Modified Artificial Gorilla Troop Optimization Algorithm for Solving Constrained Engineering Optimization Problems

Jinhua You, Heming Jia (), Di Wu (), Honghua Rao, Changsheng Wen, Qingxin Liu and Laith Abualigah
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Jinhua You: 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
Honghua Rao: School of Information Engineering, Sanming University, Sanming 365004, China
Changsheng Wen: School of Information Engineering, Sanming University, Sanming 365004, China
Qingxin Liu: School of Computer Science and Technology, Hainan University, Haikou 570228, China
Laith Abualigah: Computer Science Department, Prince Hussein Bin Abdullah Faculty for Information Technology, Al al-Bayt University, Mafraq 25113, Jordan

Mathematics, 2023, vol. 11, issue 5, 1-42

Abstract: The artificial Gorilla Troop Optimization (GTO) algorithm (GTO) is a metaheuristic optimization algorithm that simulates the social life of gorillas. This paper proposes three innovative strategies considering the GTO algorithm’s insufficient convergence accuracy and low convergence speed. First, a shrinkage control factor fusion strategy is proposed to expand the search space and reduce search blindness by strengthening the communication between silverback gorillas and other gorillas to improve global optimization performance. Second, a sine cosine interaction fusion strategy based on closeness is proposed to stabilize the performance of silverback gorillas and other gorilla individuals and improve the convergence ability and speed of the algorithm. Finally, a gorilla individual difference identification strategy is proposed to reduce the difference between gorilla and silverback gorillas to improve the quality of the optimal solution. In order to verify the optimization effect of the modified artificial gorilla troop optimization (MGTO) algorithm, we used 23 classic benchmark functions, 30 CEC2014 benchmark functions, and 10 CEC2020 benchmark functions to test the performance of the proposed MGTO algorithm. In this study, we used a total of 63 functions for algorithm comparison. At the same time, we carried out the exploitation and exploration balance experiment of 30 CEC2014 and 10 CEC2020 functions for the MGTO algorithm. In addition, the MGTO algorithm was also applied to test seven practical engineering problems, and it achieved good results.

Keywords: artificial gorilla troop optimization algorithm; convergence strategy of contraction control factors; sine cosine interaction fusion strategy; identification strategies of individual differences in gorillas (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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