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by Eric Han, Chung Min Kim, and Kathryn Tarver Investigations of Ant Colony Optimization
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An Introduction to Ants 10,000+ species of ants around the world Eat seeds, nectar, fungi, insects, etc. Colonies led by queens
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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
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ACO in Action
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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
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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
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Pseudocode for each iteration: 1.run ants 2.add pheromone 3.evaporate pheromone
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Objects 0 1 2 3 4 2 2 1 5 3 1
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Parameters ParameterDescription pRate of pheromone evaporation q Scalar, proportional to amount of pheromone added to path colsizeNumber of ants
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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
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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)
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Simulation
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Observation 1 More ants → Less Noise, fewer convergences onto local optimal
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Observation 2 Weight scaling → decreases # ants taking optimal path
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Observation 3 Increasing pheromone evaporation rate → increases % of ants taking optimal path
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Observation 4 Increasing amount of pheromone added → no effect
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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
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Conclusions ●To maximize ants taking best path: o high evaporation rate o large colony o smaller path weights
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Discussion: What Now? ●Dynamic graphs ●Eliminate convergences onto local optimal ●Optimize running time ●Analytically determine effects of changing parameters
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Bibliography Ants, 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!
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