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.

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.

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.

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.

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.

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。