Well-Implemented Artificial Fish Swarm Algorithm

In this article, I would like to share a highly effective artificial fish swarm algorithm that is particularly suitable for those beginning their learning journey. The artificial fish swarm algorithm is an optimization technique that mimics the collective behavior of fish schools, applicable to solving various practical problems. The algorithm's principle is based on information exchange and cooperation among individuals within the fish swarm. By simulating fish schooling behavior, the artificial fish swarm algorithm can effectively locate optimal solutions to problems. From an implementation perspective, the algorithm typically involves key functions such as fish initialization, prey behavior simulation, swarming mechanisms, and following behaviors. The code structure usually includes parameters for visual range, step size, crowd factor, and iteration counts. The algorithm demonstrates excellent robustness and adaptability, making it applicable across different domains and complex optimization scenarios. For beginners, studying this artificial fish swarm algorithm serves as an excellent starting point. It not only helps understand fundamental concepts of optimization algorithms but also enhances practical problem-solving capabilities. The implementation includes clear variable definitions and logical flow that facilitates understanding of population-based optimization techniques.