The accuracy of the implantation position of the MSE osseous expansion anchorage implant is a key issue in the treatment of osseous expansion, which also suffers from the drawback of falling into prematureness too early when the standard QPSO algorithm is used for its multiobjective optimisation. In this study, an adaptive improved QPSO algorithm is proposed to address the above problems. Firstly, a partitioned retrieval strategy is used to divide the population into an auxiliary class group and a main iterative population, and the respective search iteration intervals of the populations are assigned, thus optimising the initialisation mechanism of the standard algorithm, and then, the pheromone mechanism in the ACO algorithm is introduced to make the particles in the QPSO algorithm carry pheromones, and the particles determine their direction of travel by sensing the pheromone concentration in each path, thus improving the search ability of the particles. Experimental simulation results show that the improved strategy proposed in this study effectively improves the population diversity of the standard QPSO algorithm, avoids the algorithm from entering local optimum, and has good application in MSE osseous expansion position optimisation.