Ant Colony Clustering Algorithm with MATLAB Source Code

Basic Ant Colony Clustering Algorithm and Its Improved Version [With MATLAB Source Code] Ant Colony Algorithm is a heuristic approach simulating ant foraging behavior, which finds optimal solutions by mimicking how ants search for food. The basic ant colony clustering algorithm applies this methodology to clustering problems, effectively resolving convergence issues while achieving superior clustering results. Performance visualization is available in the attachments. The improved ant colony algorithm enhances the basic genetic algorithm framework by incorporating mutation factors that introduce genetic variations, thereby accelerating convergence rates. This optimized version demonstrates higher efficiency in locating optimal solutions. Key Program Features: 1. Includes MATLAB visualization routines that implement color-coded point identification for different clusters using scatter plots and colormap functions 2. Implements file I/O operations to load clustering data from data.txt, allowing users to modify datasets through simple text file edits 3. Contains comprehensive code annotations explaining algorithm parameters like pheromone update rules, heuristic information calculation, and mutation operations 4. All programs have been debugged and tested for direct execution without modifications Attachment Specifications: The attachment comprises two m-files: the basic ant colony clustering algorithm (ant_cluster.m) and the improved version (improved_ant_cluster.m). The data.txt file contains 3D array data for clustering experiments. The PPT file includes presentation slides used for project defense, detailing algorithm implementation and performance comparisons.