Deep Sea Shark Optimization Algorithm

To address the common problems in complex high-dimensional optimization problems, such as low search efficiency, susceptibility to local optima, and reliance on experience for parameter tuning, this paper proposes a novel swarm intelligence optimization method—the Deep-Sea Shark Optimization Algorithm. This algorithm uses the foraging and survival behavior of deep-sea sharks in extreme deep-sea environments as a biological prototype, comprehensively incorporating mechanisms such as deep-sea pressure gradient, acoustic echo memory, undercurrent disturbance, energy and cognitive dual-variable control, and self-organized depth clustering to construct an environment-driven, adaptive evolutionary optimization model. Through systematic mathematical modeling and mechanism analysis, the individual representation method, state variable evolution law, and swarm collaborative search mechanism of the algorithm are elaborated in detail. Theoretical analysis shows that the algorithm has good comprehensive performance in terms of global exploration capability, local development stability, and preservation of search diversity, providing a new theoretical tool for complex optimization problems.