An adaptive Particle Swarm Optimization algorithm for fire source identification of the utility tunnel fire

Abstract

In order to overcome serious utility tunnel fires and minimize economic loss, it is essential to establish an effective and practical method for finding the fire source. An adaptive Particle Swarm Optimization (PSO) algorithm was proposed for fire source identification of utility tunnel fires, whose inertia weight and learning factors were adjusted dynamically. In comparison with traditional fire detection methods, the proposed algorithm is immune to the specific environment and complicated fire propagation mechanisms, the fire source location can be identified only based on several temperature values. The proposed algorithm was applied in both numerical and experimental scenarios. The results showed that the adaptive algorithm showed greater global searchability in the early exploration, and stronger local searchability in the later exploration compared with the basic one, the improvement ratio of convergence performance could achieve 50%. Additionally, all of the cases demonstrated that the fire source location can be identified by the proposed algorithm with good accuracy. Critically, the spatial temperature distribution of the whole utility tunnel was obtained based on the fire source location and the attenuation coefficient. The reasonable results indicate that the proposed algorithm can provide a reference for fire protection and intervention in the utility tunnel.