An improved dung beetle optimizer algorithm for solving engineering optimization problems

To address the limitations of the dung beetle optimizer algorithm, such as its tendency to fall into local optima during the later phases of the iterative process, limited global exploration capability, and relatively slow convergence speed, this paper proposes a multi-strategy improved dung beetle optimizer algorithm. The improvement integrates the Sobol sequence, the nonlinear convergence factor, Lévy flight, the adaptive Cauchy–Gaussian hybrid mutation, and the greedy strategy. These improvements effectively enhance population diversity, the global exploration ability, and local exploitation performance. Specifically, the Sobol sequence is employed to initialize the population, thereby ensuring a more uniform and comprehensive population distribution. The nonlinear convergence factor is introduced to better balance the algorithm's global exploration and local exploitation. Lévy flight is applied to perturb the global best solution, improving the algorithm's ability to escape from local optima. Finally, the adaptive Cauchy–Gaussian hybrid mutation, combined with the greedy strategy, is designed to accelerate convergence and preserve elite individuals. To comprehensively evaluate the performance of the proposed algorithm, comparative experiments are conducted on the CEC2017 benchmark test set against seven widely recognized intelligent optimization algorithms. The experimental results demonstrate that the improved algorithm achieves superior performance in both optimization accuracy and convergence speed. Finally, the proposed algorithm is applied to actual engineering optimization problem, yielding the best results in all cases, thereby validating its effectiveness and practical applicability in solving complex optimization problem.