Ant Colony Optimization (ACO) for Continuous Function Optimization

This MATLAB source code implements Ant Colony Optimization (ACO) for finding optimal solutions to continuous functions, specifically employing the grid strategy method described in Duan Haibin's "Ant Colony Algorithm: Principles and Applications". The algorithm simulates ant foraging behavior to solve optimization problems where we seek maximum or minimum values of functions within given intervals. Key implementation components include: - Grid-based search space partitioning where each grid cell contains an ant - Pheromone initialization and update mechanisms to guide ant movement - Probabilistic transition rules for selecting next positions based on pheromone concentrations The code implements core ACO steps: initializing ant positions and pheromone distributions, updating pheromone levels through evaporation and reinforcement, and determining movement paths using selection probabilities. The grid strategy discretizes the continuous search space, allowing ants to explore and exploit solutions through coordinated pheromone communication. This implementation demonstrates how ACO can be adapted for continuous optimization problems through: 1) Spatial discretization using grid cells 2) Pheromone matrix maintenance representing solution quality 3) Iterative pheromone update and path selection cycles 4) Convergence to optimal solutions through collective intelligence The source code provides a practical reference for implementing grid-based ACO algorithms, with modular functions handling initialization, probability calculations, and optimization processes suitable for various continuous functions.