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Learn Ant Colony Optimization PowerPoint Presentation

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  • Slide 1 - Ant Colony Optimization Theresa Meggie Barker von Haartman IE 516 Spring 2005
  • Slide 2 - Overview
  • Slide 3 - ACO Concept Ants (blind) navigate from nest to food source Shortest path is discovered via pheromone trails each ant moves at random pheromone is deposited on path ants detect lead ant’s path, inclined to follow more pheromone on path increases probability of path being followed
  • Slide 4 - ACO System Virtual “trail” accumulated on path segments Starting node selected at random Path selected at random based on amount of “trail” present on possible paths from starting node higher probability for paths with more “trail” Ant reaches next node, selects next path Continues until reaches starting node Finished “tour” is a solution
  • Slide 5 - ACO System, cont. A completed tour is analyzed for optimality “Trail” amount adjusted to favor better solutions better solutions receive more trail worse solutions receive less trail higher probability of ant selecting path that is part of a better-performing tour New cycle is performed Repeated until most ants select the same tour on every cycle (convergence to solution)
  • Slide 6 - ACO System, cont. Often applied to TSP (Travelling Salesman Problem): shortest path between n nodes Algorithm in Pseudocode: Initialize Trail Do While (Stopping Criteria Not Satisfied) – Cycle Loop Do Until (Each Ant Completes a Tour) – Tour Loop Local Trail Update End Do Analyze Tours Global Trail Update End Do
  • Slide 7 - Background Discrete optimization problems difficult to solve “Soft computing techniques” developed in past ten years: Genetic algorithms (GAs) based on natural selection and genetics Ant Colony Optimization (ACO) modeling ant colony behavior
  • Slide 8 - Background, cont. Developed by Marco Dorigo (Milan, Italy), and others in early 1990s Some common applications: Quadratic assignment problems Scheduling problems Dynamic routing problems in networks Theoretical analysis difficult algorithm is based on a series of random decisions (by artificial ants) probability of decisions changes on each iteration
  • Slide 9 - Implementation
  • Slide 10 - Ant Algorithms
  • Slide 11 - Ant Algorithms
  • Slide 12 - Implementation Can be used for both Static and Dynamic Combinatorial optimization problems Convergence is guaranteed, although the speed is unknown Value Solution
  • Slide 13 - The Algorithm Ant Colony Algorithms are typically use to solve minimum cost problems. We may usually have N nodes and A undirected arcs There are two working modes for the ants: either forwards or backwards. Pheromones are only deposited in backward mode.
  • Slide 14 - The Algorithm The ants memory allows them to retrace the path it has followed while searching for the destination node Before moving backward on their memorized path, they eliminate any loops from it. While moving backwards, the ants leave pheromones on the arcs they traversed.
  • Slide 15 - The Algorithm The ants evaluate the cost of the paths they have traversed. The shorter paths will receive a greater deposit of pheromones. An evaporation rule will be tied with the pheromones, which will reduce the chance for poor quality solutions.
  • Slide 16 - The Algorithm At the beginning of the search process, a constant amount of pheromone is assigned to all arcs. When located at a node i an ant k uses the pheromone trail to compute the probability of choosing j as the next node: where is the neighborhood of ant k when in node i.
  • Slide 17 - The Algorithm When the arc (i,j) is traversed , the pheromone value changes as follows: By using this rule, the probability increases that forthcoming ants will use this arc.
  • Slide 18 - The Algorithm After each ant k has moved to the next node, the pheromones evaporate by the following equation to all the arcs: where is a parameter. An iteration is a completer cycle involving ants’ movement, pheromone evaporation, and pheromone deposit.
  • Slide 19 - Steps for Solving a Problem by ACO Represent the problem in the form of sets of components and transitions, or by a set of weighted graphs, on which ants can build solutions Define the meaning of the pheromone trails Define the heuristic preference for the ant while constructing a solution If possible implement a efficient local search algorithm for the problem to be solved. Choose a specific ACO algorithm and apply to problem being solved Tune the parameter of the ACO algorithm.
  • Slide 20 - Applications Efficiently Solves NP hard Problems Routing TSP (Traveling Salesman Problem) Vehicle Routing Sequential Ordering Assignment QAP (Quadratic Assignment Problem) Graph Coloring Generalized Assignment Frequency Assignment University Course Time Scheduling
  • Slide 21 - Applications Scheduling Job Shop Open Shop Flow Shop Total tardiness (weighted/non-weighted) Project Scheduling Group Shop Subset Multi-Knapsack Max Independent Set Redundancy Allocation Set Covering Weight Constrained Graph Tree partition Arc-weighted L cardinality tree Maximum Clique
  • Slide 22 - Applications Other Shortest Common Sequence Constraint Satisfaction 2D-HP protein folding Bin Packing Machine Learning Classification Rules Bayesian networks Fuzzy systems Network Routing Connection oriented network routing Connection network routing Optical network routing
  • Slide 23 - Advantages and Disadvantages
  • Slide 24 - Advantages and Disadvantages For TSPs (Traveling Salesman Problem), relatively efficient for a small number of nodes, TSPs can be solved by exhaustive search for a large number of nodes, TSPs are very computationally difficult to solve (NP-hard) – exponential time to convergence Performs better against other global optimization techniques for TSP (neural net, genetic algorithms, simulated annealing) Compared to GAs (Genetic Algorithms): retains memory of entire colony instead of previous generation only less affected by poor initial solutions (due to combination of random path selection and colony memory)
  • Slide 25 - Advantages and Disadvantages, cont. Can be used in dynamic applications (adapts to changes such as new distances, etc.) Has been applied to a wide variety of applications As with GAs, good choice for constrained discrete problems (not a gradient-based algorithm)
  • Slide 26 - Advantages and Disadvantages, cont. Theoretical analysis is difficult: Due to sequences of random decisions (not independent) Probability distribution changes by iteration Research is experimental rather than theoretical Convergence is guaranteed, but time to convergence uncertain
  • Slide 27 - Advantages and Disadvantages, cont. Tradeoffs in evaluating convergence: In NP-hard problems, need high-quality solutions quickly – focus is on quality of solutions In dynamic network routing problems, need solutions for changing conditions – focus is on effective evaluation of alternative paths Coding is somewhat complicated, not straightforward Pheromone “trail” additions/deletions, global updates and local updates Large number of different ACO algorithms to exploit different problem characteristics
  • Slide 28 - Sources Dorigo, Marco and Stützle, Thomas. (2004) Ant Colony Optimization, Cambridge, MA: The MIT Press. Dorigo, Marco, Gambardella, Luca M., Middendorf, Martin. (2002) “Guest Editorial,” IEEE Transactions on Evolutionary Computation, 6(4): 317-320. Thompson, Jonathan, “Ant Colony Optimization.” http://www.orsoc.org.uk/region/regional/swords/swords.ppt, accessed April 24, 2005. Camp, Charles V., Bichon, Barron, J. and Stovall, Scott P. (2005) “Design of Steel Frames Using Ant Colony Optimization,” Journal of Structural Engineeering, 131 (3):369-379. Fjalldal, Johann Bragi, “An Introduction to Ant Colony Algorithms.” http://www.informatics.sussex.ac.uk/research/nlp/gazdar/teach/atc/1999/web/johannf/ants.html, accessed April 24, 2005.
  • Slide 29 - Questions?

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