INTRODUCTION ANT COLONY OPTIMIZATION(ACO) MODEL PARTICLE SWARM OPTIMIZATION(PSO) MODEL COMPARISON META-HEURISTIC

1. Swarm Intelligence
“The emergent collective intelligence of group of
simple agents.”
DEPARTMENT OF
ELECTRONICS AND COMMUNICATION
Seminar By:-
RADHIKA GUPTA
(ROLL NO. - GCET/O7/15)
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2. CONTENTS
2
SLIDE NO.S CONTENTS
 3 INTRODUCTION
 7 SI MODEL
 8 ANT COLONT OPTIMIZATION
 15 PARTICLE SWARM OPTIMIZATION
 21 CONCLUSION
 22 REFERENCE
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3. • “SWARM INTELLIGENCE’’ WAS FIRST INTRODUCED BY G.RENI AND J.WANG
IN 1989.
• WHY SI?
• WHAT IS SI?
WHY SI?
 DISTRIBUTED SYSTEM OF AUTONOMOUS AGENTS(having freedom to govern itself)
 GOALS: PERFORMANCE OPTIMIZATION AND ROBUSTNESS
 DIVISION OF LABOUR AND DISTRIBUTED TASK ALLOCATION
 SELF_ORGANISED CONTROL AND COOPERATION(DECENTRALIZED)
3
INTRODUCTION
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4. WHAT ARE AGENTS?
 AGENTS CAN BE VIEWED AS ANYTHING THAT PERCEIVES ITS ENVIRONMENT
THROUGH SENSORS AND ACT UPON THE ENVIRONMENT THROUGH
ACTUATORS.
AGENT FUNCTION
 AGENT PROGRAM
(implementation of agent
function by using some
programming language)
 MAPPING A PERCEPTION OF
ACTION
(to plan what output should be given
in acc. To input)
EXAMPLE OF NATURAL AGENTS:
ANTS,HONEY BEE,BIRDS,ETC.
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5. SWARM INTELLIGENCE
 large number of
 homogenous, simple agents
 relatively unsophisticated
with limited capabilities on
their own
 locally among themselves
&
their environment
 with no central control
 global interesting behaviour
 ability
 acquire & apply
 knowledge &
skill
interacting
allow to emerge
(Behavioural patterns to achieve
task necessary for survival.)
WHAT IS SI?
 Swarm intelligence is the collective behaviour of decentralized, self-organized systems,
natural or artificial swarm system.
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6. THE GENERAL FRAMEWORK USED TO MOVE FROM A NATURAL PHENOMENON TO A
NATURE INSPIRED ALGORITHM
the production of
a computer
model
PROBLEM
INDEPENDENT
TECHNIQUES
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7. SI MODELS
COMPUTATIONAL MODELS INSPIRED BY NATURAL SWARM SYSTEMS
 ANT COLONY OPTIMIZATION
 PARTICLE SWARM OPTIMIZATION
 ARTIFICAL BEE COLONY
 BACTERIAL FORAGING
 CAT SWARM OPTIMIZATION
 ARTIFICIAL IMMUNE SYSTEM
 GLOWWORM SWARM OPTIMIZATION
 ANTS
 FLOCKING OF BIRDS
 WAGGLE DANCE OF HONEY BEE
 FORAGING AND CHEMOTATIC
PHENOMENON OF BACTERIA
 CATS
 VERTEVRATE IMMUNE SYSTEM
 LIGHTINING WORMS
MODELS INSPIRED BY
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8. ANT COLONY OPTIMIZATION(ACO)MODEL
INTRODUCED BY M.DORIGO ET AL.
• INSPIRED BY SOCIAL BEHAVIOUR OF ANT COLONIES
 BLIND
 SHOW
STIGMERGIC
BHEAVIOUR
 COOPERATE
 COMMUNICATE
 DIVIDE TASK
ORGANISED SOCITIES
 USE
PHEROMONE(volatile
chemical substance)
 FOR
COMMUNICATION
 ANTENNAE ACT AS
SENSORS
• ALARM
• FOOD TRAIL
 TRAIL LAYING
 TRAIL FOLLOWING
STIGMERGY
FOOD
FOOD
FOOD
NEST
NEST
NEST
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9. DOUBLE BRIDGE EXPERIMENT
(i) (ii)
REASON FOR VARATION
IN BEHAVIOUR IN THE
TWO CASES:
 HIGH-LEVEL
PHEROMONE
CONCENTRATION
 VERY SLOW
EVAPORATION RATE OF
PHEROMONE
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10. LESSON
Pheromone is the key
parameter
 Path exploration
(Diversification)
 Path exploitation
(Intensification)
Controls
LEARNED LESSON APPLIED ON
ARTIFICIAL ANTS
Better optimization
 High pheromone evaporation
rate
 Forgetting of errors
 Being trapped on suboptimal
solution
for
by
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11. REAL ANT VS. ARTIFICIAL ANTS
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12. ANT COLONY OPTIMIZATION METAHEURISTIC
Amount of pheromone
by one ant in which
pheromone is
dependent on quality of
path
Total pheromone
How to add pheromone :
• Equal pheromone
• Quality of path
• Food source(big or quality)
r -> evaporation rate 12MADE BY RADHIKA GUPTA

13. Heuristic value of arc
& a and b
weighted parameters
control relative importance of each component
COST GRAPH PHEROMONE GRAPH
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15. PARTICLE SWARM OPTIMIZATION (PSO) MODEL
INTRODUCED BY RUSHELL EBERHART
 Originally used for non linear continuous optimization
problem
 Inspired by birds in nature
• Vision (sense used)
• “Nearest neighbour
principle”(interaction bases)
 Three flocking rules :
i. Flock centring (closer to centroid of near by flock mates)
ii. Collision avoidance (“establish” the minimum required distance)
iii. Velocity matching (“maintain” such separation distance )
Position & velocity
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16. PARTICLE SWARM OPTIMIZATION METAHEURISTIC
(POSITION VECTOR)
VELOCITY
CONSTANT
WEIGHTING
PARAMETER
FOR PARTICLE’S
PERSONAL
EXPERIENCE
CONSTANT
WEIGHTING
PARAMETER
FOR SWARM’S
SOCIAL
EXPERIENCE
PARTICLE’S BEST
POSITION
GLOBAL BEST
POSITION
RANDOM NO. [0.0,1.0] TO
INTRODUCE
RANDOMNESS 16MADE BY RADHIKA GUPTA

17. PSEUDO CODE OF PSO
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21. 21
CONCLUSION
COMPARISON BETWEEN ACO AND PSO
Criteria ACO PSO
Communication
Mechanism
indirect direct
Problem Types solve combinatorial (discrete)
optimization problems, but it was
later modified to adapt
continuous problems.
solve continuous problems, but it
was later modified to adapt
binary/ discrete optimization
problems.
Problem
Representation
weighted graph, called
construction graph
set of n-
dimensional points
Algorithm
Applicability
where source and
destination are predefined
and specific
where previous and next
particle positions at each point
are clear and uniquely define
Algorithm
Objective
searching for an optimal path
in the construction graph
finding the location of an
optimal point in a Cartesian
coordinate system
Examples of
Algorithm
Application
Sequential ordering ,
scheduling , assembly line
balancing , probabilistic TSP,
DNA sequencing
Track dynamic systems ,
evolve NN weights , analyse
human tremor , register 3D-
to3D biomedical image ,
control reactive power and
voltage , and even play games
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22. REFERENCE
22
 Swarm Intelligence: Concepts, Models and Applications -
Technical Report 2012-585 -Hazem Ahmed Janice Glasgow
 Ali mirjalili-youtube
 R. C. Eberhart and J. Kennedy. A new optimizer using particle
swarm theory. In Proceedings of the Sixth International
Symposium on Micro Machine and Human Science, Nagoya,
Japan, pp. 39–43, 1995
 B. K. Panigrahi, Y.
 Shi, and M.-H. Lim (eds.): Handbook of Swarm Intelligence.
Series: Adaptation, Learning, and Optimization, Vol 7, Springer-
Verlag Berlin Heidelberg, 2011. ISBN 978-3-642-17389-9.
 C. Blum and D. Merkle (eds.). Swarm Intelligence – Introduction
and Applications. Natural Computing. Springer, Berlin, 2008.
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