An improved beetle optimization algorithm(IDBO)is proposed to adress 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 is compared with other algorithms on 10 benchmark test functions,and the results show that the IDBO algorithm reaches the value in terms of the optimal value, and the standard deviation is the smallest except for the function and,and in terms of convergence,it converges to the optimal at a faster speed,and the convergence curve tends to the optimal value faster and with less fluctuation,which verifies the effectiveness,stability and convergence of the IDBO.In addition,the enhanced IDBO algorithm was utilized to address the traveling salesman problem,further verifying its feasibility and effectiveness in practical applications.