Multiclass Interactive Martial Arts Teaching Optimization Method Based on Euclidean Distance

Aiming at the problems of low optimization accuracy, poor effect, and long running time in current teaching algorithms, a multiclass interactive martial arts method based on Euclidean distance is proposed. Using K-means algorithm, initial population divided into several subgroups distance, so as to effectively use information neighborhood strengthen local search ability algorithm. Imitating school's selection excellent teachers guide students with performance, after “teaching” stage, worst individual each subgroup will learn from best population, interaction evolutionary process be enhanced, that individuals quickly move closer individuals. According different learning levels situations students, stages contents are divided, mainly by grade, supplemented types groups form random matching, improve members weak group, which guarantees diversity realizes optimization. Experimental results show effect proposed better, can accuracy algorithm shorten