Ant Optimizationin NetLogo

By: Stephen Johnson

Optimization

Wide spread applicability

Much easier through the use of computers

Very clear results

Computer Optimization

Simulated Annealing

Genetic Algorithms

Taboo Lists Limited to static

scenarios

Ant Optimization

Marco Dorigo in 1992

Simplistic agents Imprinting the

environment Dynamic solution

Why Use NetLogo?

Agent based environment

Easy to use Graphical

solution Appropriate

output

Elements of my Model

Patches - hold pheromone values Walls

Food Source Hive or Ant Hill Ants – Carry food

and read pheromone values

Ant Harvesting 101

Have food? Laying “pheromone

highs” Pheromone

gradients Find the strongest

pheromone Walls and wrapping

Ant Harvesting 102

Found your destination?

Pick up or deposit

Switch modes

Put to the Test

Double bridge experiments Originally performed by

Deneubourg and colleagues (Deneubourg, Aron, Gross, and Pasteel) on real ants

Testing ant optimization and foraging habits

Test 1 – Equal Length

Test 2 – Unequal Length

Test 3 – Appearing Bridges

Pheromone Evaporation

Too slow and you get stuck on food sources

Too fast and you can’t form trails

Must be an optimal level

Testing Conditions

Created a static environment

Tested evaporation rates from 0%-1%

Ants return all food to the nest

Initial Results

0.0%

0.1%

0.2%

0.3%

0.4%

0.5%

0.6%

0.7%

0.8%

0.9%

1.0%

0

250

500

750

1000

1250

1500

1750

2000

2250

2500

2750

3000

3250

Test 1

Row 2

Evaporation Rate

Tim

est

ep

s

Refining My Test

0.0%

0.5%

1.0%

1.5%

2.0%

2.5%

3.0%

3.5%

4.0%

4.5%

5.0%

0

100

200

300

400

500

600

700

800

900

1000

1100

1200

1300

Test 2

Row 4

Evaporation Rate

Tim

est

ep

s

Conclusions

Slow Evaporation Form trails faster

and farther Pocketing

Fast Evaporation Eliminates

pocketing Relies on higher

ant density

The End