| Summary: | This paper uses an adaptive differential evolution algorithm to handle multiple conflicting optimization objectives by randomly generating uniformly distributed values corresponding to the upper and lower bounds so that the variance vectors are crossed over. A diversity metric measures the degree of uniformity of distribution between solutions, using a pre-given interval of static variables and introducing a special differential mutation pattern for iteration. The highest value of the adaptive differential evolution algorithm piano performance time was 13.5 s, and the highest value of the teaching quality situation was 0.864. With the adaptive differential evolution algorithm, the piano teaching content can be enhanced, students' interest in folk music can be nurtured, and folk music can be inherited and promoted.
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