Swarm Intelligence
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(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.
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Swarm Intelligence
Aravind Raj D & Sarath B V
Electronics & Communication Department
Mohandas College Of Engineering&Technology

.pdf   Swarm Intelligence.pdf (Size: 249.46 KB / Downloads: 131)

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 and implimentation 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.
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Introduction about Swarm Intelligence

•SI is the property of a system whereby the collective behaviors of agents interacting locally with their environment cause coherent functional global patterns to emerge .
•SI provides a basis with which it is possible to explore distributed problem solving without centralized control or the provision of a global model.
•One of the cores tenets of SI work is that often a decentralized, bottom-up approach to controlling a system is much more effective than traditional, centralized approach.

•“Groups performing tasks effectively by using only a small set of rules for individual behaviour is called swarm intelligence.”
•“Swarm Intelligence is a property of systems of non-intelligent agents exhibiting collectively intelligent behaviour.”
•In Swarm Intelligence, “…two individuals interact indirectly when one of them modifies the environment and the other responds to the new environment at a later time.”
Where did the Concept arise?

•For years scientists have been studying about insects like ants, bees, termites etc.
•The most amazing thing about social insect colonies is that there’s no individual in charge. For eg: consider the case of ants.
•But the way social insects form highways and other amazing structures such as bridges, chains, nests and can perform complex tasks is very different: they self-organize through direct and indirect interactions.
Self Organization

•Errors and randomness are not “bugs”; rather they contribute to success by enabling them to discover and explore in addition to exploiting.
•Self-organization feeds itself upon errors to provide the colony with flexibility and robustness..
•A very different mindset from the prevailing approach to software development and managing vast amounts of information: no central control, errors are good, flexibility, robustness (or self-repair).
Characteristics of Social Insects

Human beings suffer from a “centralized mindset”; But inserting the human factor into the loop is against SI.
Big Issue

How should we program the individual virtual ants so that the network behaves appropriately at the system level?
There is always a fear of these systems going out of control as there is no central control nor the emergent behaviour of the whole system is predefined only the agents are predefined.
We don’t always know ahead of time what emergent solutions will come out because emergent behaviour is unpredictable.
If applied well, self-organization endows your swarm with the ability to adapt to situations that you didn’t think of.
Characteristics of Self Organizing Behaviour

1.Positive reinforcement
2.Negative reinforcement
3.Amplification of fluctuations
4.Multiple interactions

1.Positive reinforcement
•Many ant species forage for food using a trail-laying trail-following behaviour.
•It is a self-fulfilling prophecy, “ants following pheromone trails will tend to congregate simply from the fact the pheromone density increases with each additional ant that follows”.
•This self- perpetuating mechanism is known as “mass recruitment” and is the primary reinforcement of the foraging behaviour.
Pheramone Trails

•Individual ants lay pheromone trails while travelling from the nest, to the nest or possibly in both directions.
•The pheromone trail gradually evaporates over time.
•But pheromone trail strength accumulate with multiple ants using path.

2.Negative Reinforcement
•Negative reinforcement can be seen in crowding at the food source, limitation of population, or food source exhaustion.
•In case food source exhaustion, then no more pheromone is deposited on the trail. The pheromone currently on the trail will evaporate, eventually falling to zero.
•No pattern is formed if the pheromone signal is too weak.

4.Multiple interactions

•A minimum saturation is required for a pattern to emerge
•Self organization “usually requires a minimal density of mutually tolerant individuals”.
•Each individual should be able to use the results of its efforts and those of nest mates possibly being able to distinguish exactly which individual performed the task.

In order to understand swarm intelligence we have to analyze few questions:
•How are jobs scheduled and assigned?
•How are jobs executed?
•How do individuals choose or change a job?
•How is equilibrium achieved among all individuals for all jobs in the colony?
We can analyze the job assignment strategy as it is the only question which can be give an answer in specifics.
Job Assignment: Four Ways

•Individual – Individual (I-I) communication
•Environment – Individual (E-I) communication
1) Age Job Assignment

•Among certain social insects, individuals prefer to take certain jobs based on their age.
•With honey bees
– Young workers do hive chores such as building and repairing the hive, ventilation, defense, food preparation, etc…
– Older workers gather nectar, pollen, and water.
•In SI our virtual agents work in the same way older agents will have more updated rules, newer agents will have some specific job.
2) Morphology Job Assignment

•Some individuals may be better suited for one job over another due to their physical form.
•Thus they tend toward a certain set of jobs (or may be capable of only those jobs).
3) Individual - Individual Communication Job Assignment

•For those species with evolved communication skills, one individual may recruit another, via direct communication, to help with a certain job.
•We have applied these concepts to a variety of technology problems, such as distributed data storage in a computer network, and the creation and management of ad hoc wireless networks.”
4)Environment - Individual Communication Job Assignment

•Stigmergy: a kind of indirect communication and learning by the environment found in social insects is a well known example of self-organization, providing not only vital clues in order to understand how the components can interact to produce a complex patterns as can pinpoint simple biological non-linear rules and means to achieve an improved design of artificial intelligent systems.
Adventages of using Mobile Agents and Stigmentory

2.Fault tolerance
Swarm Systems Exhibits

•Multiple lower level competences
•Situated in environments
•Limited time to act
•Autonomous with no explicit control provided
•Problem solving is emergent behaviour
•Strong emphasis on reaction and adaptations
Applications of SI

1.Ant-Based Control: developed for telephone networks.
2.AntNet: Adaptive agent-based routing algorithm
Ant Net

•Routing is determined by complex interactions of forward & backward network exploration agents.
•Forward ants: No node routing updates. They report N/W delay conditions to Backward ants.
•Backward ants: inherit the raw data & update routing table of nodes.
•Entries of routing table are probabilities.
Probabilities serve a dual purpose
1.To decide the next hop to a destination.
2.Data packets deterministically select the path with the highest probability for the next hop.
Actionsin Ant Net

1.Each n/w node launches forward ants to all destinations in regular time intervals
2.Ant finds path to destination based on current routing tables.
3.Forward ants create Stacks, pushing Trip times for every node.
4.When the destination is reached, backward ant inherits the stack.
5.It pops the stack entries & follows path in reverse.
6.Node tables of each visited node are updated based on trip times.
Practical Applications

•UAV (Unmanned Air Vehicle) & Robots
UAV (Unmanned Air Vehicles) and Robots
Being able to control swarms or teams of UV vehicles could lead to novel peace time applications.

•Fisheries: to track schools of fish or whales in the ocean
•Robots: to explore and clean up hazardous sites
•Pico-satellites: survey asteroid belts and gather scientific information
–Only small simulation exists.
–Formal modeling is currently underway.
–Conceptual development needs to be done.
Business Applications of Swarm Intelligence

“Swarm Intelligence” had been used successfully to address notoriously difficult business problems. Some clients are:
•South West Airlines
•Pina Petroli
•Distribution Centres (“Bucket Brigade”)

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