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Name: Faizan HyderRoll No: 08040619-098

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Swarm Intelligence

What is Swarm?A large or dense group of insects.

Swarm intelligence (SI) is the collective behaviorof decentralized, self-organized systems, natural orartificial.

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Characteristics of Swarms

Composed of many individuals Individuals are Of the same kind

Local interaction based on simple

rules Self-organization

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Two principles of Swarm Intelligence

Self-organization is based on: activity amplification by positive feedback

activity balancing by negative feedback

multiple interactions

Stigmergy - stimulation by work - is basedon:

Stigmergy is a mechanism of indirect coordinationbetween agents or actions

Work as behavioural response to the environmental

state work that does not depend on specific agents

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Ant Colony Optimization:

A heuristic optimization method forshortest path and other optimizationproblems which borrows ideas frombiological ants.

ACO is implemented as a team ofintelligent agents which simulate theants behavior, walking around the graph

representing the problem to solve usingmechanisms of cooperation andadaptation.

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Ant Colony Optimization - BiologicalInspiration

Inspired by foraging behavior of ants. Ants find shortest path to food source

from nest.

Ants deposit pheromone along traveledpath which is used by other ants to followthe trail.

This kind of indirect communication via the

local environment is called stigmergy.

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Pheromone

Substances secreted by an animal toinfluence the behavior of other animalsof the same species.

Two minutes after a ant has marked a

trail, the pheromone has evaporated toa point that is below detection level.

Only if food is found does the workeremit a pheromone trail and only on theway back to the nest.

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Pheromone(Cont..)

If a worker loses the trail, it travels insmall circles until the trail is eitherfound, or another worker interceptsthe lost ant by antennae touching

and guides it back to the trail

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Foraging behavior of Ants

2 ants start with equal probability ofgoing on either path.

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Foraging behavior of Ants

The ant on shorter path has a shorterto-and-fro time from its nest to thefood.

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Foraging behavior of Ants

The density of pheromone on theshorter path is higher because of 2passes by the ant (as compared to 1by the other).

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Foraging behavior of Ants

The next ant takes the shorter route.

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Foraging behavior of Ants

Over many iterations, more ants beginusing the path with higher pheromone,thereby further reinforcing it.

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Foraging behavior of Ants

After some time, the shorter path isalmost exclusively used.

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ACO algorithm

Main steps of the ACO algorithm aregiven below:

Pheromone trail initialization

Solution construction using pheromone

trail Each ant constructs a complete solution to

the problem according to a probabilistic

State transition rule. The state transition

rule depends mainly on the state of thepheromone .

Pheromone trailupdate.

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Ant Colony Optimization Algorithm

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Choosing Next Node

When being at a node, an ant chooses to go toa unvisited node at time twith a probabilitygiven by

wherejkiis, the setof cities which antkhas not yet visited; i,j(t) is the pheromone value on the edge(i, j) at the time t, is

the weight of pheromone; i,j (t) is a priori available heuristicinformation on the edge (i, j) at the time t, is the weight ofheuristic information. Two parameters and determine therelative influence of pheromone trail and heuristic information.

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Pheromone Update

When all the ants have completed asolution, the trails are updated by

where is the pheromone trailevaporation rate (0

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Advantages

For TSPs (Traveling Salesman Problem), relatively

efficient

Performs better against other global optimizationtechniques for TSP (neural net, geneticalgorithms)

Compared to GAs (Genetic Algorithms):lessaffected by poor initial solutions (due tocombination of random path selection and colonymemory)

Can be used in dynamic applications (adapts tochanges such as new distances, etc.)

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