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.
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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 some
the lower path. (c) Since ants move at approximately a constant speed, the ants which choose the lower, shorter, path reach the opposite decision point faster
than those which choose the upper, longer, path. (d) Pheromone accumulates at a higher rate on the shorter path. The number of dashed lines is approximately
proportional to the amount of pheromone deposited by ants.
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5. The ACO Algorithm
Cingshui
Cliffs
Taroko
National
Park Chihsingtan
Beach
How to go ??
East
Coast
Hualien Distance: 566
East
Rift
Valley
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6. The ACO Algorithm (cont.)
Initialize the
parameters Begin max uJ ( 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 iterations
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