Swarm Intelligenge_gaurav Agrawal

8/6/2019 Swarm Intelligenge_gaurav Agrawal

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Swarm Intelligence

Gaurav Agrawal

Phd scholar (computer science)

Sharda University


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OUTLINE

Background

What is a Swarm Intelligence (SI)?

Examples from nature

Origins and Inspirations of SI

Ant Colony Optimization

Particle Swarm Optimization

Properties of a Swarm Intelligence System

ADVANTAGES OF SI

SI APPLICATIONS


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swarm behavior

The behaviors of a flock of

birds, a group of ants, a school

of fish, etc.,were the field of

study during earlier days. Such

collective motion of insectsand birds is known to as

swarm behavior.


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WHAT IS A SWARM?

A loosely structured collection of interacting agents

Agents:

Individuals that belong to a group (but are not necessarily

identical)

They contribute to and benefit from the group

They can recognize, communicate, and/or interact with each

other

A swarm is better understood if thought of as agents exhibiting a

collective behavior

A type of self-organization emerges from the collection of actions of thegroup.


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SWARM INTELLIGENCE (SI)

Gerardo Beni and Jing Wang introduced the term swarm intelligence

in a 1989.

Swarm intelligence is the collective intelligence of groups of simple

autonomous agents.

The autonomous agent is a subsystem that interacts

with its environment, relatively independently from all other agents.

There is no global plan or leader to control the entire group of

autonomous agents.

Based on group behavior found in nature


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Swarm intelligence techniques are population

based stochastic methods used in

combinatorial optimization problems

in which the collective behavior of relatively

simple individuals arises from their local

interactions with their environment to produce

functional global patterns.


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EXAMPLES OF SWARMS IN

NATURE:

Classic Example: Swarm ofBees

Can be extended to other similar systems:

Ant colony

Agents: ants

Flock of birds

Agents: birds

Traffic

Agents: cars

CrowdAgents: humans

Immune system

Agents: cells and molecules


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Boid model

In 1987, a boid = Bird-oids (bird like) model was created by Reynold.

This boid is a distributed behavioral model, which is used to simulate

the motion of a flock of birds on a personal computer.

Each boid serves as an independent actor that navigates based on

its own perception of the dynamic environment.

There are a certain set of rules that are to be observed by the boid.

The avoidance rule states that an individual boid must move awayfrom boids that are too close, so as to reduce the chance of in-air

collisions.


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TWO COMMON SI ALGORITHMS

Ant Colony Optimization

Particle Swarm Optimization


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PARTICLE SWARMOPTIMIZATION (PSO)

Particle Swarm Optimization (PSO) was proposed by JamesKennedy and R. C. Eberhart in 1995, inspired by social behavior oforganisms such as bird flocking and fish schooling.

The underlying concept is that, for every time instant, the velocity ofeach particle also known as the potential solution, changes betweenits pbest and lbest locations.

The particle associated with the best solution (fitness value) seemsto be the leader and each particle keeps track of its coordinates inthe problem space.

This fitness value is stored which is referred to as pbest. Another best value that is tracked by the particle swarm optimizer

is the best value, obtained so far by any particle in the neighbors ofthe particle. This location is called lbest.


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when a particle takes all the population as its topological neighbors,

the best value is a global best and is called gbest.

Thus a PSO system combines local search methods

with global search methods


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Ant ColonyOptimization (ACO)

Is a class of algorithms, whose firstmember, called Ant System, was initiallyproposed by Colorni, Dorigo and Maniezzo1992.

In nature, ants usually wander randomly,and upon finding food return to their nestwhile laying down pheromone trails. The

other ants find the path (pheromone trail),and follow the trail, returning and reinforcingit if they eventually find food. Thepheromone starts to evaporate as timepasses. If the time taken for an ant to traveldown the path and back again to the nest,the pheromone evaporates thereby making

the path less prominent. A shorter path, incomparison will be visited by more ants (canbe described as a loop of positive feedback)and thus the pheromone density remainshigh for a longer time.


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An ant runs more or less at random

around the colony;

1. On discovering food, it returns more

or less directly to the nest, leavingin its path a trail of pheromone;

2.These pheromones are attractive,

nearby ants will be inclined to

follow, more or less directly, the

track

3.Returning to the colony, these antswill strengthen the route;

4. If there are two routes to reach the

same food source then, in a given

amount of time, the shorter one will

be traveled by more ants than the

long route;The short route will be increasingly

enhanced, and therefore become

more attractive;

The long route will eventually isappear

because pheromones are volatile;

Eventually, all the ants havedetermined and therefore "chosen"


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ACO is implemented as a collective group of intelligent agents,which simulate the ants behavior, walking around the graphrepresenting the problem to solve using mechanisms of cooperationand adaptation. ACO algorithm requires the following definitions:

The problem needs to be represented appropriately, which would

allow the ants to incrementally update the solutions through the useof a probabilistic transition rules, based on the amount ofpheromone in the trail and other problem specific knowledge. It isalso important to enforce a strategy to construct only valid solutions

corresponding to the problem definition

A problem-dependent heuristic function that measures the qualityof components that can be added to the current partial solution

A rule set for pheromone updating, which specifies how to modify

the pheromone value .

A probabilistic transition rule based on the value of the heuristicfunction and the pheromone value that is used to iteratively

construct a solution


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Properties of a Swarm Intelligence System

Unity:A swarm is a combination of several individuals.

Fault tolerance: Swarm intelligent processes do not rely on acentralized control mechanism. Therefore the loss of a few nodes orlinks does not result in catastrophic failure.

Rule-based behavior:A certain set of rules are observed by the

individuals that exploit only local information that the individualsexchange directly or through the environment.

Autonomy: The overall behavior of the swarm system is selforganized, it does not depend on orders external to the system

itself. No human supervision is required.

Scalability: Population of the agents can be adapted according to

the network size. Scalability is also promoted by local anddistributed agent interactions.


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SI APPLICATIONS

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 fornanobots to fight cancer (Swarm Robotics)

SI techniques are applied to load balancing/ routing in

telecommunication networks

Entertainment industry is applying SI techniques forbattle and crowd scenes


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Thank You

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