Deep-Sea Sea Cucumber Optimization Algorithm

To address the common problems of convergent search mechanisms, significant positive feedback convergence, and susceptibility to local optima in existing swarm intelligence optimization algorithms, this paper proposes a novel continuous optimization method—the Deep-Sea Sea Cucumber Optimization Algorithm (DSSCOA)—based on the survival and foraging behavior of deep-sea sea cucumbers in extreme environments. This algorithm constructs a self-organizing search mechanism centered on negative feedback suppression and environmental perception by simulating the sedimentary memory, energy regulation, dual-timescale crawling migration, and non-optimal guidance behavior of deep-sea sea cucumbers. At the mathematical modeling level, the algorithm introduces a sedimentary memory field, an energy-risk coupling model, and a crowding suppression mechanism, enabling the search process to move beyond simply relying on global optimal attraction. Instead, it coordinates global exploration and local development by avoiding redundant searches and dynamically adjusting exploration strategies. Theoretical analysis shows that this algorithm differs from existing typical swarm intelligence optimization methods in its structure and search logic, exhibiting strong resistance to premature convergence and high stability.