Dust Storm Optimization

Dust storms in nature are a typical large-scale particle motion phenomenon, characterized by multi-center rotation, random drift, and dynamic dispersion. In optimization problems, these characteristics can be used to inspire search strategies, achieving a balance between global exploration and local exploitation. This paper proposes an optimization algorithm based on dust storm behavior—the Dust Storm Optimization (DSO). This algorithm incorporates a multi-layer vortex mechanism, random wind redirection, adaptive sand particle splitting, and vortex interference and mutual exclusion mechanisms. Through a mathematical model, it rigorously describes the sand particle renewal, vortex center evolution, and perturbation mechanisms, ensuring excellent search performance in complex high-dimensional, multimodal functions. Theoretical analysis shows that DSO exhibits excellent global exploration capabilities, local exploitation capabilities, and dynamic diversity maintenance capabilities, while also ensuring convergence. This paper provides new insights for further optimizing nature-inspired algorithms.