The document discusses ant colony optimization, a nature-inspired metaheuristic algorithm. It describes how real ants communicate indirectly via pheromone trails to find the shortest path between their nest and food sources. The algorithm mimics this behavior to solve complex optimization problems like the traveling salesman problem, with artificial "ants" probabilistically building solutions through pheromone-guided movements.

1. Ant Colony Systems and the Ant Algorithm

2. REAL ANT BEHAVIOR

4. EXPERIMENTAL STUDY OF ANTS The more ants follow a trail, the more attractive that trail becomes for being followed NEST FOOD NEST FOOD NEST FOOD

5. ANT Behavior The more ants follow a trail, the more attractive that trail becomes for being followed

6. ANT Behavior Even when the tracks are equal the behavior will encourage one over the other--convergence (Deneubourg et al )

8. TRAVELING SALESMAN PROBLEM

12. General Formulation - Symmetric

13. General Formulation - Asymmetric

15. Simple Examples

16. Nearest Neighbor Solution

17. Larger TSP Example Obj Fx: min d

18. Initial Order Solution d=3138

19. Nearest Neighbor Solution d=2108

20. Tabu Search Solution d=1830

21. THE ANT COLONY OPTIMIZATION METAHEURISTIC

25. FLOWCHART OF ACO Have all cities been visited Have the maximum Iterations been performed START ACO Locate ants randomly in cities across the grid and store the current city in a tabu list Determine probabilistically as to which city to visit next Move to next city and place this city in the tabu list Record the length of tour and clear tabu list Determine the shortest tour till now and update pheromone NO YES STOP ACO YES NO

30. Consider the Case Studies in Papers

32. APPLICATIONS

33. APPLICATIONS

37. QUESTIONS??