Abstract: The dung beetle optimization algorithm suffers from the problems of easily falling into local optimum, imbalance between global exploration and local exploitation ability. In order to improve the searching ability of the dung beetle optimization algorithm, a mixed-strategy dung beetle optimization algorithm is proposed. The Sobol sequence is used to initialize the population in order to make the dung beetle population better traverse the whole solution space. The golden sine algorithm is added to the ball-rolling dung beetle position updating stage to improve the convergence speed and searching accuracy. And the hybrid variation operator is introduced for perturbation to improve the algorithm’s ability to jump out of the local optimum. The improved algorithms are tested on eight benchmark functions and compared with the gray wolf optimization algorithm, the whale optimization algorithm and the dung beetle optimization algorithm to verify the effectiveness of the three improved strategies. The results show that the dung beetle optimization algorithm with mixed strategies has significant enhancement in convergence speed, robustness and optimization search accuracy.

Key words: dung beetle optimizer , Sobol sequence, golden sine algorithm, mix mutation operator