A Multi-Strategy Optimized Dung Beetle Algorithm

To address the shortcomings of the Dung Beetle Optimizer (DBO) in solving complex optimization problems—namely insufficient global exploration capability, relatively slow convergence, and a tendency to become trapped in local optima—this paper proposes a multi-strategy improved DBO, termed CWSADBO, and applies it to the economic dispatch problem of microgrids. The proposed algorithm integrates Cubic chaotic initialization, a black widow behavior mechanism, a sparrow-inspired perturbation strategy, and a Cauchy–Gaussian hybrid mutation mechanism to enhance both search efficiency and solution accuracy. Specifically, the Cubic chaotic map is employed to increase the diversity of the initial population; the black widow behavior mechanism strengthens global exploration during the search process; the sparrow-inspired perturbation strategy helps the algorithm escape local optima; and the Cauchy–Gaussian hybrid mutation mechanism achieves a dynamic balance between global exploratory jumps and local fine-grained exploitation. To evaluate its performance, 16 standard benchmark functions are tested, and comparative experiments are conducted against DBO, IDBO, SSA, and SABO. The results demonstrate that CWSADBO achieves faster convergence and higher optimization accuracy on most benchmark functions, with particularly notable advantages in unimodal, multimodal, and complex multimodal cases. Moreover, its application to microgrid economic dispatch case studies confirms its ability to effectively reduce operating costs, exhibiting strong stability and engineering applicability.