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Swarm Intelligence
Post: #1

(SI) is an artificial intelligence technique based around the study of collective behavior in decentralized, self-organized systems. The expression swarm intelligence was introduced by Beni & Wang in 1989, in the context of cellular robotic systems. SI systems are typically made up of a population of simple agents interacting locally with one another and with their environment. Although there is normally no centralized control structure dictating how individual agents should behave, local interactions between such agents often lead to the emergence of global behavior. Examples of systems like this can be found in nature, including ant colonies, bird flocking, animal herding, bacteria molding and fish schooling. Application of swarm principles to large numbers of robots is called as swarm robotics.
Post: #2
Swarm Intelligence
Aravind Raj D & Sarath B V
Electronics & Communication Department
Mohandas College Of Engineering&Technology


Swarm intelligence (SI) involves multiple simple agents interacting with each other and the environment to
solve complex problems through their collective global behaviour. This is inspired by the intelligent
behaviour seen in swarms of animals such as a colony of ants, flocks of birds or schools of fish.
SI systems can handle many problems that are not suitable by traditional means. These include problems
that are dynamic, non predictable, not defined or computationally hard. SI systems have a number of
features such as flexibility,robustness,decentralized and self organization.
As SI systems are inspired by natural biological
swarms, standard algorithms are based on the
search for food. The differences in food searching
techniques lead to different SI algorithms,

Ant Colony Optimisation (Aco)
ACO replicate the natural behaviour of ants. Ants
will randomly spread out and search for food.
When food is discovered an ant will return to its
base leaving a pheromone trail. Upon finding a
pheromone trail another ant will follow that train
and if it finds food on this trail it too will return to
base, leaving its own pheromone trail. If an ant is
on a pheromone trail and crosses a stronger
pheromone trail it will follow the stronger trail.
Pheromones decay over time allowing the removal
of non optimal solutions. The ACO algorithm finds
optimal solutions because shorter paths are traveled

over faster and hence more often quickly leading to
strong pheromone trails. Introducing new ants
randomly over time allows responses to dynamic
changes in the environment. ACO is typically used
to find an optimal path.

Particle Swarm Optimisation (Pso)
This form of Swarm intelligence is based on
schools of fish and flocks of birds finding food.
PSO is used to find an optimal point in space.
Agents begin by being randomly spread out in the
environment with random velocities. As the agents
move they examine the area around them and
communicate with the other agents their
evaluations. This communication can either be a
global communication or a local ‘neighbourhood’
communication. Based on their own findings and
the findings communicated to them, agents will
adjust their velocities to follow better solutions.
As a result agents will begin to head into areas
where the best solutions are being found and this
leads to an optimal solution.

Intelligent Water Drops
Intelligent Water Drops algorithm (IWD) is a
swarm-based nature-inspired optimization
algorithm, which has been inspired from natural
rivers and how they find almost optimal paths to
their destination.These near optimal or optimal
paths follow from actions and reactions occurring
among the water drops and the water drops with
their riverbeds. In the IWD algorithm, several
artificial water drops cooperate to change their
environment in such a way that the optimal path is
revealed as the one with the lowest soil on its links.
The solutions are incrementally constructed by the
IWD algorithm. Consequently, the IWD algorithm
is generally a constructive population-based
optimization algorithm.

System Design
The difficult task in swarm intelligence is to answer
the question:
How do we program an individual agent so the
entire global system behaves as we want it to?
The techniques to design and control individual
agents are a standard AI problem and techniques
like reinforcement learning, fuzzy logic, neural
networks etc, can be used.
When designing an SI system both the individual
agents’ ability to search and evaluate its area as
well as a means for communication need to be
considered. Many of the global emergent
behaviour are difficult to predict.The major steps in
SI system design are:
• Identification of analogies: in swarm
biology and IT systems
• Understanding: computer modelling of
realistic swarm biology
• Engineering: model simplification and
tuning for IT applications

Applications& Scope
Swarm Intelligence is utilised in the following
• Swarm robotics:
It is a new approach to the coordination of
multirobot systems which consist of large numbers

of mostly simple physical robots. It is supposed that
a desired collective behavior emerges from the
interactions between the robots and interactions of
robots with the environment. This approach
emerged on the field of artificial swarm
intelligence, as well as the biological studies of
insects, ants and other fields in nature, where
swarm behaviour occurs.
One project that might deploy such methods in the
near future is ANTS — Autonomous Nano
Technology Swarm. The acronym is apt, because
ANTS is all about collective, emergent intelligence
of the sort that appears in insect colonies. What
scientists at NASA’s Goddard Space Flight Center
envision is a massive cluster of tiny probes that use
artificial intelligence to explore the asteroid belt.
Each probe, weighing perhaps 1 kilogram (2.2
pounds) would have its role — while a small
number of them direct the exploration, perhaps 900
of the probes would proceed to do the work, with
only a few returning to Earth with data.One key
factor here is redundancy; the mission succeeds
even if a large number of individual probes are lost.
ANTS could serve as a testbed for numerous
technologies as it spreads computing intelligence
across intelligent, networked spacecraft. In
particular, computer autonomy would be critical to
ensuring the success of the mission.
• Crowd simulation :
It is the process of simulating the movement of a
large number of objects or characters, now often
appearing in 3D computer graphics for film. While
simulating these crowds, observed human behavior
interaction is taken into account, to replicate the
collective behavior.
The need for crowd simulation arises when a scene
calls for more characters than can be practically
animated using conventional systems, such as
skeletons/bones. Simulating crowds offer the
advantages of being cost effective as well as allow
for total control of each simulated character or
The actual movement and interactions of the crowd
is typically done in one of two ways:

Read the report for further details.

2. Science Daily. 2008 (April 1). "Planes, Trains
and Ant Hills: Computer scientists simulate activity
of ants to reduce airline delays.
Post: #3
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