Snow avalanches algorithm (SAA): A new optimization algorithm for engineering applications
This paper proposes a novel efficient inspired algorithm based on snow avalanches in nature which is named the Snow Avalanches Algorithm (SAA), for solving the benchmark and engineering optimization problems and determining the global solution. The proposed algorithm is modeled using four phases inc...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-11-01
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| Series: | Alexandria Engineering Journal |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1110016823009316 |
| _version_ | 1797448713721872384 |
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| author | Keyvan Golalipour Saber Arabi Nowdeh Ebrahim Akbari Seyed Saeed Hamidi Danyal Ghasemi Almoataz Y. Abdelaziz Hossam Kotb Amr Yousef |
| author_facet | Keyvan Golalipour Saber Arabi Nowdeh Ebrahim Akbari Seyed Saeed Hamidi Danyal Ghasemi Almoataz Y. Abdelaziz Hossam Kotb Amr Yousef |
| author_sort | Keyvan Golalipour |
| collection | DOAJ |
| description | This paper proposes a novel efficient inspired algorithm based on snow avalanches in nature which is named the Snow Avalanches Algorithm (SAA), for solving the benchmark and engineering optimization problems and determining the global solution. The proposed algorithm is modeled using four phases including avalanche due to mountain slope, human factors, weather in the region as well as normal conditions and it has only one control parameter. The advantages of this algorithm are low control parameters, simple structure and also easy implementation. The effectiveness of the SAA algorithm is examined on 23 classic benchmark test functions. Then, the effectiveness of the SAA to achieve accurate results in different aspects is examined and proved on engineering problems including six different cases. The superiority of the SAA to solve the classic benchmark test functions is compared with spotted hyena optimization (SHO), particle swarm optimization (PSO), Aquila optimizer (AO), differential evolution (DE), bat algorithm (BA), dwarf mongoose optimization (DMO), genetic algorithm (GA), artificial bee colony (ABC), and ant colony optimization (ACO). The simulation results provide evidence for the well-organized and efficient performance of the SAA in solving a great diversity of engineering problems. The results demonstrated that the SAA can be more effective than other algorithms to solve the test functions in terms of optimization accuracy and convergence rate. Moreover, the results proved that the SAA obtained more competitive results than the previous methods to solve constrained engineering optimization problems, especially hybrid energy system design as well as economic load dispatch problems. |
| first_indexed | 2024-03-09T14:15:26Z |
| format | Article |
| id | doaj.art-1323970a161a43e0ad6a78fa4e4d4512 |
| institution | Directory Open Access Journal |
| issn | 1110-0168 |
| language | English |
| last_indexed | 2024-03-09T14:15:26Z |
| publishDate | 2023-11-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Alexandria Engineering Journal |
| spelling | doaj.art-1323970a161a43e0ad6a78fa4e4d45122023-11-29T04:23:52ZengElsevierAlexandria Engineering Journal1110-01682023-11-0183257285Snow avalanches algorithm (SAA): A new optimization algorithm for engineering applicationsKeyvan Golalipour0Saber Arabi Nowdeh1Ebrahim Akbari2Seyed Saeed Hamidi3Danyal Ghasemi4Almoataz Y. Abdelaziz5Hossam Kotb6Amr Yousef7Department of Computer Engineering, Sari Branch, Islamic Azad University, Sari, IranGolestan Technical and Vocational Training Center, Gorgan, IranDepartment of Computer Engineering, Islamic Azad University, Sari Branch, Sari, IranDepartment of Computer Engineering, Qaemshahr Branch, Islamic Azad University, Qaemshahr, IranDepartment of Electrical Engineering, Aliabad Katoul Branch, Islamic Azad University, Aliabad Katoul, IranFaculty of Engineering and Technology, Future University in Egypt, Cairo 11835, EgyptDepartment of Electrical Power and Machines, Faculty of Engineering, Alexandria University, Alexandria 21544, Egypt; Corresponding author.Engineering Mathematics Department, Faculty of Engineering, Alexandria University, Alexandria 21544, Egypt; Electrical Engineering Department, University of Business and Technology, Ar Rawdah, Jeddah, 23435, Saudi ArabiaThis paper proposes a novel efficient inspired algorithm based on snow avalanches in nature which is named the Snow Avalanches Algorithm (SAA), for solving the benchmark and engineering optimization problems and determining the global solution. The proposed algorithm is modeled using four phases including avalanche due to mountain slope, human factors, weather in the region as well as normal conditions and it has only one control parameter. The advantages of this algorithm are low control parameters, simple structure and also easy implementation. The effectiveness of the SAA algorithm is examined on 23 classic benchmark test functions. Then, the effectiveness of the SAA to achieve accurate results in different aspects is examined and proved on engineering problems including six different cases. The superiority of the SAA to solve the classic benchmark test functions is compared with spotted hyena optimization (SHO), particle swarm optimization (PSO), Aquila optimizer (AO), differential evolution (DE), bat algorithm (BA), dwarf mongoose optimization (DMO), genetic algorithm (GA), artificial bee colony (ABC), and ant colony optimization (ACO). The simulation results provide evidence for the well-organized and efficient performance of the SAA in solving a great diversity of engineering problems. The results demonstrated that the SAA can be more effective than other algorithms to solve the test functions in terms of optimization accuracy and convergence rate. Moreover, the results proved that the SAA obtained more competitive results than the previous methods to solve constrained engineering optimization problems, especially hybrid energy system design as well as economic load dispatch problems.http://www.sciencedirect.com/science/article/pii/S1110016823009316OptimizationMeta-heuristic AlgorithmEngineering Optimization ProblemBenchmark Test FunctionsSnow Avalanches Algorithm |
| spellingShingle | Keyvan Golalipour Saber Arabi Nowdeh Ebrahim Akbari Seyed Saeed Hamidi Danyal Ghasemi Almoataz Y. Abdelaziz Hossam Kotb Amr Yousef Snow avalanches algorithm (SAA): A new optimization algorithm for engineering applications Alexandria Engineering Journal Optimization Meta-heuristic Algorithm Engineering Optimization Problem Benchmark Test Functions Snow Avalanches Algorithm |
| title | Snow avalanches algorithm (SAA): A new optimization algorithm for engineering applications |
| title_full | Snow avalanches algorithm (SAA): A new optimization algorithm for engineering applications |
| title_fullStr | Snow avalanches algorithm (SAA): A new optimization algorithm for engineering applications |
| title_full_unstemmed | Snow avalanches algorithm (SAA): A new optimization algorithm for engineering applications |
| title_short | Snow avalanches algorithm (SAA): A new optimization algorithm for engineering applications |
| title_sort | snow avalanches algorithm saa a new optimization algorithm for engineering applications |
| topic | Optimization Meta-heuristic Algorithm Engineering Optimization Problem Benchmark Test Functions Snow Avalanches Algorithm |
| url | http://www.sciencedirect.com/science/article/pii/S1110016823009316 |
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