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 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.

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.

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.