1. ANT COLONY OPTIMIZATION
By,
Name: Suraj Padhy
Roll: S/07/73
Regd.:
Branch: CSE

2. Introduction
In the next generation of wireless communication systems, there will be need of networks that can establish themselves without any requirement of preexisting infrastructure.
Mobile Ad-Hoc Networks (MANETS). Mobile implies that the interconnecting nodes are not succumbed to be remain at one place, rather they can move from one place to the other. Ad-Hoc implies that the network does not depend on any preexisting infrastructure such as routers.
One of the most important performance parameter in ad- hoc networks is minimizing the total transmission energy in the path and extending the battery life of the nodes.

3. Introduction cont…
Conventional Routing algorithms were developed for that such as AODV [1], DSR[2] and TORA[3] .
These protocols generally focus on finding the shortest path available from source node to the destination node.
There exists a protocol Minimum Transmission Power Routing (MTPR) [4] which tries to minimize the total transmission power And the whole concept of ANT COLONY OPTIMIZATION is to minimize the path and power consumption.

4. General Tendency Of 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.

5. Ant Moves
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.

6. Applications Scheduling Telecommunication Network Graph Coloring
Traveling Salesman Problem
Quadratic Assignment Problem
Network Model Problem
Vehicle routing
Scheduling
Telecommunication Network
Graph Coloring
Water Distribution Network
etc . . .

7. Traveling Salesman Problem
TSP PROBLEM : Given N cities, and a distance function d between cities, find a tour that:
1. Goes through every city once and only once
2. Minimizes the total distance.
Problem is NP-hard
Classical combinatorial
optimization problem to
test.

8. ACO for Traveling Salesman Problem
The TSP is a very important problem in the context of Ant Colony Optimization because it is the problem to which the original AS was first applied, and it has later often been used as a benchmark to test a new idea and algorithmic variants.
The TSP was chosen for many reasons:
It is a problem to which the ant colony metaphor
It is one of the most studied NP-hard problems in the combinatorial optimization
it is very easily to explain. So that the algorithm behavior is not obscured by too many technicalities.

9. Algorithm for TSP Initialize Place each ant in a randomly chosen city
For Each Ant
Choose NextCity(For Each Ant)
yes
more cities
to visit No
Return to the initial cities
Update pheromone level using the tour cost for each ant No
Stopping
criteria
yes
Print Best tour

10. Iteration 1 2
[B] 1
[A] A B 3
[C] C 5
[E] 4
[D] D E

11. Iteration 2 5
[E,A] 3
[C,B] A B 2
[B,C] C 1
[A,D] 4
[D,E] D E

12. Iteration 3 A B C D E [D,E,A] [E,A,B] [A,D,C] [B,C,D] [C,B,E] 4 5 1 2

13. Iteration 4 A B C D E [B,C,D,A] [D,E,A,B] [E,A,B,C] [C,B,E,D] [A,DCE]2
[B,C,D,A] 4
[D,E,A,B] A B 5
[E,A,B,C] C 3
[C,B,E,D] 1
[A,DCE] D E

14. Iteration 5 A B C D E [A,D,C,E,B] [C,B,E,D,A] [D,E,A,B,C] [E,A,B,C,D]1
[A,D,C,E,B] 3
[C,B,E,D,A] A B 4
[D,E,A,B,C] C 5
[E,A,B,C,D] 2
[B,C,D,A,E] D E

15. ACO Algorithms : An Overview
Problem name
Authors
Algorithm name
Year
Traveling salesman
Dorigo, Maniezzo & Colorni AS
1991
Gamberdella & Dorigo
Ant-Q
1995
Dorigo & Gamberdella
ACS &ACS 3 opt
1996
Stutzle & Hoos
MMAS
1997
Bullnheimer, Hartl & Strauss
ASrank
Cordon, et al.
BWAS
2000
Quadratic assignment
Maniezzo, Colorni & Dorigo
AS-QAP
1994
Gamberdella, Taillard & Dorigo
HAS-QAP
MMAS-QAP
1998
Maniezzo
ANTS-QAP
1999
Maniezzo & Colorni
Scheduling problems
Colorni, Dorigo & Maniezzo
AS-JSP
Stutzle
AS-SMTTP
Barker et al
ACS-SMTTP
den Besten, Stutzle & Dorigo
ACS-SMTWTP
Merkle, Middenderf & Schmeck
ACO-RCPS
Vehicle routing
AS-VRP
Gamberdella, Taillard & Agazzi
HAS-VRP

16. ACO Algorithms : An Overview cont…
Problem name
Authors
Algorithm name
Year
Connection-oriented
Schoonderwood et al.
ABC
1996
network routing
White, Pagurek & Oppacher
ASGA
1998
Di Caro & Dorigo
AntNet-FS
Bonabeau et al.
ABC-smart ants
Connection-less
AntNet & AntNet-FA
1997
Subramanian, Druschel & Chen
Regular ants
Heusse et al.
CAF
van der Put & Rethkrantz
ABC-backward
Sequential ordering
Gamberdella& Dorigo
HAS-SOP
Graph coloring
Costa & Hertz
ANTCOL
Shortest common supersequence
Michel & Middendorf
AS_SCS
Frequency assignment
Maniezzo & Carbonaro
ANTS-FAP
Generalized assignment
Ramalhinho Lourenco & Serra
MMAS-GAP
Multiple knapsack
Leguizamon & Michalewicz
AS-MKP
1999
Optical networks routing
Navarro Varela & Sinclair
ACO-VWP
Redundancy allocation
Liang & Smith
ACO-RAP
Constraint satisfaction
Solnon
Ant-P-solver
2000

17. Advantages
Positive Feedback accounts for rapid discovery of good solutions
Distributed computation avoids premature convergence
The greedy heuristic helps find acceptable solution in the early solution in the early stages of the search process.
The collective interaction of a population of agents.

18. Disadvantages Slower convergence than other Heuristics
Performed poorly for TSP problems larger than 75 cities.
No centralized processor to guide the AS towards good solutions

19. Conclusion
ACO is a recently proposed metaheuristic approach for solving hard combinatorial optimization problems.
Artificial ants implement a randomized construction heuristic which makes probabilistic decisions.
The a cumulated search experience is taken into account by the adaptation of the pheromone trail.
ACO Shows great performance with the “ill-structured” problems like network routing.
In ACO Local search is extremely important to obtain good results.

20. Thank You