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SWARM INTELLIGENCE
A SEMINAR REPORT
BY
ARPIT GANDHI
www.powerpointpresentationon.blogspot.com
ARTIFICIAL INTELLIGENCE
• Artificial intelligence (AI) is the intelligence of
machines and the branch of computer science that
aims to create such machines.
• The central problems of AI include such traits as
reasoning, knowledge, planning, learning,
communication, perception and the ability to move
and manipulate objects.
NEED OF AI
• Environment changes dynamically and cannot be
framed by calculations and algorithms.
• Scientists have proposed many solutions to cope up
with the limitations and exception of environment.
• Insects and birds are successful in surviving for
years and are efficient , flexible and robust.
• They solve many problems like finding food ,
building nest etc.. Hence they are self organized and
optimize their path.
Particle swarm optimisation
• Idea: Used to optimize continuous functions.
• PSO is a population-based search algorithm and is
initialized with a population of random solutions
called particles.
• The particles have the tendency to fly towards the
better and better search area over the course of
search process.
• Function is evaluated at each time step for the
agent’s current position.
• Each agent “remembers” personal/local best value
of the function
ANT COLONY
OPTIMIZATION
• ACO is inspired by the behavior of ant colonies.
• Ant colonies have the ability to find the shortest path
for the food.
• Ants leave a chemical pheromone trail . This
pheromone trial enables them to find shortest path
between their nest and the food sources.
• Ants find the shortest path via an experimental setup
shown below.
Real World Insect
Examples
BEES
WASPS
TERMITES
ANTS
SUMMARY OF INSECTS
• The complexity and sophistication of
Self-organization is carried out with no clear
leader.
• What we learn about social insects can be
applied to the field of Intelligent System Design.
• The modeling of social insects by means of
Self-Organization can help design artificial
distributed problem solving devices. This is also
known as Swarm Intelligent Systems.
From Ants to Algorithms
Swarm intelligence information allows us to
address modeling via:
• Problem solving
• Algorithms
• Real world applications
Traveling Sales Ants
SOCIAL INSECTS
Problem solving benefits include:
• Flexible
• Robust
• Decentralized
• Self-Organized
ROBOTS
• Collective task completion
• No need for overly complex algorithms
• Adaptable to changing environment
The Future?
Medical
Telecommunications
Cleaning Ship
Pipe InspectionHulls
Satellite
Maintenance
Self-Assembling
Robots
Engine
Maintenance
Job Scheduling
Pest Eradication
Data Clustering
Interacting Chips in
Mundane Objects Vehicle Routing