A Modified Gorilla Troops Optimizer for Global Optimization Problem
The Gorilla Troops Optimizer (GTO) is a novel Metaheuristic Algorithm that was proposed in 2021. Its design was inspired by the lifestyle characteristics of gorillas, including migration to a known position, migration to an undiscovered position, moving toward the other gorillas, following silverbac...
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MDPI AG
2022-10-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/19/10144 |
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| author | Tingyao Wu Di Wu Heming Jia Nuohan Zhang Khaled H. Almotairi Qingxin Liu Laith Abualigah |
| author_facet | Tingyao Wu Di Wu Heming Jia Nuohan Zhang Khaled H. Almotairi Qingxin Liu Laith Abualigah |
| author_sort | Tingyao Wu |
| collection | DOAJ |
| description | The Gorilla Troops Optimizer (GTO) is a novel Metaheuristic Algorithm that was proposed in 2021. Its design was inspired by the lifestyle characteristics of gorillas, including migration to a known position, migration to an undiscovered position, moving toward the other gorillas, following silverback gorillas and competing with silverback gorillas for females. However, like other Metaheuristic Algorithms, the GTO still suffers from local optimum, low diversity, imbalanced utilization, etc. In order to improve the performance of the GTO, this paper proposes a modified Gorilla Troops Optimizer (MGTO). The improvement strategies include three parts: Beetle-Antennae Search Based on Quadratic Interpolation (QIBAS), Teaching–Learning-Based Optimization (TLBO) and Quasi-Reflection-Based Learning (QRBL). Firstly, QIBAS is utilized to enhance the diversity of the position of the silverback. Secondly, the teacher phase of TLBO is introduced to the update the behavior of following the silverback with 50% probability. Finally, the quasi-reflection position of the silverback is generated by QRBL. The optimal solution can be updated by comparing these fitness values. The performance of the proposed MGTO is comprehensively evaluated by 23 classical benchmark functions, 30 CEC2014 benchmark functions, 10 CEC2020 benchmark functions and 7 engineering problems. The experimental results show that MGTO has competitive performance and promising prospects in real-world optimization tasks. |
| first_indexed | 2024-03-09T22:00:09Z |
| format | Article |
| id | doaj.art-f9c40196b8e243758bf68dbfbbfee6a0 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T22:00:09Z |
| publishDate | 2022-10-01 |
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| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-f9c40196b8e243758bf68dbfbbfee6a02023-11-23T19:51:46ZengMDPI AGApplied Sciences2076-34172022-10-0112191014410.3390/app121910144A Modified Gorilla Troops Optimizer for Global Optimization ProblemTingyao Wu0Di Wu1Heming Jia2Nuohan Zhang3Khaled H. Almotairi4Qingxin Liu5Laith Abualigah6School of Education and Music, Sanming University, Sanming 365004, ChinaSchool of Education and Music, Sanming University, Sanming 365004, ChinaSchool of Information Engineering, Sanming University, Sanming 365004, ChinaSchool of Education and Music, Sanming University, Sanming 365004, ChinaDepartment of Computer Engineering, Computer and Information Systems College, Umm Al-Qura University, Makkah 21955, Saudi ArabiaSchool of Computer Science and Technology, Hainan University, Haikou 570228, ChinaHourani Center for Applied Scientific Research, Al-Ahliyya Amman University, Amman 19328, JordanThe Gorilla Troops Optimizer (GTO) is a novel Metaheuristic Algorithm that was proposed in 2021. Its design was inspired by the lifestyle characteristics of gorillas, including migration to a known position, migration to an undiscovered position, moving toward the other gorillas, following silverback gorillas and competing with silverback gorillas for females. However, like other Metaheuristic Algorithms, the GTO still suffers from local optimum, low diversity, imbalanced utilization, etc. In order to improve the performance of the GTO, this paper proposes a modified Gorilla Troops Optimizer (MGTO). The improvement strategies include three parts: Beetle-Antennae Search Based on Quadratic Interpolation (QIBAS), Teaching–Learning-Based Optimization (TLBO) and Quasi-Reflection-Based Learning (QRBL). Firstly, QIBAS is utilized to enhance the diversity of the position of the silverback. Secondly, the teacher phase of TLBO is introduced to the update the behavior of following the silverback with 50% probability. Finally, the quasi-reflection position of the silverback is generated by QRBL. The optimal solution can be updated by comparing these fitness values. The performance of the proposed MGTO is comprehensively evaluated by 23 classical benchmark functions, 30 CEC2014 benchmark functions, 10 CEC2020 benchmark functions and 7 engineering problems. The experimental results show that MGTO has competitive performance and promising prospects in real-world optimization tasks.https://www.mdpi.com/2076-3417/12/19/10144gorilla troops optimizerbeetle-antennae search based on quadratic interpolationteaching–learning-based optimizationquasi-reflection-based learningfunction optimizationengineering design |
| spellingShingle | Tingyao Wu Di Wu Heming Jia Nuohan Zhang Khaled H. Almotairi Qingxin Liu Laith Abualigah A Modified Gorilla Troops Optimizer for Global Optimization Problem Applied Sciences gorilla troops optimizer beetle-antennae search based on quadratic interpolation teaching–learning-based optimization quasi-reflection-based learning function optimization engineering design |
| title | A Modified Gorilla Troops Optimizer for Global Optimization Problem |
| title_full | A Modified Gorilla Troops Optimizer for Global Optimization Problem |
| title_fullStr | A Modified Gorilla Troops Optimizer for Global Optimization Problem |
| title_full_unstemmed | A Modified Gorilla Troops Optimizer for Global Optimization Problem |
| title_short | A Modified Gorilla Troops Optimizer for Global Optimization Problem |
| title_sort | modified gorilla troops optimizer for global optimization problem |
| topic | gorilla troops optimizer beetle-antennae search based on quadratic interpolation teaching–learning-based optimization quasi-reflection-based learning function optimization engineering design |
| url | https://www.mdpi.com/2076-3417/12/19/10144 |
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