To enhance the optimization ability of quantum potential well-based particle swarm optimization algorithm, the improved quantum potential well-based particle swarm optimization algorithms are proposed by analyzing the design process of current quantum potential well-based particle swarm optimization algorithms. Firstly, three improved quantum particle swarm optimization algorithms are proposed based on delta potential well, harmonic oscillator and square potential well, respectively, and then a statistic mean-based control parameter design method is presented for the proposed models. Secondly, to highlight the guiding role of the global optimal particle in designing potential well centers, two strategies are presented based on a weighted average of all self-optimal particles and dynamic random variables. The experimental results show that the performances of three improved algorithms are relatively close, the model based delta potential well are slightly better than the other two kinds of model, and the performances of three improved algorithms are superior to that of the original algorithm.