1. Ant colonies for the travelling salesman problem Macro Dorigo, Luca Maria Gambardella 資工三 91610032 李明杰

2. Introduction Ants are known to use pheromones to communicate to coordinate activities like the location and collection of food. ACO (ant colony optimization). Based on the foraging behavior of ant colonies, ACO has generally used a single kind of pheromone to communicate between its ants. However, natural pheromone communication often consist of a number of different pheromones. ACO ’s reliance on positive feedback alone may make difficult for it to successfully escape local optima.

3. Ant colony

4. ACS-TSP TSP: find the shortest path that traverses all cities in the problem exactly once, returning to the starting city. ACS-TSP: Using Ant Colony System to solve TSP

5. ACS-TSP (cont.) Brief summary of ACS-TSP 每隻 ant 都有 memory 記錄走過的 city => Mk 再根據下面的機率來選擇下一個 city.

6. Comparison Comparison of ACS with GA, EP, SA and the AG.

7. ACS performance for some bigger TSPs

8. Summary It’s a new idea to identify an problem across graph searched by many artificial ants. Pheromone V.S. heuristic function. Local optimal to global trail updating is performed. ACS enables parallelization to improve the performance for finding good solution.