Modified Artificial Gorilla Troop Optimization Algorithm for Solving Constrained Engineering Optimization Problems
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...
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-03-01
|
| Series: | Mathematics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2227-7390/11/5/1256 |
| _version_ | 1827752548284497920 |
|---|---|
| author | Jinhua You Heming Jia Di Wu Honghua Rao Changsheng Wen Qingxin Liu Laith Abualigah |
| author_facet | Jinhua You Heming Jia Di Wu Honghua Rao Changsheng Wen Qingxin Liu Laith Abualigah |
| author_sort | Jinhua You |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-11T07:17:57Z |
| format | Article |
| id | doaj.art-c8b67755592d45bf82b1cbccf8c83eca |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-11T07:17:57Z |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-c8b67755592d45bf82b1cbccf8c83eca2023-11-17T08:10:21ZengMDPI AGMathematics2227-73902023-03-01115125610.3390/math11051256Modified Artificial Gorilla Troop Optimization Algorithm for Solving Constrained Engineering Optimization ProblemsJinhua You0Heming Jia1Di Wu2Honghua Rao3Changsheng Wen4Qingxin Liu5Laith Abualigah6School of Information Engineering, Sanming University, Sanming 365004, ChinaSchool of Information Engineering, Sanming University, Sanming 365004, ChinaSchool of Education and Music, Sanming University, Sanming 365004, ChinaSchool of Information Engineering, Sanming University, Sanming 365004, ChinaSchool of Information Engineering, Sanming University, Sanming 365004, ChinaSchool of Computer Science and Technology, Hainan University, Haikou 570228, ChinaComputer Science Department, Prince Hussein Bin Abdullah Faculty for Information Technology, Al al-Bayt University, Mafraq 25113, JordanThe 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.https://www.mdpi.com/2227-7390/11/5/1256artificial gorilla troop optimization algorithmconvergence strategy of contraction control factorssine cosine interaction fusion strategyidentification strategies of individual differences in gorillas |
| spellingShingle | Jinhua You Heming Jia Di Wu Honghua Rao Changsheng Wen Qingxin Liu Laith Abualigah Modified Artificial Gorilla Troop Optimization Algorithm for Solving Constrained Engineering Optimization Problems Mathematics artificial gorilla troop optimization algorithm convergence strategy of contraction control factors sine cosine interaction fusion strategy identification strategies of individual differences in gorillas |
| title | Modified Artificial Gorilla Troop Optimization Algorithm for Solving Constrained Engineering Optimization Problems |
| title_full | Modified Artificial Gorilla Troop Optimization Algorithm for Solving Constrained Engineering Optimization Problems |
| title_fullStr | Modified Artificial Gorilla Troop Optimization Algorithm for Solving Constrained Engineering Optimization Problems |
| title_full_unstemmed | Modified Artificial Gorilla Troop Optimization Algorithm for Solving Constrained Engineering Optimization Problems |
| title_short | Modified Artificial Gorilla Troop Optimization Algorithm for Solving Constrained Engineering Optimization Problems |
| title_sort | modified artificial gorilla troop optimization algorithm for solving constrained engineering optimization problems |
| topic | artificial gorilla troop optimization algorithm convergence strategy of contraction control factors sine cosine interaction fusion strategy identification strategies of individual differences in gorillas |
| url | https://www.mdpi.com/2227-7390/11/5/1256 |
| work_keys_str_mv | AT jinhuayou modifiedartificialgorillatroopoptimizationalgorithmforsolvingconstrainedengineeringoptimizationproblems AT hemingjia modifiedartificialgorillatroopoptimizationalgorithmforsolvingconstrainedengineeringoptimizationproblems AT diwu modifiedartificialgorillatroopoptimizationalgorithmforsolvingconstrainedengineeringoptimizationproblems AT honghuarao modifiedartificialgorillatroopoptimizationalgorithmforsolvingconstrainedengineeringoptimizationproblems AT changshengwen modifiedartificialgorillatroopoptimizationalgorithmforsolvingconstrainedengineeringoptimizationproblems AT qingxinliu modifiedartificialgorillatroopoptimizationalgorithmforsolvingconstrainedengineeringoptimizationproblems AT laithabualigah modifiedartificialgorillatroopoptimizationalgorithmforsolvingconstrainedengineeringoptimizationproblems |