Swarm Technology

1
Swarm Technology
The Power of Simplicity

• -Abhilash Nayak
• Regd. No. 0801227285
• CS1(B)
• abhilash.nayak01_at_gmail.com

2
Introduction

• What is Swarm Intelligence (SI)?
• The emergent collective intelligence of
  groups of simple agents.
• Swarm intelligence (SI) as defined by Bonabeau,
  Dorigo and Theraulaz is any attempt to design
  algorithms or distributed problem-solving devices
  inspired by the collective behavior of social
  insect colonies and other animal societies

3
Examples

• Swarms build colonies and work in a coordinated
  manner yet no single member of the swarm is in
  control.
• Flocks of birds coordinate to move without
  collision.
• Ants manage to find food sources quickly and
  efficiently.
• Termites build giant structures.
• Schools of fish fend off predators and move as
  one body

4
Why do we need new computing techniques?

• The computer revolution changed human societies
• Communication Transportation
• Industrial production
• Administration, writing and bookkeeping
• Technological advances
• Entertainment
• However, some problems cannot be tackled with
  traditional hardware and software!

5
Drawbacks of traditional techniques

• Computing tasks have to be -
• Well-defined
• Fairly predictable
• Computable in reasonable time with serial
  computers.

6
What are the alternatives?

• DNA based computing (chemical computation)
• Quantum computing (quantum-physical computation)
• Bio-computing (simulation of biological
  mechanisms)

7
Working
8
Two principles of Swarm Intelligence

• self-organization is based on
• activity amplification by positive feedback
• activity balancing by negative feedback
• amplification of random fluctuations
• multiple interactions
• stigmergy - stimulation by work - is based on
• work as behavioural response to the environmental
  state
• an environment that serves as a work state memory
• work that does not depend on specific agents

9
Why Social Colony is a source of inspiration?

• Flexible the colony can respond to internal
  perturbations and external challenges
• Robust tasks are completed even if some
  individuals fail
• Decentralized there is no central control(ler)
  in the colony
• Self-organized paths to solutions are emergent
  rather than predefined

10
Ants

• Why are ants interesting?
• ants solve complex tasks by simple local means
• ant productivity is better than the sum of their
  single activities
• ants are grand masters in search and
  exploitation
• Which mechanisms are important?
• cooperation and division of labour
• adaptive task allocation
• work stimulation by cultivation
• pheromones

11
Pheromone Trails

• Species lay chemical substance pheromone while
  travelling from nest, to nest or possibly in both
  directions.
• Pheromones evaporate.
• Pheromones accumulate with multiple ants using
  same path.

12
Ant Colony Optimization (ACO)

• Is inspired by the behavior of ant colonies .
• Ability of Optimization in finding shortest path.
• Ants leave a chemical pheromone trail.
• Pheromone trails enables them to find shortest
  paths between their nest and food sources
• Ants find the shorter path in an experimental
  setup

13

• A bridge leads from a nest to a foraging area,
  (a) 4 minutes after bridge placement, (b) 8
  minutes after bridge placement

14
Ant Foraging
Cooperative search by pheromone trails

1. The natural behavior of these ants and be
   programmed into an ant algorithm, which we can
   use to find the shortest path within graphs.
2. As ants move they leave behind a chemical
   substance called pheromone, which other ants can
   smell and identify that an ant has been there
   before.

Swarm Technology
15
ACO algorithm

• Main steps of the ACO algorithm are given below
• Pheromone trail initialization
• Solution construction using pheromone trail
• Each ant constructs a complete solution to the
  problem according to a probabilistic
• State transition rule. The state transition rule
  depends mainly on the state of the pheromone .
• Pheromone trail update.

16
algorithm

• 1 repeat
• 2 if antCount lt maxAnts then
• 3 create a new ant
• 4 set initial state
• 5 end if
• 6 for all ants do
• 7 determine all feasible neighbor states
• considering the ant's visited
  states
• 8 if solution found V no feasible neighbor
  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

17

• 15 stochastically select a feasible neighbor
  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

18
Applications of Ant Colony Optimization

• Traffic on telecommunications systems, the
  internet, roads, rail, and sea would all benefit
  from the reduction in congestion that efficient
  routing algorithms could provide.
• Modern airlines are actually putting the ant
  colony research to work, with impressive payback

Airlines
Telecommunication System
19
Particle Swarm Optimization (PSO)

• Idea Used to optimize continuous functions
• Function is evaluated at each time step for the
  agents current position

20

• Each agent remembers personal best value of the
  function (pbest)
• Globally best personal value is known (gbest)
• Both points are attracting the agent

21

• Formula for one agent in one dimension
• vx vx 2.rand().(pbestx - presentx)
• 2.rand().(gbestx - presentx) ----(a)
• Where 0 rand() 1
• presentx presentxvx ----(b)

22
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
Swarm Technology
23
Solving some of the NP-Hard Problems using Swarm
Intelligence
24
Hard Problems

• Well-defined, but computational hard problems
• NP hard problems (Travelling Salesman Problem)
• Action-response planning (Chess playing)

Swarm Technology
25
Hard Problems

• Fuzzy problems
• intelligent human-machine interaction
• natural language understanding

Swarm Technology
26
Hard Problems

• Hardly predictable and dynamic problems
• real-world autonomous robots
• management and business planning

Swarm Technology
27
Problems

• Complex NP complete problems.
• Vehicle routing.
• Network maintenance.
• The traveling salesperson.
• Computing the shortest route between two points.

28
Scientists, now, are looking into the world of
insects in search of new methods and approaches
of attacking complex problems.
29
Travelling Salesman Problem

1. Visit cities in order to make sales.
2. Save on travel costs.
3. Visit each city once (Hamiltonian circuit).

30
Combinatorial Explosion in Travelling Salesman
Problem

• If there are N cities, then the number of
  different paths among them is 1.2(N-1).
• Time to examine single path N.
• Total time to perform the search (N-1)!
• For 10 cities, time reqd. 10! 3,268,800.

31
Solution of TSP by Swarm Intelligence

• Use of agents for TSP problem.
• They sense and dispense pheromone.
• Memory to back step through the graph.
• Each agent starts at a random starting city.
• Once agent finishes a tour, it determines the
  size of the tour.
• Then pheromone is added to the tour, the shorter
  the tour, the higher the pheromone level.

32

• No guarantee that the first tour the agents will
  converge the shortest path.
• Agents explore other tours.
• The stray agent finds a shorter path.
• Adjusts the pheromone levels.
• Plenty of computing time needed to converge on
  the optimal tour.
• The ant algorithm approach will still solve
  faster than other algorithms.

33
Vehicle routing by Swarm Optimization

• Vehicle routing is similar to the TSP problem.
• Employee services the client by going to them.
• Minimize cost.
• Use the same optimal Hamiltonian circuit as in
  the TSP problem.

34
Use of Swarm Intelligence in Economy

• The economy is an example of SI that most
  researchers forget to consider.
• SI demonstrates complex behavior that arises from
  simple individual interactions.
• No one can control the economy, as there are no
  groups that can consistently control the economy.

35

• The reaction of the population causes the the
  economy to slow down.
• Simulating an economy using ant algorithms.
• Makes it possible to control or predict the ebb
  and flow of this complex behavior.
• Swarm intelligence, is still in its infancy.
• A project such as simulating the economy is still
  far beyond the its capability.

36
Why is Swarm Intelligence interesting for
IT?Analogies in IT and social insects

• distributed system of interacting autonomous
  agents
• goals performance optimization and robustness
• self-organized control and cooperation
  (decentralized)
• division of labor and distributed task allocation
• indirect interactions

37
Failure in solving these problems

• Learning algorithms developed with artificial
  intelligence systems such as neural networks but
  imperfections and inefficiencies in both the
  hardware and software have prevent reliable
  results.
• Genetic algorithms also made an attempt at these
  problems, and had some success. The algorithms
  were considered too complex to re-implement.

38
How do we design Swarm Intelligence Systems?

• It is a 3-step process.
• Identification of analogies in swarm biology and
  IT systems.
• Understanding computer modeling of realistic
  swarm biology.
• Engineering model simplification and tuning for
  IT applications.

39
Bad News , Good News

• Bad news
• Difficult to predict collective behaviour from
  individual rules.
• Interrogate one of the participants, it wont
  tell you anything
• about the function of the group.
• Small changes in rules lead to different
  group-level behaviour.
• Individual behaviour looks like noise how do
  you detect
• threats?
• Good news
• Possible to efficiently control organization or
• manipulate groups using simple rules.
• Possible to predict group-level outcome using
  bottom
• simulation.

Swarm Technology
40
Applications of SI

• Swarm/crowd simulation programming
• Computer Networks Adaptive Routing
• Robotics/Artificial Intelligence
• Process optimization /Staff Scheduling

41
Conclusion

• Scientists are realizing SIs potential.
• The use of ant algorithms within computing
  systems has helped to solidify swarm
  intelligences place in the computing world.
• Already researchers are observing other social
  animals, such as bees and schools of fish in
  order to utilize it in future applications and
  algorithms.

42
Any Questions?
43
Thank You!