Tunnel ventilation control system based on Lévy Flight Dynamics Improved Particle Swarm Optimization Algorithm

Due to the swift progress of China’s transportation sector, the quantity of constructed tunnels in the country is on the rise. The majority of current road tunnels have imprecise or fragmented controls to regulate the activation of the tunnel’s ventilation system. However, employing these strategies is likely to lead to unnecessary electricity use and higher operational expenses.This paper introduces an enhanced particle group optimisation algorithm (LD-IPSO) for optimising the ventilator control model in tunnels. The algorithm aims to achieve more precise control of the ventilation group during tunnel operation, enhance fan utilisation, decrease power consumption in road tunnel ventilation, and enhance tunnel operation safety.The utilisation of the Levy Flight Avoidance algorithm in this paper is premature.To enhance the precision and effectiveness of particle search, the utilisation of adaptive inertia weights and adaptive learning factors ensures a harmonious equilibrium between the particles’ global and local search capabilities. Four test functions were chosen for evaluation, and the LD-IPSO algorithms demonstrated superior result quality compared to the PSO method, the genetic algorithm, the artificial fish stocks algorithm, and the simulated bees stocks algorithm.Ultimately, the system’s effectiveness is evaluated by comparing it to the scenario of a tunnel fan opening. The simulation findings demonstrate that the LD-IPSO algorithm can achieve more precise optimisation of the number of fan openings in real-world situations, resulting in reduced energy consumption during fan operation.