By Eric Han, Chung Min Kim, and Kathryn Tarver Investigations of Ant Colony Optimization.
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Transcript of By Eric Han, Chung Min Kim, and Kathryn Tarver Investigations of Ant Colony Optimization.
by Eric Han, Chung Min Kim, and Kathryn Tarver
Investigations of Ant Colony Optimization
An Introduction to Ants
10,000+ species of ants around the world
Eat seeds, nectar, fungi, insects, etc.
Colonies led by queens
How Ants Forage for Food
1. Random walk
2. Pheromone is dropped
3. Food source quality affects pheromone amount
4. More pheromone = favored path
5. Pheromone evaporates
ACO in Action
ACO in Action
ACO in Action
ACO in Action
ACO: Ant Colony Optimization
● First suggested by Marco Dorigo (1992)
● Inspired by foraging ant colonies
● Algorithm sends particles on random walks to optimize
pathways
● Currently applied to problems such as Internet routing
and protein folding
Our goal is to:
1. create an algorithm to find the shortest path between
two points in a network, and
2. explore the effects of changing parameters in the
algorithm.
Project Goal
Pseudocode
for each iteration:
1. run ants
2. add pheromone
3. evaporate pheromone
Objects
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23
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2
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Parameters
Parameter Description
p Rate of pheromone evaporation
q Scalar, proportional to amount of pheromone added to path
colsize Number of ants
Pseudocode: The Antsrun antsfor each ant: while not at end vertex: mark current vertex as visited for all unvisited vertices: roll RNG to see if traveling this vertex if traveling: move to the vertex add vertex to path
Pseudocode: The Edgesadd pheromone: for each ant for each edge along its path add (Q÷L) pheromone to the edge
evaporate pheromone: for each edge multiply pheromone value by (1-p)
Simulation
Observation 1More ants → Less Noise, fewer convergences onto local optimal
Observation 2
Weight scaling → decreases # ants taking optimal path
Observation 3
Increasing pheromone evaporation rate → increases % of ants taking optimal path
Observation 4
Increasing amount of pheromone added → no effect
General Observations● Large, dense graph
o sometimes will find global optimalo usually will converge on local optimal
comes close to the global optimalo need many ants to avoid local optimal
● Small, dense graph o ants almost always find global optimalo don’t need as many ants or iterations to do soo converges more slowly
Conclusions
● To maximize ants taking best path:
o high evaporation rate
o large colony
o smaller path weights
Discussion: What Now?● Dynamic graphs● Eliminate convergences onto local optimal● Optimize running time● Analytically determine effects of changing parameters
BibliographyAnts, Ant Pictures, Ant Facts - National Geographic. (n.d.). Retrieved July 20, 2015.
Argentine Ant l Globe spanning insect society - Our Breathing Planet. (n.d.). Retrieved July 20, 2015.
Blum, C., & Li, X. (2008). Swarm Intelligence in Optimization. Natural Computing Series Swarm Intelligence, 43-85. Retrieved July 20, 2015.
Priyadi, A. Ant fire [Online image]. Retrieved July 20, 2015 from http://yourshot.nationalgeographic.com/photos/3098725/?source=gallery.
Ant clipart [Online image]. (2014). Retrieved July 30, 2015 from ……… http://www.clipartpanda.com/clipart_images/ant-clipart-158500
http://www.pageresource.com/clipart/clipart/animals/insects/ants/ant-3.png
Thank you!