[1]WU Di,JIA Heming,LIU Qingxin,et al.Teaching and learning optimization algorithm based on empirical reflection mechanism[J].CAAI Transactions on Intelligent Systems,2023,18(3):629-641.[doi:10.11992/tis.202112043]
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Teaching and learning optimization algorithm based on empirical reflection mechanism

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 18 Number of periods: 2023 3 Page number: 629-641 Column: 学术论文—人工智能基础 Public date: 2023-07-05
Title:
Teaching and learning optimization algorithm based on empirical reflection mechanism
Author(s):
WU Di1;  JIA Heming2;  LIU Qingxin3;  QI Qi3;  WANG Shuang2
1. School of Education and Music, Sanming University, Sanming 365004, China;
2. School of Information Engineering, Sanming University, Sanming 365004, China;
3. School of Computer Science and Technology, Hainan University, Haikou 570228, China
Keywords:
teaching and learning-based optimization; empirical reflection mechanism; dynamic adaptive weight; meta-heuristic algorithm; benchmark functions; pressure vessel design problem; welded beam design problem; Wilcoxon rank-sum test
CLC:
TP301.6
DOI:
10.11992/tis.202112043
Abstract:
In this paper, an empirical reflection teaching learning based optimization (ERTLBO) algorithm is proposed to solve the problems of easy falling into local optimum, slow convergence speed and low accuracy in traditional teaching-learning-based optimization. Firstly, the empirical reflection mechanism is introduced in the teaching stage to select elite individuals, guide ordinary individuals to approach teachers, and improve the overall level of the class, so as to improve the overall exploration ability of the algorithm. Secondly, the dynamic adaptive weight is introduced in the learning stage, which can adaptively disturb the position according to the fitness value of students, so as to realize dynamic updating of individual position and improve the ability of the algorithm to jump out of local optimum. In the experiment, 23 benchmark functions are selected to test the performance of ERTLBO, variant, and popular algorithms. Experimental results show that ERTLBO has better optimization performance and solution stability. Finally, the effectiveness and superiority of ERTLBO in solving practical problems are further verified through two engineering design problems.
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