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: 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

Parameters ParameterDescription pRate of pheromone evaporation q Scalar, proportional to amount of pheromone added to path colsizeNumber of ants

Pseudocode: The Ants run ants for 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 Edges add 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 1 More 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 optimal o usually will converge on local optimal  comes close to the global optimal o need many ants to avoid local optimal ●Small, dense graph o ants almost always find global optimal o don’t need as many ants or iterations to do so o 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

Bibliography Ants, Ant Pictures, Ant Facts - National Geographic. (n.d.). Retrieved July 20, Argentine Ant l Globe spanning insect society - Our Breathing Planet. (n.d.). Retrieved July 20, Blum, C., & Li, X. (2008). Swarm Intelligence in Optimization. Natural Computing Series Swarm Intelligence, Retrieved July 20, Priyadi, A. Ant fire [Online image]. Retrieved July 20, 2015 from Ant clipart [Online image]. (2014). Retrieved July 30, 2015 from ……… Thank you!