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.

Based on tile clonal selection theory and immune memory mcchanism in the natural immune system a novel artificial imnltmc system algorithnl，Clonal Strategy Algorithm based on the Immune Memory(CSAIM)，is proposed in this paper The Mgorithm realizes tile evolution of antibody population and tile evolution of nlenlory unit at the Same time and by using clonal selection operator the global optimal computation can be combined with tile local searching According to amibody antibody(Ab-Ag)affinity and antibody antigen(Ab-Ag)affinity．tile algorithm can allot adaptively the scales of memory unit and antibody population It is proved theoretically that CSAIM is convergent with probability 1 And with the conlputer simulations of eight benchmark functions and one instance of traveling salcsillan problem(TSP)．it is shown thm CSAIM has strong abilities in having high convergence speed enhancing the diversity of the population and avoiding the prenlature convergence to some extent.

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.

公共管理问题日益复杂，其研究范围、重点不断发展变化，单一学科、简单还原论的方法已经难以满足公共管理学科发展需要。本文在简要回顾公共管理科学和复杂性科学研究进展的基础上，通过复杂性科学的农民工流动问题问题研究中的应用实例，说明管理复杂性科学用于公共管理科学研究的必要性的可行性；对复杂性科学视野下公共管理的未来重要研究领域的展望也是本文探讨的问题之一。

从故障诊断的模式识别本质出发，利用网络表示故障数据结构，通过网络结构反映故障状态及其特征，把故障诊断聚类问题建模为子网络探测问题，提出基于网络结构分析的故障诊断策略。为了解决子网络划分中数据间相似度测度和划分测度设计这两个重要问题，引入复杂网络社群结构分析中的模块性概念，设计状态区分准则函数，并采用自底向上模块合并层次过程优化准则函数实现故障状态聚类，提出一种基于模块合并的故障诊断聚类算法。通过算法在标准数据集分类和真实压缩机故障系统诊断上的应用，分析相似度测度对算法的影响并验证了算法的性能。试验结果表明，与遗传算法，人工免疫网络等人工智能诊断方法相比，本文提出的算法能以较少的计算耗时，有效提取故障特征，获得理想的诊断正确率。