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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