Glider snake optimizer (GSO): a nature-inspired metaheuristic algorithm for global and engineering optimization problems

Abstract The rapid expansion of complex engineering and real-world optimization problems necessitates the development of efficient, adaptable, and computationally lightweight metaheuristic algorithms. In this study, a novel nature-inspired algorithm called glider snake optimization (GSO) is proposed, which draws behavioral inspiration from the gliding and serpentine locomotion patterns of arboreal snakes to enhance solution exploration and convergence control. The GSO algorithm incorporates a multi-segment movement mechanism, a flexible gliding path generator, and an elite guidance model to ensure effective balance between exploration and exploitation. Extensive experimental validation is conducted using a…