OPTIMIZATION BY ANT

COLONY METHOD

CONTENT

defination of optimization

ACO concept

ACO system

ACO system cont.

ANT foraging

Implementation

Applications

Advantages & Disadvantages

Sources

conclusions

References

What is Optimization?

Procedure to make a system or design as

effective, especially the mathematical

techniques involved. (Meta-Heuristics)

Finding Best Solution

Minimal Cost (Design)

Minimal Error (Parameter Calibration)

Maximal Profit (Management)

Maximal Utility (Economics)

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ACO Concept

Ants (blind) navigate from nest to food source

Shortest path is discovered via pheromone trails

First ant moves at random

pheromone is deposited on path

ants detect lead ant’s path, inclined to follow

more pheromone on path increases probability of path

being followed

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ACO System

Virtual “trail” accumulated on path segments

Starting node selected at random

Path selected at random

based on amount of “trail” present on possible paths

from starting node

higher probability for paths with more “trail”

Ant reaches next node, selects next path

Continues until reaches starting node

Finished “tour” is a solution

6

ACO System, cont.

A completed tour is analyzed for optimality

“Trail” amount adjusted to favor better solutions

better solutions receive more trail

worse solutions receive less trail

higher probability of ant selecting path that is part of a better-performing tour

New cycle is performed

Repeated until most ants select the same tour on every cycle (convergence to solution)

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Implementation

Can be used for both Static and Dynamic

Combinatorial optimization problems

Convergence is guaranteed, although the

speed is unknown

Value

Solution

Existing Nature-Inspired Algorithms

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Applications

Efficiently Solves NP hard Problems

Routing TSP (Traveling Salesman Problem)

Vehicle Routing

Sequential Ordering

Assignment QAP (Quadratic Assignment Problem)

Graph Coloring

Generalized Assignment

Frequency Assignment

University Course Time Scheduling

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Applications

Other Shortest Common Sequence

Constraint Satisfaction

2D-HP protein folding

Bin Packing

Machine Learning Classification Rules

Bayesian networks

Fuzzy systems

Network Routing Connection oriented network routing

Connection network routing

Optical network routing

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Advantages and Disadvantages,

cont.

Can be used in dynamic applications (adapts to

changes such as new distances, etc.)

Has been applied to a wide variety of applications

As with GAs, good choice for constrained discrete

problems (not a gradient-based algorithm)

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Sources

Dorigo, Marco and Stützle, Thomas. (2004) Ant Colony Optimization,Cambridge, MA: The MIT Press.

Dorigo, Marco, Gambardella, Luca M., Middendorf, Martin. (2002) “Guest Editorial,” IEEE Transactions on Evolutionary Computation,6(4): 317-320.

Thompson, Jonathan, “Ant Colony Optimization.” http://www.orsoc.org.uk/region/regional/swords/swords.ppt, accessed April 24, 2005.

Camp, Charles V., Bichon, Barron, J. and Stovall, Scott P. (2005) “Design of Steel Frames Using Ant Colony Optimization,” Journal of Structural Engineeering, 131 (3):369-379.

Fjalldal, Johann Bragi, “An Introduction to Ant Colony Algorithms.” http://www.informatics.sussex.ac.uk/research/nlp/gazdar/teach/atc/1999/web/johannf/ants.html, accessed April 24, 2005.

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Advantages and Disadvantages

For TSPs (Traveling Salesman Problem), relatively efficient

for a small number of nodes, TSPs can be solved by exhaustive search

for a large number of nodes, TSPs are very computationally difficult to solve (NP-hard) – exponential time to convergence

Performs better against other global optimization techniques for TSP (neural net, genetic algorithms, simulated annealing)

Compared to GAs (Genetic Algorithms):

retains memory of entire colony instead of previous generation only

less affected by poor initial solutions (due to combination of random path selection and colony memory)

Estimation and simulation, end

users; field work – tracer studies,

pressure tests, case studies;

contaminant and water security –

detection, source identification,

response; network vulnerability –

security assessments, network

reliability,

CONCLUSION