, two heuristic search operators, and elitism. The unique selection technique of NNIA only selects minority isolated nondominated individuals in the population. The selected individuals are then cloned proportionally to their crowding-distance values before heuristic search. By using the nondominated neighbor-based selection and proportional cloning, NNIA pays more attention to the less-crowded regions of the current trade-off front. We compare NNIA with NSGA-II, SPEA2, PESA-II, and MISA in solving five DTLZ problems, five ZDT problems and three low-dimensional problems. The statistical analysis based on three performance metrics including the Coverage of two sets, the Convergence metric, and the Spacing, show that the unique selection method is effective, and NNIA is an effective algorithm for solving multiobjective optimization problems. The empirical study on NNIA's scalability with respect to the number of objectives shows that the new algorithm scales well along the number of objectives.

An algorithm is proposed to detect community structures in social networks. The algorithm begins with a community division based on prior knowledge of the degrees of the nodes and then combines the communities until a clear partition is obtained. In applications such as a computer-generated network, Ucinet networks, and Chinese rural-urban migrants' social networks, the algorithm can achieve higher modularity and greater speed than others in the recent literature.