Snow Avalanches Algorithm (SAA): A New Optimization Algorithm for Engineering Applications

• Published in: Alexandria engineering journal Vol. 83; pp. 257 - 285
• Main Authors: Golalipour, Keyvan, Arabi Nowdeh, Saber, Akbari, Ebrahim, Saeed Hamidi, Seyed, Ghasemi, Danyal, Abdelaziz, Almoataz Y., Kotb, Hossam, Yousef, Amr
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 15.11.2023; Elsevier
• Subjects: Benchmark Test Functions; Engineering Optimization Problem; Meta-heuristic Algorithm; Optimization; Snow Avalanches Algorithm; Snow Avalanches Algorithm; Engineering Optimization Problem; Meta-heuristic Algorithm; Benchmark Test Functions; Optimization
• ISSN: 1110-0168
• DOI: 10.1016/j.aej.2023.10.029

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.