Improving the Dung Beetle Optimizer with Multiple Strategies: An Application to Complex Engineering Problems

Although the Dung Beetle Optimizer (DBO) is a promising new metaheuristic for global optimization, it often struggles with premature convergence and lacks the necessary precision when applied to complex optimization challenges. Therefore, we developed the Multi-Strategy Improved Dung Beetle Optimizer (MIDBO), an algorithm that incorporates several new strategies to enhance the performance of the standard DBO. The algorithm enhances initial population diversity by improving the distribution uniformity of the Circle chaotic map and combining it with a dynamic opposition-based learning strategy for initialization. A nonlinear oscillating balance factor and an improved foraging strategy are introduced to achieve a dynamic equilibrium between the algorithm's global search and local refinement, thereby accelerating convergence. A multi-population differential co-evolutionary mechanism is designed, wherein the population is partitioned into three categories according to fitness, with each category using a unique mutation operator to execute targeted searches and avoid local optima. A comparative study against multiple metaheuristics on the CEC2017 and CEC2022 benchmarks was performed to comprehensively evaluate MIDBO's performance. The practical effectiveness of the MIDBO algorithm was validated by applying it to three practical engineering challenges. The results demonstrate that MIDBO significantly outperformed the other algorithms, a success attributed to its superior optimization performance.