Improved dung beetle optimization algorithm based on gaussian mutation and elite traction strategy

Abstract Dung Beetle Optimization algorithm (DBO) is a novel meta-heuristic algorithm proposed in 2022, which simulates the common behaviors of dung beetle populations. Due to the restriction of dung beetles’ activity areas and move lengths in the DBO algorithm, the algorithm suffers from the lack of both global search ability and local exploration ability. Therefore, an improved dung beetle algorithm GEDBO (Improved Dung Beetle Optimization algorithm Based on Gaussian Mutation and Elite Traction Strategy) is proposed in this paper. First, the differential mutation operator, Gaussian cross mutation operator, elite traction and random perturbation strategies, and neighborhood hunting strategy are embedded into the basic DBO algorithm to form the GEDBO algorithm. Secondly, the GEDBO algorithm is compared with seven state-of-the-art algorithms on 23 benchmark functions of CEC2014, and the experimental results show that the GEDBO algorithm has good convergence and accuracy. Finally, the GEDBO algorithm is applied to the high-dimensional optimization problem, and compared with the original algorithm, the improved algorithm has faster convergence speed, higher optimization accuracy and better robustness, which further proves the superiority of the GEDBO algorithm.