Sea Bass Optimization Algorithm

Swarm optimization algorithms have been widely applied in continuous optimization, combinatorial optimization, and constrained optimization problems in recent years. Sea bass exhibit highly cooperative group predation behavior, flexible predator avoidance strategies, and highly adaptable environmental exploration characteristics in their natural environment. This paper proposes an optimization algorithm based on sea bass behavior, called the Sea Bass Optimization Algorithm (SBOA), and introduces a dynamic energy-velocity model, a global-local intelligent switching mechanism, and an ecological cooperation multi-strategy mechanism to improve the algorithm's exploration and development capabilities in complex search spaces. This paper provides detailed mathematical modeling and theoretical analysis of the algorithm, including individual representation, velocity and energy update formulas, exploration tendency factors, and neighbor cooperation mechanisms. Results show that SBOA can effectively combine global search and local optimization while maintaining group diversity, providing a new theoretical framework for swarm optimization algorithms.