In complex optimization problems, traditional swarm intelligence algorithms often rely on historical optimal solutions for incremental searches. Their search behavior is prone to over-convergence and structural fragility under multimodal, strongly nonlinear, or dynamically changing objective functions. Especially when the search process is highly stable, the algorithm's ability to explore potential global optima significantly decreases. To address this, this paper proposes a novel swarm intelligence optimization method—the Black Swan Optimization Algorithm. Inspired by the theory of "Black Swan events," this algorithm systematically introduces low-probability but high-impact search behavior into the optimization process and, for the first time, treats risk as a regulatory variable endogenously generated by the swarm's search state. By constructing a swarm risk tension function, a structural deviation jump mechanism, and a negative Black Swan memory learning strategy, the algorithm can effectively avoid premature convergence while maintaining convergence capability. This paper systematically elucidates the algorithm's search mechanism from a theoretical perspective, providing a new risk-aware search paradigm for complex optimization problems.