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Ant Colony Optimization

Ryan Ward. Ant Colony Optimization. Overview. Ant Colony Optimization (ACO) uses ants finding food as inspiration for algorithms to find near optimal solutions to computationally intensive problems

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Ant Colony Optimization

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  1. Ryan Ward Ant Colony Optimization

  2. Overview • Ant Colony Optimization (ACO) uses ants finding food as inspiration for algorithms to find near optimal solutions to computationally intensive problems • Has been applied to multiple NP problems such as the Traveling Salesman Problem, Job-Shop Scheduling Problem, and the Quadratic Assignment Problem

  3. Traveling Salesman Problem • Given n nodes on a graph, find the circuit that visits all the nodes with the lowest cost • 2D Euclidean Symmetric

  4. Natural Ant System • Initially, ants explore randomly • Leave behind pheromone when they travel back to colony from food • Pheromone evaporates over time • Ants follow strong pheromone left behind by other ants • Short routes to food have more pheromone (less distance = less evaporation)

  5. Consequences of Natural System • Independent agents communicating to each other by effecting the environment • Agents act independently by exploring solution space early, then together by converging on and exploring good solutions • Continuously updating and changing solution (partial on-line algorithm)

  6. Create n agents, initialize cost and pheromone matrices • While end conditions are not met • agents create circuits, deciding where to go at each step depending on cost and pheromone • update pheromone • Pheromone updating can be done in multiple ways: • All ants add pheromone to their best route • The ant(s) with the best route adds pheromone

  7. Exploration vs. Exploitation • Pheromone vs. known cost • Evaporation • Pheromone added by ants is a function of distance • Pheromone subtracted after move • How ants add pheromone

  8. Comparisons to Other Algorithms • Performs at near the same time and effectiveness as GA, TS, SA • Since it involves agents, can be multi-threaded • Biggest advantage comes when applied to dynamic problems

  9. Dynamic TSP • Set of nodes changes over time • Learned information (pheromone) may become obsolete after changes • Necessary to modify pheromone values after a change in the problem to keep useful pheromone while removing obsolete pheromone • Reset method, distance-based method

  10. Research • Examined reset and distance-based methods for varying frequencies and severities of changes • Reset for low frequency and large changes • Distance-based for high frequency and small changes

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