A Modified Group Teaching Optimization Algorithm for Solving Constrained Engineering Optimization Problems

Honghua Rao, Heming Jia (), Di Wu (), Changsheng Wen, Shanglong Li, Qingxin Liu and Laith Abualigah
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Honghua Rao: School of Information Engineering, Sanming University, Sanming 365004, China
Heming Jia: School of Information Engineering, Sanming University, Sanming 365004, China
Di Wu: School of Education and Music, Sanming University, Sanming 365004, China
Changsheng Wen: School of Information Engineering, Sanming University, Sanming 365004, China
Shanglong Li: School of Information Engineering, Sanming University, Sanming 365004, China
Qingxin Liu: School of Computer Science and Technology, Hainan University, Haikou 570228, China
Laith Abualigah: Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, Amman 19328, Jordan

Mathematics, 2022, vol. 10, issue 20, 1-36

Abstract: The group teaching optimization algorithm (GTOA) is a meta heuristic optimization algorithm simulating the group teaching mechanism. The inspiration of GTOA comes from the group teaching mechanism. Each student will learn the knowledge obtained in the teacher phase, but each student’s autonomy is weak. This paper considers that each student has different learning motivations. Elite students have strong self-learning ability, while ordinary students have general self-learning motivation. To solve this problem, this paper proposes a learning motivation strategy and adds random opposition-based learning and restart strategy to enhance the global performance of the optimization algorithm (MGTOA). In order to verify the optimization effect of MGTOA, 23 standard benchmark functions and 30 test functions of IEEE Evolutionary Computation 2014 (CEC2014) are adopted to verify the performance of the proposed MGTOA. In addition, MGTOA is also applied to six engineering problems for practical testing and achieved good results.

Keywords: group teaching optimization algorithm; learning motivation strategy; random opposition-based learning; restart strategy; engineering problems (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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