MSO: A Modified Snake Optimizer for Engineering Applications

Many complex engineering problems can be formulated as mathematical optimization tasks, for which bio-inspired metaheuristic algorithms have demonstrated outstanding effectiveness. Drawing inspiration from snake behavior, the Snake Optimizer (SO) algorithm provides a promising framework but suffers from random population initialization, insufficient global search capability, and slow convergence. To address these drawbacks, the study proposes a Modified Snake Optimizer (MSO) that integrates three key strategies: a dual mapping strategy based on Latin hypercube sampling and logistic mapping for population initialization; an opposition-based learning mechanism with scaling factors for exploration; and integration of the soft-rime search strategy from RIME optimization during exploitation. The performance of MSO was benchmarked against nine representative algorithms using the CEC2017 and further validated on three engineering application problems—pressure vessel, tension/compression spring, and hydrostatic thrust bearing design, and two UAV path planning scenarios. Experimental results show that MSO achieves faster convergence speed, stronger robustness and greater stability, effectively extending the biomimetic principles of the original SO and confirming its superiority for solving optimization problems.