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Ant Colony Optimization                         Yung-Chun ChangIntelligent Agent Systems Lab Institute of Information Scie...
Outline Biological Inspiration The ACO Algorithm Application2011/6/13                  2
Biological Inspiration     Swarm intelligence is a relatively new approach to problem solving that takes      inspiration...
Biological Inspiration (cont.)                             (a)                                                 (b)        ...
The ACO Algorithm              Cingshui               Cliffs   Taroko   National    Park                 Chihsingtan      ...
The ACO Algorithm (cont.)                      Initialize the                       parameters                   Begin    ...
Application Travel Salesman Problem Scheduling2011/6/13                   7
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Ant colony optimization

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Ant colony optimization

  1. 1. Ant Colony Optimization Yung-Chun ChangIntelligent Agent Systems Lab Institute of Information Science, Academia Sinica ycchang@twgrid.org
  2. 2. Outline Biological Inspiration The ACO Algorithm Application2011/6/13 2
  3. 3. Biological Inspiration Swarm intelligence is a relatively new approach to problem solving that takes inspiration from the social behaviors of insects and of other animals. In particular, ants have inspired a number of methods and techniques among which the most studied and the most successful is the general purpose optimization technique known as ant colony optimization. The natural metaphor on which ant algorithms are based is that of ant colonies. Real ants are capable of finding the shortest path from a food source to their nest without using visual cues by exploiting pheromone information. While walking, ants deposit pheromone on the ground and follow, in probability, pheromone previously deposited by other ants.2011/6/13 3
  4. 4. Biological Inspiration (cont.) (a) (b) (c) (d)Fig. 2. How real ants find a shortest path. (a) Ants work between nest and food. (b) Ants arrive at a decision point. Some ants choose the upper path and somethe lower path. (c) Since ants move at approximately a constant speed, the ants which choose the lower, shorter, path reach the opposite decision point fasterthan those which choose the upper, longer, path. (d) Pheromone accumulates at a higher rate on the shorter path. The number of dashed lines is approximatelyproportional to the amount of pheromone deposited by ants. 2011/6/13 4
  5. 5. The ACO Algorithm Cingshui Cliffs Taroko National Park Chihsingtan Beach How to go ?? East Coast Hualien Distance: 566East RiftValley 2011/6/13 5
  6. 6. The ACO Algorithm (cont.) Initialize the parameters Begin  max uJ ( r ) {[r , u )]  (r , u )] }, if q  0 (Exploitation Rule) arg ( [ q s k Eq. (1) Lay equal pheromone , S otherwise (Exploration Rule) on each path Each ant k positioned on node r chooses the city s to move by Eqs. (5) and (6)  [r , s )]  (r , s )]  ( [ Ant k visits No  , if s J k ( r ) all cities? p k (r , s)    [r , u )]  ( r , u )]  J ( r ) ( [ Eq. (2) Update Yes  k u local pheromone , 0 otherwise By Eq. (7) All ants K have visited No all cities at this iteration? Update Yes r , s )  (1  ) ( r , s )    (r , s ) (    k Eq. (3) global pheromone by Eq. (8) No Reach the number of iterations N? Yes Stop r , s )  (1  ( r , s )   ( r , s ) ( )  k Eq. (4) Note: K is the total number of the ants N is the number of iterations2011/6/13 6
  7. 7. Application Travel Salesman Problem Scheduling2011/6/13 7
  8. 8. Thanks for your attention

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