Model Study on the Impact of Foreign Trade on Economic Resilience Based on Improved Self-Regulating Annealing Particle Swarm Optimization (China Yangtze River Delta as an Example)

In order to improve the global search ability of PSO and avoid falling into local extreme value, an improved self-regulating tabu annealing PSO was proposed to study the impact of foreign trade on economic resilience model (China Yangtze River Delta as an example). Firstly, improving the inertial adaptive weights and introducing adjustment factors to constrain the learning particles can make the algorithm have stronger searching ability. The improved algorithm can realize the self-adjustment of inertia weights. Secondly, the improved simulated annealing particle swarm optimization algorithm is verified and compared with the former algorithm through two different functions, and the results show that the convergence speed and accuracy are improved. According to the sensitivity calculation, the compensation node is determined for reactive power compensation. The final results show that the resilience of tourism economy in the Yangtze River Delta urban agglomerations presents a changing trend of "rising first and then decreasing", and the resilience level of all cities is on the rise. The cities with high resilience are mainly distributed in the central and eastern regions, and the resilience level of tourism economy in each city has a certain spatial agglomeration characteristic. The resilience of tourism economy is comprehensively affected by economic development level, economic development structure, infrastructure and government intervention, among which the speed of tourism development has a significant negative impact on the resilience of tourism economy, and other factors have a significant promoting effect.