The purpose of this project is to provide a clear understanting of the Ants-based algorithm, by
giving a formal and comprehensive systematization of the subject. The simulation developed in
Java will be a support of a deeper analysis of the factors of the algorithm, its potentialities and its
Swarm intelligence (SI) is a type of artificial intelligence based on the collective behavior of
decentralized, self-organized systems. The expression was introduced by Gerardo Beni and Jing
Wang in 1989, in the context of cellular robotic systems.
SI systems are typically made up of a population of simple agents or boids interacting locally
with one another and with their environment. The agents follow very simple rules, and although
there is no centraized control structure dictating how individual agents should behave, local, and
to a certain degree random, interactions between such agents.
PARTICLE SWARM OPTIMISATION
Particle swarm optimization (PSO) is a swarm intelligence based algorithm to find a solution to
an optimization problem in a search space, or model.
ANT COLONY OPTIMISATION
The ant colony optimization algorithm (ACO), is a probabilistic technique for solving
computational problems which can be reduced to finding good paths through graphs.
This algorithm is a member of ant colony algorithms family, in swarm intelligence methods,the
first algorithm was aiming to search for an optimal path in a graph; based on the behavior of ants
seeking a path between their colony and a source of food. The original idea has since diversified
to solve a wider class of Numerical problems, and as a result, several problems have emerged,
drawing on various aspects of the behavior of ants.
§ Propose an easy approach to the Ant Colony Algorithm, with appropriated vocabulary and
global explanation, as well as details about its behaviour.
§ Develop a Java application which shows the working of the algorithm and gives a better
§ Give a straightforward analysis of the state-of-the-art studies on Ants-based Routing
Algorithms and the implementations which have been done.
THE SOURCE OF INSPIRATION: THE ANTS
Ant as a single individual has a very limited effectiveness. But as a part of a well-organised
colony, it becomes one powerful agent, working for the development of the colony. The ant lives
for the colony and exists only as a part of it.
Each ant is able to communicate, learn, cooperate, and all together they are capable of develop
themselves and colonise a large area. They manage such great successes by increasing the
number of individuals and being exceptionally well organised. The self organising principles
they are using allow a highly coordinated behaviour of the colony.
Pierre Paul Grassé, a French entomologist, was one of the first researchers who investigate the
social behaviour of insects. He discoveredi that these insects are capable to react to what he
called significant stimuli," signals that activate a genetically encoded reaction. He observed thatthe effects of these reactions can act as new significant stimuli for both the insect that produced
them and for the other insects in the colony. Grassé used the term stigmergy to describe this
particular type of indirect communication in which the workers are stimulated by the
performance they have achieved
Stigmergy is defined as a method of indirect communication in a self-organizing emergent
system where its individual parts communicate with one another by modifying their local
Ants communicate to one another by laying down pheromones along their trails, so where ants
go within and around their ant colony is a stigmergic system
In many ant species, ants walking from or to a food source, deposit on the ground a substance
called pheromone. Other ants are able to smell this pheromone, and its presence influences the
choice of their path, that is, they tend to follow strong pheromone concentrations. The
pheromone deposited on the ground forms a pheromone trail, which allows the ants to find good
sources of food that have been previously identified by other ants.
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