Research on the Evaluation Model Combining AHP and PSO Algorithms for Higher Education

With the development of science and technology, the use of modern information technology can effectively develop educational resources, optimize the educational process, and cultivate and improve students' information literacy. Therefore, in order to accurately evaluate the quality of higher education, a research proposes an education evaluation model based on improved particle swarm optimization analytic hierarchy process. The model used particle swarm optimization algorithm to perform consistency testing on the Analytic Hierarchy Process to confirm the objectivity of the evaluation system. The experimental results show that although the particle swarm optimization analytic hierarchy process model improved compared to other evaluation models has a slower iteration speed, its convergence value is smaller. In the initial judgment matrix, the consistency ratio of the improved particle swarm optimization analytic hierarchy process model is only 0.026, and the weights of the initial matrix are 0.087, 0.443, 0.160, and 0.310, respectively. In addition, the consistency ratios of each judgment matrix in the improved particle swarm optimization analytic hierarchy process model are the smallest, with consistency ratios of 0.015, 0.004, 0.018, and 0.006 for matrices G1 to G4, respectively. The above results indicate that the improved particle swarm optimization analytic hierarchy process model can optimize the weights of each indicator, reduce the consistency ratio of each judgment matrix, and achieve objective evaluation of education quality.