Abstract:Aiming at the problems that the original dung beetle optimizer (DBO) is easy to prone to local optimum and the low convergence precision, a multi-strategy fusion improved dung beetle optimizer (TDBO) is proposed. Using the Tent chaos initialization population mapping strategy to makes the initial position of dung beetle distribution more uniform, and improve the diversity of population, adaptive inertia weight was applied during the breeding stage to improve the optimization ability. Levy flight was introduced into the dung beetle stealing behavior formula to improve the search ability of the algorithm, make the algorithm jump out of the local optimal, equates the relationship between diversity and convergence accuracy. Compared with the basic DBO algorithm, four comparison algorithms and single-strategy improved DBO algorithm on 9 test functions, and Wilcoxon rank sum test is used to verify the performance of the TDBO algorithm. The results show that the speed and accuracy of the TDBO algorithm are better than the comparison algorithm on multiple functions, and the TDBO algorithm has significant difference. Through the test of benchmark functions, the verification of Wilcoxon rank sum test, and validation of three engineering optimization problems, the TDBO algorithm has better convergence accuracy and speed.