To solve the problems that Particle Swarm Optimization (PSO) algorithm is easy to fall into the local extremum due to the lack of interaction between individuals in the search process, the diversity of the population is gradually lost, a Perturbation Particle Swarm Optimization algorithm based on Local Far-neighbor Differential Enhancement (LFDE-PPSO) was proposed. Firstly, in order to enlarge the population search space, the disturbance factor was introduced to make inertia weight and learning factor fluctuate within a small range. Secondly, the reconstruction probability was introduced, and the population with low fitness value was selected to reconstruct intermediate population. Finally, in order to increase the population diversity, the excellent individuals of poor individuals were retained, the irrelevant and far-neighbor individuals were introduced. The far-neighbors with large differences from differential individual genes were used for differential enhancement. The experimental results show that the proposed algorithm can preserve individuals with high fitness in the intermediate population, effectively increase the population diversity, make the population have strong ability to jump out of local extremum, speed up the particle approximation to the global aptimum, and have the advantages of fast convergence and high precision.