Swarm intelligence refers to the design of algorithms and problem-solving systems inspired by the collective behavior of social insects and other animal societies, where simple agents exhibit complex group behaviors without a central leader. Examples in nature include ant colonies, bird flocks, and fish schools, and this form of intelligence is implemented in various AI algorithms like Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO). Applications of swarm intelligence span multiple fields, including robotics, military operations, and medical research, highlighting both its advantages in scalability and flexibility as well as its challenges related to efficiency and control.
Introduces swarm intelligence, defining it as behavior inspired by social insects and other animal societies.
Discusses swarm intelligence in robots and insects, emphasizing decentralized control, local interactions, and self-organization.
Details on self-organization principles and examples including ants, birds, and traffic systems showcasing collective behavior.
Examines specific roles of leafcutter, weaver, and army ants in effective foraging and colony organization.
Explains direct and indirect interactions among swarm members, highlighting communication methods.
Introduction to two algorithms: Ant Colony Optimization and Particle Swarm Optimization, explaining their functions and origins.
Describes the pheromone marking process in ants for food search, emphasizing shortest path discovery.
Lists applications of swarm intelligence algorithms in fields like robotics, military, and medical research.
Explains benefits of swarm systems, including scalability, flexibility, robustness, and adaptability.
Discusses inefficiencies, uncontrollable nature, unpredictability, and challenges in understanding complex swarm systems.Raises questions about the origins of intelligence in swarms and how individual actions contribute to group dynamics.
Highlights collective behaviors in natural settings including bird flocks, fish schools, and bacteria.
Describes various group dynamics, including flocks escaping threats and synchronized actions during attacks.
Discusses the rapid communication and decision-making processes in swarms, including migration and food search.
Highlights innovative applications of swarm principles in fields like robotics, aircraft operations, and logistics optimization.
Explores how groups, like bees and traders, utilize democratic methods for decision-making to choose optimal outcomes.
Examines how individuals in groups can exhibit different behavior compared to when alone, showcasing locusts and fish.
Describes the fluid and artistic patterns of flocking birds, emphasizing non-leadership in coordination.
Concludes the presentation, thanking viewers for their attention.
Swarm intelligence
Definition:-
Any attemptto design algorithms or distributed problem-
solving devices inspired by the collective behavior of social
insect colonies and other animal societies” (Bonabeau, Dorigo,
Theraulaz: Swarm Intelligence)
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It’s “the behaviourof a population of simple agents who aggregate
behaviour exhibits intelligence unknown to the individual agents.
Groups exhibiting swarm intelligence have no central leader but
rather members interact with each other based solely on
information they have locally. Examples in nature include ant
colonies, flocks of birds, schools of fish, and bacterial growth.”
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swarm of robots swarm of Ants
Swarm of birds
Swarm of Flying robots
cooperating together
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Swarm intelligence
What ismeant by Swarm Intelligence?
• It is an artificial intelligence (AI) technique based on the
collective behavior in decentralized, self-organized systems
• Generally made up of agents who interact with
each other and the environment
• No centralized control structures
• Based on group behavior found in nature
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What ismeant by Swarm Intelligence?
• Insects have a few hundred brain cells
• However, organized insects have been known for:
• Architectural marvels
• Complex communication systems
• Resistance to hazards in nature
• In the 1950’s E.O.Wilson observed:
• A single ant acts (almost) randomly
• often leading to its own demise
• A colony of ants provides food and protection for the
entire population
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What is meant by Swarm Intelligence?
• This huge Ant colony Concrete, that has been Excavated from
earth in several weeks.
• This Colony has roads with shortest path between every two
points.
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What is meant by Swarm Intelligence?
• Characteristic
• Composed of many individuals
• Individuals are homogeneous
• Local interaction based on simple rules
• Self-organization
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What is meant by Swarm Intelligence?
• Four Ingredients of Self Organization
• Positive Feedback
• Negative Feedback
• Amplification of Fluctuations – randomness
• Reliance on multiple interactions
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Example Original Example: Swarm of Bees
• Ant colony - Agents: ants
• Flock of birds- Agents: birds
• Traffic - Agents: cars
• Crowd - Agents: humans
• Immune system - Agents: cells and molecules
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Cont. Example…
• Ant Colony
• Every single insect in a social insect colony seems to have its
own agenda, and yet an insect colony looks so organized.
• The seamless integration of all individual activities does not
seem to require any supervisor.
• For Example there is in one colony different type of workers:
• Leafcutter Ants
• Weaver Ants
• Army Ants
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• Leafcutter Ants
• cut leaves from plants and trees
• Workers forage for leaves hundreds of meters away from the
nest
• literally organizing highways to and from their foraging sites
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• Weaver Ants
• workers form chains of their own bodies, allowing them to
cross wide gaps and pull stiff leaf edges together to form a
nest
• Several chains can join to form a bigger one over which
workers run back and forth.
• Such chains create enough force to pull leaf edges together.
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• Army Ants
• organize impressive hunting raids, involving up to 200,000
workers, during which they collect thousands of prey
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• Ant Colony Swarm benefits:
• Ants forage better.
• Settle in organized home.
• Defend it self against predators
• Social Insects have survived for millions of years.
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Cont. Examples, How to Interact?
• Direct Interactions
• Food/liquid exchange, visual contact, chemical
contact (pheromones)
• Indirect Interactions (Stigmergy)
• Individual behavior modifies the environment, which
in turn modifies the behavior of other individuals
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ACO and PSO Algorithms
• Two Common SI Algorithms
• Ant Colony Optimization (ACO)
• Particle Swarm Optimization (PSO)
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PSO Algorithms
• PSO
• A population based stochastic optimization technique
Searches for an optimal solution in the computable search
space.
• Developed in 1995 by Dr. Eberhart and Dr. Kennedy.
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PSO Algorithms Cont.…
• PSO
• In PSO individuals strive to improve themselves and often
achieve this by observing and imitating their neighbors.
• Each PSO individual has the ability to remember.
• Inspiration: Swarms of Bees, Flocks of Birds, Schools of Fish.
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ACO Algorithms
• ACO
• Optimization Technique Proposed by Marco Dorigo in the
early ’90
• Heuristic optimization method inspired by biological systems
• Multi-agent approach for solving difficult combinatorial
optimization problems
• Has become new and fruitful research area
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ACO Algorithms Cont.…
• ACO
• The way ants find their food in shortest path is interesting.
• Ants secrete pheromones to remember their path.
• These pheromones evaporate with time.
• Whenever an ant finds food , it marks its return journey with
pheromones.
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ACO Algorithms Cont.…
• ACO
• Pheromones evaporate faster on longer paths. (Evaporation)
• Shorter paths serve as the way to food for most of the
other ants.
• The shorter path will be reinforced by the pheromones
further. (Reinforcement)
• Finally , the ants arrive at the shortest path. (Establishment)
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Applications and Recent Developments
• Some applications Uses S.I Algorithms :
• Movie effects
• Lord of the Rings
• Happy Feet
• Network Routing
• ACO Routing
• Swarm Robotics
• Swarm bots
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Movies Used Swarm Intelligence
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Applications and Recent Developments Cont.…
Other Recent developed
• Human tremor analysis
• Human performance assessment
• Ingredient mix optimization
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Applications and Recent Developments Cont.…
Other Recent developed
• Evolving neural networks to solve problems.
• U.S. Military is applying SI techniques to control of unmanned
vehicles.
• NASA is applying SI techniques for planetary mapping.
• Medical Research is trying SI based controls for nanobots to
fight cancer.
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Advantages and Disadvantages
• Advantages
• The systems are scalable because the same control
architecture can be applied to a couple of agents or
thousands of agents
• The systems are flexible because agents can be easily added
or removed without influencing the structure
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Advantages and Disadvantages Cont.…
• Advantages
• The systems are robust because agents are simple in design,
the reliance on individual agents is small, and failure of a
single agents has little impact on the system’s performance
• The systems are able to adapt to new situations easily
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Swarm intelligence
Advantages and Disadvantages Cont.…
• Disadvantages
• Non-optimal – Because swarm systems are highly redundant
and have no central control, they tend to be inefficient. The
allocation of resources is not efficient, and duplication of
effort is always rampant.
• Uncontrollable – It is very difficult to exercise control over a
swarm.
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Swarm intelligence
Advantages and Disadvantages Cont.…
• Disadvantages
• Non-optimal – Because swarm systems are highly redundant
and have no central control, they tend to be inefficient. The
allocation of resources is not efficient, and duplication of
effort is always rampant.
• Uncontrollable – It is very difficult to exercise control over a
swarm.
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Swarm intelligence
Advantages and Disadvantages Cont.…
• Disadvantages
• Unpredictable – The complexity of a swarm system leads to
unforeseeable results.
• Non-understandable – Sequential systems are
understandable; complex adaptive systems, instead, are a
jumble of intersecting logic.
• Non-immediate – complex swarm systems with rich
hierarchies take time. The more complex the swarm, the
longer it takes to shift states
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Swarm intelligence
o Where this intelligence comes from raises a fundamental
question in nature?
o How do the simple actions of individuals add up to the
complex behaviour of a group?
o How do hundreds of honeybees make a critical decision about
their hive if many of them disagree?
o What enables a school of herring to coordinate its movements
so precisely it can change direction in a flash, like a single,
silvery organism?
The collective abilities of such animals—none of which grasps the
big picture, but each of which contributes to the group's success
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Ants are notsmart. But colonies are smart. So what’s amazing about ants is that
in the aggregate, all of these inept creatures accomplish amazing feats as
colonies. In an ant colony, there’s nobody in charge. There are no managers.
There is nobody telling anybody what to do. The queen does not give rules.
She just sits there and lays eggs.”
– CBS News - Small wonders :-What ants can teach us.
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Political agitation (No comments)
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Cinema first show (No comments)
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Driving @ Intersection (No comments)
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Harmonious Flight
The ability of animal groups - such as this flock of starlings - to shift
shape as one, even when they have no leader, reflects the genius of
collective behaviour - something scientists are now tapping to solve
human problems.
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Harmonious Flight
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Mass Escape
A peregrine falcon on the attack forces a flock of starlings to take
evasive action, moving together as one.
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Mass Escape
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Instant Messaging
Because each individual is paying close attention to its neighbours,
news travels fast through a school of bigeye jack near Cocos Island
in the Pacific. The fish follow simple rules that keep the group alert:
stick together, avoid collisions, and swim in the same direction.
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Instant Messaging
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On the Move
Wildebeests crossing the Mara River in Kenya may be able to
follow a migration route even if only a few of them know the way,
say researchers using a computer model of herd behaviour. Never
mind that the informed animals aren't trying to lead. The rest
follow anyway.
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On the Move
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Modern-day Plague
Locusts beyond number rise in a single black cloud in Mauritania,
devouring every crop in their path and leaving hunger or starvation
in their wake. Finding ways to prevent such plagues depends on a
deeper understanding of swarm theory and the surprising ways it
affects our lives.
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Modern-day Plague
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Conveyer-belt Behaviour
Leafcutter ants (Atta colombica) in Panama carry bits of vegetation
to their nest, where collaborating teams of ants transport, clean,
cut up, crush, mold, and pack the material into compost piles. Still
other ants tend the piles to grow fungi, the main food source for
the colony's young. Because a colony of several million leafcutters
relies upon cooperation to survive, biologists sometimes describe
it as a superorganism.
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Conveyer-belt Behaviour
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Conveyer-belt Behaviour
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Arboreal Light Show
A tree ablaze with fireflies in Indonesia blinks on and off as each
insect adjusts its flashes to match the others. Such self-organized
behaviour resembles the synchronized firing of heart muscle cells
or the rhythmic applause of a crowd - but seems more mysterious.
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Arboreal Light Show
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Colour-coordinated
A red colour ring means "grab me," a blue one "stay away," as
robots in a Brussels lab converge to form a single unit. Their goal:
to accomplish something together they can't do alone, such as
moving a heavy object.
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Colour-coordinated
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High-tech Teamwork
A team of inch-long (three centimetres) robots spreads out
through a mock-up of a turbine engine in a laboratory of the
Swarm-Intelligent Systems Group at the Ecole Polytechnique
Federale de Lausanne in Switzerland. To speed the task of
inspecting each blade, these experimental units can signal
neighbours through infrared sensors.
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High-tech Teamwork
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Communal Breadwinners
Army ants work together to find food to haul back to the group.
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Communal Breadwinners
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From Ant Trails to Truck Routes
Tanker trucks with cargoes of liquid nitrogen, oxygen, and argon in
Pasadena, Texas, are assigned delivery routes by dispatchers using a
computer program inspired by the foraging behaviour of ants.
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From Ant Trails to Truck Routes
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Working Smarter
When Southwest Airlines wanted to expand its package delivery business, but
ramp workers complained that they were already doing a lot of extra handling
of packages, the airline asked a consulting firm to look into it. Using a
computer simulation based on the foraging behaviour of ants, the consultants
advised the workers to change their rules of thumb for routing packages.
Instead of using the "hot potato" strategy—putting a package on the next
flight heading in the general direction of the final destination—they
recommended waiting for the next plane going to that destination, even if it
meant a delay of several hours. The result: a 50 to 85 percent reduction in the
number of packages transferred by ramp workers at their six busiest locations.
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Working Smarter
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Democratic Decisions
Even though swarming honeybees frequently differ about where to
establish a new nest, the group usually chooses the best site. Bees
reach this decision by gathering information, conducting
independent evaluations, and holding a kind of vote.
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Democratic Decisions
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Driving Force
Chicago traders swarm on the stock exchange floor, driving the
price of soybean futures with the same practices - fact-finding,
independent study, and voting - used by swarming honeybees in
search of a new site to nest.
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Driving Force
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Mob Mentality
In high spirits, a well-dressed crowd at Ascot Racecourse near
London celebrates a day of horse races with singing and patriotic
flag waving. Individuals in a densely packed group tend to act
differently from the way they would on their own, scientists say, not
unlike a herd of animals. So event organizers need to take special
care to keep participants from panicking as they exit such events.
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Mob Mentality
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Water Ballet
Kids from a summer day camp watch a school of golden trevally
swim by at the Georgia Aquarium in Atlanta. The ability of schools
to stick together as they move through the water, which is
beautiful to observe, still holds mysteries for biologists trying to
understand the principles of collective motion.
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Water Ballet
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Gathering Storm
Biologists in an Oxford lab show that when otherwise harmless
juvenile locusts get too crowded, they will suddenly align
themselves and march in the same direction, triggering a potentially
devastating swarm.
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Gathering Storm
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Aerial Art
Flocks of starlings in Rome, Italy, twist and turn into curious shapes.
The birds are not following leaders as they perform such
manoeuvres, biologists say, but rather acting as a group in which
individual birds constantly change their position.
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Aerial Art
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