Improved Dung Beetle Optimization Algorithm with Multi-strategy and Its Application

To solve problems such as incompatibility between global search performance and local development ability, low accuracy, and slow search speed, a multistrategy-improved dung beetle optimization algorithm is proposed. Circle sequence and lens imaging strategies are adopted to ensure a more balanced distribution of generated dung beetles, thereby expanding search scope and diversity. The somersault strategy is introduced to optimize the position-updating process of dung beetles during their foraging process in the dung beetle algorithm, aiding dung beetles in conducting global searches more effectively and avoiding premature convergence while balancing global exploration and local exploitation. Combined with the Cauchy-Gauss variation strategy, the probability of the algorithm jumping out of the local optimum is increased. The optimization results of 12 benchmark functions are compared and analyzed. The Wilcoxon rank sum statistical test results show that the improved algorithm has a better convergence effect, robustness, and optimization speed. Finally, through the comparison of the optimizing results of welding beam design and pressure vessel design in engineering applications, the effectiveness and superiority of the improved algorithm in practical engineering applications are further verified.