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ANT COLONY OPTIMIZATION

ANT COLONY OPTIMIZATION. By, Name: Suraj Padhy Roll: S/07/73 Regd.: 0701204232 Branch: CSE. Introduction.

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ANT COLONY OPTIMIZATION

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  1. ANT COLONY OPTIMIZATION By, Name: Suraj Padhy Roll: S/07/73 Regd.: 0701204232 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 • 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. [A] [E] [C] [B] [D] 1 5 3 2 4 Iteration 1 A B C D E

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

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

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

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

  15. ACO Algorithms : An Overview

  16. ACO Algorithms : An Overview cont…

  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

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