Aiming at the problems that Butterfly Optimization Algorithm (BOA) tends to fall into local optimum and has poor convergence, a Multi-Strategy Improved BOA (MSBOA) was proposed. Firstly, the cosine similarity position adjustment strategy was introduced to the algorithm, rotation transformation operator and scaling transformation operator were used to update the positions, so as to effectively maintain the population diversity of the algorithm. Secondly, dynamic switching probability was introduced to balance the transformation between the local phase and the global phase of the algorithm. Finally, a hybrid inertia weight strategy was added to accelerate convergence. Solving 16 benchmark test functions, as well as the Wilcoxon rank-sum test and CEC2014 test functions were to verify, the effectiveness and robustness of the proposed algorithm. Experimental results show that compared with BOA, some BOAs with different improvement strategies and some swarm intelligence algorithms, MSBOA has significant improvement in convergence accuracy and convergence speed.

In order to improve the performance of Wireless Sensor Network (WSN), a node deployment optimization method based on Enhanced Sine Cosine Algorithm (ESCA) was proposed. Firstly, hyperbolic sine regulatory factor and dynamic cosine wave weight coefficient were introduced to balance the global exploration and local exploitation capability of the algorithm. Then, a mutation strategy based on Laplacian and Gaussian distribution was proposed to avoid the algorithm falling into local optimum. The experimental results of benchmark function optimization show that, compared with gravitational search algorithm, whale optimization algorithm, basic Sine Cosine Algorithm (SCA) and improved algorithms, ESCA has better convergence accuracy and convergence speed. Finally, ESCA was applied to WSN node deployment optimization. The results show that, compared with enhanced particle swarm optimization algorithm, extrapolation artificial bee colony algorithm, improved grey wolf optimization algorithm and self-adaptive chaotic quantum particle swarm algorithm, ESCA has improved the coverage rate by 1.55 percentage points, 7.72 percentage points, 2.99 percentage points and 7.63 percentage points respectively, and achieves the same target precision with fewer nodes.

Concerning the problem that in complex and non-cooperative situations the number of matching feature pairs and the accuracy of feature matching results in video stitching can not meet the requirements of subsequent image stabilization and stitching at the same time, a method of constructing matching model to match features accurately after feature points being scored by grayscale tower was proposed. Firstly, the phenomenon that the similiar grayscales would merged together after grayscale compression was used to establish a grayscale tower to realize the scoring of feature points. Then, the feature points with high score were selected to establish the matching model based on position information. Finally, according to the positioning of the matching model, regional block matching was performed to avoid the influence of global feature point interference and large error noise matching, and the feature matching pair with the smallest error was selected as the final result of matching pair. In addition, in a motion video stream, regional feature extraction could be performed by using the information of previous and next frames to establish a mask, and the matching model could be selectively passed on to the next frame to save the computation time. The simulation results show that after using this matching model based on grayscale tower score, the feature matching accuracy is about 95% and the number of matching feature pairs of the same frame is nearly 10 times higher than that of the traditional method. The proposed method has good robustness to environment and illumination while guaranteeing the matching number and the matching accuracy without large error matching result.

In Wireless Sensor Network (WSN), to deal with the energy limitation of nodes and the energy consumption of broadcast routing, a new WSN broadcast routing algorithm based on the improved Discrete Fruit fly Optimization Algorithm (DFOA) was proposed. Firstly, the swap and swap sequence were introduced into the Fruit fly Optimization Algorithm (FOA) to obtain DFOA, which expands the applications field of FOA. Secondly, the step of fruit fly was controlled by the Lévy flight to increase the diversity of the samples, and the position updating strategy of population was also improved by the roulette selection to avoid the local optimum. Finally,the improved DFOA was used to optimize the broadcast routing of WSN to find the broadcast path with minimum energy consumption. The simulation results show that the improved DFOA reduces the energy consumption of broadcast and has better performance than comparison algorithms including the original DFOA, Simulated Annealing Genetic Algorithm (SAGA), Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO) in different network. The improved DFOA can increase the diversity of the samples, enhance the ability of escaping from local optimum and improve the network performance.

Concerning the low efficiency of serialized execution in database instance recovery and relying on ShenTong database, a parallel instance recovery method based on multi-thread was proposed. First, two steps including "building dirty page table" and "prefetching dirty pages" were added to the original database instance recovery model to get an improved model. Second, the improved model was processed by the multi-threaded parallel processing way and a parallel instance recovery model was generated. Finally, by using rollback segment management strategy, undo log was managed as normal data and the parallel instance recovery could be finished earlier. In the comparison experiments with the original method, Transaction Processing performance Council-C (TPC-C) benchmark test result of the parallel recovery method showed that the efficiency of reading and parsing redo log increased by 2-7 times, the efficiency of redoing increased by 4-9 times, and the total time for recovery reduced to 20%-40%. The results prove that the parallel instance recovery method can accomplish parallel processing of each stage, reduce the time needed for recovery and ensure the high efficiency of database in practical applications.

To build a power grid fault model which is closer to the actual power grid, a new model named FCA was proposed combined with fuzzy theory and Cellular Automata (CA) to simulate the evolution of power grid failure, and the fuzzy rule bases of cellular status, power status and degree of fault transmission in the model was defined. Meanwhile, simulation with FCA model based on IEEE39 node system was conducted. The simulation results further validate the Self-Organized Criticality (SOC) of power grid, and show that the absolute value of losses load power law curve slope of this model increases by 17% than the model without fuzzy rule, the grid is more stable, and the FCA model is closer to the actual operation of grid.

Using steerablity of the steerable pyramid,the orientation of the image could be obtained to search the best rotation transform,meanwhile the amount of translation could be searched together so that the method could deal with registration of both rotation and translation.Mutual information was used for similarity metric.Experiment results show that this approach performs well.