www.ijecs.in
International Journal Of Engineering And Computer Science ISSN:2319-7242
Volume 2. Issue 10 October 2013 Page No. 3015-3018
Swarm Intelligence and Knowledge Acquisition
1
Nivedita Joshi,2Pooja Singh
1
Student, B.Tech (IT), Dronacharya College of Engineering, Maharishi Dayanand University
Gurgaon, Haryana,India
Nivedita.joshi92@yahoo.co.in
2
Student, B.Tech (IT), Dronacharya College of Engineering, Maharishi Dayanand University
Gurgaon, Haryana,India
Poojasingh.8dec@gmail.com
ABSTRACT
Computing is rapidly moving away from traditional computers. Programs in the future will run on collections of mobile processors that interact
with the physical world and communicate over ad hoc networks. We can view such collections as swarms. As with natural swarms, such as a
beehive or ant colony, the behavior of a computational swarm emerges from the behaviors of its individual members. This research paper
focuses on swarm techniques for creating, understanding and validating properties of programs that execute on swarms of computing
devices.The paper strives on identifying and understanding about swarm practices in principled ways and investigates the technique based on
both experimental and analytical approaches.
Keywords: Self-Organization, Stigmergy, Data Mining, Ant Colonization, Particle Swarm Organization (PSO).
1. Introduction
Swarm intelligence is a collection of decentralized and selforganized, artificial or natural agents forming an intelligent
network.Gerardo Beni and Jing Wang first introduced the
term “SWARM INTELLIGENCE” in 1989 in the context of
cellular robotic systems.These agents,artificial or natural,are
interacting locally with each other as well as their
surrounding environment without a centralized plan.
The inspiration comes from nature such as ants
foraging,bees, and social insects.These agents follow very
simple protocols without involving a detailed structure and
still each individual agent is aware of behaving and
performing intelligently.The interactions occurring between
the agents lead to an intelligent global behavior unknown to
individual agents.
Examples of natural agents of SI include ant colonies, bird
flocking, animal herding, bacterial growth, and fish
schooling.
‘Self-organization is a set of dynamical mechanisms
whereby structures appear at the global level of a system
from interactions of its lower-level components.’
(Bonabeau et al, in Swarm Intelligence, 1999)
2.2.1 Ant Foraging
A colony of ants is able to forage to the nearest and most
promising food source. The other foragers of the same
colony are recruited by pheromone trails. A successful ant
returns to the nest leaving behind a "smell" on the ground.
This smell guides the movements of other foragers. When
the latter foragers also find a food source at the end of the
pheromone trail they reinforce the earlier trail by leaving
behind new pheromone marks.
Thus a network of pheromone trail is constructed in a selforganized manner.So the foragers are able to adapt to
different food sources,thus avoiding over-recruitment. The
features of such resulting distribution are behavior
updating,adaptive patterns and trail following.The models of
adaptive foraging form the basis for algorithms that can be
applied to routing in communication networks.
2. Principal Mechanism of Swarm Intelligence
2.2 Self-Organization
1
These algorithms are applied to problems like the Travelling
Salesman
and
related
network-based
problems.
Nivedita Joshi, IJECS Volume 2. Issue 10 October 2013 Page No.3015-3018
Page 3015
Reinforcement of edges that are rated as good is combined
with a mechanism for pheromone decay leading an optimal
solution.
Fig. 1 Pheromone Trail
Fig. 2 Nest Building
Features of self-organization:
2.2.2 Division of Labour
1. Multiple interactions
A colony of ant adjusts to its allocated task. Even if a large
partition of specialized workers are artificially removed, it
still maintains its functions.This type of adaptation occurs
without any evaluation of the global situation, any direct
communication or centralized control.Such models are
based on self-organization and deals with response
thresholds.Every ant in the colony is able to make out the
need for fulfillment of some task such as storing the
incoming food, brood care etc.
If this spur is strong enough, the ant has a higher chance of
getting involved in the task associated with the
spur(stimulus).The ant adapts its threshold for the task it
completes successfully. The strengthening of such task,
which the ant has done often, leads to specification in a
colony of identical workers. Such model can be used for
task allocation mechanism in an artificial multi-agent
system.
2. Strengthening activities by positive feedback
3. Balancing activities by negative feedback
4. Strengthening random variations
2.3 Stigmergy
The term Stigmergy is derived from the Greek word stigma
(sign) + ergon (work). It is the indirect interaction between
agents and captures the notion that an agent’s action leave
signs in the environment, which the other agents sense that
stimulate their subsequent actions.In this way the
subsequent actions of the agents tend to reinforce, leading to
modification of the environment. The agents are extremely
simple, lacks any memory or intelligence and are unaware
of each other.
2.2.3 Cemetery Organization
In ant colonies, distributed objects are loaded and stored in
clusters, such as collecting corpses or larval sorting, without
any central plan as where to load the items.A simple agent
based model explains this phenomenon. The agent carrying
an object drops it when it finds similar objects and picks up
an item when the surrounding objects are of different kind.
Such models can be used for data analysis or graph
partitioning in a two-dimensional space or implemented in
swarm of robots moving around and picking pucks.
2.2.4 Nest Building
The social insects construct complex nest architectures
without any global representation. This can be explained by
the
framework of micro-rules such as the configuration
of bricks for example surrounding a simulated wasp
indicating that whether agent should deposit a new brick
or move on. Such rules can be used to examine random
space filling activities (eggs are likely to be layered in the
neighborhood of brood)and to determine the behavior of
single entity(random movement of queen over the combs).
1
Fig. 3 Mechanism
2.3.1 Termite Mound
Initially termites wander around randomly carrying mud and
depositing it at random places. The deposits are created in a
haphazard manner, which tends to stimulate the other insects
to add more mud in the same place. The small heaps grow
into large complex columns that eventually come together to
form intricate cathedral of arches. That is the traces left in
the environment by an agent or action incites the
performance of a next action. The partially executed work of
the ones provides information to the others about where to
make their own contribution.
Features of Stigmergy:
Nivedita Joshi, IJECS Volume 2. Issue 10 October 2013 Page No.3015-3018
Page 3016
1.
Planning: the agent needs to know the current state
of their activity; the next step or result is irrelevant
for their present work.
2.
Memory: the agents lack any memory or
intelligence; no information about the last state of
the work needs to be stored except in the medium.
3.
Communication: there is no need for the agents to
know who does what that is no information needs
to be transferred between the agents.
4.
Mutual awareness: each agent works independently
5.
Simultaneous presence: there is no need for the
agents to be present at the same time or at the same
place; tasks are registered in the medium.
6.
Imposed sequence: actions are not started until
right conditions are in place. Workflow emerges
spontaneously. Completion of one task triggers the
initiation of further tasks.
7.
Commitment: agents do not need to commit to a
particular task. When an agent becomes
unavailable or quits, another one automatically
replaces it.
8.
Centralized control: there is no centralized control;
global
organization
emerges
from local
interactions.
3. Knowledge Discovery
Data mining, also known as information discovery,
knowledge extraction; is a process of extraction of
information from a data set and transforming it into an
understandable structure for further use.
Steps in knowledge discovery:
1. Understanding the application data and classifying the
goals of KDD process.
2. Creating target data set
3. Data preprocessing by performing operations such as
removal of noise, identifying and handling missing data
fields etc.
4. Using data reduction and transformation methods to
reduce the effective number of variables and finding useful
features for representing the data.
5. Achieving the goals of knowledge discovery through data
mining methods like clustering, summation, classification
etc.
Fig. 4 Steps in Knowledge Discovery
Swarm intelligence based methods such as Ant Colony
Optimization and Particle Swarm Optimization (PSO) can
be used for discovering patterns in large data sets. Such
methods can be used in KDD(Knowledge Discovery in
Databases) process. Deneubourg et al first introduced the
ant colony based clustering algorithms by imitating the
naturally occurring phenomena. In ant colonies, the items
are loaded in heaps such as clusters of corpses and
cemeteries without any central plan. The basis of this
mechanism is the attraction between dead items mediated by
the ant workers. This positive & auto- catalytic feedback
leads to formation of larger clusters.
Lumer and Faieta proposed an algorithm based on a
response threshold function. The behavior of ants associated
with loading and unloading of objects use a combination of
different thresholds. The general idea of data clustering is
that isolated objects should be picked up and dropped at a
location where more such items are present.
Abraham and Ramos proposed an algorithm to discover
Web usage patterns. Web usage mining has become very
important for effective
web
site management,
personalization, business and support services, network
traffic flow analysis etc.
Particle Swarm Optimization (PSO) method can be used for
recognizing patterns and image processing. PSO is a
population based search method that optimizes a problem by
iteratively improving a candidate solution. In PSO each
particle is associated with velocity.
The particles are moved in search space according to some
mathematical formulae until it is guided towards the bestknown positions in the search space. Particle swarm
intelligence combined with classical optimization methods
can be used for Visual data mining. Visual data mining
becomes difficult when applied to massive data sets due to
limits of both screen resolution, human visual system
resolution as well as the limits of available computational
resources.
4. Conclusion
1
Nivedita Joshi, IJECS Volume 2. Issue 10 October 2013 Page No.3015-3018
Page 3017
Swarm intelligence is a computational intelligence
technique to solve complex real-world problems. It involves
the study of collective behavior of individuals in a
population who interact locally with one another and with
their environment in a decentralized control system.
Through this research paper we introduced the basic concept
of swarm intelligence: self-organization where the agents
perform tasks without any centralized plan; Stigmergy
where the agent’s (unaware of each other) actionleave signs
in the environment, which the other agents sense and incite
their subsequent actions. . We then described the knowledge
discovery terminologies and also illustrated the ongoing
works of swarm intelligence in knowledge discovery with
an emphasis on ant colony optimization and particle swarm
optimization.
References
[1] Bernstein, Jeremy. "Project Swarm"
technology inspired by swarms in nature.
[2]Karaboga, Dervis (2010)
algorithm" Scholarpedia 5.
"Artificial
Report on
bee
colony
[3] Engelbrecht, Andries “Fundamentals of Computational
Swarm Intelligence”.
[4]Miller, Peter (July 2007), "Swarm Theory", National
Geographic Magazine
[5]C Grosan, Abraham, M Chis - Swarm Intelligence in
Data Mining, 2006 - Springer
[6]Marsh, L. &Onof, C. (2007) "Stigmergic epistemology,
stigmergic cognition."
[7]Ant Colony Optimization by M acroDoriga and Thomas
Stutzle, MIT Press
[8]Shell, D., &Mataric, M. (2003). On the use of the term
“Stigmergy.” In Proceedings of the second international
workshop on the mathematics and algorithms of social
insects
1
Nivedita Joshi, IJECS Volume 2. Issue 10 October 2013 Page No.3015-3018
Page 3018