Route planning is very important for the task execution of Unmanned Aerial Vehicle （UAV） swarm， and the computation is usually complex in high dimensional scenarios. Swarm intelligence has provided a good solution for this problem. Particle Swarm Optimization （PSO） algorithm is especially suitable for route planning problem because of its advantages such as few parameters， fast convergence and simple operation. However， PSO algorithm has poor global search ability and is easy to fall into local optimum when applied to route planning. In order to solve the problems above and improve the effect of UAV swarm route planning， a Dynamic Cluster Particle Swarm Optimization （DCPSO） algorithm was proposed. Firstly， artificial potential field method and receding horizon control principle were used to model the task scenario of route planning problem of UAV swarm. Secondly， Tent chaotic map and dynamic cluster mechanism were introduced to further improve the global search ability and search accuracy. Finally， DCPSO algorithm was used to optimize the objective function of the model to obtain each trajectory point selection of UAV swarm. On 10 benchmark functions with different combinations of unimodal/multimodal and low-dimension/high-dimension， simulation experiments were carried out. The results show that compared with PSO algorithm， Pigeon-Inspired Optimization （PIO）， Sparrow Search Algorithm （SSA） and Chaotic Disturbance Pigeon-Inspired Optimization （CDPIO） algorithm， DCPSO algorithm has better optimal value， mean value and variance， better search accuracy and stronger stability. Besides， the performance and effect of DCPSO algorithm were demonstrated in the route planning application instances of UAV swarm simulation experiments.

Focusing on the problems of complex process and long response time of task offloading in multi-cloudlet mode， a computing task offloading model based on multi-cloudlet collaboration was constructed， and a Weighted self-Adaptive Inertia Weight Particle Swarm Optimization （WAIW-PSO） algorithm was proposed to solve the optimal offloading scheme quickly. Firstly， the task execution process of mobile terminal-cloudlet-remote cloud was modeled. Secondly， considering the competition of computing resources by multiple users， the task offloading model based on multi-cloudlet collaboration was constructed. Finally， since the complexity of solving the optimal offloading scheme was excessively high， the WAIW-PSO was proposed to solve the offloading problem. Simulation results show that compared with the standard Particle Swarm Optimization （PSO） algorithm and the PSO algorithm with Decreasing Inertia Weight based on Gaussian function （GDIWPSO）， WAIW-PSO algorithm can adjust the inertia weight according to evolutionary generation and individual fitness， and it has the better optimization ability and the shortest time for finding the optimal offloading scheme. Experimental results on different task unloading schemes with different numbers of equipments and tasks show that the WAIW-PSO algorithm based offloading schemes can significantly shorten the total task completion time.