Ant Colony Algorithm for Continuous Function Optimization

The MATLAB-based ant colony algorithm is an optimization method designed for continuous function problems. Ant Colony Optimization (ACO) is a heuristic algorithm that simulates the foraging behavior of ants, employing pheromone-based communication and cooperative mechanisms to find optimal solutions. In this implementation, each artificial ant determines its movement direction based on both local position information and global pheromone trails deposited by other ants, gradually converging toward the optimal solution through iterative search processes. The MATLAB implementation includes key algorithmic components such as: - Pheromone initialization and update mechanisms using evaporation coefficients - Probabilistic path selection based on heuristic information and pheromone concentrations - Fitness evaluation for continuous functions through objective function calculations - Adaptive parameter tuning for convergence control The package provides detailed documentation and supporting research papers containing: - Theoretical foundations of ACO algorithms for continuous optimization domains - Step-by-step implementation guidelines with code comments - Parameter configuration examples for different function types - Application case studies demonstrating optimization performance on benchmark functions - Comparative analysis with other optimization techniques All code features modular design with separate functions for initialization, ant movement simulation, pheromone management, and result visualization, ensuring easy customization for specific optimization requirements.