Optimizing college English curriculum structure with an adaptive particle swarm algorithm

Abstract

In this study, an intelligent system is built based on adaptive particle swarm optimization (APSO) algorithm, and multidimensional optimization is carried out for college English curriculum structure. By analyzing the problems existing in the current curriculum system, such as rigid modules, inefficient resources and single evaluation, this paper puts forward a three-dimensional optimization objective function with the allocation of class hours, module weight and resource allocation as the core. The improved APSO algorithm is adopted to dynamically adjust the inertia weight and mutation strategy, and the Pareto frontier and TOPSIS method are combined to screen the optimal solution, so as to realize the collaborative optimization of language ability improvement rate, resource utilization rate and student satisfaction. The empirical results show that the optimized curriculum structure significantly increases the proportion of oral communication (+8%) and cross-cultural practice (+3%), reduces the proportion of reading, writing and translation (-5%) and academic English (-5%), and improves the input-output ratio of foreign teachers' resources (+1.2 times) and VR lab (+1.9 times). In the experimental group, the CET-6 pass rate increased by 12.5%, the oral advanced ability increased by 17.4%, the average value of cross-cultural communication test increased by 13.8 points, and the standard deviation of ability decreased by 33.3%. The research shows that APSO algorithm effectively balances the diversity of teaching objectives and resource constraints through the dynamic optimization of curriculum structure driven by intelligent system, and provides quantitative decision-making tools and scalable solutions for college English teaching reform.