In order to further improve the exploration ability, convergence speed and optimization ability in high dimensional problems of random dritf particle swarm optimization(RDPSO) algorithm. The frequently coverage RDPSO(FC-RDPSO) algorithm based on the strategy of frequent coverage is proposed. The probability statistical method and Monte Carlo method are used to analyze the feasibility of the frequently coverage strategy. The results show that the proposed FC-RDPSO algorithm has better performance than some classical and state-of-the-art variants of PSO algorithms on the test suite for CEC'2013 special session on real-parameter optimization(RPO) benchmark functions. Results on a set of widely used large-scale global optimization(LSGO) benchmark functions demonstrate that the proposed FC-RDPSO algorithm also has strong optimization ability on a high-dimensional problem.