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。

Inspired by the mechanism of small-world phenomenon, some smallworld optimization operators, mainly including the local short-range searching operator and random long-range searching operator, are constructed in this paper. And a new optimization algorithm, Small-World Optimization Algorithm (SWOA) is explored. Compared with the corresponding Genetic Algorithms (GAs), the simulation experiment results of some complex functions optimization indicate that SWOA can enhance the diversity of the population, avoid the prematurity and GA deceptive problem to some extent, and have the high convergence speed. SWOA is shown to be an effective strategy to solve complex tasks.

采用2005年深圳外来农村流动人口的调查数据，利用指数随机图模型（p*模型），分析了农民工的社会网络（包括社会支持网和社会讨论网）结构。分析结果显示，农民工社会网络关系稀疏，尤其是社会讨论网;无论在聚敛性还是扩张性方面，农民工社会网络的核心-边缘的局部结构均较明显，且有小团体现象产生;社会支持和社会讨论关系都更可能受到中间人的控制或约束。属性变量对社会支持网的影响较多，而对社会讨论网的影响较少。指数随机图模型为基于社会网络来认识农民工的社会化过程提供了新的方法。

Though clone selection algorithm has been used successfully in many instances of optimizations, there is still difficultness when solving much complicated problems. Using prior knowledge of problems themselves leads a feasible approach. In this paper, two operators, named clonal adjust operator and immunodominance operator are designed based on clonal mechanisms and prior knowledge. With these, an improved clone selection algorithm is put forward to solve NPhard combinatorial optimization. The simulations show that when applied to 0-1 knapsack benchmark data, the algorithm is effective and that achieves better results with quicker convergence than evolutionary algorithm.

Based on the mechanisms of immunodominance and clonal selection theory, we propose a new multiobjective optimization algorithm, immune dominance clonal multiobjective algorithm (IDCMA). IDCMA is unique in that its fitness values of current dominated individuals are assigned as the values of a custom distance measure, termed as Ab-Ab affinity, between the dominated individuals and one of the nondominated individuals found so far. According to the values of Ab-Ab affinity, all dominated individuals (antibodies) are divided into two kinds, subdominant antibodies and cryptic antibodies. Moreover, local search only applies to the subdominant antibodies, while the cryptic antibodies are redundant and have no function during local search, but they can become subdominant (active) antibodies during the subsequent evolution. Furthermore, a new immune operation, clonal proliferation is provided to enhance local search. Using the clonal proliferation operation, IDCMA reproduces individuals and selects their improved maturated progenies after local search, so single individuals can exploit their surrounding space effectively and the newcomers yield a broader exploration of the search space. The performance comparison of IDCMA with MISA, NSGA-II, SPEA, PAES, NSGA, VEGA, NPGA, and HLGA in solving six well-known multiobjective function optimization problems and nine multiobjective 0/1 knapsack problems shows that IDCMA has a good performance in converging to approximate Pareto-optimal fronts with a good distribution.