5. • The high number of individuals and the decen-
tralized approach to task coordination means
that
▫ ant colonies show high degrees of parallelism, self-
organization and fault tolerance.
▫ All of which are desired characteristics in modern
computer systems.
6. • The work on ant-based techniques is considered to have
started by the Ant Colony Optimization my Dorigo et. al.,
[6].
• In this work, a group of ant- agents is used to solve the
TSP problem.
• While each ant walks on the graph, it leaves a
pheromone signal through the path it used.
• Shorter paths will leave stronger signals.
• The next ants, when deciding which path to take, tend to
choose paths with stronger signals with a higher
probability, so as shorter paths are found, more ants try
to explore these paths, by a positive reinforcement cycle.
7. • One important characteristic of the ant-inspired
technologies is shown on the work on network
routing by Ant Colony Optimization, ANT NET
[4].
8. stigmergy
• The root of this adaptation is the stigmergic
nature of the ant-system.
• Stigmergy is a central idea of all ant-based
algorithms.
• Here, it happens, as described before, by the
leaving of “pheromone" trails.
9. • The pheromone value of the path that composes
the optimal solution is higher than that of the
non- optimal solutions after the end of the
algorithm.
• If there is any change in the topology, like a
route that fails or a new route, the system can
use the existing values of the pheromone trails to
adapt to the changes while online.
10.
11. • we will call this bi-dimensional grid the
workspace, and the n-dimensional space the
feature space.
12. • The first step in a canonical" ant-clustering
system is to distribute the data (objects)
randomly into the workspace.
• Each object is projected onto one grid of the
workspace
13. • Then a number na of ants is put on random
positions of the workspace.
• It is defined that only one ant and only one
object can be put into one grid of the work space.
• Each ant is also able to carry one data object
with itself.
14. • At each time step, each ant will, if loaded, try to
unload its object onto its current position or, if
unloaded, try to pick an object in the same grid
as itself.
• The probability of picking or dropping an object
is based on the disparity (or distance in feature
space) between that object and other objects in
its neighborhood.
15.
16. • Ant-based clustering usually came first or in a
close second (as shown in the data selected for
this work).
• When observing the run-time, it was observed
that for low dimensionality data, ant-based
clustering would be slightly slower than the
other techniques, but its runtime scales linearly,
so that it becomes the fastest algorithm for high-
dimensionality data.
17. • ant-based clustering techniques are an
appropriate alternative to traditional clustering
algorithms.
• it has the ability of automatically discovering the
number of clusters.
• Also, it linearly scales against the dimensionality
of data.
• It automatically generates a representation of
the formed clusters that can be intuitively
understood by humans.
18. Towards Improving Clustering Ants:
An Adaptive Ant Clustering Algorithm
André L. Vizine, Leandro N. de Castro, Eduardo
R. Hruschka, Ricardo R. Gudwin
2005
19. • Among the many bio-inspired techniques, ant-
based clustering algorithms have received special
attention from the community over the past few
years for two main reasons.
• First, they are particularly suitable to perform
exploratory data analysis and,
• second, they still require much investigation to
improve performance, stability, convergence, and
other key features that would make such
algorithms mature tools for diverse applications.
20. Cooling Schedule for kp
• a cooling schedule for the parameter that drives
the picking probability kp is employed.
• The adopted scheme is simple: after one cycle
(10,000 ant steps) has passed, the value of the
parameter kp starts being geometrically
decreased, at each cycle, until it reaches a
minimal allowed value, kpmin, which
corresponds to the stopping criterion for the
algorithm.
21. • In the current implementation, kp is cooled
based on a geometric scheme presented in Eq.
(4).
22. Progressive Vision
• The definition of a fixed value for s^2 may
sometimes cause inappropriate behaviors,
because a fixed perceptual area does not allow
distinguishing between clusters of different
sizes.
• A small area of vision implies a small perception
of the cluster at a global level.
• Thus, small clusters and large clusters are all the
same in this sense, for the agent only perceives a
limited area of the environment.
23. Progressive Vision
• On the other hand, a large vision field may be
inefficient in the initial iterations, when the data
elements are scattered at random on the grid,
because analyzing a broad area may imply in
analyzing a large number of small clusters
simultaneously.
25. ‘How can an ant agent detect the size of a cluster
so as to control the size of its vision field?’
• There is a relationship between the size of a
cluster and its density dependent function: the
average value of f(i) increases as the clustering
proceeds, and this happens because larger
clusters tend to be formed.
• When f(i) achieves a value greater than a pre-
specified threshold θ, the parameter s2 is
incremented by ns units until it reaches its
maximum value.
26. Pheromone Heuristics
• Sherafat et al. (2004a,b) introduced a pheromone
function, Phe( φmax, φmin,P, φ(i)), given by Eq.
(6), that influences the probability of picking up and
dropping off objects from and on the grid.
• The proposed pheromone function varies linearly
with the pheromone level at each grid position, φ(i),
and depends on a number of userdefined
parameters, such as
the φmax and φmin values of pheromone perceived by
the agent, and
the maximal influence of pheromone allowed, P.
27. Pheromone Heuristics
• To accommodate the addition of pheromone on
the grid, some variations on the picking and
dropping probability functions of SACA were
proposed in (Sherafat et al.,2004a,b), as
described in Eqs. (7) and (8), respectively:
28. Pheromone Heuristics
• where φmax represents the current largest
amount of pheromone perceived by this agent;
• φmin corresponds to the current smallest
amount of pheromone perceived by this agent;
• P is the maximum influence of the pheromone in
changing the probability of picking and dropping
data elements;
• and φ(i) is the quantity of pheromone in the
current position i.
29. • the probability that an ant picks up an item from
the grid is inversely proportional to the amount
of pheromone at that position and also to the
density of objects around i.
30. • The rate at which pheromone evaporates is
preset
32. The effect of using evolutionary
algorithms on Ant Clustering
Techniques
Claus Aranha1 and Hitoshi Iba
33. • Ant-based clustering algorithms can be
considered non-hierarchical, hard,
agglomerative clustering methods.
▫ Non-hierarchical means that there is no parent-
child relationship between the objects or the
clusters formed by the technique.
▫ Hard means that each object is assigned to only
one cluster.
▫ Agglomerative means that the clusters are formed
bottom-up - in other words, isolated objects are
progressively put together to form bigger clusters.
34. • Ramos et al. [15]. applied ant-based clustering to
the classification of stone images.
• In their works, they noticed that the normal LF
algorithm would generate a large quantity of
small clusters, and that many actions were
wasted when the ants moved through empty
space.
• To address this concerns they used pheromones
to guide the ant movement.
35. • Handl et al. [9] changed the ants’ movement
policy so that the ants, after dropping an object,
would “teleport” to the next isolated object, and
pick it automatically.
• In this way, an ant would never give a step while
not carrying an object, which did not add
anything to the clustering effort.
• They also added limited local memory to each
ant, which would give them “hints” to the best
place to drop the carried object.
36. • Hartmann [11] proposes the use of Neural
Networks to replace the pick and drop functions.
37. neighborhood disparity function
• xs is an object within the neighborhood radius of i,
• Md is the maximum distance between any two
objects,
• and St is the total number of objects in the
neighborhood of I
• The neighborhood of an object is given by all the
objects within Manhattan distance sight of the
object
39. Use of Genetic Algorithms
• it is commented that the sensitivity of the many
parameters in ant-clustering is a topic worthy of
study
• To improve the ant clustering algorithm, we’ll
try to optimize its parameters (presented in table
1) by using Genetic algorithms.
40. each individual
• each individual is represented by the set of
configuration parameters in table 1.
• For each generation, we run the program once
with each set of parameters, and take the fitness
from each run.
41. Elite selection
• We use the Elite selection strategy for GA,
where, for each generation, the best elite size
individuals are directly copied into the next
generation, and the remaining individuals of the
population are deleted and replaced by crossover
between this elite.
42. • For the crossover operator, we randomly choose two
parents from the elite, and create a new individual
by choosing one parameter value from each parent
(equal probability for both parents).
• After that we run the mutation operator (with a
probability equal to the mutation parameter for each
individual).
• The mutation operator can either change the value
of one parameter by 10%, or generate a new random
value for that parameter.
43. • The key in a successful application of GA to a
problem is an appropriate choice of the fitness
function.
• One of the strong points of ant clustering is the
ability to auto-detect the number of clusters.
44. To extract the clusters
• To extract the clusters from the workspace, we
define a cluster as a group of objects within 2
units of Manhattan distance from any member
of the group.
• In this way, in figure 2, we can see 2 such
different clusters.
45. • However, the number of clusters alone does not
tell us how good the clustering is, so we must
also account for the quality of the clusters.
• We Average Local Linkage, to measure the
quality of one cluster.
46. Local Linkage
• First, we take the neighborhood disparity
function f(i) to determine the local Linkage of
one object.
• From this value, we calculate the ALL for the
cluster as:
• Where Csize is the number of objects in the
cluster, and each is an object belonging to
the cluster.
47. fitness of one individual
• To calculate the fitness of one individual, then,
we identify the clusters by using the definition in
figure 2,
• and then averaging ALL(C) for all clusters where
Csize > 1.
48. • There is, however, one extra thing that must be
taken care with when calculating the fitness of
ant clustering algorithms.
• As reported in [18], LF does not reach an stable
configuration - since the pick and drop
probabilities are not deterministic, the ants may
pick some pieces from established clusters,
lowering the fitness, just to put them back a few
turns later.
49.
50. • Therefore, if we just pick any one time step, and
measure the fitness at that moment, we can get a
lucky high or low unstable state
• In order to avoid that, after a given turn t, we
start measuring the fitness for the next fit turns,
and take the average fitness of this period as the
individual’s fitness.
51. Experiments
• Running the experiment, we found out that in
fact the evolved solutions could generate a
smaller number of clusters with the passing of
the generations
52.
53. Effects of Inter-agent
Communication in Ant-Based
Clustering Algorithms: A case
Study on Communication Policies
in Swarm Systems
Marco A. Montes de Oca,
Leonardo Garrido, and Jos´e L.
Aguirre
Springer-Verlag Berlin Heidelberg
2005
54. • In natural settings, stigmergy [6] plays a key
role as it provides the means for indirect
communication among insects through the
environment.
• we need to consider the question of whether
agents should/could communicate in other ways
to achieve organization or better solutions to
problems.
• we need to study the effects of letting agents use
different communication policies.
55. pheromone
• In ACO, we can see stigmergy in action whenever an
artificial ant deposits a pheromone trail on a
problem solution space.
• If an artificial ant come across a pheromone trail, it
is attracted to it, very much like termites are
attracted by clusters of soil pellets.
• By means of this indirect communication channel,
ants share knowledge and the pheromone trail is a
“blue print” to build a good solution to the problem
at hand.
56. • The similarity measure used in all the experiments was
the cosine metric
• S is the steepness of the response curve and D serves as
a displacement factor.
• S was fixed to 5 because it provides a similarity value
close to 0 when the cosine measure is minimum,
• when the cosine measure gives a value of −1, and D to 1
because this allows us to better distinguish vectors with
separation angles between 0 and π/2.
57. • All algorithms were tested 30 times with every database
for 1,000,000 simulation cycles.
• We tried with populations of 10 and 30 agents within an
environment of 100 × 100 locations in all the
experiments.
58. Direct Information Exchange
• direct information exchange occurs only when
two or more agents meet at a location on the
grid.
• Hence, the probability of an encounter between
two agents moving randomly raises as the
number of agents is increased
59. Indirect Information Exchange
• agents lay packets which contain information about
data distribution on the environment for others to
pick and use.
• Direct communication among agents in ant-based
clustering has two disadvantages:
• (i) even when the number of exchanges increases,
we cannot expect many of them to happen since the
number of agents must be kept small (for
performance reasons),
• and (ii) many exchanges do not have any effect since
agents walk in a randomly fashion, i.e., two agents
coincide many times, over and over again, before
they follow different trajectories.
60. • So the idea is that if we let agents lay information on
their environment, it could be possible to increase
dramatically the number of exchanges without even
increasing the number of agents.
• Two information laying policies were studied:
▫ a periodic laying policy and
With the periodic laying policy, an agent drops
information packets every given number of simulation
cycles.
▫ an adaptive laying policy.
With the adaptive laying policy, an agent drops
information after it has modified the environment and a
given number of simulation cycles have passed.
61. AN ANT COLONY CLUSTERING
ALGORITHM
BAO-JIANG ZHAO
2007
62. • The algorithm considers R agents, namely
artificial ants, to build solutions.
• An agent starts with an empty solution string S
of length N where each element of string
corresponds to one of the test samples.
• The value assigned to an element of solution
string S represents the cluster number to which
the test sample is assigned in S.
63. • To construct a solution, the agent uses the
pheromone trail information to allocate each
element of string S to an appropriate cluster
label.
• At the start of the algorithm, the pheromone
matrix, τ is initialized to a value τ0.
• The trail value, τij at location (i, j) represents
the pheromone concentration of sample i
associated to the cluster j.
64. • For the problem of separating N samples into K clusters
the pheromone matrix is of size.
• The pheromone trail matrix evolves as we iterate.
• At any iteration level, each one of the agents will develop
such trial solutions using the process of pheromone-
mediated communication with a view to obtain a near-
optimal partition of the given N test samples into K
groups satisfying the defined objective.
• After generating a population of R trial solutions,
crossover operator is performed to further improve
fitness of these solutions.
• The pheromone matrix is then updated depending on
the quality of solutions produced by the agents.
• the above steps are repeated for certain number of
iterations.
65. • the agent select cluster number for each element of
string S by the following way:
is a parameter which determines the relative
influence of the heuristic information.
J is a random variable selected according to the
probability distribution given by
• probability for element i belongs to cluster j:
66.
67. Cluster Analysis Based on
Artificial Immune System and
Ant Algorithm
Chui-Yu Chiu and Chia-Hao Lin
IEEE 2007
68. Immunity-based Ant Clustering
Algorithm(IACA)
• immune system utilizes problem-specific
heuristic to conduct local search and fine-tuning
in the solution space.
• using artificial immune system to fine-tune the
objects between two different clusters is the
most important characteristic of IACA
