Artificial Ant Colony Algorithm for Unconstrained Continuous Function Optimization

General MATLAB source code for artificial ant colony algorithm applied to unconstrained continuous function optimization. This implementation features pheromone-based probability selection, local search mechanisms, and global updating strategies. For constrained optimization problems, penalty function methods can first transform them into unconstrained models before applying this code. The algorithm demonstrates efficient exploration-exploitation balance through parameter-adjustable evaporation rates and ant movement patterns.