ABSTRACT: Swarm Intelligence (SI) is a relatively new paradigm being applied in a host of research settings to improve the management and control of large numbers of interacting entities such as communication, computer and sensor networks, satellite constellations and more. Attempts to take advantage of this paradigm and mimic the behavior of insect swarms however often lead to many different implementations of SI. The rather vague notions of what constitutes self-organized behavior lead to rather ad hoc approaches that make it difficult to ascertain just what SI is, assess its true potential and more fully take advantage of it. This work provides a set of general principles for SI research and development. A precise definition of self-organized behavior is described and provides the basis for a more axiomatic and logical approach to research and development as opposed to the more prevalent ad hoc approach in using SI concepts. The advances and applications of Swarm Intelligence is also dealt with in this work. 1
Swarm Intelligence (SI) is a relatively new paradigm being applied in a host of research settings to improve the management and control of large numbers of interacting entities such as communication, computer and sensor networks, satellite constellations and more. Attempts to take advantage of this paradigm and mimic the behavior of insect swarms however often lead to many different implementations of SI. The rather vague notions of what constitutes self-organized behavior lead to rather ad hoc approaches that make it difficult to ascertain just what SI is, assess its true potential and more fully take advantage of it. This work provides a set of general principles for SI research and development. A precise definition of self-organized behavior is described and provides the basis for a more axiomatic and logical approach to research and development as opposed to the more prevalent ad hoc approach in using SI concepts. The advances and applications of Swarm Intelligence is also dealt with in this work.
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ABSTRACT:
Swarm Intelligence (SI) is a relatively new paradigm being applied in a host of research
settings to improve the management and control of large numbers of interacting entities such
as communication, computer and sensor networks, satellite constellations and more. Attempts
to take advantage of this paradigm and mimic the behavior of insect swarms however often
lead to many different implementations of SI. The rather vague notions of what constitutes self-
organized behavior lead to rather ad hoc approaches that make it difficult to ascertain just what
SI is, assess its true potential and more fully take advantage of it.
This work provides a set of general principles for SI research and development. A precise
definition of self-organized behavior is described and provides the basis for a more axiomatic
and logical approach to research and development as opposed to the more prevalent ad hoc
approach in using SI concepts. The advances and applications of Swarm Intelligence is also dealt
with in this work.
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CHAPTER ONE
INTRODUCTION1.1 Background of the study
Swarm Intelligence (SI) is the property of a system whereby the collective behaviors of agents
interacting locally with their environment cause coherent functional global patterns to emerge. SI
provides a basis with which it is possible to explore distributed problem solving without
centralized control or the provision of a global model. One of the cores tenets of SI work is that
often a decentralized, bottom-up approach to controlling a system is much more effective than
traditional, centralized approach. Groups performing tasks effectively by using only a small set
of rules for individual behaviour is called swarm intelligence. Swarm Intelligence is a property
of systems of non-intelligent agents exhibiting collectively intelligent behaviour. In Swarm
Intelligence, two individuals interact indirectly when one of them modifies the environment and
the other responds to the new environment at a later time. For years scientists have been studying
about insects like ants, bees, termites etc. The most amazing thing about social insect colonies is
that there’s no individual in charge. For example consider the case of ants. But the way social
insects form highways and other amazing structures such as bridges, chains, nests and can
perform complex tasks is very different: they self-organize through direct and indirect
interactions. The characteristics of social insects are (Bonabeau, 1999.)
1. Flexibility
2. Robustness
3. Self-Organization
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1.2 Aim and Objectives of the study
The aim of this study is to highlight the most significant recent developments on the topics of
swarm intelligence.
The objectives of this work are
To highlights the area of applications of swarm intelligence
To highlight benefits /advantages of swarm intelligence
To identify future research directions,
To publicize swarm intelligence algorithms to a wider audience.
1.3 Scope of the Study
Swarm intelligence is a relatively new discipline that deals with the study of self-organizing
processes both in nature and in artificial systems. Researchers in ethology and animal behavior
have proposed many models to explain interesting aspects of social insect behavior such as self-
organization and shape-formation. Recently, algorithms inspired by these models have been
proposed to solve difficult computational problems.
An example of a particularly successful research direction in swarm intelligence is ant colony
optimization, the main focus of which is on discrete optimization problems. Ant colony
optimization has been applied successfully to a large number of difficult discrete optimization
problems. Another interesting approach is that of particle swarm optimization, that focuses on
continuous optimization problems. Here too, a number of successful applications can be found in
the recent literature. Swarm robotics is another relevant field. Here, the focus is on applying
swarm intelligence techniques to the control of large groups of cooperating autonomous robots.
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1.4 Significance of the Study
This study is important to the field of science and engineering as it will improve the applications
the following applications of swarm intelligence;
Particle swarm optimization
Artificial bees and firefly algorithms
Bacterial foraging optimization
Ant colony optimization
Swarm robotics
Artificial immune systems
Hybridization of swarm intelligence methods
Theory and practice of swarm intelligence methods in different domains
Real-world problem solving using swarm intelligence methods
1.5 Definition of terms and abbreviations
Artificial: made or produced by human beings rather than occurring naturally, especially as a
copy of something natural.
Swarm: A large or dense group of flying insects.
Intelligence: The ability to acquire and apply knowledge and skills.
Swarm intelligence: this is the collective behavior of decentralized, self-organized systems,
natural or artificial.
A.C.O: Ant Colony Optimization
CSS: Charged System Search
CHAPTER TWO
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LITERATURE REVIEW
Swarm intelligence is inspired by emergent behaviour in nature, such as bird flock, ant
colony, and fish school. It is first introduced by Beni and Wang in \Swarm Intelligence in
Cellular Robotic System". In the paper, swarm intelligence is described as \systems of non-
intelligent robots exhibiting collectively intelligent behaviour evident in the ability to
unpredictably produce `specific' ([i.e.] not in a statistical sense) ordered patterns of matter in the
external environment" Beni and Wang (1989).
Craig Reynolds' flocking system is one of the influential swarm intelligence example.
Individuals in flocking system are called boids. They have three layers of motion, action
selection, steering and locomotion. The three simple steering behaviours {aggregation,
separation, alignment {can emergent into complicated result that looks like a flock of bird or a
school of _sh. In his paper, other steering behaviours such as obstacle avoidance, seeking,
foraging are also added to the system to enhance the over all realism. Reynolds (1999)
Figure 2.1: Craig Reynold's flocking system Reynolds (1999)
Another well known example of emergent behaviour would be Conway's Game of Life. It is a
cellular automation with initial state specified and rules are set to decide whether the life live or
die. With different initial state given, different pattern generate after certain time with the three
rules. Conway's idea is a simplified implementation of John Von Neumann's attempt to build a
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machine that can build itself, which is also an analogy of the rise, fall and alterations of a society
of living organisms Wikipedia (2010).
2.1.1 Properties of Swarm Intelligence
The typical swarm intelligence system has the following properties:
It is composed of many individuals;
The individuals are relatively homogeneous
The interactions among the individuals are based on simple behavioral rules that exploit
only local information that the individuals exchange directly or via the environment
The overall behaviour of the system results from the interactions of individuals with each
other and with their environment, that is, the group behavior self-organizes.
Modelling Swarm Behaviour
The simplest mathematical models of animal swarms generally represent individual
animals as following three rules:
1. Move in the same direction as your neighbor
2. Remain close to your neighbors
3. Avoid collisions with your neighbors
Many current models use variations on these rules, often implementing them by means of
concentric "zones" around each animal. In the zone of repulsion, very close to the animal, the
focal animal will seek to distance itself from its neighbors to avoid collision. Slightly further
away, in the zone of alignment, the focal animal will seek to align its direction of motion with its
neighbors. In the outermost zone of attraction, which extends as far away from the focal animal
as it is able to sense, the focal animal will seek to move towards a neighbor.
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The shape of these zones will necessarily be affected by the sensory capabilities of the given
animal. For example the visual field of a bird does not extend behind its body. Fish rely on both
vision and on hydrodynamic perceptions relayed through their lateral line, while Antarctic krill
rely both on vision and hydrodynamic signals relayed through antennae. Some of the animals