Aiming at the problems that swarm intelligence optimization algorithms are easy to fall into local optimum as well as have low population diversity in the optimization process and are difficult to optimize high-dimensional functions， an Alternately Optimizing Algorithm based on Brownian-movement and Gradient-information （AOABG） was proposed. First， a global and local alternately optimizing strategy was used in the proposed algorithm， which means the local search was switched in the range of getting better and the global search was switched in the range of getting worse. Then， the random walk of uniform distribution probability based on gradient information was introduced into local search， and the random walk of Brownian motion based on optimal solution position was introduced into global search. The proposed AOABG algorithm was compared with Harris Hawk Optimization （HHO）， Sparrow Search Algorithm （SSA） and Special Forces Algorithm （SFA） on 10 test functions. When the dimension of test function is 2 and 10， the mean value and standard deviation of AOABG’s 100 final optimization results on 10 test functions are better than those of HHO， SSA and SFA. When the test function is 30-dimensional， except for Levy function where HHO performs better than AOABG but the mean value of the two is in the same order of magnitude， AOABG performs best on the other nine test functions with an increase of 4.64%-94.89% in the average optimization results compared with the above algorithms. Experimental results show that AOABG algorithm has faster convergence speed， better stability and higher accuracy in high-dimensional function optimization.

Aiming at the characteristics of Synthetic Aperture Radar (SAR) images and the problem of the standard graph cut segmentation algorithm's high computational complexity, a method of strong scattering objects segmentation based on graph cut and Mean Shift algorithm was proposed. Firstly, the image was pre-processed with the Mean Shift algorithm to produce over-segmentation areas. Then, a graph was built with nodes responding to over-segmentation areas, and then the results of SAR strong scattering targets segmentation were obtained by using graph cut algorithm. Compared with nodes responding to pixels in the standard graph cut algorithm, the number of nodes and edges in the graph were reduced by two orders of magnitude and the computational efficiency was significantly improved. Furthermore, according to the strong scattering characteristics of the targets in SAR images, the “object” terminal and the “background” terminal were defined automatically to reduce human interaction. The experiments show that the proposed method combines the advantages of Mean Shift and graph cut effectively, and it can effectively extract SAR strong scattering targets from the background clutter.

In order to solve the two key problems of the short text classification, very sparse features and strong context dependency, a new method based on latent Dirichlet allocation was proposed. The generated topics not only discriminate contexts of common words and decrease their weights, but also reduce sparsity by connecting distinguishing words and increase their weights. In addition, a short text dataset was constructed by crawling titles of Netease pages. Experiments were done by classifying these short titles using K-nearest neighbors. The proposed method outperforms vector space model and topic-based similarity.

Providing Quality of Service (QoS) guarantee for mobile Ad Hoc network is very important due to the rapid growth of multimedia applications. The Multi-bounded QoS-aware Ad Hoc Routing (MQAR) protocol based on AODV was proposed, which allowed intermediate node to return Route Reply (RREP) packet and reserve resources by extending QoS routing table, and adopted local route recovery as well. The simulation results show that MQAR protocol can effectively reduce route establishing time, route recovery time and routing overload as well, and the packet delivery ratio remains high.

A QoS multicast routing algorithm for DiffServ networks called PQMRD(Per-class QoS Multicast Routing in DiffServ networks) was proposed. It used different routing strategy to select route in terms of different QoS requirement; in addition, admission control and resource reservation were performed while selecting route. It is proved by the simulation result that inter-class unfairness problem is alleviated.