MATLAB Implementation of Artificial Fish Swarm Algorithm with Enhanced Practical Applications

This MATLAB implementation of the Artificial Fish Swarm Algorithm has been enhanced to address various practical engineering challenges. The algorithm simulates natural fish schooling behavior, where individual artificial fishes interact and exchange information to collectively locate optimal solutions. Key implementation features include: The code implements core algorithmic components such as: - Fish behavior modeling including prey searching, swarming, and following mechanisms - Distance calculation functions for neighborhood detection - Visual scope and step size parameters for movement optimization - Fitness evaluation functions for solution quality assessment The algorithm demonstrates significant potential in solving complex optimization problems and enhancing system performance. The MATLAB implementation provides a flexible framework that allows for: - Customizable parameter tuning for specific problem domains - Modular design enabling easy integration with existing engineering systems - Visualization tools for monitoring convergence behavior and swarm dynamics This enhanced version incorporates improvements in convergence speed and solution accuracy, making it particularly valuable for practical engineering applications where traditional optimization methods may underperform. The implementation supports various problem types including constrained optimization, multi-objective optimization, and dynamic environment adaptation.