The construction of binary Locally Repairable Code (LRC) achieving C-M (Cadambe-Mazumdar) bound has been fully studied while there are few researches on general fields. In order to solve the problem, the construction of LRC on general fields was studied. Firstly, a method for determining the number of elements in sunflower was proposed by projective geometry theory. Then, the parameters such as code length, dimension and locality of LRC were clearly described by depicting LRC through the disjoint repair group. Finally, based on the parity-check matrix with disjoint local repair group, two families of LRC on general fields with the minimum distance of 6 were constructed by sunflower, many of which were optimal or almost optimal. Compared with the existing LRC constructed by methods such as subfield subcode, generalized concatenated code and algebraic curve, the constructed two families of codes improve the information rate under the same code minimum distance and locality. These results can be applied to the construction of other LRC on general fields.

Concerning the problem that how to detect spammer groups writing fake reviews on the e-commerce platforms, a Weighted reviewer Graph based Spammer group detection Algorithm (WGSA) was proposed. Firstly, a weighted reviewer graph was built based on the co-reviewing feature with the weight calculated by a series of group spam indicators. Then, a threshold was set for the edge weight to filter the suspicious subgraphs. Finally, considering the community structure of the graph, the community discovery algorithm was used to generate the spammer groups. Compared with K-Means clustering algorithm ( KMeans), Density-Based spatial clustering of applications with noise (DBscan) and hierarchical clustering algorithm on the large dataset Yelp, the accuracy of WGSA is higher. The characteristics and distinction of the detected spammer groups were also analyzed, which show that spammer groups with different activeness have different harm. The high-active group is more harmful and should be concerned more.

Aiming at the problems of low convergence precision and premature convergence in Teaching-Learning-Based Optimization (TLBO) algorithms, an improved Self-Learning mechanism-based TLBO (SLTLBO) algorithm was proposed. A more complete learning framework was constructed for students in SLTLBO algorithm. Besides, after completing nomal learning in "teaching" and "learning" stage, students would further compare their differences from the teachers and the worst students, then various learning operations were implemented independently, so as to enhance their knowledge level and improve the convergence accuracy of the algorithm. Meanwhile, the students carried out self-examination through Gaussian searching to jump out of the local area and achieved better global search. The performance of SLTLBO was tested on 10 benchmark functions and compared with the algorithms including Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC) and TLBO. The experimental results verify the effectiveness of the proposed SLTLBO algorithm.

Particle Swarm Optimization (PSO) is easy to fall into local minimum, and has poor global search ability. Many improved algorithms cannot optimize PSO performance fully by using a single search strategy in a way. In order to solve the problem, a novel PSO with Firefly Behavior and Levy Flight (FBLFPSO) was proposed. The local search ability of PSO was improved to avoid falling into local optimum by using improved self-regulating step firefly search strategy. Then, the principle of Levy flight was taken to enhance population diversity and improve the global search ability of PSO, which contributed to escape from local optimal solution. The simulation results show that, compared with the existing correlation algorithms, the global search ability and the search accuracy of FBLFPSO are greatly improved.

According to the problem of premature convergence and local optimum in Firefly Algorithm (FA), this paper came up with a kind of multi-group firefly algorithm based on simulated annealing mechanism (MFA_SA), which equally divided firefly populations into many child populations with different parameter. To prevent algorithm fall into local optimum, simulated annealing mechanism was adopted to accept good solutions by the big probability, and keep bad solutions by the small probability. Meanwhile, variable distance weight was led into the process of population optimization to dynamically adjust the "vision" of firefly individual. Experiments were conducted on 5 kinds of benchmark functions between MFA_SA and three comparison algorithms. The experimental results show that, MFA_SA can find the global optimal solutions in 4 testing function, and achieve much better optimal solution, average and variance than other comparison algorithms. which demonstrates the effectiveness of the new algorithm.

For the plots data outputted by radar receiver, the states of plots spread and the forming reasons of target redundancy phenomenon was discussed. And the Adaptive Fuzzy C-Means Clustering(AFCMC) algorithm which was used in agglomerate processing for the detected plots was proposed. This algorithm has provided a way for target redundancy processing. The simulation result has verified the validity of it.