A Faculty Matching Model for Medical Laboratory Programmes Based on Discrete Particle Swarm Optimization Algorithm

Aiming at the deficiencies of the course faculty configuration of medical testing in higher vocational colleges at the present stage, the study proposes a novel faculty collocation model for higher vocational medical testing courses based on the discrete particle swarm algorithm by using the cluster analysis technique and the discrete binary particle swarm algorithm. The experimental results show that the best fitness value of the improved discrete binary particle swarm algorithm proposed by the study is 0.21, and only 36 iterations are needed to reach a stable state. After the faculty reconfiguration using the novel model proposed by the Institute, the change in the degree of variance of instructional matching is not obvious, with a minimum of only 0.16, and the curve distribution is also smooth. The variation curve of the teaching match variability without faculty reconfiguration is obvious, with the lowest matching variability of 0.23. The new faculty reconfiguration model proposed by the study has the highest P-value of 97.12%, the highest R-value of 97.24%, the highest F1-value of 97.25%, and the average detection time of 8.25 s. This is a good example of how the new faculty reconfiguration model can be used to achieve a stable state. Thus, it can be seen that the method proposed by the study can provide certain feasible suggestions for the management of the teachers of the medical testing programme in higher vocational colleges and universities, and promote the progress of education in higher vocational colleges and universities.