Sperm flagellum motility algorithm: a novel metaheuristic optimiser for solving optimisation problems

The challenges and intricacies associated with real-world numerical optimisation problems have significantly increased, necessitating the development of efficient optimisation methods. While numerous metaheuristic approaches have been proposed, only a select few have gained recognition within the research community. This paper introduces a novel metaheuristic algorithm known as the sperm flagellum motility algorithm (SFMA) to address optimisation problems. SFMA emulates the motility principle of a sperm flagellum immersed in a two-dimensional fluid. Representing a population of solutions, the sperm flagellum navigates the search space to discover an optimal solution for the given problem. To evaluate the effectiveness of SFMA, a suite of 23 benchmark functions and five engineering problems were solved and compared against the performance of other widely recognised optimisers. The optimisation results demonstrate that the SFMA is highly competitive when compared to both conventional methods and state-of-the-art meta-heuristic algorithms.