Analysis of Tourism Management Based on Genetic Algorithm and Simulated Annealing Algorithm

Abstract

Based on genetic algorithm and simulated annealing algorithm, this paper constructs a strategy for sustainable urban tourism management. Firstly, the dynamic relationship between tourist flow, environmental impact and resource consumption is modeled by differential equations, in which the tourist number model realizes flow regulation through seasonal function and fare sensitivity coefficient; secondly, genetic algorithm is introduced for multi-objective optimization to maximize the tourism revenue under the premise of satisfying the capacity constraints of the attractions, and its evolutionary process is evaluated visually through the fitness function; lastly, simulated annealing algorithm is used to deal with complex nonlinear constraints, balancing global search and local optimization through temperature decay mechanism, and realizing synergistic optimization of minimizing environmental impact and rationalizing resource utilization. This study provides a quantifiable decision support framework for smart tourism management.