SLIDE 1
Bio-inspired vs. Classic approaches
- verall throughput
Micha Kaliszan Jakub Suchecki Bio-inspired vs. Classic approaches - - PowerPoint PPT Presentation
Micha Kaliszan Jakub Suchecki Bio-inspired vs. Classic approaches - - PowerPoint PPT Presentation
The bridge experiment applied to network routing Micha Kaliszan Jakub Suchecki Bio-inspired vs. Classic approaches overall throughput reaction to failures non-deterministic vs. deterministic Graph generator Parameters:
SLIDE 2
SLIDE 3
Graph generator
Parameters:
number of nodes length of the shortest path between two
designated nodes
SLIDE 4
Graph generator
Generated graph with
shortest path (blue)
Time savings on large
graph structures
SLIDE 5
Graph generator
Pheromone
distribution (green)
Shortest path (blue)
SLIDE 6
Model of the optimized network
Two nodes chosen, with a known shortest path
between them
Link between the nodes optimized
simultaneously for transmission in both directions
Possibility of generation of an initial, suboptimal
path between the nodes, marked by the initial pheromone distribution
SLIDE 7
Ant-based routing algorithm
Two groups of ants: ‘forward’ and
‘backward’
Each time step new ants sent from the
source node and the destination node
Two groups using different types of
pheromones
SLIDE 8
Ant-based routing algorithm
Each group of ants update differently both
groups of pheromones
‘Backward’ ants update much stronger
pheromones for the ‘forward’ ants, and
- pposite.
SLIDE 9
Parameters of the simulation
new ants sent each time step speed of pheromone evaporation pheromone drop TTL of each ant
SLIDE 10
Simulation results
Parameters:
100 ants per time
step
TTL 50 pheromone drop 0.2 evaporation rate 0.8 200 nodes shortest path: 15
SLIDE 11