Résumé: Archimedes optimization algorithm (AOA) is a recent metaheuristic method inspired by the Archimedes principle, which is the law of physics. Like other metaheuristic methods, it suffers from the disadvantages of being stuck in local areas, suffering from weak exploitation abilities, and an inability to maintain a balance between exploration and exploitation. To overcome these weaknesses, a new hybrid Mutualism Archimedes Optimization Algorithm (MAOA) method has been proposed by combining the AOA and the mutation phase in the Symbiosis organism search (SOS) method. SOS algorithm is known for its exploitation ability. With the mutation phase, it has been used to improve local search for swarm agents, help prevent premature convergence and increase population diversity. To verify the applicability and performance of the proposed algorithm, extensive analysis of standard benchmark functions, CEC’17 test suites, and engineering design problems were performed. The proposed method is compared with the recently emerged and popular AOA, SOS, Harris Hawks Optimization (HHO), COOT Optimization Algorithm (COOT), Aquila Optimizer (AO), Salp Swarm Algorithm (SSA), and Multi-Verse Optimization (MVO) methods, and statistical analyses were performed. The results obtained from the experiments show that the proposed MAOA method has superior global search performance and faster convergence speed compared to AOA, SOS, and other recently emerged and popular metaheuristic methods. Furthermore, this study compares MAOA to five well-established and recent algorithms constructed using various metaheuristic methodologies utilizing nine benchmark datasets to assess the general competence of MAOA in feature selection. Therefore, the proposed method is considered to be a promising optimization method for real-world engineering design problems, global optimization problems, and feature selection.

Langue: Anglais