Artificial Fish Swarm Algorithm Application in IEEE Power System Planning

The Artificial Fish Swarm Algorithm (AFSA) is a biologically-inspired optimization technique based on swarm intelligence, applied to IEEE power system planning. This algorithm simulates fish schooling behaviors such as foraging, swarming, and following to locate optimal solutions. In power system planning contexts, AFSA is widely implemented to address challenges including power dispatch optimization and transmission line configuration. The algorithm demonstrates strong global search capabilities and adaptability through key functions like visual scope simulation, stochastic movement operators, and crowding factor controls. Its implementation typically involves coding fish position updates, fitness evaluations using power flow equations, and dynamic parameter adjustments for constraint handling. AFSA's efficient convergence properties enable rapid identification of near-optimal solutions, making it particularly valuable for complex power system optimization problems. Consequently, its application in power system planning has gained significant attention and achieved remarkable results in reducing operational costs and improving system reliability.