MATLAB Implementation of Firefly Algorithm

This implementation presents a firefly algorithm, a highly practical optimization technique easily adaptable through test function modifications. The core implementation involves three key components: attractiveness calculation based on light intensity, distance-based movement vectors, and brightness updating mechanisms. When customizing, pay attention to critical parameters including firefly population size, maximum iteration count, and light absorption coefficients to ensure optimal performance. As a heuristic optimization algorithm, the firefly algorithm mimics firefly behavior to solve complex optimization problems. In this computational model, each firefly represents a potential solution, where its brightness corresponds to solution quality (typically the objective function value), and movement patterns symbolize solution refinement through iterative improvements. The algorithm employs attractiveness proportional to light intensity, causing fireflies to move toward brighter companions, effectively implementing a neighborhood search strategy. Key implementation aspects include: - Distance calculation between fireflies using Euclidean or other metrics - Light intensity decay formula incorporating absorption coefficients - Position update rules combining current location and attractiveness factors - Boundary handling mechanisms for constraint satisfaction This algorithm has demonstrated effectiveness across diverse domains including image processing (for feature selection), data mining (in cluster analysis), and machine learning (hyperparameter optimization). The MATLAB implementation provides modular structure allowing straightforward integration of custom objective functions and constraint handling, making it a valuable tool for both educational and research purposes in computational intelligence.