Mine Fire Emergency Path Planning Based on Imprroved Salpa Algorithm

Abstract To tackle the challenge of personnel evacuation following a mine fire, we propose an enhanced SSA (Scyphozoa Swarm Algorithm) simulation algorithm inspired by the intelligent behavior of marine organisms for personnel evacuation path planning. Our approach addresses the issues of local optimal solution trapping and slow convergence in the standard SSA algorithm by incorporating chaos optimization, differential evolution mutation, and non-uniform Gaussian mutation. We evaluate the performance of our improved SSA algorithm in path optimization through comparative testing with the standard SSA algorithm, particle swarm optimization (PSO) algorithm, and genetic algorithm (GA). Subsequently, we conduct grid-based path planning simulation experiments. Our findings demonstrate that our enhanced SSA algorithm excels in path optimization, achieving a 6% shorter optimal length compared to GA, an 11% improvement over PSO, and a 9% enhancement over the ant colony algorithm. Furthermore, in terms of path stability, our improved SSA algorithm outperforms GA by 75%, PSO by 89%, and the standard SSA algorithm by 83%. These results establish that our enhanced SSA algorithm surpasses other algorithms in obtaining the shortest and optimal paths.