Ant Colony Algorithm-presentation

8/4/2019 Ant Colony Algorithm-presentation

1/30

Department of Computer Science & Engineering, RGMCETDepartment of Computer Science & Engineering, RGMCET


2/30

An Ant Colony Algorithm

Optimization

By


3/30

Natural behavior of ants have inspired scientists to mimic insect

operational methods to solve real-life complex problems

By observing ant behavior, scientists have begun to understand

their means of communication

Ant-based behavioral patterns to address combinatorial

problems - first proposed by Marco Dorigo

REAL ANT BEHAVIOR

Ants secrete pheromone while

traveling from the nest to food,

and vice versa in order to

communicate with one another to

find the shortest path


4/30

EXPERIMENTAL STUDY OF ANTS

The more ants follow a trail, the more attractive that trail

becomes for being followed

NEST FOOD

NEST FOOD

NEST FOOD


5/30

ANT Behavior

The more ants follow a trail, the more attractive that trail

becomes for being followed


6/30

ANT Behavior

Even when the tracks are equal the behavior will encourage one

over the other--convergence (Deneubourg et al)


7/30

ROUTE SELECTION

Ants are forced to decide whether they should go left or right, and

the choice that is made is a random decision

Pheromone accumulation is faster on the shorter path

The difference in pheromone content between the two paths overtime makes the ants choose the shorter path

Positive feedback mechanism to arrive at the shortest route while

foraging

Stygmergy or stigmergetic model of communication

Different optimization problems have been explored using a

simulation of this real ant behavior


8/30

REAL vs. ARTIFICIAL ANTS

Discrete time steps

Memory Allocation Quality of Solution

Time of Pheromone

deposition

Distance Estimation

REAL ANT ARTIFICIAL ANT


9/30

Department of Computer Science &Engineerig, RGMCET

THE ANT COLONY

OPTIMIZATIONMETAHEURISTIC


10/30

GOAL OF ACO HEURISTIC

Artificial ants form a multi-agent system performing the

functions as observed in the real ant system

Exploit stigmergistic communication

The ACO meta-heuristic relies on the co-operation of a

group of artificial ants to obtain a good solution to a

discrete optimization problem such as the TSP

Artificial ants are mutants of a real ant system

The resulting shortest route mapping determined by the agents

can be applied to the optimization problem


11/30

ACO CHARACTERISTICS

Exploit a positive feedback mechanism

Demonstrate a distributed computational architecture

Exploit a global data structure that changes dynamically as

each ant transverses the route

Has an element of distributed computation to it involving the

population of ants

Involves probabilistic transitions among states or rather

between nodes


12/30


FLOWCHART OF ACO

Have all

cities been

visited

Have the

maximum

Iterations been

performed

STARTACO

Locate ants randomly

in cities across the

grid and store the

current city

in a tabu list

Determine probabilistically

as to which city to visit next

Move to next city and

place this city in the

tabu list

Record the length of

tour and cleartabu list

Determine the shortest

tour till now and

update pheromone

NO

YES

STOP

ACO

YESNO


13/30


KEY PARAMETERS

Trail intensity is given by value ofXij which indicates the intensity of

the pheromone on the trail segment, (ij)

Trail visibility is Lij = 1/dij

The importance of the intensity in the probabilistic transition is E

The importance of the visibility of the trail segment is F

The trail persistence or evaporation rate is given as V

Q is a constant and the amount of peromone laid on a trail

segment employed by an Ant; this amount may be modified invarious manners


14/30


PROBABILISTIC CITY SELECTION

Helps determine the city to visit next while the ant is in a tour

Determined by variables such as the pheromone content in an

edge (i,j) at time instant t, heuristic function of the desirability of

adding edge, and their control parameters

? A ? A? A ? A

)(

)(

0

)(

)(

)(

)(iJjf

iJjift

t

tp

k

kilil

ijij

k

ij

ikJl

!

FE

FE

LX

LX


15/30


PHEROMONE UPDATING

Using the tour length for the k-th Ant, Lk, the quantity of pheromone

added to each edge belonging to the completed tour is given by

tTjiedgeiftTjiedgewhere

L

Q

tk

k

k

k

ij

(

),(

),(

0

X

)()()1()1( ttt ijijij XXVX (

The pheromone decay in each edge of a tour is given by


16/30


Ant Colony Optimization

An adaptative nature inspired algorithmexplaination

Concretely implementation


17/30


The ants

The double bridge experiment

From biological ants to agents

Java Implementation Demonstration 1

The different moves of the ants Demonstration 2

Adaptation of the Ants-based algorithm to routing protocols

ACO compared to RIP and OSPF Examples of effective implementations

Results of the analysed reports

Questions

Plan


18/30


The ants

Can explore vast areas without global view of the

ground.

Can find the food and bring it back to the nest.

Will converge to the shortest path.


19/30


How can they manage such great

tasks?

By leaving pheromones behind them.

Wherever they go, they let pheromones behind here, marking

the area as explored and communicating to the other ants thatthe way is known stigmergisticcommunication.

Double Bridge experiment


20/30


From biological ants to ant-agent

Distributed process:

local decision-taking

Autonomous

Simultaneous

Macroscopic development from microscopic probabilistic

decisions

Problem: adaptation to reality


21/30


From biological ants to ant-agent

Solution:

Pheromone upgrade: evaporation.

Ant aging: after a given time, ants are tired and have to

come back to the nest.

2 different pheromones : away (from nest) and back (fromsource of food).


22/30


Java Implementation

Object modeling:

Definition of the objects:

Ant

Playground

Traces

Playground: central object, contains a list of ants, an arrayof traces. Manages the processes and the graphical output.

Ant: can move by itself, according to the traces around itand a random decision.

Traces: amount of pheromones of 2 types, Away and Back.


23/30


Demonstration 1

2-Bridge Experiment

Interesting Convergence


24/30


Double Bridge experiment

Food


25/30

Department of Computer Science &

Engineerig, RGMCET

Possible moves of Ants

Four types:

From home to food

Goal has never been reached: moveStraightAwayFromAway();

Goal reached: moveTowardAway();

Back to home

Goal has never been reached: moveFromFoodToHome();

Goal reached: moveFromHomeToFood();

Idea: generates several random moves and see which one is the best among

them.


26/30


Engineerig, RGMCET

Demonstration 2

A difficult playground


27/30


Engineerig, RGMCET

Adaptation of the Ants-based

algorithm to routing protocols

E

D

B

A

F

CNest

Food

Ants will start from A the nest and look for D the food. At every step, theywill upgrade the routing tables and as soon as the first one reaches the food,the best path will be known, thus allowing communication from D to A.


28/30


Engineerig, RGMCET

ACO Compared to RIP and OSPF RIP / OSPF:

Transmit routing table or flood LSPs at regular interval

High routing overhead

Update the entire table

Based on transmission time / delay

ACO algorithm:

Can be attached to data

Frequent transmissions of ants

Low routing overhead

Update an entry in a pheromone table independently


29/30


Engineerig, RGMCET

Questions ?


30/30


Engineerig, RGMCET

Thank you !

Ant Colony Algorithm-presentation

Documents

Transcript of Ant Colony Algorithm-presentation