An improved beetle optimization algorithm (IDBO) is proposed to address the problems of traditional beetle optimization algorithm (DBO) being prone to local optima and slow convergence. Firstly, the population is initialized using logistic chaotic mapping, and optimized using the refraction reverse learning strategy to increase the diversity of the population; Secondly, during the breeding stage, the formula for position update was improved using a spiral search strategy, which increased the convergence speed of the algorithm; Finally, during the foraging phase, the current optimal value is introduced through the optimal value strategy to guide the generation of candidate solutions, enhancing the algorithm's global search capability. The proposed IDBO algorithm was compared and analyzed with other algorithms on 10 benchmark test functions to verify its effectiveness, stability, and convergence. In addition, the enhanced IDBO algorithm was utilized to address the traveling salesman problem, further verifying its feasibility and effectiveness in practical applications.