Research on the Optimization of English-speaking Teaching Strategies based on Genetic Algorithm

Abstract

This exploration examines the optimization of English-speaking showing techniques through the use of genetic algorithms (GAs), particle swarm optimization (PSO), ant colony optimization (ACO), and simulated annealing (SA). By blending bits of knowledge from related work and directing analyses, it exhibits the adequacy of these optimization algorithms in upgrading language learning results. Our examinations uncover that genetic algorithms and ant colony optimization reliably outflank different algorithms concerning arrangement quality and viability in working on English-speaking capability. In particular, genetic algorithms and ant colony optimization show higher assembly velocities and produce better arrangements contrasted with particle swarm optimization and simulated annealing. Also, these algorithms show more prominent adequacy in upgrading English-speaking capability, as confirmed by significant enhancements in student execution measurements and language capability evaluations. In general, this exploration adds to propelling the talk on optimization procedures in language schooling and features the capability of computational optimization algorithms in fitting educational strategies to meet the different necessities of language students.