Advanced Artificial Intelligence and Ant Colonies

Pages: 11 (3662 words) Published: August 4, 2010
East West University
Department of Computer Science and Engineering
Course Title: Advanced Artificial Intelligence
Course Code: CSE 532

Assignment 1

Section 1

Course Teacher: Dr. M. Ameer Ali

Q1. Write a research article for ANT COLONY OPTIMIZATION.

Name:A.K.M Abdul Halim

1. Abstract
2. Introduction
3. Existing Literature Survey
4. Analysis of the Existing Algorithms
5. Conclusions
6. References


Ants: Small animals (insects) that live in colonies in/on the ground. With this real life definition, ant colony optimization is an optimization method in which imaginary agents are used. Daemon Actions: These are the actions that can be taken to centralize the solution. The aim of Daemon Actions is to prevent quick convergence of the algorithm. Decentralized Control: A term which is related to robustness and flexibility. Robust systems are desired because of their ability to continue to function in the event of breakdown of one of their components (Dréo et al., 2006). Dense Heterarchy: A term which is taken from biology and represents the organization of ant colonies. It is different from the managerial term hierarchy. In dense heterarchy, the structure is horizontal, contrary to hierarchy Pheromone: In real life, pheromone refers to the chemical material that an ant spreads over the path it goes and the level of it changes over time by evaporating. On the other hand, in ant colony optimization, pheromone is a parameter. The amount of this parameter determines the intensity of the trail. The intensity of the trail can be viewed as a global memory of the system (Dréo et al., 2006).


In the real world, ants (initially) wander randomly, and upon finding food return to their colony while laying down pheromone trails. If other ants find such a path, they are likely not to keep travelling at random, but to instead follow the trail, returning and reinforcing it if they eventually find food. Over time, however, the pheromone trail starts to evaporate, thus reducing its attractive strength. The more time it takes for an ant to travel down the path and back again, the more time the pheromones have to evaporate. A short path, by comparison, gets marched over faster, and thus the pheromone density remains high as it is laid on the path as fast as it can evaporate. Pheromone evaporation has also the advantage of avoiding the convergence to a locally optimal solution. The original idea comes from observing the exploitation of food resources among ants, in which ants’ individually limited cognitive abilities have collectively been able to find the shortest path between a food source and the nest.The first ant finds the food source (F), via any way (a), then returns to the nest (N), leaving behind a trail pheromone (b)Ants indiscriminately follow four possible ways, but the strengthening of the runway makes it more attractive as the shortest route. Ants take the shortest route, long portions of other ways lose their trail pheromones.

In a series of experiments on a colony of ants with a choice between two unequal length paths leading to a source of food, biologists have observed that ants tended to use the shortest route. A model explaining this behaviour is as follows: An ant (called "blitz") runs more or less at random around the colony; If it discovers a food source, it returns more or less directly to the nest, leaving in its path a trail of pheromone; These pheromones are attractive, nearby ants will be inclined to follow, more or less directly, the track; Returning to the colony, these ants will strengthen the route; If two routes are possible to reach the same food source, the shorter one will be, in the same time, traveled by more ants than the long route will; The short route will be increasingly enhanced, and therefore become more attractive; The long route will eventually disappear, pheromones are volatile; Eventually, all the...

References: J.-L. Deneubourg, S. Aron, S. Goss, and J. M. Pasteels. The self-organizing exploratory pattern of the Argentine ant. Journal of Insect Behavior, 3:159–168, 1990.
G. Di Caro and M. Dorigo. AntNet: Distributed stigmergetic control for communications networks. Journal of Artificial Intelligence Research, 9:317–365, 1998.
M. Dorigo. Optimization, Learning and Natural Algorithms (in Italian). PhD thesis, Dipartimento di Elettronica, Politecnico di Milano, Milan, Italy, 1992.
M. Dorigo and C. Blum. Ant colony optimization theory: A survey. Theoretical Computer Science, 344(2–3):243–278, 2005.
M. Dorigo and L. M. Gambardella. Ant Colony System: A cooperative learning approach to the traveling salesman problem. IEEE Transactions on Evolutionary Computation, 1(1):53–66, 1997.
M. Dorigo, V. Maniezzo, and A. Colorni. Positive feedback as a search strategy. Technical Report 91-016, Dipartimento di Elettronica, Politecnico di Milano, Milan, Italy, 1991.
M. Dorigo, V. Maniezzo, and A. Colorni. Ant System: Optimization by a colony of cooperating agents. IEEE Transactions on Systems, Man, and Cybernetics – Part B, 26(1):29–41, 1996.
M. Dorigo and T. Stützle. Ant Colony Optimization. MIT Press, Cambridge, MA, 2004.
S. Goss, S. Aron, J.-L. Deneubourg, and J. M. Pasteels. Self-organized shortcuts in the Argentine ant. Naturwissenschaften, 76:579–581, 1989.
W. J. Gutjahr. A Graph-based Ant System and its convergence. Future Generation Computer Systems, 16(8):873–888, 2000.
T. Stützle and H. H. Hoos. MAX–MIN Ant System. Future Generation Computer Systems, 16(8):889–914, 2000.
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