Benchmark functions.

To address the challenge of personnel evacuation during mine fires, an enhanced Whale Optimization Algorithm (WOA) incorporating a hybrid strategy inspired by the intelligent behavior of marine life is proposed and applied to mine escape route planning. Initially, to overcome the limitations of the original WOA—such as poor optimization accuracy, susceptibility to local optima, and slow convergence—five improvement strategies are introduced: Sobol sequence for population initialization, nonlinear time-varying factors, adaptive weighting, stochastic learning, and Cauchy mutation. These enhancements are compared against single-strategy improved WOAs.Subsequently, path planning simulations were conducted using several extracted algorithms and grid-based methods. The results demonstrate that the optimal path length achieved by the Multi-Strategy WOA (MSWOA) is 41.7% shorter than that of the standard WOA, 42.3% shorter than WOA-1, and 48.5% shorter than PSO for the shortest path. Additionally, the average path length of MSWOA is 32.2% shorter than WOA, 40.5% shorter than WOA-1, and 41.4% shorter than PSO. The MSWOA algorithm generates the shortest and smoothest path among the tested methods.Based on the analysis of the path graph and iteration frequency graph, it is recommended to apply the MSWOA algorithm to path planning experiments. The findings indicate that the WOA with the five integrated strategies significantly enhances optimization accuracy and convergence speed, making it a robust solution for mine evacuation route planning.