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Aiming at the problems of uneven distribution and low coverage when nodes are randomly deployed in wireless sensor network (WSN), a hybrid particle swarm-butterfly algorithm (HPSBA) is proposed for node deployment optimization. Firstly, logistic mapping and adaptive adjustment strategies are designed to control parameter values, so that the optimization speed, convergence accuracy and global search capability of HPSBA are improved. Then, four benchmark functions are used to analyze the performance of HPSBA. The simulation results show that HPSBA has higher optimization accuracy, faster optimization speed, and better stability. Finally, HPSBA is used in WSN node deployment optimization and compared with other six typical algorithms such as PSO, BOA, IGWO and so on. The results show that HPSBSA has higher coverage rate, which can effectively reduce the redundancy of nodes and prolong the survival time of WSN. [ABSTRACT FROM AUTHOR]