Mountaineering Team-Based Optimization: A Novel Human-Based Metaheuristic Algorithm
This paper proposes a novel optimization method for solving real-world optimization problems. It is inspired by a cooperative human phenomenon named the mountaineering team-based optimization (MTBO) algorithm. Proposed for the first time, the MTBO algorithm is mathematically modeled to achieve a rob...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-03-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/11/5/1273 |
| _version_ | 1797614816412565504 |
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| author | Iman Faridmehr Moncef L. Nehdi Iraj Faraji Davoudkhani Alireza Poolad |
| author_facet | Iman Faridmehr Moncef L. Nehdi Iraj Faraji Davoudkhani Alireza Poolad |
| author_sort | Iman Faridmehr |
| collection | DOAJ |
| description | This paper proposes a novel optimization method for solving real-world optimization problems. It is inspired by a cooperative human phenomenon named the mountaineering team-based optimization (MTBO) algorithm. Proposed for the first time, the MTBO algorithm is mathematically modeled to achieve a robust optimization algorithm based on the social behavior and human cooperation needed in considering the natural phenomena to reach a mountaintop, which represents the optimal global solution. To solve optimization problems, the proposed MTBO algorithm captures the phases of the regular and guided movement of climbers based on the leader’s experience, obstacles against reaching the peak and getting stuck in local optimality, and the coordination and social cooperation of the group to save members from natural hazards. The performance of the MTBO algorithm was tested with 30 known CEC 2014 test functions, as well as on classical engineering design problems, and the results were compared with that of well-known methods. It is shown that the MTBO algorithm is very competitive in comparison with state-of-art metaheuristic methods. The superiority of the proposed MTBO algorithm is further confirmed by statistical validation, as well as the Wilcoxon signed-rank test with advanced optimization algorithms. Compared to the other algorithms, the MTBO algorithm is more robust, easier to implement, exhibits effective optimization performance for a wide range of real-world test functions, and attains faster convergence to optimal global solutions. |
| first_indexed | 2024-03-11T07:17:36Z |
| format | Article |
| id | doaj.art-5e43faff36a14fceb21ff7ea8aa2d850 |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-11T07:17:36Z |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-5e43faff36a14fceb21ff7ea8aa2d8502023-11-17T08:10:35ZengMDPI AGMathematics2227-73902023-03-01115127310.3390/math11051273Mountaineering Team-Based Optimization: A Novel Human-Based Metaheuristic AlgorithmIman Faridmehr0Moncef L. Nehdi1Iraj Faraji Davoudkhani2Alireza Poolad3Department of Construction Production and Theory of Structures, Institute of Architecture and Construction, South Ural State University, 454080 Chelyabinsk, RussiaDepartment of Civil Engineering, McMaster University, Hamilton, ON L8S 4M6, CanadaEnergy Management Research Center, University of Mohaghegh Ardabili, Ardabil 5619911367, IranDepartment of Electrical Engineering, Islamic Azad University, Bushehr Branch, Bushehr 7519619555, IranThis paper proposes a novel optimization method for solving real-world optimization problems. It is inspired by a cooperative human phenomenon named the mountaineering team-based optimization (MTBO) algorithm. Proposed for the first time, the MTBO algorithm is mathematically modeled to achieve a robust optimization algorithm based on the social behavior and human cooperation needed in considering the natural phenomena to reach a mountaintop, which represents the optimal global solution. To solve optimization problems, the proposed MTBO algorithm captures the phases of the regular and guided movement of climbers based on the leader’s experience, obstacles against reaching the peak and getting stuck in local optimality, and the coordination and social cooperation of the group to save members from natural hazards. The performance of the MTBO algorithm was tested with 30 known CEC 2014 test functions, as well as on classical engineering design problems, and the results were compared with that of well-known methods. It is shown that the MTBO algorithm is very competitive in comparison with state-of-art metaheuristic methods. The superiority of the proposed MTBO algorithm is further confirmed by statistical validation, as well as the Wilcoxon signed-rank test with advanced optimization algorithms. Compared to the other algorithms, the MTBO algorithm is more robust, easier to implement, exhibits effective optimization performance for a wide range of real-world test functions, and attains faster convergence to optimal global solutions.https://www.mdpi.com/2227-7390/11/5/1273optimizationmountaineering team-based optimizationhuman cooperationbenchmark functionheuristic algorithm |
| spellingShingle | Iman Faridmehr Moncef L. Nehdi Iraj Faraji Davoudkhani Alireza Poolad Mountaineering Team-Based Optimization: A Novel Human-Based Metaheuristic Algorithm Mathematics optimization mountaineering team-based optimization human cooperation benchmark function heuristic algorithm |
| title | Mountaineering Team-Based Optimization: A Novel Human-Based Metaheuristic Algorithm |
| title_full | Mountaineering Team-Based Optimization: A Novel Human-Based Metaheuristic Algorithm |
| title_fullStr | Mountaineering Team-Based Optimization: A Novel Human-Based Metaheuristic Algorithm |
| title_full_unstemmed | Mountaineering Team-Based Optimization: A Novel Human-Based Metaheuristic Algorithm |
| title_short | Mountaineering Team-Based Optimization: A Novel Human-Based Metaheuristic Algorithm |
| title_sort | mountaineering team based optimization a novel human based metaheuristic algorithm |
| topic | optimization mountaineering team-based optimization human cooperation benchmark function heuristic algorithm |
| url | https://www.mdpi.com/2227-7390/11/5/1273 |
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