Research on the Application of Mathematical Optimization Algorithms in Civil Engineering Courses and Strategies for Improving Teaching Effectiveness

Abstract

Accurate recommendation of teaching resources broadens the feasible path for realizing students' personalized development. This paper constructs a civil engineering course resource recommendation system based on multi-dimensional data view to improve the level of intelligent recommendation of resources. Quantitative data weights are used to convert the multi-objective optimization problem into the optimal solution of objective function value, which reduces the difficulty of solving. The multi-objective particle swarm optimization algorithm (NEMOPSO) is introduced to enhance the global optimization capability and improve the accuracy of resource recommendation. Research shows that NEMOPSO can maintain population diversity and solution comprehensiveness in the process of optimization solving. The recommendation accuracy of single resource or multiple resource tasks ≧90%, memory occupation space ≦66MB, and running time ≦24 s. After the experiment, the mean values of the experimental class in the four engineering thinking dimensions were improved by 1.3486, 1.2787, 1.1631, and 1.2769, respectively, which were significantly different from the pre-experiment at the 0.01 level.