This lecture extends further the discussion on any colony technique. Ants can be considered artificial agents which work together and improve candidate solution in each iteration. A simple ant colony algorithm is discussed. Individual ants may not be that smart but when they work in group, they are one of the smartest creatures. How ant find the food quickly and slowly optimize their route to shortest route from source to their home, the same concept we can apply to many real world optimization problems e.g. traveling salesperson, routing in networks, stochastic diffusion search, swarm intelligence and optimization, and many more. Ants leaves their foot print or pheromone or fragrance or smell along the path they have visited. The shortest path will be the one which will have higher intensity of the fragrance. Over time fragrance evaporate, which one can model as probabilistic model, ants take into consideration the fragrance and choose a path at a intersection, which path they gonna take, one can model the choice as probabilistic system. There are many more statistical techniques one many use to improve the performance of the algorithm. Put simply ant colony system are pretty good for solving real world optimization problems of NP-hard nature.
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