Adaptive Differential Evolutionary Algorithm for Innovative Teaching and Learning in High School Project-Based Courses

Abstract

Abstract In this paper, for the innovative teaching of high school project-based courses, an adaptive differential evolutionary algorithm is used as one of the tools of the project to help students solve complex problems. The traditional adaptive differential evolution algorithm has the problems of local optimum and low convergence accuracy; to overcome these limitations, this paper proposes an enhanced reverse learning adaptive differential evolution algorithm. The OL-ADE algorithm is improved by introducing the reverse learning strategy in machine learning to extract the reverse elite individual population from the original individual population and select the better-adapted individuals for mutation operation. After the project-based teaching, the students’ works have high scores in layout color matching, technical operation, organization, information awareness, and information social responsibility, and their scores are 8.6, 9.75, 9, 9.35, and 8.75, respectively. Therefore, this paper proves the Adaptive Difference Evolution algorithm’s effectiveness in innovative teaching of high school project-based courses.