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To solve the problems that the nodes is non-uniform distribution and the coverage is incomplete in wireless sensor network by using the traditional method, an improved leap frog algorithm is proposed. In order to increase the population diversity and accelerate the algorithm convergence speed of the algorithm, the improved leapfrog algorithm respectively increased a gaussian learning mechanism and a improved update strategy based on particle swarm thought. Let the worst individual of groups search in their own local nearby, if invalid, the worst individual approach to the best individual of groups and the global optimal individual. In the performance evaluation experiments, the standard function test and wireless sensor network coverage optimization test were performed. The results show that, comparing with other algorithms, the convergence accuracy of improved leapfrog algorithm has increased significantly on four of the six test functions. What’s more, the improved leapfrog algorithm is more effective to solve wireless sensor network coverage problem. The distribution of nodes is more uniform, and the network coverage rate is up to 85.6%.