Fang Fish Optimization Algorithm

Swarm optimization algorithms have demonstrated wide application value in continuous optimization, multimodal function optimization, and constrained optimization problems in recent years. Their core idea is to solve complex problems by simulating the collective behavior of organisms in nature. This paper proposes a swarm optimization algorithm based on the behavioral characteristics of bream—the Fang Fish Optimization Algorithm (FFOA). The algorithm simulates the bream's group cooperation, local predation, energy regulation, and global jump behavior to form an efficient hybrid search mechanism that balances global exploration and local exploitation capabilities. The key to the algorithm lies in: individuals adaptively adjusting their positions within the group using neighbor and global information; dynamically adjusting the exploration step size using energy; improving search accuracy through local predation and local bursts; and preventing getting trapped in local optima through global jumps. This paper details the modeling, mathematical formulas, iterative process, and characteristic analysis of FFOA, providing new design ideas for swarm optimization algorithms.