Nodes in the Content-Centric Network (CCN) cache all the passed content by default, without selective caching and optimally placing of the content. In order to solve the problems, a new Cooperative Caching strategy based on User Preference (CCUP) was proposed. Firstly, user's preference for content type and content popularity were considered as user's local preference indexes to realize the selection of cached content. Then, the differentiated caching strategy was executed on the content that needed to be cached, the globally active content was cached at the important central node, and the inactive content was cached according to the matching of the local preference and the distance level between node and user. Finally, the near access of user to local preference content and the quick distribution of global active content were both achieved. The simulation results show that, compared with typical caching strategies, such as LCE (Leave Copy Everywhere)、Prob(0.6) (Probabilistic caching with 0.6)、Betw (cache "less for more"), the proposed CCUP has obvious advantages in average cache hit rate and average request delay.

To improve the efficiency of Particle Swarm Optimization (PSO), a quantum particle swarm optimization algorithm combined with quantum theory on the basis of Bloch sphere was proposed. In Bloch spherical coordinates, the particle automatically updated rotation angle and particle position, without setting the rotation angle in the form of look-up table (or setting fixed value of the interval), making up for the deficiency of quantum evolutionary algorithm and quantum genetic algorithm on the basis of Bloch sphere, and the algorithm is more generalizable. Using quantum Hadamard gate to realize the variation of particle enhanced the diversity of population, and prompted particle jump out of local extreme value. The simulation results of the typical function optimization problem show that the algorithm is stable with high precision and fast convergence rate, and it is practical.