SWARM INTELLIGENCE

A Technical Seminar Report submitted to the Faculty of Computer Science and Engineering

Geethanjali College of Engineering & Technology

(Cheeryal (V), Keesara(M), R.R. Dist., Hyderabad-A.P.)

Accredited by NBA

(Affiliated to J.N.T.U.H, Approved by AICTE, New Delhi)

In partial fulfillment of the requirement for the award of degree of

BACHELOR OF TECHNOLOGY IN

COMPUTER SCIENCE AND ENGINEERING

Under the esteemed guidance of

Mr. P. Srinivas, M.Tech, (Ph.D)Sr. Associate Professor

By

G.RAHUL

09R11A0549

Department of Computer Science & Engineering

Year: 2012-2013

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Geethanjali College of Engineering &

Technology

(Affiliated to J.N.T.U.H, Approved by AICTE, NEW DELHI.)

Accredited by NBA

DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING

Date:

CERTIFICATE

This is to Certify that the Technical Seminar report on “Swarm Intelligence ” is a bonafide work done by G.Rahul (09R11A0549) in partial fulfillment of the requirement of the award for the degree of Bachelor of Technology in “Computer Science and Engineering” J.N.T.U.H, Hyderabad during the year 2012 - 2013.

Technical Seminar Co-Ordinator HOD-CSE (Mr. P. Srinivas) (Prof. Dr. P.V.S. Srinivas) Sr. Associate Professor

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ABSTRACT

Swarm intelligence (SI) is the collective behaviour of decentralized, self-

organized systems, natural or artificial. The concept is employed in work on

artificial intelligence. The expression was introduced by Gerardo Beni and Jing

Wang in 1989, in the context of cellular robotic systems.

SI systems are typically made up of a population of simple agents or

boids interacting locally with one another and with their environment. The agents

follow very Simple rules, and although there is no centralized control

Structure dictating how individual agents should behave, local, and to a

certain degree random, interactions between such agents lead to the emergence

of "intelligent" global behavior, unknown to the individual agents. Natural

examples of SI include ant colonies, bird flocking, animal herding, bacterial

growth, and fish schooling. The application of swarm principles to robots is

called swarm robotics, while ‘Swarm Intelligence’ refers to the more general set

of algorithms. 'Swarm prediction' has been used in the context of forecasting

problems.

Swarm describes behaviour of an aggregate of animals of similar size and body

orientation, often moving en masse or migrating in the same direction.

Swarming is a general term that can be applied to any animal that swarms. The

term is applied particularly to insects, but can also be applied to birds, fish,

various microorganisms such as bacteria, and people.

The term flocking is usually used to refer to swarming behaviour in birds, while

the terms shoaling or schooling are used to refer to swarming behaviour in fish.

The swarm size is a major parameter of a swarm.

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Contents

Chapters Page No.

1. Introduction

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2. Properties of Swarm Intelligence

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3. Modelling Swarm behaviour

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4. Algorithms of Swarm Intelligence

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5. Applications of Swarm Intelligence

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6. Advanatages & Disadvantages of Swarm Intelligence

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7. Conclusion

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8. Future Scope

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9. List of Abbreviations

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10. References

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Chapter 1

INTRODUCTION

Swarm Intelligence is the property of a system whereby the collective behaviours

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 no 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

1. Flexibility

2. Robustness

3. Self-Organization

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Chapter 2

PROPERTIES OF SWARM INTELLIGENCE

The typical swarm intelligence system has the following properties:

it is composed of many individuals;

the individuals are relatively homogeneous (i.e., they are either all identical or

they belong to a few typologies);

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 (stigmergy);

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.

The characterizing property of a swarm intelligence system is its ability to act in a

coordinated way without the presence of a coordinator or of an external controller.

Many examples can be observed in nature of swarms that perform some collective

behavior without any individual controlling the group, or being aware of the overall

group behavior. Notwithstanding the lack of individuals in charge of the group, the

swarm as a whole can show an intelligent behavior. This is the result of the

interaction of spatially neighboring individuals that act on the basis of simple rules.

Most often, the behavior of each individual of the swarm is described in

probabilistic terms: Each individual has a stochastic behavior that depends on his

local perception of the neighborhood.

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Because of the above properties, it is possible to design swarm intelligence system

that are scalable, parallel, and fault tolerant.

Scalability means that a system can maintain its function while increasing its

size without the need to redefine the way its parts interact.

Because in a swarm intelligence system interactions involve only neighboring

individuals, the number of interactions tends not to grow with the overall number of

individuals in the swarm: each individual's behavior is only loosely influenced by

the swarm dimension. In artificial systems, scalability is interesting because a

scalable system can increase its performance by simply increasing its size, without

the need for any reprogramming.

Parallel action is possible in swarm intelligence systems because individuals

composing the swarm can perform different actions in different places at the same

time. In artificial systems, parallel action is desirable because it can help to make

the system more flexible, that is, capable to self-organize in teams that take care

simultaneously of different aspects of a complex task.

Fault tolerance is an inherent property of swarm intelligence systems due to the

decentralized, self-organized nature of their control structures. Because the system

is composed of many interchangeable individuals and none of them is in charge of

controlling the overall system behavior, a failing individual can be easily dismissed

and substituted by another one that is fully functioning.

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Chapter 3

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 neighbour

2. Remain close to your neighbours

3. Avoid collisions with your neighbours

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

neighbours to avoid collision. Slightly further away, in the zone of alignment,

the focal animal will seek to align its direction of motion with its neighbours.

In the outermost zone of attraction, this extends as far away from the focal animal

as it is able to sense, the focal animal will seek to move towards a neighbour.

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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 that

exhibit swarm behaviour are

1. Insects – Ants, bees, locusts, termites, mosquitoes and insects migration.

2. Bacteria

3. Birds

4. Land animals

5. Aquatic animals – fish, krill and other aquatic animals

6. People

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Chapter 4

ALGORITHMS OF SWARM INTELLIGENCE

Algorithms of Swarm Intelligence are

Ant colony optimization(ACO)

River formation dynamics

Particle swarm optimization(PSO)

Stochastic diffusion search

Gravitational search algorithm(GSA)

Intelligent Water Drops

Charged System Search

Backtracking optimization Search Algorithm(BSA)

Bat Algorithm

Differential Search Algorithm

Firefly Algorithm

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Glowworm Swarm Optimization

Krill Herd Algorithm

Magnetic Optimization Algorithm

Self-propelled Particles

I. Ant Colony Optimization

Ant colony optimization (ACO) is a class of optimization algorithms modeled on

the actions of an ant colony. ACO methods are useful in problems that need to

find paths to goals. Artificial 'ants'—Stimulation agents—locate optimal solutions

by moving through a parameter space representing all possible solutions. Real

ants lay down pheromones directing each other to resources while exploring their

environment. The Stimulated 'ants' similarly record their positions and the quality

of their solutions, so that in later Stimulation iterations more ants locate better

solutions. One variation on this approach is the bees algorithm, which is more

analogous to the foraging patterns of the honey bee.

In other words we can say that , the ant colony optimization algorithm

(ACO) is a probabilistic technique for solving computational problems which

can be reduced to finding good paths through graphs. In the real world, ants

wander randomly, and upon finding food return to their colony while laying down

pheromone trails. If other ants find such a path, they are likely not to keep

travelling at random, but to instead follow the trail, returning and reinforcing it if

they eventually find food through that way.

This algorithm is inspired by forgiving behaviour of the ants.

1. The first ant finds the food source (F), via any way (a), then returns to the

nest (N), leaving behind a trail pheromone (b)

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2. Ants indiscriminately follow four possible ways, but the Strengthening of

the runway makes it more attractive as the shortest route.

3. Ants take the shortest route, long portions of other ways lose

their trail pheromones.

FIG: Ant Colony Optimization

In a series of experiments on a colony of ants with a choice between two unequal

length paths leading to a source of food, biologists have observed that ants

tended to use the shortest route. A model explaining this behaviour is as follows:

1. An ant (called "blitz") runs more or less at random around the colony;

2. If it discovers a food source, it returns more or less directly to the nest, leaving

in its path

3. A trail of pheromone;

4. These pheromones are attractive, nearby ants will be inclined to follow, more

or less

5. Directly, the track;

6. Returning to the colony, these ants will strengthen the route;

7. If there are two routes to reach the same food source then, in a given amount

of time,

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8. The shorter one will be travelled by more ants than the long route;

9. The short route will be increasingly enhanced, and therefore become more

10. attractive;

11. The long route will eventually disappear because pheromones are volatile;

12. Eventually, all the ants have determined and therefore "chosen" the shortest

route.

Pseudo code of ACO

1: repeat

2: if antCount < maxAnts then

3: create a new ant

4: set initial State

5: end if

6: for all ants do

7: determine all feasible neighbour States {considering the ant's visited

States}

8: if solution found V no feasible neighbour State then

9: kill ant

10: if we use delayed pheromone update then

11: evaluate solution

12: deposit pheromone on all used edges

13: end if

14: else

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15: Stochastically select a feasible neighbour State {directed by the ants

memory,

The pheromone concentration on the edges and local heuristics}

16: if we use Step-by-Step pheromone update then

17: deposit pheromone on the used edge

18: end if

19: end if

20: end for

21: evaporate pheromone until termination criterion satisfied {e.g., found a

Satisfying solution}

II. River Formation Dynamics

River formation dynamics (RFD) is an heuristic method Similar to ant

colony optimization (ACO). In fact, RFD can be seen as a gradient version of ACO,

based on copying how water forms rivers by eroding the ground and

depositing sediments. As water transforms the environment, altitudes of places

are dynamically modified, and decreasing gradients are constructed. The

gradients are followed by subsequent drops to create new gradients, reinforcing

the best ones. By doing so, good solutions are given in the form of decreasing

altitudes. This method has been applied to solve different NP-complete

problems (for example, the problems of finding a minimum distances tree and

finding a minimum spanning tree in a variable-cost graph). The gradient

orientation of RFD makes it especially suitable for solving these problems and

provides a good tradeoff between finding good results and not spending

much computational time. In fact, RFD fits particularly well for problems

consisting in forming a kind of covering tree.

III. Particle Swarm Optimization

Particle swarm optimization (PSO) is a population based Stochastic

optimization technique developed by Dr. Eberhart and Dr. Kennedy in 1995,

inspired by social behavior of bird flocking or fish schooling. Particle

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swarm optimization (PSO) is a global optimization algorithm for dealing

with problems in which a best solution can be represented as a point or

surface in an n-dimensional space. Hypotheses are plotted in this space and

seeded with an initial velocity, as well as a communication channel between the

particles. Particles then move through the solution space, and are evaluated

according to some fitness criterion after each time Step. Over time, particles are

accelerated towards those particles within their communication grouping which

have better fitness values. The main advantage of such an approach over other

global minimization Strategies such as Stimulated annealing is that the large

numbers of members that make up the particle swarm make the technique

impressively resilient to the problem of local minima.

PSO shares many Simi lar i t ies with evolutionary computation techniques such

as Genetic Algorithms (GA). The system is initialized with a population of random

solutions and searches for optima by updating generations. However, unlike

GA, PSO has no evolution operators such as crossover and mutation. In PSO, the

potential solutions, called particles, fly through the problem space by following the

current optimum particles.

Ex. Birds flocking

FIG: PARTICLE SWARM OPTIMIZATION

Algorithm of PSO

As Stated before, PSO stimulates the behaviors of bird flocking. Suppose the

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following scenario: a group of birds are randomly searching food in an area. There

is only one piece of food in the area being searched. All the birds do not know

where the food is. But they know how far the food is in each iteration. So what's

the best Strategy to find the food? The effective one is to follow the bird which is

nearest to the food.

PSO learned from the scenario and used it to solve the optimization problems. In

PSO, each Single solution is a "bird" in the search space. We call it "particle". All

of particles have fitness values which are evaluated by the fitness function

to be optimized, and have velocities which direct the flying of the particles.

The particles fly through the problem space by following the current optimum

particles.

PSO is initialized with a group of random particles (solutions) and then searches for

optima by updating generations. In every iteration, each particle is updated by

following two "best" values. The first one is the best solution (fitness) it has

achieved so far. (The fitness value is also stored.) This value is called pbest.

Another "best" value that is tracked by the particle swarm optimizer is the best

value, obtained so far by any particle in the population. This best value is a

global best and called best. When a particle takes part of the population as its

topological neighbors, the best value is a local best and is called lbest.

After finding the two best values, the particle updates its velocity and

positions with following equation (a) and (b).

v[] = v[] + c1 * rand() * (pbest[] - present[]) + c2 * rand() * (gbest[] -

present[])----------(a)

present [] = present[] + v[] ----------------------------------------------------- (b)

Where:-

v[] is the particle velocity,16

present [] is the current particle (solution).

pbest [] and gbest[] are defined as Stated before. i.e. personal best and global best

respv. rand () is a random number between (0,1).

c1, c2 are learning factors. Usually c1 = c2 = 2.

The pseudo code of the procedure is as follows

For each particle

Initialize particle

END

Do

For each particle

Calculate fitness value

If the fitness value is better than the best fitness value (pBeST) in history

Set current value as the new pBeST

End

Choose the particle with the best fitness value of all the particles as the gbest

For each particle

Calculate particle velocity according equation (a) Update particle position

according equation (b)

End

While maximum iterations or minimum error criteria is not attained

Particles' velocities on each dimension are clamped to a maximum velocity Vmax.

If the sum of accelerations would cause the velocity on that dimension to exceed

Vmax, which is a parameter specified by the user. Then the velocity on that

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dimension is limited to Vmax.

IV. Stochastic Diffusion Search

Stochastic diffusion search (SDS) is an agent-based probabilistic global

search and optimization technique best suited to problems where the

objective function can be decomposed into multiple independent partial-

functions. Each agent maintains a hypothesis which is iteratively tested by

evaluating a randomly selected partial objective function parameterized by the

agent's current hypothesis. In the standard version of SDS such partial function

evaluations are binary, resulting in each agent becoming active or inactive.

Information on hypotheses is diffused across the population via inter-agent

communication. Unlike the stigmergic communication used in ACO, in

SDS agents communicate hypotheses via a one-to-one communication

strategy analogous to the tandem running procedure observed in some

species of ant. A positive feedback mechanism ensures that, over time, a

population of agents Stabilize around the global-best solution. SDS is both an

efficient and robust search and optimization algorithm, which has been

extensively mathematically described.

Or in simple words we can say that It belongs to a family of swarm intelligence

and naturally inspired search and optimization algorithms which includes

ant colony optimization, particle swarm optimization and genetic algorithms.

It is an agent-based probabilistic global search and optimization technique best

suited to problems where the objective function can be decomposed into multiple

independent partial-functions. Each agent maintains a hypothesis which is

iteratively tested by evaluating a randomly selected partial objective function

parameterized by the agent's current hypothesis.

V. Gravitational Search Algorithm

Gravitational search algorithm (GSA) is constructed based on the law of Gravity

and the notion of mass interactions. The GSA algorithm uses the theory of

Newtonian physics and its searcher agents are the collection of masses. In GSA,

we have an isolated system of masses. Using the gravitational force, every mass

in the system can see the situation of other masses. The gravitational force is

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therefore a way of transferring information between different masses. In GSA,

agents are considered as objects and their performance is measured by their

masses. All these objects attract each other by a gravity force, and this force

causes a movement of all objects globally towards the objects with heavier masses.

The heavy masses correspond to good solutions of the problem. The position of the

agent corresponds to a solution of the problem, and its mass is determined using a

fitness function. By lapse of time, masses are attracted by the heaviest mass. We

hope that this mass would present an optimum solution in the search space. The

GSA could be considered as an isolated system of masses. It is like a small artificial

world of masses obeying the Newtonian laws of gravitation and motion. A multi-

objective variant of GSA, called Non-dominated Sorting Gravitational Search

Algorithm (NSGSA), was proposed by Nobahari and Nikusokhan in 2011.

VI. Intelligent Water Drops

Intelligent Water Drops algorithm (IWD) is a swarm-based nature-inspired

optimization algorithm, which has been inspired from natural rivers and how

they find almost optimal paths to their destination. These near optimal or optimal

paths follow from actions and reactions occurring among the water drops and the

water drops with their riverbeds. In the IWD algorithm, several artificial water

drops cooperate to change their environment in such a way that the optimal path

is revealed as the one with the lowest soil on its links. The solutions are

incrementally constructed by the IWD algorithm. Consequently, the IWD

algorithm is generally a constructive population-based optimization algorithm.

VII. Charged System Search

Charged System Search (CSS) is a new optimization algorithm based on some

principles from physics and mechanics. CSS utilizes the governing laws of

Coulomb and Gauss from electrostatics and the Newtonian laws of mechanics. CSS

is a multi-agent approach in which each agent is a Charged Particle (CP). CPs can

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affect each other based on their fitness values and their separation distances. The

quantity of the resultant force is determined by using the electrostatics laws and

the quality of the movement is determined using Newtonian mechanics laws. CSS

is applicable to all optimization fields; especially it is suitable for non- smooth or

non-convex domains. This algorithm provides a good balance between the

exploration and the exploitation paradigms of the algorithm which can

considerably improve the efficiency of the algorithm and therefore the CSS also

can be considered as a good global and local optimizer simultaneously.

VIII. Backtracking optimization Search Algorithm

Backtracking Optimization Search Algorithm (BSA), a new evolutionary algorithm

(EA) for solving real-valued numerical optimization problems. EAs are popular

stochastic search algorithms that are widely used to solve non-linear, non-

differentiable and complex numerical optimization problems. Current research aims

at mitigating the effects of problems that are frequently encountered in EAs, such

as excessive sensitivity to control parameters, premature convergence and slow

computation. In this vein, development of BSA was motivated by studies that

attempt to develop simpler and more effective search algorithms. Unlike many

search algorithms, BSA has a single control parameter. Moreover, BSA’s problem-

solving performance is not over sensitive to the initial value of this parameter. BSA

has a simple structure that is effective, fast and capable of solving multimodal

problems and that enables it to easily adapt to different numerical optimization

problems. BSA’s strategy for generating a trial population includes two new

crossover and mutation operators. BSA’s strategies for generating trial populations

and controlling the amplitude of the search-direction matrix and search-space

boundaries give it very powerful exploration and exploitation capabilities. In

particular, BSA possesses a memory in which it stores a population from a

randomly chosen previous generation for use in generating the search-direction

matrix. Thus, BSA’s memory allows it to take advantage of experiences gained from

previous generations when it generates a trial preparation. This paper uses the

Wilcoxon Signed-Rank Test to statistically compare BSA’s effectiveness in solving

numerical optimization problems with the performances of six widely used EA

algorithms: PSO, CMAES, ABC, JDE, CLPSO and SADE. The comparison, which uses

75 boundary-constrained benchmark problems and three constrained real-world

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benchmark problems, shows that in general, BSA can solve the benchmark

problems more successfully than the comparison algorithms.

IX. Differential search algorithm

Differential search algorithm (DSA) has been inspired by migration of super

organisms. DSA is population based, single/multi objective optimization algorithm

utilizing the concept of Brownian like motion. The problem solving success of DSA

was compared to the successes of ABC, JDE, JADE, SADE, EPSDE, GSA, PSO2011

and CMA-ES algorithms for solution of numerical optimization problems in 2012.

X. Firefly algorithm

The Firefly algorithm (FA) is a metaheuristic algorithm, inspired by the flashing

behaviour of fireflies. The primary purpose for a firefly's flash is to act as a signal

system to attract other fireflies. Xin-She Yang formulated this firefly algorithm by

assuming:

All fireflies are unisexual, so that one firefly will be attracted to all other fireflies;

Attractiveness is proportional to their brightness, and for any two fireflies, the

less brighter one will be attracted by (and thus move to) the brighter one; however,

the brightness can decrease as their distance increases;

If there are no fireflies brighter than a given firefly, it will move randomly.

The brightness should be associated with the objective function.

Firefly algorithm is a nature-inspired metaheuristic optimization algorithm.

XI. Glowworm swarm optimization

Glowworm swarm optimization (GSO), introduced by Krishnan and Ghose in 2005

for simultaneous computation of multiple optima of multimodal functions. The

algorithm shares a few features with some better known algorithms, such as ant

colony optimization and particle swarm optimization, but with several significant

differences. The agents in GSO are thought of as glowworms that carry a

luminescence quantity called luciferin along with them. The glowworms encode the

fitness of their current locations, evaluated using the objective function, into a

luciferin value that they broadcast to their neighbors. The glowworm identifies its

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neighbors and computes its movements by exploiting an adaptive neighborhood,

which is bounded above by its sensor range. Each glowworm selects, using a

probabilistic mechanism, a neighbor that has a luciferin value higher than its own

and moves toward it. These movements—based only on local information and

selective neighbor interactions—enable the swarm of glowworms to partition into

disjoint subgroups that converge on multiple optima of a given multimodal function.

XII. Krill herd algorithm

Krill herd (KH) is a novel biologically inspired algorithm proposed by Gandomi and

Alavi in 2012. The KH algorithm is based on simulating the herding behavior

of krill individuals. The minimum distances of each individual krill from food and

from highest density of the herd are considered as the objective function for the

krill movement.

The time-dependent position of the krill individuals is formulated by three main

factors:

movement induced by the presence of other individuals;

foraging activity; and

random diffusion.

The derivative information is not necessary in the KH algorithm because it uses a

stochastic random search instead of a gradient search. For

each metaheuristic algorithm, it is important to tune its related parameters. One of

interesting parts of the proposed algorithm is that it carefully simulates the krill

behavior and it uses the real world empirical studies to obtain the coefficients.

Because of this fact, only time interval should be fine-tuned in the KH algorithm.

This can be considered as a remarkable advantage of the proposed algorithm in

comparison with other nature-inspired algorithms. The validation phases indicate

that the KH method is very encouraging for its future application to optimization

tasks.

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XIII. Magnetic optimization algorithm

Magnetic Optimization Algorithm (MOA), proposed by Tayarani in 2008, is an

optimization algorithm inspired by the interaction among some magnetic particles

with different masses. In this algorithm, the possible solutions are some particles

with different masses and different magnetic fields. Based on the fitness of the

particles, the mass and the magnetic field of each particle is determined, thus the

better particles are more massive objects with stronger magnetic fields. The

particles in the population apply attractive forces to each other and so move in the

search space. Since the better solutions have greater mass and magnetic field, the

inferior particles tend to move toward the fitter solutions and thus migrate to area

around the better local optima, where they wander in search of better solutions.

XIV. Self-Propelled Particles

Self-propelled particles (SPP), also referred to as the Vicsek model, was introduced

in 1995 by Vicsek et al. as a special case of the boids model introduced in 1986

by Reynolds. A swarm is modeled in SPP by a collection of particles that move with

a constant speed but respond to a random perturbation by adopting at each time

increment the average direction of motion of the other particles in their local

neighbourhood. SPP models predict that swarming animals share certain properties

at the group level, regardless of the type of animals in the swarm. Swarming

systems give rise to emergent behaviours which occur at many different scales,

some of which are turning out to be both universal and robust. It has become a

challenge in theoretical physics to find minimal statistical models that capture

these behaviours.

The SPP model is based on a collection of points or particles, each functioning

individually as an autonomous agent, and each following the same simple rules

which govern their behaviour. The particles move in a plane with constant speed

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but in different directions. The direction of each particle is updated using a "nearest

neighbor rule", a local rule which replaces the direction of each particle with the

average of the particle's own direction plus the directions of its immediate

neighbours.

Simulations demonstrate that a suitable "nearest neighbour rule" eventually results

in all the particles swarming together, or moving in the same direction. This

emerges, even though there is no centralized coordination, and even though the

neighbours for each particle constantly change over time.

Although more realistic swarming models have been explored, the SPP model

remains important because of its simplicity and the strength and the variety of its

emergent phenomena. The SPP model is an agent-based model based on

a Lagrangian viewpoint, which follows individual particles rather than working with

the density of the swarm. It is a discrete switched linear system which is stable,

even though no common quadratic Lyapunov function exists. It is an analogue of

the Ising model in ferromagnetism, where temperature corresponds to particle

randomness and spin clusters correspond to particle clusters.

SPP models have been applied in areas, such as marching locusts, bird landings ,

schooling fish, robotic swarms, molecular motors, the development of human

stampedes and the evolution of human trails in urban green spaces.

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FIG : Flocks of birds , make an unanimous group decision to land

Chapter 5

APPLICATIONS OF SWARM INTELLIGENCE

Swarm Intelligence-based techniques can be used in a number of applications.

The U.S. military i s investigating s w a r m t e c h n i q u e s f o r c o n t r o l l i n g

unmanned v e h i c l e s . The European Space Agency is thinking about an

orbital swarm for self assembly and interferometer. NASA is investigating the

use of swarm intelligence for planetary mapping. A 1992 paper by M. Anthony

Lewis and George A. Bekey discusses the possibility of using swarm intelligence

to control nanobots within the body for the purpose of killing cancer tumors! Here

are some of the applications of Swarm intelligence.

a. Crowd Simulation

Artists are using swarm intelligence as a means of creating complex interactive

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systems or simulating crowds.

Stanley and Stella in: Breaking the Ice was the first movie to make use of swarm

intelligence for rendering, realistically depicting the movements of groups of fish

and birds using the Boids system. Tim Burton's Batman Returns also made

use of swarm technology for showing the movements of a group of bats. The

Lord of the Rings film trilogy made use of similar technology, known as

Massive, during battle scenes. Swarm technology is particularly attractive

because it is cheap, robust, and simple.

Airlines have used swarm theory to Simulate passengers boarding a plane.

Southwest Airlines researcher Douglas A. Lawson used an ant-based computer

Simulation employing only six interaction rules to evaluate boarding times using

various boarding methods.

FIG: Crowd Simulation in Maya

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b. Ant-Based Routing

The use of Swarm Intelligence in Telecommunication Networks has also

been researched, in the form of Ant Based Routing. This was pioneered separately

by Dorigo et al. and Hewlett Packard in the mid-1990s, with a number of

variations since. Basically this uses a probabilistic routing table

rewarding/reinforcing the route successfully traversed by each "ant" (a small

control packet) which flood the network. Reinforcement of the route in the

forwards, reverse direction and both Simultaneously have been researched:

backwards reinforcement requires a symmetric network and couples the two

directions together; forwards reinforcement rewards a route before the outcome

is known (but then you pay for the cinema before you know how good the film is).

As the system behaves stochastically and is therefore lacking repeatability, there

are large hurdles to commercial deployment. Mobile media and new

technologies have the potential to change the threshold for collective action

due to swarm intelligence.

Airlines have also used ant-based routing in assigning aircraft arrivals to airport

gates. At Southwest Airlines software program uses swarm theory, or swarm

intelligence -- the idea that a colony of ants works better than one alone. Each

pilot acts like an ant searching for the best airport gate. "The pilot learns from his

experience what the best is for him, and it turns out that that's the best solution for

the airline," Dr. Douglas A. Lawson explains. As a result, the "colony" of pilots

always go to gates they can arrive and depart quickly. The program can even alert

a pilot of plane back-ups before they happen. "We can anticipate that it's going to

happen, so we'll have a gate available," Dr. Lawson says.

27

FIG: Swarm Intelligence used in Airlines

c. Clustering Behavior Of Ants

Ants build cemeteries by collecting dead bodies into a single place in the nest.

They also organize the spatial disposition of larvae into clusters with the younger,

smaller larvae in the cluster center and the older ones at its periphery.

This clustering behavior has motivated a number of scientific studies.

28

FIG: Clustering Behaviour of Ants

d. Nest Building Behaviour of Wasps and Termites

Wasps build nests with a highly complex internal Structure that is well beyond

the cognitive capabilities of a Single wasp. Termites build nests whose dimensions

are enormous when compared to a Single individual, which can measure as little

as a few millimeters. Scientists have been studying the coordination mechanisms

that allow the construction of these Structures and have proposed probabilistic

models exploiting insects behavior. Some of these models are implemented in

computer programs to produce Simulated Structures that recall the morphology of

the real nests.

29

FIG: Nest building behaviour of Wasps and Termites

e. Flocking and Schooling In Birds and Fish

Scientists have shown that these elegant swarm-level behaviors can be

understood as the result of a self-organized process where no leader is in charge

and each individual bases i t s movement dec i s ions solely on l oca l l y

ava i l ab le i n fo rmat i on ; the d i s tance , perceived speed, and direction of

movement of neighbours. These Studies have inspired a number of computer

Simulations that are now used in the computer graphics industry for the realistic

reproduction of flocking in movies and computer games.

30

FIG: Flock of Birds

FIG: Flocking Simulation

f. Ant Colony Optimization

31

In ant colony optimization (ACO), a set of software agents called "artificial ants"

search for good solutions to a given optimization problem transformed into the

problem of finding the minimum cost path on a weighted graph. The artificial

ants incrementally build solutions by moving on the graph. The solution

construction process is Stochastic and is biased by a pheromone model, that

is, a set of parameters associated with graph components the values of which

are modified at runtime by the ants.

FIG: Ant Colony Optimization

g. Particle Swarm Optimization

It is inspired by social behaviors in flocks of birds and schools of fish. In practice, in

the initialization phase each particle is given a random initial position and an

initial velocity. The position of the particle represents a solution of the problem

and has therefore a value, given by the objective function. At each iteration of the

algorithm, each particle moves with a velocity that is a weighted sum of

three components: the old velocity, a velocity component that drives the

32

particle towards the location in the search space where it previously found

the best solution so far, and a velocity component that drives the particle towards

the location in the search space where the neighbor particles found the

best solution so far.

FIG: Graph based on Particle Swarm Optimization

h. Swarm Based Network Management

Schoonderwoerd et al. proposed Ant-based Control (ABC), an algorithm for routing

and load balancing in circuit-switched networks; Di Caro and Dorigo

proposed AntNet, an algorithm for routing in packet-switched networks. While

ABC was a proof of- concept, AntNet, which is an ACO algorithm, was compared

to many State-of-the-art algorithms and its performance was found to be

competitive especially in situation of highly dynamic and stochastic data traffic as

can be observed in Internet-like networks. An extension of AntNet has been

successfully applied to ad-hoc networks.

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FIG: Network Management using Swarm Intelligence

i. Cooperative Behaviour in Swarms of Robots

There are a number of swarm behaviours observed in natural systems that have

inspired innovative ways of solving problems by using swarms of robots. This is

what is called swarm robotics. In other words, swarm robotics is the application of

swarm intelligence principles to the control of swarms of robots. As with swarm

intelligence systems in general, swarm robotics systems can have either a

scientific or an engineering flavour. Clustering in a swarm of robots was mentioned

above as an example of artificial/scientific system.

34

FIG: Swarm Robotics

FIG: Swarm Robot

j. Swarmic art

35

In a series of works al-Rifaie et al[50] have successfully used two swarm

intelligence algorithms – one mimicking the behaviour of one species of ants

(Leptothorax acervorum) foraging (Stochastic diffusion search (SDS)) and the other

algorithm mimicking the behaviour of birds flocking (Particle swarm

optimization PSO) – to describe a novel integration strategy exploiting the local

search properties of the PSO with global SDS behaviour. The resulting hybrid

algorithm is used to sketch novel drawings of an input image, exploiting an artistic

tension between the local behaviour of the ‘birds flocking’ - as they seek to follow

the input sketch - and the global behaviour of the ‘ants foraging’ - as they seek to

encourage the flock to explore novel regions of the canvas. The 'creativity’ of this

hybrid swarm system has been analyzed under the philosophical light of the

‘rhizome’ in the context of Deleuze’s well known ‘Orchid and Wasp’ metaphor.

36

Chapter 6

ADVANTAGES & DISADVANTAGES OF SWARM INTELLIGENCE

Advantages of Swarm Intelligence

It is easily adoptable as conventional workgroups devise various

Standard operating procedures to react to predetermined Stimuli. But swarms

have better ability to adjust to new Situations or to change beyond a narrow

range of options.

Evolution is the result of adaptation. Conventional bureaucratic systems can

shift the locus of adaptation (slowly) from one part of the system to another. In

swarm systems, individual variation and imperfection lead to perpetual

novelty, which leads to evolution Resilient.

A swarm is a collective system made up of multitudes in parallel, which

results in enormous redundancy. Because the swarm is highly adaptable

and evolves quickly, failures tend to be minimal.

Disadvantages of Swarm Intelligence

37

It is non-optimal and uncontrollable as it is very difficult to exercise control

over a swarm. Swarm systems require guidance in the way that a shepherd

drives a herd by applying force at crucial leverage points.

It is unpredictable as the complexity of a swarm system leads to

unforeseeable results. Emergent novelty i s a primary characteristic of self-

organization by adaptive systems.

Non-understandable – Sequential systems are understandable; complex

adaptive systems, instead, are a jumble of intersecting logic. Instead of A

causing B, which in turn causes C, A indirectly causes everything, and everything

indirectly causes A.

It is non-immediate as linear systems tend to be very direct. Flip a switch and

the light comes on. Simple collective systems tend to operate simply. But

complex swarm systems with rich hierarchies take time.

CONCLUSION

38

The idea of swarm behavior may still seem Strange because we are used to

relatively linear bureaucratic models. In fact, this kind of behaviour characterizes

natural systems ranging from flocks of birds to schools of fish. Humans are more

complex than ants or fish and have lots more capacity for novel behavior, some

unexpected results are likely, and for this reason, leading scientists and

organizations will further pursue swarm approaches. Swarm Intelligence provides

a distributive approach to the problem solving mimicking the very Simple natural

process of cooperation. According to my survey many solutions that had been

previously solved using other Artificial Intelligence (AI) approach like genetic

algorithm neural network are also solve able by this approach also. Due to its

Simple architecture and adaptive nature like Ant Colony Optimization (ACO) has it

is more likely to be seen much more in the future.

39

FUTURE SCOPE

In the future, Swarm Intelligence will be an important tool for researchers and

engineers interested in solving certain classes of complex problems. To build the

foundations of this discipline and to develop an appropriate methodology, we

should proceed in parallel both at an abstract level and by tackling a number of

challenging problems in selected research domains. The research domains that

have chosen are optimization, robotics, networks and data mining, Pipe Inspection,

Miniaturization, Telecommunications, Medical, Self assembling Robots, Engine

maintenance, cleaning Ship hulls, Satellite maintenance, Pest eradication.

LIST OF ABBREVATIONS

40

SI – Swarm Intelligence

ST - Swarm Technology

ACO - Ant colony optimization

PSO - Particle swarm optimization

GSA - Gravitational search algorithm

BSA - Backtracking optimization Search Algorithm

SPP - Self-propelled Particles

REFERENCES

41

1. http://en.wikipedia.org/wiki/Swarm_intelligence

2. Beni, G., Wang, J. Swarm Intelligence in Cellular Robotic Systems, Proceed.

NATO Advanced Workshop on Robots and Biological Systems, Tuscany, Italy, June

26–30 (1989).

3.  Altruism helps swarming robots fly bettergenevalunch.com, 4 May 2011.

4.    Ant Colony Optimization by Marco Dorigo and Thomas Stützle, MIT Press,

2004. ISBN 0-262-04219-3

5.  Karaboga, Dervis (2010). "Artificial bee colony algorithm".Scholarpedia 5 (3):

6915.

6.  Civicioglu, Pinar (2013). "Artificial cooperative search algorithm for numerical

optimization problems". Information Sciences 229: 58–76.

7.  Civicioglu, P. (2013). "Backtracking Search Optimization Algorithm for numerical

optimization problems". Applied Mathematics and Computation 219: 8121–8144.

8. oglich, M.; Maschwitz, U.; Holldobler, B., Tandem Calling: A New Kind of Signal in

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9.  Nasuto, S.J., Bishop, J.M. & Lauria, S., Time complexity analysis of the Stochastic

Diffusion Search, Proc. Neural Computation '98, pp. 260-266, Vienna, Austria,

(1998).

10.  Nasuto, S.J., & Bishop, J.M., (1999), Convergence of the Stochastic Diffusion

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