In view of the redundancy of dataset and the risk of privacy leakage caused by the similarity of track shape when the interference track was noised and publicated by the historical track， an IGSO-SDTP （Trajectory Protection of Simplification and Differential privacy of the track data based on Improved Glowworm Swarm Optimization） was proposed. Firstly， the historical trajectory dataset was reduced based on the position salient points. Secondly， the simplified trajectory dataset was generalized and noised by combining k-anonymity and differential privacy. Finally， a weighted distance was designed to take into account the distance error and track similarity， and the weighted distance was used as the evaluation index to solve the interference track with a small weighted distance based on IGSO （Improved Glowworm Swarm Optimization） algorithm. Experimental results on multiple datasets show that compared with the RD（Differential privacy for Raw trajectory data）， SDTP（Trajectory Protection of Simplification and Differential privacy）， LIC（Linear Index Clustering algorithm）， and DPKTS（Differential Privacy based on K-means Trajectory shape Similarity）， the weighted distances obtained by IGSO-SDTP are reduced by 21.94%， 9，15%， 14.25% and 10.55%， respectively. It can be seen that the interference trajectory publicated by IGSO-SDTP has better usability and stability.

In view of the actual situations such as the preference and the delay waiting of spatial crowdsourcing users of ride-hailing in life， a task allocation method of spatial crowdsourcing based on user satisfaction utility called IGSO（Improved discrete Glowworm Swarm Optimization）-SSCTA（Spatial Crowdsourcing Task Allocation based on user Satisfaction utility） was proposed. Firstly， user satisfaction utility was defined， which was composed of user preference utility， delay waiting utility and task completion expectation. Secondly， SSCTA model was constructed based on user satisfaction utility. Thirdly， IGSO algorithm was proposed by discrete coding， the initialization of reverse learning collaboration， four improved mobile strategies， adaptive selection strategy and treatment of infeasible solutions. Finally， IGSO algorithm was used to solve the above model. Experimental results on different scale datasets show that compared with the three allocation strategies of time minimization， distance minimization and random allocation， the user satisfaction utility of the proposed method is improved by 9.64%， 11.77% and 15.70% respectively， and the proposed algorithm has better stability and convergence than the greedy algorithm and other improved glowworm algorithms.

Focusing on the task allocation problem in spatial crowdsourcing, with the consideration of the influence of the spatial crowdsourcing workers' service quality on the allocation results, a task allocation strategy with the quality evaluation of worker's service was proposed. Firstly, in each spatio-temporal environment, the evaluation element of spatial crowdsourcing workers was added to establish a multi-objective model that fully considers the service quality and distance cost of the workers. Secondly, the algorithm convergence speed was increased and the global optimization ability was improved by improving the initialization and coding strategy, position movement strategy and neighborhood search strategy of the discrete glowworm swarm optimization algorithm. Finally, the improved algorithm was used to solve the model. Experimental results on the simulated and real datasets show that, compared with other swarm intelligence algorithms, the proposed algorithm can improve the total score of task allocation by 2% to 25% on datasets with different scales. By considering the service quality of workers, the proposed algorithm can effectively improve the efficiency of task allocation and the final total score.

In order to prolong the lifespan of heterogeneous directional sensor network, a node scheduling algorithm based on Enhanced Coral Reef Optimization algorithm (ECRO) and with different monitoring requirements for different monitoring targets was proposed. ECRO was utilized to divide the sensor set into multiple sets satisfying the coverage requirements, so that the network lifespan was able to be prolonged by the scheduling among sets. The improvement of Coral Reef Optimization algorithm (CRO) was reflected in four aspects. Firstly, the migration operation in biogeography-based optimization algorithm was introduced into the brooding of coral reef to preserve the excellent solutions of the original population. Secondly, the differential mutation operator with chaotic parameter was adopted in brooding to enhance the optimization ability of the offspring. Thirdly, a random reverse learning strategy were performed on the worst individual of population in order to improve the diversity of population. Forthly, by combining CRO and simulated annealing algorithm, the local searching capability of algorithm was increased. Extensive simulation experiments on both numerical benchmark functions and node scheduling were conducted. The results of numerical test show that, compared with genetic algorithm, simulated annealing algorithm, differential evolution algorithm and the improved differential evolution algorithm, ECRO has better optimization ability. The results of sensor network node scheduling show that, compared with greedy algorithm, the Learning Automata Differential Evolution (LADE) algorithm, the original CRO, ECRO has the network lifespan improved by 53.8%, 19.0% and 26.6% respectively, which demonstrates the effectiveness of the proposed algorithm.

To solve the problem that high dimension of descriptor decreases the matching speed of Scale Invariant Feature Transform (SIFT) algorithm, an improved SIFT algorithm was proposed. The feature point was acted as the center, the circular rotation invariance structure was used to construct feature descriptor in the approximate size circular feature points' neighborhood, which was divided into several sub-rings. In each sub-ring, the pixel information was to maintain a relatively constant and positions changed only. The accumulated value of the gradient within each ring element was sorted to generate the feature vector descriptor when the image was rotated. The dimensions and complexity of the algorithm was reduced and the dimensions of feature descriptor were reduced from 128 to 48. The experimental results show that, the improved algorithm can improve rotating registration repetition rate to more than 85%. Compared with the SIFT algorithm, the average matching registration rate increases by 5%, the average time of image registration reduces by about 30% in the image rotation, zoom and illumination change cases. The improved SIFT algorithm is effective.

Concerning the problem of large computation, slow response and repeated computation in travel sky-based Calendar Shopping (CS) system, an efficient method was proposed to cache calculation results of unit in this paper. The system architecture was redesigned and the performance of calendar shopping system was optimized. The experimental results show that the presented method can reduce the system response time and improve the performance of the system significantly, also it provides the method and theoretical support for calendar shopping system in civil aviation field.