Based on the chaos movement and the clonal selection theory, a novel artificial immune system algorithm, Adaptive Chaos Clonal Evolutionary Programming Algorithm (ACCEP), is proposed in this paper. The new algorithm uses the Logistic Sequence to control the mutation scale and uses the Chaos Mutation Operator to control the clonal selection. Compared with SGA and Clonal Selection Algorithm, ACCEP can enhance the precision and stability, avoid prematurity to some extent, and have the high convergence speed. The results of the experiment indicate that ACCEP has the capability to solve complex machine learning tasks, like Multimodal Function Optimization.

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.

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.

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.

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。