Blackfish Optimization Algorithm

Swarm optimization algorithms are a class of effective methods for solving complex optimization problems by simulating the collective behavior of organisms in nature. The snakehead fish is a type of fish that lives in dark waters and possesses excellent predation abilities and cooperative group behavior. This paper proposes a novel Blackfish Optimization Algorithm (BFOA), which simulates the swimming, predation, and cooperative behavior of snakehead fish in their natural environment. Combining multimodal search, adaptive parameter control, a memory mechanism, and dynamic neighborhood cooperation, it achieves global search and local exploitation for high-dimensional continuous optimization problems. The algorithm rigorously defines the movement and update rules of individuals using mathematical formulas and analyzes its convergence and global search capabilities. BFOA is applicable to multimodal optimization, constrained optimization, and continuous optimization problems, possessing advantages such as thorough theoretical analysis, strong convergence stability, and significant swarm intelligence characteristics.