Bio-inspired Approaches
Dr.-Ing. Abdalkarim Awad
Page 1
Prof. Dr.-Ing. habil. Andreas Mitschele-Thiel
Integrated Communication Systems Group
www.tu-ilmenau.de/ics
Lecture 2: Bio-inspired Approaches
Self-Organizing
Dr.-Ing. Abdalkarim Awad
20.10.2011
Bio-inspired Approaches
Dr.-Ing. Abdalkarim Awad
Page 2
Prof. Dr.-Ing. habil. Andreas Mitschele-Thiel
Integrated Communication Systems Group
www.tu-ilmenau.de/ics
Lecture 1: Review
• Why Self-Organization
• Emergent Phenomena
• Direct and Indirect communication
• Feedback
• Decentralized Control
Bio-inspired Approaches
Dr.-Ing. Abdalkarim Awad
Page 3
Prof. Dr.-Ing. habil. Andreas Mitschele-Thiel
Integrated Communication Systems Group
www.tu-ilmenau.de/ics
Characteristics of Biological Systems
• Complex behaviors on basis of limited set of basic rules
– Ant colony optimization
• Adaptive to the varying environmental circumstances
– Firefly synchronization
• Resilient to failures by internal or external factors
– Human Immune system
• Able to learn and evolve itself under new conditions
– Neural networks
Bio-inspired Approaches
Dr.-Ing. Abdalkarim Awad
Page 4
Prof. Dr.-Ing. habil. Andreas Mitschele-Thiel
Integrated Communication Systems Group
www.tu-ilmenau.de/ics
Biological Models: Application Domains
• Application domains of bio-inspired solutions:
– Bio-inspired computing – a class of algorithms
focusing on efficient computing, e.g. for optimization processes and pattern recognition
– Bio-inspired systems – a class of system architectures for massively distributed and collaborative systems, e.g. for distributed sensing and exploration
– Bio-inspired networking – a class of strategies for efficient and scalable communication under uncertain network conditions, e.g. for autonomic organization in distributed network architectures
Bio-inspired Approaches
Dr.-Ing. Abdalkarim Awad
Page 5
Prof. Dr.-Ing. habil. Andreas Mitschele-Thiel
Integrated Communication Systems Group
www.tu-ilmenau.de/ics
Biological Models: Application Domains
• Biological principles with application to
networking (examples):
– Ant Colony Optimization (ACO)
– Firefly Synchronization
– Artificial Immune System (AIS)
– Neural Networks (next week)
Bio-inspired Approaches
Dr.-Ing. Abdalkarim Awad
Page 6
Prof. Dr.-Ing. habil. Andreas Mitschele-Thiel
Integrated Communication Systems Group
www.tu-ilmenau.de/ics
Swarm Intelligence (SI)
• Swarm Intelligence (SI) is
– computational techniques for solving distributed problems
inspired from biological examples provided by
• social insects such as ants, termites, bees, and wasps and
by swarm, herd, flock, and shoal phenomena such as fish
shoals (school of fish)
– Approaches to controlling and optimizing distributed
systems
– Resilient, decentralized, self-organized techniques
Bio-inspired Approaches
Dr.-Ing. Abdalkarim Awad
Page 7
Prof. Dr.-Ing. habil. Andreas Mitschele-Thiel
Integrated Communication Systems Group
www.tu-ilmenau.de/ics
SI Organizing Principles
SI has the following notable features:
• Autonomy:
– The system does not require outside management or maintenance. Individuals are autonomous, controlling their own behavior both at the detector and effector levels in a self-organized way
• Adaptability:
– Based on local interaction, the system is able to adapt itself to changes
• Scalability:
– SI abilities can be performed using groups consisting of a few, up to thousands of individuals with the same control architecture.
• Flexibility:
– No single individual of the swarm is essential, that is, any individual can be dynamically added, removed, or replaced.
Bio-inspired Approaches
Dr.-Ing. Abdalkarim Awad
Page 8
Prof. Dr.-Ing. habil. Andreas Mitschele-Thiel
Integrated Communication Systems Group
www.tu-ilmenau.de/ics
SI Organizing Principles
SI has the following notable features:
• Robustness:
– No central coordination takes place, which means that
there is no single point of failure
– It can recover form unexpected disruptions
• Massively parallel:
– Tasks can be performed by individuals within the group
same time.
• Emergence:
– The intelligence exhibited is not present in the individuals,
but rather emerges somehow out of the entire swarm..
Bio-inspired Approaches
Dr.-Ing. Abdalkarim Awad
Page 9
Prof. Dr.-Ing. habil. Andreas Mitschele-Thiel
Integrated Communication Systems Group
www.tu-ilmenau.de/ics
SI Communication Forms
• Indirect communication
– Implicit communication that takes place between
individuals via the surrounding environment.
– Known as Stigmergy communication..
• Example
– Ants leave a chemical substance (pheromone )
• Direct Communication
– Explicit communication that can also take place between
individuals.
– Example:
• trophallaxis (food or liquid exchange
• , e.g. mouth-to-mouth food exchange)
Bio-inspired Approaches
Dr.-Ing. Abdalkarim Awad
Page 10
Prof. Dr.-Ing. habil. Andreas Mitschele-Thiel
Integrated Communication Systems Group
www.tu-ilmenau.de/ics
Stigmergy
• Stigmergy: stigma (sign, mark) + ergon (work)
• Characteristics of stigmergy
– Indirect agent interaction modification of the environment
– The information is local: it can only be accessed by insects
that visit the locus in which it was released
– Work can be continued by any individual
Bio-inspired Approaches
Dr.-Ing. Abdalkarim Awad
Page 11
Prof. Dr.-Ing. habil. Andreas Mitschele-Thiel
Integrated Communication Systems Group
www.tu-ilmenau.de/ics
SI: Main application
• SI principles have been successfully applied in a variety
of problem domains and applications:
Ant colony optimization (ACO),
• Which focuses on discrete optimization problems
Bio-inspired Approaches
Dr.-Ing. Abdalkarim Awad
Page 12
Prof. Dr.-Ing. habil. Andreas Mitschele-Thiel
Integrated Communication Systems Group
www.tu-ilmenau.de/ics
• Ant Colony Optimization (ACO)
Bio-inspired Approaches
Dr.-Ing. Abdalkarim Awad
Page 13
Prof. Dr.-Ing. habil. Andreas Mitschele-Thiel
Integrated Communication Systems Group
www.tu-ilmenau.de/ics
Ants
• Why are ants interesting?
– Ants solve complex tasks by simple local means
– Ants productivity is better than the sum of their single
activities
– Ants are grand masters in search and exploitation
Bio-inspired Approaches
Dr.-Ing. Abdalkarim Awad
Page 14
Prof. Dr.-Ing. habil. Andreas Mitschele-Thiel
Integrated Communication Systems Group
www.tu-ilmenau.de/ics
Foraging behavior of Ants
• (Double bridge experiment)
Ants start with equal probability
Bio-inspired Approaches
Dr.-Ing. Abdalkarim Awad
Page 15
Prof. Dr.-Ing. habil. Andreas Mitschele-Thiel
Integrated Communication Systems Group
www.tu-ilmenau.de/ics
Foraging behavior of Ants
• (Double bridge experiment)
The ant on shorter path has a shorter to-and-fro time from it’s nest to the food.
Bio-inspired Approaches
Dr.-Ing. Abdalkarim Awad
Page 16
Prof. Dr.-Ing. habil. Andreas Mitschele-Thiel
Integrated Communication Systems Group
www.tu-ilmenau.de/ics
Foraging behavior of Ants
• (Double bridge experiment)
The density of pheromone on the shorter path is higher
Bio-inspired Approaches
Dr.-Ing. Abdalkarim Awad
Page 17
Prof. Dr.-Ing. habil. Andreas Mitschele-Thiel
Integrated Communication Systems Group
www.tu-ilmenau.de/ics
Foraging behavior of Ants
• (Double bridge experiment)
The next ant takes the shorter route
Bio-inspired Approaches
Dr.-Ing. Abdalkarim Awad
Page 18
Prof. Dr.-Ing. habil. Andreas Mitschele-Thiel
Integrated Communication Systems Group
www.tu-ilmenau.de/ics
Foraging behavior of Ants
• (Double bridge experiment)
Eventually, the shorter path is almost exclusively used
Bio-inspired Approaches
Dr.-Ing. Abdalkarim Awad
Page 19
Prof. Dr.-Ing. habil. Andreas Mitschele-Thiel
Integrated Communication Systems Group
www.tu-ilmenau.de/ics
Travelling Salesman Problem (TSP)
• TSP description:
– Visit cities in order to make sales
– Save on travel costs
– Visit each city once (Hamiltonian circuit)
• A Hamiltonian cycle (or Hamiltonian circuit) is a cycle in an undirected graph which visits each vertex exactly once and also returns to the starting vertex.
Bio-inspired Approaches
Dr.-Ing. Abdalkarim Awad
Page 20
Prof. Dr.-Ing. habil. Andreas Mitschele-Thiel
Integrated Communication Systems Group
www.tu-ilmenau.de/ics
From nature to computers
• The use of simple computational agents that work cooperatively
• In an iterative fashion, each ant moves from state Si to state Sj guided by two main factors:
1. Heuristic information:
A measure of the heuristic preference for moving from state Si to state Sj
This information is known a priori to the algorithm run, and is not modified during
2. Artificial pheromone trail(s):
A measurement of the pheromone deposition from ants previous transitions from state Si to state Sj
This information is modified during the algorithm run by the artificial ants
Bio-inspired Approaches
Dr.-Ing. Abdalkarim Awad
Page 21
Prof. Dr.-Ing. habil. Andreas Mitschele-Thiel
Integrated Communication Systems Group
www.tu-ilmenau.de/ics
Ant Colony Optimization (ACO)
• Developed by Dorigo and Di Caro
• It is a population-based metaheuristic used to find
approximate solutions to difficult optimization problems
• ACO is structured into three main functions:
• AntSolutionsConstruct( )
– Performs the solution construction process
• PheromoneUpdate( )
– Performs pheromone trail updates
– Includes also pheromone trail evaporation
• DaemonActions( )
– An optional step in the algorithm which involves applying
additional updates from a global perspective
Bio-inspired Approaches
Dr.-Ing. Abdalkarim Awad
Page 22
Prof. Dr.-Ing. habil. Andreas Mitschele-Thiel
Integrated Communication Systems Group
www.tu-ilmenau.de/ics
What is Metaheuristic?
• “A metaheuristic is a set of algorithmic concepts that can
be used to define heuristic methods applicable to a wide
set of different problems”
• In other words: “a metaheuristic is a general-purpose
algorithmic framework that can be applied to different
optimization problems with relatively few modifications”
…Dorigo, 1999
• Examples of metaheuristics:
– Tabu search
– Iterated local search
– …
Bio-inspired Approaches
Dr.-Ing. Abdalkarim Awad
Page 23
Prof. Dr.-Ing. habil. Andreas Mitschele-Thiel
Integrated Communication Systems Group
www.tu-ilmenau.de/ics
Properties of the artificial ant
• Each artificial ant has an internal memory
• Starting in an initial state Sinitial each ant tries to build a feasible solution to the given problem, moving in an iterative fashion through its search space
• The guidance factors for ants movement take is a transition rule which is applied before every move from state Si to state Sj
• The amount of pheromone each ant deposits is governed by a problem specific pheromone update rule
• Pheromone deposition may occur at every state transition during the solution construction (pheromone trial update)
• Ants may retrace their paths once a solution has been constructed and only then deposit pheromone, all along their individual paths.
Bio-inspired Approaches
Dr.-Ing. Abdalkarim Awad
Page 24
Prof. Dr.-Ing. habil. Andreas Mitschele-Thiel
Integrated Communication Systems Group
www.tu-ilmenau.de/ics
The original Ant System
• Developed by Dorigo et al. (1996)
• Ant system (AS)
– First ACO algorithm
– Pheromone updated by all ants in the iteration
• Travelling Salesman Problem (TSP) was used as a test-
bed for this algorithm.
Bio-inspired Approaches
Dr.-Ing. Abdalkarim Awad
Page 25
Prof. Dr.-Ing. habil. Andreas Mitschele-Thiel
Integrated Communication Systems Group
www.tu-ilmenau.de/ics
PROBLEM DEFINITION
TSP:
Given a set of n cities, the Traveling
Salesman Problem requires a
salesman to find the shortest route
between the given cities and return
to the starting city, while keeping in
mind that each city can be visited
only once
Bio-inspired Approaches
Dr.-Ing. Abdalkarim Awad
Page 26
Prof. Dr.-Ing. habil. Andreas Mitschele-Thiel
Integrated Communication Systems Group
www.tu-ilmenau.de/ics
TSP Problem
• Try all O(n!)
• 20 cities
• The resulting factorial of 20! is 19 digits long.
• 2432902008176640000 =2.4 e+18
• 100 cites
• The resulting factorial of 100! is 158 digits long.
• 933262154439441526816992388562667004907159682
643816214685929638952175999932299156089414639
761565182862536979208272237582511852109168640
000000000000000 00000000 =9.3 e+157
0 1
3 2
4
Bio-inspired Approaches
Dr.-Ing. Abdalkarim Awad
Page 27
Prof. Dr.-Ing. habil. Andreas Mitschele-Thiel
Integrated Communication Systems Group
www.tu-ilmenau.de/ics
Solution for TSP
• A connected graph G=(V,E), where
– V is a set of vertices (cities)
– E is a set of edges (connection between cities)
• A variable called pheromone is associated with each edge and can be read and modified by ants
• Ant system is an iterative algorithm: at each iteration,
– A number of artificial ants are considered
– Each ant build a solution by walking from vertex to vertex
– Each vertex is visited one time only
– An ant selects the following vertex to be visited according to a
stochastic mechanism that is biased by the pheromone
• At the end, the pheromone values are updated on order to bias ants in the future iteration to construct solutions similar to the previously constructed
Bio-inspired Approaches
Dr.-Ing. Abdalkarim Awad
Page 28
Prof. Dr.-Ing. habil. Andreas Mitschele-Thiel
Integrated Communication Systems Group
www.tu-ilmenau.de/ics
Ant System and the TSP
• The following steps is used to solve the TSP:
– Pheromone trail
– Memory
– Awareness of environment
– Probability function
Bio-inspired Approaches
Dr.-Ing. Abdalkarim Awad
Page 29
Prof. Dr.-Ing. habil. Andreas Mitschele-Thiel
Integrated Communication Systems Group
www.tu-ilmenau.de/ics
Ant System and the TSP 1. Pheromone trail
– Iteration is defined as the interval in (t,t+1) where each of the N ants moves once
– Epoch n iterations (when each ant has completed a tour)
– Intensity of trail:
– Trail update function after each epoch:
– is the evaporation rate
– is the quantity of pheromone laid on path (i,j) by the ant k and is given by:
where is a constant and is the tour length of kth ant.
29
otherwise,
tour,itsin edge used ant If
0
/ (i,j)kLQ kk
ij
ij
1
(1 )N
k
ij ij
k
)(ij t
i
j
k
ij
kLQ
Bio-inspired Approaches
Dr.-Ing. Abdalkarim Awad
Page 30
Prof. Dr.-Ing. habil. Andreas Mitschele-Thiel
Integrated Communication Systems Group
www.tu-ilmenau.de/ics
Ant System and the TSP 2. Memory
– Prevents town repeats
– Tabu list
3. Awareness of environment
– City distance
– Visibility:
where
4. Probability function
30
otherwise 0
allowed if )( allowed
][)]([
][)]([j
tp j ijij
ijij
t
t
k
ij
2 2( ) ( )ij i j i jd x x y y
1ij
ijd
i
j
I,j, m o,p
Bio-inspired Approaches
Dr.-Ing. Abdalkarim Awad
Page 31
Prof. Dr.-Ing. habil. Andreas Mitschele-Thiel
Integrated Communication Systems Group
www.tu-ilmenau.de/ics
Ant System and the TSP
• Various improvements were made which gave rise to
several other ant algorithms which collectively form the
main ACO algorithms, such as:
– Max–Min Ant System
– Ant Colony System
– …..
Bio-inspired Approaches
Dr.-Ing. Abdalkarim Awad
Page 32
Prof. Dr.-Ing. habil. Andreas Mitschele-Thiel
Integrated Communication Systems Group
www.tu-ilmenau.de/ics
Solving a Problem by ACO
Steps:
1. Represent the problem in the form a weighted graph, on
which ants can build solutions
2. Define the meaning of the pheromone trails
3. Define the heuristic preference for the ant while
constructing a solution
4. Choose a specific ACO algorithm and apply to problem
being solved
5. Tune the parameters of the ACO algorithm
Bio-inspired Approaches
Dr.-Ing. Abdalkarim Awad
Page 33
Prof. Dr.-Ing. habil. Andreas Mitschele-Thiel
Integrated Communication Systems Group
www.tu-ilmenau.de/ics
Solving a Problem by ACO
• Scheduling
• Routing problems
– Traveling Salesman Problem (TSP)
– Vehicle routing
– Network routing
– …
Bio-inspired Approaches
Dr.-Ing. Abdalkarim Awad
Page 34
Prof. Dr.-Ing. habil. Andreas Mitschele-Thiel
Integrated Communication Systems Group
www.tu-ilmenau.de/ics
TRAVELING SALESMAN PROBLEM (TSP)
Bio-inspired Approaches
Dr.-Ing. Abdalkarim Awad
Page 35
Prof. Dr.-Ing. habil. Andreas Mitschele-Thiel
Integrated Communication Systems Group
www.tu-ilmenau.de/ics
REAL vs. ARTIFICIAL ANTS
• Discrete time steps
• Memory Allocation
• Quality of Solution
• Time of Pheromone
deposition
• Distance Estimation
REAL ANT ARTIFICIAL ANT
Bio-inspired Approaches
Dr.-Ing. Abdalkarim Awad
Page 36
Prof. Dr.-Ing. habil. Andreas Mitschele-Thiel
Integrated Communication Systems Group
www.tu-ilmenau.de/ics
FLOWCHART OF ACO
Have all
cities been
visited
Have the
maximum
Iterations been
performed
START ACO
Locate ants randomly
in cities across the
grid and store the
current city
in a tabu list
Determine probabilistically
as to which city to visit next
Move to next city and
place this city in the
tabu list
Record the length of
tour and clear tabu list
Determine the shortest
tour till now and
update pheromone
NO
YES
STOP
ACO
YES NO
Bio-inspired Approaches
Dr.-Ing. Abdalkarim Awad
Page 37
Prof. Dr.-Ing. habil. Andreas Mitschele-Thiel
Integrated Communication Systems Group
www.tu-ilmenau.de/ics
TSP-Example
Bio-inspired Approaches
Dr.-Ing. Abdalkarim Awad
Page 38
Prof. Dr.-Ing. habil. Andreas Mitschele-Thiel
Integrated Communication Systems Group
www.tu-ilmenau.de/ics
TSP-Example
Bio-inspired Approaches
Dr.-Ing. Abdalkarim Awad
Page 39
Prof. Dr.-Ing. habil. Andreas Mitschele-Thiel
Integrated Communication Systems Group
www.tu-ilmenau.de/ics
TSP-Example
Bio-inspired Approaches
Dr.-Ing. Abdalkarim Awad
Page 40
Prof. Dr.-Ing. habil. Andreas Mitschele-Thiel
Integrated Communication Systems Group
www.tu-ilmenau.de/ics
TSP-Example
Bio-inspired Approaches
Dr.-Ing. Abdalkarim Awad
Page 41
Prof. Dr.-Ing. habil. Andreas Mitschele-Thiel
Integrated Communication Systems Group
www.tu-ilmenau.de/ics
TSP-Example
Bio-inspired Approaches
Dr.-Ing. Abdalkarim Awad
Page 42
Prof. Dr.-Ing. habil. Andreas Mitschele-Thiel
Integrated Communication Systems Group
www.tu-ilmenau.de/ics
TSP-Example
Bio-inspired Approaches
Dr.-Ing. Abdalkarim Awad
Page 43
Prof. Dr.-Ing. habil. Andreas Mitschele-Thiel
Integrated Communication Systems Group
www.tu-ilmenau.de/ics
TSP-Example
Bio-inspired Approaches
Dr.-Ing. Abdalkarim Awad
Page 44
Prof. Dr.-Ing. habil. Andreas Mitschele-Thiel
Integrated Communication Systems Group
www.tu-ilmenau.de/ics
Performance of TSP with ACO
heuristic
• Performs better than state-of-the-art TSP algorithms
for small (50-100) of nodes
• The main point to appreciate is that Swarms give us new algorithms for optimization
Bio-inspired Approaches
Dr.-Ing. Abdalkarim Awad
Page 45
Prof. Dr.-Ing. habil. Andreas Mitschele-Thiel
Integrated Communication Systems Group
www.tu-ilmenau.de/ics
Firefly Synchronization
Bio-inspired Approaches
Dr.-Ing. Abdalkarim Awad
Page 46
Prof. Dr.-Ing. habil. Andreas Mitschele-Thiel
Integrated Communication Systems Group
www.tu-ilmenau.de/ics
Firefly Synchronization
• Distributed precise synchronization hard to achieve
• Nature has surprising synchrony among independent actors
– Chirping crickets
– Flashing of fireflies
• Male fireflies flash to attract females (or meals!)
• Do not follow a leader
• Period between flashes is constant
– internal clock
• Flash at beginning of cycle
– initially random
• Each firefly is exposed to flashes of neighbors
– Resets the timing of flashes
– Self-organized synchrony
Bio-inspired Approaches
Dr.-Ing. Abdalkarim Awad
Page 47
Prof. Dr.-Ing. habil. Andreas Mitschele-Thiel
Integrated Communication Systems Group
www.tu-ilmenau.de/ics
• Oscillators assume to interact by a simple
form of pulse coupling
– When a given oscillator fires, it pulls all the
other oscillators up by an amount , or pulls
them up to firing.
– Whichever is less
Firefly Synchronization
Bio-inspired Approaches
Dr.-Ing. Abdalkarim Awad
Page 48
Prof. Dr.-Ing. habil. Andreas Mitschele-Thiel
Integrated Communication Systems Group
www.tu-ilmenau.de/ics
Firefly Synchronization - The Model
• New models inspired from synchronization principles of fireflies
• Firefly synchronization is based on pulse-coupled oscillators Integrate-and-fire oscillator → xi from 0 to 1 → oscillator fires when xi = 1, then xi comes back to 0
• Multiple oscillators interact in form of simple pulse coupling:
• When a given oscillator fires, it pulls the others up by a fixed amount, or brings them to the firing threshold, whichever is less:
• For almost all initial conditions the population evolves to a state in which all the oscillators are firing synchronously
Bio-inspired Approaches
Dr.-Ing. Abdalkarim Awad
Page 49
Prof. Dr.-Ing. habil. Andreas Mitschele-Thiel
Integrated Communication Systems Group
www.tu-ilmenau.de/ics
• Generalize the integrate and fire dynamics
– Assume only that x evolves according to
– ƒ:[0,1][0,1] is smooth, monotonically increasing and concave down
– Here is a phase variable such that •
• Ф=0 when the oscillator is at its lowest state x=0
• Ф=1 at the end of the cycle when the oscillator reaches the threshold x=1.
Two Oscillators – The Model
d
dt
1
T, T is the cycle period
•ƒ satisfies ƒ(0)=0, ƒ(1)=1
Bio-inspired Approaches
Dr.-Ing. Abdalkarim Awad
Page 50
Prof. Dr.-Ing. habil. Andreas Mitschele-Thiel
Integrated Communication Systems Group
www.tu-ilmenau.de/ics
• Example
– Here
–
– Consider Two oscillators governed by ƒ
• Interact by pulse coupling rule
Two Oscillators – The Model
Bio-inspired Approaches
Dr.-Ing. Abdalkarim Awad
Page 51
Prof. Dr.-Ing. habil. Andreas Mitschele-Thiel
Integrated Communication Systems Group
www.tu-ilmenau.de/ics
• (a) Two Points on a fixed curve
• (b) Just before firing
• (c) Immediately After Firing
Two Oscillators – The Model cont…
Bio-inspired Approaches
Dr.-Ing. Abdalkarim Awad
Page 52
Prof. Dr.-Ing. habil. Andreas Mitschele-Thiel
Integrated Communication Systems Group
www.tu-ilmenau.de/ics
Two Oscillators
• Strategy
– To prove that the two oscillators always become
synchronized, we first calculate the return map and
then show the oscillators are driven closer together
each time the map is iterated.
– Perfect Synchrony when the oscillators have gotten
so close together that the firing of one brings the
other to threshold.
– They remain synchronized thereafter because there
dynamics are identical.
Bio-inspired Approaches
Dr.-Ing. Abdalkarim Awad
Page 53
Prof. Dr.-Ing. habil. Andreas Mitschele-Thiel
Integrated Communication Systems Group
www.tu-ilmenau.de/ics
How does it work!
• Return Map
– Two Oscillators A and B
– Consider the system just after A has fired
– Ф denotes the phase of B
– The return map R(Ф) is defined to be the phase of B
immediately after the next firing of A.
Bio-inspired Approaches
Dr.-Ing. Abdalkarim Awad
Page 54
Prof. Dr.-Ing. habil. Andreas Mitschele-Thiel
Integrated Communication Systems Group
www.tu-ilmenau.de/ics
How does it work
• Observe that after a time 1-Ф oscillator B reaches threshold
• A moves from zero to an x-value given by xA= ƒ(1-Ф).
Bio-inspired Approaches
Dr.-Ing. Abdalkarim Awad
Page 55
Prof. Dr.-Ing. habil. Andreas Mitschele-Thiel
Integrated Communication Systems Group
www.tu-ilmenau.de/ics
• An instant later B fires and xA jumps to ε+ƒ(1-Ф) or 1. whichever is less.
• If xA=1, we are done, the oscillators have synchronized.
How does it work
Bio-inspired Approaches
Dr.-Ing. Abdalkarim Awad
Page 56
Prof. Dr.-Ing. habil. Andreas Mitschele-Thiel
Integrated Communication Systems Group
www.tu-ilmenau.de/ics
Immune System
Bio-inspired Approaches
Dr.-Ing. Abdalkarim Awad
Page 57
Prof. Dr.-Ing. habil. Andreas Mitschele-Thiel
Integrated Communication Systems Group
www.tu-ilmenau.de/ics
Immune System
• Aim
– To provide responses to attacks from outside the body
• Antigen
– Any substance that elicits an immune response (e.g. virus,
bacteria)
• Self/Non-Self recognition
– Each cell has a marker (self)
– Cells without marker (non-self) are considered antigen
Immune system attacks antigen
Bio-inspired Approaches
Dr.-Ing. Abdalkarim Awad
Page 58
Prof. Dr.-Ing. habil. Andreas Mitschele-Thiel
Integrated Communication Systems Group
www.tu-ilmenau.de/ics
Immune System
• Characteristics:
– Antigen-specific
– Systemic (throughout the body)
– Memory
• next attack recognized quicker
• level of response higher
• Dysfunctions
– Autoimmune disease
• System attacks self cells
– Allergies
• System attacks innocuous substances (allergen)
Bio-inspired Approaches
Dr.-Ing. Abdalkarim Awad
Page 59
Prof. Dr.-Ing. habil. Andreas Mitschele-Thiel
Integrated Communication Systems Group
www.tu-ilmenau.de/ics
Immune System
• Agents of Immune System
– Lymphocites (white blood cells)
• Organs of lymphatic system: bone marrow, thymus gland,
spleen
• Lymph: colorless fluid transported by lymphatic vessels
– Lymphocites (B and T cells)
• Cells transported by blood and lymphatic vessels across the
whole body
• Cells exchanged between blood and lymphatic vessels
– Foreign organisms enter body through lymph nodes
Bio-inspired Approaches
Dr.-Ing. Abdalkarim Awad
Page 60
Prof. Dr.-Ing. habil. Andreas Mitschele-Thiel
Integrated Communication Systems Group
www.tu-ilmenau.de/ics
Immune System
Bio-inspired Approaches
Dr.-Ing. Abdalkarim Awad
Page 61
Prof. Dr.-Ing. habil. Andreas Mitschele-Thiel
Integrated Communication Systems Group
www.tu-ilmenau.de/ics
Immune System
– B cells (Bone)
• Produced by bone marrow / enter blood system
• Wait for antigen (in blood cells)
• Activation of B cells requires help of T cells when encounter antigen
• B cells replicate and release antibodies
• Antibodies circulate in the blood vessels for additional antigens to
mark
• B cells antibodies mark the antigen for destruction by other immune
system cells
– T cells (Thymus)
• Produced by bone marrow / initialised in thymus enter lymphatic
vessels
• Coordinate immune response + destroy infected body cells
• T cells replicate and create memory cells (for subsequent attacks)
Bio-inspired Approaches
Dr.-Ing. Abdalkarim Awad
Page 62
Prof. Dr.-Ing. habil. Andreas Mitschele-Thiel
Integrated Communication Systems Group
www.tu-ilmenau.de/ics
Immune System
B
T
B
B
B
AB
AB
B
T
B
B
B
T
T
B and T cells circulate into body
Antigen enters body
B
B T
B cells recognise Antigen
T cell activates B cell
B cell replicates
B cells generate Antibodies (AB)
AB
AB
AB
T
AB marks Antigen as non-self
T cells destroy Antigen
AB
Bio-inspired Approaches
Dr.-Ing. Abdalkarim Awad
Page 63
Prof. Dr.-Ing. habil. Andreas Mitschele-Thiel
Integrated Communication Systems Group
www.tu-ilmenau.de/ics
Immune System
• Innate immunity – Cells of immune system bind to specific antigen
• Pattern-recognition receptors • Passed from generation to generation • Evolution
• Acquired Immunity • Characteristics
– Distributed – No centralized control – Uses learning and memory – Tolerant of self
• Artificial Immune System (AIS) – an adaptive system inspired by theoretical and experimental immunology
• Aim: to efficiently detect changes in the environment or deviations from the normal system behavior in complex problems domains
Bio-inspired Approaches
Dr.-Ing. Abdalkarim Awad
Page 64
Prof. Dr.-Ing. habil. Andreas Mitschele-Thiel
Integrated Communication Systems Group
www.tu-ilmenau.de/ics
Summary (what do I need to know)
• What make biological systems interesting ?
• What is ant colony optimization?
• How does it work?
• How can we achieve synchronization in a distributed
system?
Bio-inspired Approaches
Dr.-Ing. Abdalkarim Awad
Page 65
Prof. Dr.-Ing. habil. Andreas Mitschele-Thiel
Integrated Communication Systems Group
www.tu-ilmenau.de/ics
References
• “Synchronization of Pulse-Coupled Biological Oscillators”, Renato E. Mirollo and Steven H. Strogatz, Siam Journal of Applied Mathematics, Vol. 50, No. 6 (Dec 1990), PP. 1645-1662
• M. Dorigo, V. Maniezzo & A. Colorni, Ant System: Optimization by a colony of cooperating agents. IEEE Transactions on Systems, Man, and Cybernetics-Part B, 26(1):29-41,1996
• Slides “Adaptive Systems” by Giovanna Di Marzo Serugendo
• Slides“ Bio-Inspired Signal Processing” by Sergio Barbarossa
• Slides “Self-Organization in Sensor and Actor Networks” by Falko Dressler
• Slides “Synchronization of Pulse-Coupled Biological Oscillators” by Ramil Berner