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.

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。

This paper puts forward a novel artificial immune response algorithm for optimal approximation of linear systems. A quaternion model of artificial immune response is proposed for engineering computing. The model abstracts four elements, namely, antigen, antibody, reaction rules among antibodies, and driving algorithm describing how the rules are applied to antibodies, to simulate the process of immune response. Some reaction rules including clonal selection rules, immunological memory rules and immune regulation rules are introduced. Using the theorem of Markov chain, it is proofed that the new model is convergent. The experimental study on the optimal approximation of a stable linear system and an unstable one show that the approximate models searched by the new model have better performance indices than those obtained by some existing algorithms including the differential evolution algorithm and the multi-agent genetic algorithm.

模块性（modularity）是度量网络社区结构（community structure）的主要参数。探讨了Watts和Strogatz的小世界网络（简称W2S模型）以及Barabàsi等的B2A无标度网络（简称B2A模型）两类典型复杂网络模块性特点。结果显示，网络模块性受到网络连接稀疏的影响，W2S模型具有显著的社区结构，而B2A模型的社区结构特征不明显。因此，应用中应该分别讨论网络的小世界现象和无标度特性。社区结构不同于小世界现象和无标度特性，并可以利用模块性区别网络类型，因此网络复杂性指标应该包括社区结构。现实社会网络可能同时具有小世界现象、无标度特性和清晰的社区结构，而目前的复杂网络模型只揭示了其部分特征，为了更全面地认识社会复杂性，需要建立新的复杂网络模型。

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.