Evaluating a Bio-Inspired Approach for the Design of a Grid Information System - Advancing Artificial Intelligence Through Biological Process Applications - page 309

Tsoumakos, D., & Roussopoulos, N. (2003). A
Comparison of Peer-to-Peer Search Methods ,
Paper presented at the 6th International Work-
shop on the Web and Databases (WebDB), San
Diego, USA.

detailed description and guide to all major ACO
algorithms and a report on current theoretical
indings. The topic surveys ACO applications now
in use, including routing, assignment, scheduling,
subset, machine learning, and bioinformatics
problems.
Other suggested works in the field of swarm
intelligence are the following:

Van Dyke Parunak, H., Brueckner, S. A., Mat-
thews, R., & Sauter, J. (2005). Pheromone Learn-
ing for Self-Organizing Agents , IEEE Transac-
tions on Systems, Man, and Cybernetics, Part A:
Systems and Humans, 35(3).

Camazine, S., Franks, N. R., Sneyd, J., Bonabeau,
E., Deneubourg, J.-L., and Theraula, G. (2001).
Self-Organization in Biological Systems. Princ-
eton University Press, Princeton, NJ, USA.

ADDITIONAL READING

Deneubourg, J. L., Goss, S., Franks, N., Sendova-
Franks, A., Detrain, C., & Chretien, L. (1990)
The dynamics of collective sorting robot-like
ants and ant-like robots . Paper presented at the
1st International Conference on Simulation of
Adaptive Behavior on From Animals to Animats,
Cambridge, USA.

To better understand the concepts concerning
bio-inspired techniques for the management
of resources in distributed systems, the reader
can refer to “Swarm Intelligence: From Natural
to Artificial Systems” authored by Bonabeau,
Dorigo and Theraulaz, pioneers of so called ant
optimization and the simulation of social insects.
With their topic , they provide an overview of the
state of the art in swarm intelligence. They go
further by outlining future directions and areas
of research where the application of biologically
inspired algorithms seems to be promising. The
topic describes several phenomenona in social in-
sects that can be observed in nature and explained
by models. The topic also provides examples of
the latter being transferred successfully to algo-
rithms, multi-agent systems and robots, or at least
describes promising approaches for doing this.
Another interesting topic is “Ant Colony
Optimization” authored by Dorigo and Stützle.
This topic presents an overview of this rapidly
growing field, from its theoretical inception to
practical applications, including descriptions of
many available ACO algorithms and their uses.
The topic irst describes the translation of ob-
served ant behavior into working optimization
algorithms. The ant colony metaheuristic is then
introduced and viewed in the general context of
combinatorial optimization. This is followed by a

Grassè, P. (1959). La reconstruction du nid et les
coordinations inter-individuelles chez belicosi-
termes natalensis et cubitermes sp. la theorie
de la stigmergie: Essai d'interprtation du com-
portement des termites constructeurs , Insectes
Sociaux 6, 41-84.

Martin, M., Chopard, B., and Albuquerque, P.
(2002). Formation of an ant cemetery: swarm intel-
ligence or statistical accident? Future Generation
Computer Systems 18, 7, 951-959.

Parunak, H. V. D., Brueckner, S., Matthews, R.
S., and Sauter, J. A. (2005). Pheromone learning
for self-organizing agents. IEEE Transactions
on Systems, Man, and Cybernetics, Part A 35,
3, 316-326.

Van Dyke Parunak, H., Brueckner, S. A., Mat-
thews, R., & Sauter, J. (2005). Pheromone Learn-
ing for Self-Organizing Agents , IEEE Transac-
tions on Systems, Man, and Cybernetics, Part A:
Systems and Humans, 35(3).