In order to improve the solving efficiency and performance of Weapon Target Assignment (WTA), this paper puts forward a kind of improved quantum-behaved particle swarm optimization algorithm for solving WTA. First, by defining particle evolution speed and particle aggregation degree, the inertia weight is expressed as the function of particle evolution speed and particle aggregation degree, making the inertia weight have self-adaptivity. Secondly, the slowly varying function is introduced into the traditional location updating formula, effectively overcoming the problem of the partial optimization. Finally, a multiple weapons target assignment is built to meet the target of the minimum failure probability in allocating weapons and shooting all targets. Simulation results indicate that the new algorithm can get the optimal or suboptimal solution to WTA problems, that is, effectively solve WTA problems.