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 comprehensive set of 23 classical benchmark functions, high-dimensional test cases (100D, and 500D), the CEC 2019 benchmark suite, and several constrained engineering design problems. The results demonstrate that GSO outperforms or matches 13 state-of-the-art algorithms, including particle swarm optimization (PSO), grey wolf optimizer (GWO), whale optimization algorithm (WOA), and differential evolution (DE) in terms of accuracy, convergence speed, computational cost, and robustness. The algorithm also exhibits exceptional stability across parameter variations, as confirmed through sensitivity analysis and statistical significance testing. These findings highlight the potential of GSO as a powerful and efficient tool for solving complex optimization problems in both theoretical and practical domains. Additionally, GSO achieves leading performance on most benchmark functions, with error reductions of up to 90% compared with competing algorithms. GSO also demonstrates faster convergence and lower variance across repeated trials, confirming its robustness. These quantitative outcomes further reinforce the effectiveness of the proposed algorithm. The MATLAB and Python implementations of GSO are available at https://nimakhodadadi.com .
El-kenawy et al. (Tue,) studied this question.