Advanced Artificial Intelligence and Ant Colonies

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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
ID:2009-2-96-010

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

Abstract

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).

Introduction

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



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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