Teaching-Learning-Based Optimization Algorithm with Social Psychology Theory
Teaching-learning-based optimization (TLBO) is a heuristic optimization algorithm that simulates the teaching process. In view of the low precision and poor stability of TLBO algorithm, an improved teaching-learning-based optimization algorithm named SPTLBO (social psychology teaching-learning-based...
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| Format: | Article |
| Language: | zho |
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Journal of Computer Engineering and Applications Beijing Co., Ltd., Science Press
2022-06-01
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| Series: | Jisuanji kexue yu tansuo |
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| Online Access: | http://fcst.ceaj.org/fileup/1673-9418/PDF/2010049.pdf |
| _version_ | 1811332175489925120 |
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| author | HE Peiyuan, LIU Yong |
| author_facet | HE Peiyuan, LIU Yong |
| author_sort | HE Peiyuan, LIU Yong |
| collection | DOAJ |
| description | Teaching-learning-based optimization (TLBO) is a heuristic optimization algorithm that simulates the teaching process. In view of the low precision and poor stability of TLBO algorithm, an improved teaching-learning-based optimization algorithm named SPTLBO (social psychology teaching-learning-based optimization) is proposed. Human psychological factors are considered in the improvement of the algorithm. In the “teaching” stage, combining the “expectation effect” theory in social psychology, teachers adopt one-to-one teaching strategy for students with high expectations, which makes outstanding students approach teachers faster. According to cognitive style, students can be divided into two types, “field independence” and “field dependence”, so that it can preserve the diversity of students. Different types of students will adopt different communication methods to learn. After the “teaching” and “learning” stages, combined with the theory of self-regulation, students enter the stage of learning method adjustment. It can enhance the ability of self-exploration and improve the overall level of students. In addition, an adaptive student update factor is introduced to simulate the influence of environment on students’ learning efficiency, which increases the global search ability of the algorithm and avoids falling into local optimum in the initial iteration. The test of 25 test functions shows that, compared with the basic TLBO algorithm and other intelligent optimization algorithms, the SPTLBO algorithm has more advantages in the optimization accuracy and convergence speed. |
| first_indexed | 2024-04-13T16:31:48Z |
| format | Article |
| id | doaj.art-d582641a5de6442c88657f9ef3dade3e |
| institution | Directory Open Access Journal |
| issn | 1673-9418 |
| language | zho |
| last_indexed | 2024-04-13T16:31:48Z |
| publishDate | 2022-06-01 |
| publisher | Journal of Computer Engineering and Applications Beijing Co., Ltd., Science Press |
| record_format | Article |
| series | Jisuanji kexue yu tansuo |
| spelling | doaj.art-d582641a5de6442c88657f9ef3dade3e2022-12-22T02:39:32ZzhoJournal of Computer Engineering and Applications Beijing Co., Ltd., Science PressJisuanji kexue yu tansuo1673-94182022-06-011661362137310.3778/j.issn.1673-9418.2010049Teaching-Learning-Based Optimization Algorithm with Social Psychology TheoryHE Peiyuan, LIU Yong0School of Management, University of Shanghai for Science and Technology, Shanghai 200093, ChinaTeaching-learning-based optimization (TLBO) is a heuristic optimization algorithm that simulates the teaching process. In view of the low precision and poor stability of TLBO algorithm, an improved teaching-learning-based optimization algorithm named SPTLBO (social psychology teaching-learning-based optimization) is proposed. Human psychological factors are considered in the improvement of the algorithm. In the “teaching” stage, combining the “expectation effect” theory in social psychology, teachers adopt one-to-one teaching strategy for students with high expectations, which makes outstanding students approach teachers faster. According to cognitive style, students can be divided into two types, “field independence” and “field dependence”, so that it can preserve the diversity of students. Different types of students will adopt different communication methods to learn. After the “teaching” and “learning” stages, combined with the theory of self-regulation, students enter the stage of learning method adjustment. It can enhance the ability of self-exploration and improve the overall level of students. In addition, an adaptive student update factor is introduced to simulate the influence of environment on students’ learning efficiency, which increases the global search ability of the algorithm and avoids falling into local optimum in the initial iteration. The test of 25 test functions shows that, compared with the basic TLBO algorithm and other intelligent optimization algorithms, the SPTLBO algorithm has more advantages in the optimization accuracy and convergence speed.http://fcst.ceaj.org/fileup/1673-9418/PDF/2010049.pdf|teaching-learning-based optimization algorithm (tlbo)|social psychology|expectation effect|field independence-field dependence|self-regulation theory|adaptive update factor |
| spellingShingle | HE Peiyuan, LIU Yong Teaching-Learning-Based Optimization Algorithm with Social Psychology Theory Jisuanji kexue yu tansuo |teaching-learning-based optimization algorithm (tlbo)|social psychology|expectation effect|field independence-field dependence|self-regulation theory|adaptive update factor |
| title | Teaching-Learning-Based Optimization Algorithm with Social Psychology Theory |
| title_full | Teaching-Learning-Based Optimization Algorithm with Social Psychology Theory |
| title_fullStr | Teaching-Learning-Based Optimization Algorithm with Social Psychology Theory |
| title_full_unstemmed | Teaching-Learning-Based Optimization Algorithm with Social Psychology Theory |
| title_short | Teaching-Learning-Based Optimization Algorithm with Social Psychology Theory |
| title_sort | teaching learning based optimization algorithm with social psychology theory |
| topic | |teaching-learning-based optimization algorithm (tlbo)|social psychology|expectation effect|field independence-field dependence|self-regulation theory|adaptive update factor |
| url | http://fcst.ceaj.org/fileup/1673-9418/PDF/2010049.pdf |
| work_keys_str_mv | AT hepeiyuanliuyong teachinglearningbasedoptimizationalgorithmwithsocialpsychologytheory |