An Adaptive Particle Swarm Optimization Algorithm Based on Directed Weighted Complex Network

The disadvantages of particle swarm optimization (PSO) algorithm are that it is easy to fall into local optimum in high-dimensional space and has a low convergence rate in the iterative process. To deal with these problems, an adaptive particle swarm optimization algorithm based on directed weighted complex network (DWCNPSO) is proposed. Particles can be scattered uniformly over the search space by using the topology of small-world network to initialize the particles position. At the same time, an evolutionary mechanism of the directed dynamic network is employed to make the particles evolve into the scale-free network when the in-degree obeys power-law distribution. In the proposed method, not only the diversity of the algorithm was improved, but also particles’ falling into local optimum was avoided. The simulation results indicate that the proposed algorithm can effectively avoid the premature convergence problem. Compared with other algorithms, the convergence rate is faster

High-Risk Immunization Strategies for Multiethnic Regions

In the Multiethnic regions, like the west of China, because of the difference of religious beliefs, ethnic customs, and mode of production, the contacts and relationships are also different. The epidemic characteristics of these regions are different from other places. Based on the background, some high-risk immunization strategies for Multiethnic regions are proposed. The epidemic dynamics were analyzed both from theory and simulation experiment. The results indicate that the proposed immunization strategies are effective, and it is also economic and feasible

Vision Target Tracker Based on Incremental Dictionary Learning and Global and Local Classification

Based on sparse representation, a robust global and local classification algorithm for visual target tracking in uncertain environment was proposed in this paper. The global region of target and the position of target would be found, respectively by the proposed algorithm. Besides, overcompleted dictionary was obtained and updated by biased discriminate analysis with the divergence of positive and negative samples at current frame. And this over-completed dictionary not only discriminates the positive samples accurately but also rejects the negative samples effectively. Experiments on challenging sequences with evaluation of the state-of-the-art methods show that the proposed algorithm has better robustness to illumination changes, perspective changes, and targets rotation itself