1. Evolving a Disjunctive Predator Prey Swarm using PSO: Adapting Swarms with Swarms
A thesis Presented by: Firasath Riyaz.
Mentor: Dr. Peter M. Maurer.
Co-Advisor: Dr. Robert J. Marks II
11/10/2005
Department Of Computer Science, BU, Waco.

2. Department Of Computer Science, BU, Waco.
Outline
Introduction
Problem
Solution
Adapting Predator Prey Swarm
Simulations, Results and Emergent Patterns
Conclusions
Possibilities for future work
11/10/2005
Department Of Computer Science, BU, Waco.

3. Department Of Computer Science, BU, Waco.
Introduction
Swarm Intelligence: “the emergent collective intelligence of groups of simple agents”
Autonomous agents
Simple rules
Coordination without direct communication
Unexpected complex group behavior
11/10/2005
Department Of Computer Science, BU, Waco.

4. Introduction (cont’d)
11/10/2005
Department Of Computer Science, BU, Waco.

5. Introduction (cont’d)
Features:
Distributed and multi-agent.
Flexibility.
Robustness.
Self-organization
Group behavior unexpected and complex
Complex forms of social behavior can achieve a number of tasks.
11/10/2005
Department Of Computer Science, BU, Waco.

6. Introduction (cont’d)
SI Applications
Business Applications:
Bonabeau, E. and Myer, C. (2001). Swarm Intelligence, A Whole New Way to think about Business, Harvard Business Review, pp
Ant foraging for efficient routing of cargo, Southwest Airlines.
Honey bees model for task allocation.
“bucket-brigade”, enhanced efficiency and increased productivity
Simple rules, self organization, Capital One
11/10/2005
Department Of Computer Science, BU, Waco.

7. Introduction (cont’d)
Telecommunication:
Bonabeau, E. and Myer, C. (2001). Swarm Intelligence, A Whole New Way to think about Business, Harvard Business Review, pp
Ant foraging applied for efficient routing.
Kassabalidis, I, El-Sharkawi, M.A., Marks, R.J. Asabshahi, P. and Gray, A.A.. (2001) Swarm Intelligence for Routing in Communication Networks, IEEE Globecom, San Antonio TX.
AntNet algorithm for mobile ad-hoc networks.
11/10/2005
Department Of Computer Science, BU, Waco.

8. Introduction (cont’d)
Optimization:
Kennedy, J. and Eberhart, R. C. (1995). Particle swarm optimization, Proceedings of IEEE International Conference on Neural Networks, Piscataway, NJ. pp
A function optimization algorithm
Botee, H.M. and Bonabeau, E. ( 1999). Evolving ant colony optimization, Advances in Complex Systems, vol. 1, no. 2/3, pp
Evolving ant colony optimization for solving TSP
11/10/2005
Department Of Computer Science, BU, Waco.

9. Introduction (cont’d)
Robotics:
Mondada F., Pettinaro G.C., Kwee I., Guignard A., Gambardella L.M., Floreano D., Nolfi S., Deneubourg J.-L. and Dorigo M. (2002) SWARM-BOT: A Swarm of Autonomous Mobile Robots with Self-Assembling Capabilities, Proceedings of the International Workshop on Self-Organisation and Evolution of Social Behaviour, pages 11-22, Monte Verità, Ascona, Switzerland
Control a system governed by complex dynamics
group of autonomous robots
11/10/2005
Department Of Computer Science, BU, Waco.

10. Problem Agent characteristics Autonomous
Swarm based models have multiple agents
Agent characteristics
Autonomous
Simple rules represented by sensors
Robustness of the swarm lies in the autonomous or disjoint nature of agents.
11/10/2005
Department Of Computer Science, BU, Waco.

11. Department Of Computer Science, BU, Waco.
Problem (cont’d)
Important Issue in multi-agent systems
Aggregation of simple rules in decision making
Conjunctive aggregation
Problem is if one sensor corresponding to a rule fails, the agent is ineffective
11/10/2005
Department Of Computer Science, BU, Waco.

12. Department Of Computer Science, BU, Waco.
Solution
Disjunctive aggregation:
Since
Added robustness: Linear degradation in agent’s performance under individual rules fail.
Ability to evolve performance
Emergent behavior
Investigate robustness
11/10/2005
Department Of Computer Science, BU, Waco.

13. Department Of Computer Science, BU, Waco.
Predator Prey Swarm
Models a system governed by complex dynamics.
Unpredictable effects of decision taken at agent level
Explicit design of control is difficult
Environment and agents
A model to test Disjunctive aggregation.
Predator models the behavior of a static environment
Prey models the behavior of an agent
11/10/2005
Department Of Computer Science, BU, Waco.

14. Predator Prey Swarm (cont’d)
World is defined by a two dimensional field
Predator: Bully
Current position, velocity, kill radius and neighborhood
Follow a set of rules to capture prey.
A predator does not die.
Prey: Dweeb
Current position, velocity and neighborhood.
Follow a set of rules to evade the predators
Maximize the median prey life
11/10/2005
Department Of Computer Science, BU, Waco.

15. Predator Prey Swarm (cont’d)
Sample two dimensional field of 3 predators and 6 prey.
11/10/2005
Department Of Computer Science, BU, Waco.

16. Predator Prey Swarm (cont’d)
Predator attack rules:
Chase nearest prey
Move towards Center of Mass of prey in neighborhood
Random component to avoid coalescing
11/10/2005
Department Of Computer Science, BU, Waco.

17. Predator Prey Swarm (cont’d)
Predator attack velocity attributes
11/10/2005
Department Of Computer Science, BU, Waco.

18. Predator Prey Swarm (cont’d)
Predator velocity:
Calculate unit velocity vectors in the three strategic attack directions.
Each velocity vector has an associated weight
Disjunctive aggregation of velocity components
vnew = wnd * vnd + wdcom * vdcom + wr * vr
Normalize vnew
Physical constraint
V = vold * momentum + vnew * ( 1 – momentum)
11/10/2005
Department Of Computer Science, BU, Waco.

19. Predator Prey Swarm (cont’d)
Predator position update
NewPosition = Old Position + MaxStepSize * V
11/10/2005
Department Of Computer Science, BU, Waco.

20. Predator Prey Swarm (cont’d)
Prey survival attributes: move away from
The nearest predator
Center of mass of predators contained in the neighborhood.
Move in a random direction to avoid coalescing/overlapping.
Stay away from the boundaries to avoid being cornered.
Move away from the nearest infected prey.
The prey try to spread themselves in the field by staying away from the closest prey and moving away from center of mass prey in the neighborhood.
11/10/2005
Department Of Computer Science, BU, Waco.

21. Predator Prey Swarm (cont’d)
Prey Survival Attributes
11/10/2005
Department Of Computer Science, BU, Waco.

22. Predator Prey Swarm (cont’d)
Prey velocity:
Calculate unit velocity vectors for different survival attributes.
Fuzzy Linguistic Variables in a Fuzzy Inference Engine:
Euclidean distance classified as VN, N, A, F, VF
Each variable has a set of associated weights
11/10/2005
Department Of Computer Science, BU, Waco.

23. Predator Prey Swarm (cont’d)
Disjunctive aggregation of prey velocity components
vnew = wnb * vnb + wbcom * vbcom + wr * vr + wid * vid
+ wdcom * vdcom + wnd * vnd + wbt * vbt
+ wtp * vtp + wlw * vlw+ wrw * vrw
11/10/2005
Department Of Computer Science, BU, Waco.

24. Predator Prey Swarm (cont’d)
Normalize vnew
Physical Constraint.
V = vold * momentum + vnew * ( 1 – momentum)
Prey Position Update:
NewPosition = Old Position + MaxStepSize * V
A set of optimal weights for prey survival attributes are evolved using Particle Swarm Optimization
11/10/2005
Department Of Computer Science, BU, Waco.

25. Adapting Predator Prey Swarms
Particle Swarm Optimization:
Optimization algorithm imitating the motion of a flock of birds, or insects
The system is initialized with a population of random solutions (particles) and searches for optima by updating generations
Particles: position and velocity.
Fitness
11/10/2005
Department Of Computer Science, BU, Waco.

26. Adapting Predator Prey Swarm (cont’d)
PSO Algorithm:
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 to equation 1         Update particle position according to equation 2     End
While maximum iterations or minimum error criteria is not attained.
11/10/2005
Department Of Computer Science, BU, Waco.

27. Adapting Predator Prey Swarm (cont’d)
vnew = vold + c1 * r1 * (pbest - present)
+ c2 * r2 * (gbest - present) (1)
present = present + vnew (2)
Where:
vnew and vold is the new and old velocities of the particle,
present: Current position of the particle in search space.
pbest: particle position which gave the best fitness value so far.
gbest: best of pbests so far.
r1 and r2 are uniform random numbers in [0, 1].
c1 and c2 are learning factors usually set to 2.
11/10/2005
Department Of Computer Science, BU, Waco.

28. Adapting Predator Prey Swarm (cont’d)
PSO was used to evolve prey survival attribute weights.
Each particle represents a set of weights
For each particle predator prey swarm was repeated several times
Fitness function optimized: Average median prey life
11/10/2005
Department Of Computer Science, BU, Waco.

29. Simulations, Results and Emergent Patterns
Simulation setup
Parameters.
Results
Emergent patterns
11/10/2005
Department Of Computer Science, BU, Waco.

30. Simulations, Results and Emergent Patterns
Simulation setup
Software configuration: Java
Hardware configuration: Dell Pentium 4, 1.8 Ghz, 512MB machines in the graduate student lab, Computer Science Dept.
11/10/2005
Department Of Computer Science, BU, Waco.

31. Simulations, Results and Emergent Patterns
Parameters used
Predator prey swarm
Parameter
Description
Value
Field Size
The size of the field in which predator prey swarm is evolved.
400 x 400 (pixel applet window)
Number of predators
The number of predators in the swarm. 10
Number of prey
The number of prey in the swarm.
100
11/10/2005
Department Of Computer Science, BU, Waco.

32. Simulations, Results and Emergent Patterns
Predator parameters
Prey parameters
Parameter
Value
Kill-radius
5 ( pixel positions)
Com-radius
200 (pixels)
Max step size
Momentum
0.75
Parameter
Value
Infected prey distance
100 ( pixel positions)
Com-radius
30 (pixels)
Max step size
3 ( pixel positions)
Momentum
0.25
11/10/2005
Department Of Computer Science, BU, Waco.

33. Simulations, Results and Emergent Patterns
PSO parameters
Fitness function optimized: Average of median prey life.
Parameter
Description
Value
Number of particles
Number of the particles in PSO. 10
Vmax
Maximum particle velocity 1
c1 and c2
Learning factors 2
Predator-Prey repeat
For each particle number of times predator prey swarm is evolved in one PSO generation. 5
11/10/2005
Department Of Computer Science, BU, Waco.

34. Simulations, Results and Emergent Patterns
PSO Simulation 1: 234 generations
The Global Best and Average Personal Best Fitness function is plotted
11/10/2005
Department Of Computer Science, BU, Waco.

35. Simulations, Results and Emergent Patterns
Evolved set of prey survival attribute weights
Parameter VN N A F VF
wnb
wbcom
wdcom
wnd
wlw
wrw
wtp
wbt
wid
11/10/2005
Department Of Computer Science, BU, Waco.

36. Simulations, Results and Emergent Patterns
PSO Simulation 2: 122 generations
The Global Best and Average Personal Best Fitness function is plotted
11/10/2005
Department Of Computer Science, BU, Waco.

37. Simulations, Results and Emergent Patterns
Evolved set of prey survival attribute weights
Parameter VN N A F VF
wnb
wbcom
wdcom
wnd
wlw
wrw
2.2854
wtp
wbt
wid
11/10/2005
Department Of Computer Science, BU, Waco.

38. Simulations, Results and Emergent Patterns
PSO Simulation 3: 112 generations
The Global Best and Average Personal Best Fitness function is plotted
11/10/2005
Department Of Computer Science, BU, Waco.

39. Simulations, Results and Emergent Patterns
Evolved set of prey survival attribute weights
Parameter VN N A F VF
wnb
wbcom
-0.243
wdcom
wnd
wlw
wrw
wtp
wbt
wid
11/10/2005
Department Of Computer Science, BU, Waco.

40. Simulations, Results and Emergent Patterns
Simulation 2 produced better global fitness value
Consistency check:
3 different predator prey simulations
Predator-prey swarm repeat: 25 times
Simulation 1 gives best optimal fitness
Weights from simulation 3 are more consistent
Weights used from
Average of median prey fitness
Standard Deviation
Simulation one
Simulation two
Simulation three
763701
11/10/2005
Department Of Computer Science, BU, Waco.

41. Simulations, Results and Emergent Patterns
The predator prey swarm was simulated with the most consistent optimal set of weights.
Unexpected behavior emerged.
Self sacrificing prey
Prey evolved negative weights
Reversal of rule
Increased swarm aggregate life
11/10/2005
Department Of Computer Science, BU, Waco.

42. Simulations, Results and Emergent Patterns
Self sacrificing prey
Self sacrificing prey
11/10/2005
Department Of Computer Science, BU, Waco.

43. Simulations, Results and Emergent Patterns
Prey herding predators
Prey evolved a strategy to surround the predators
Vacillating predator motion and herding
Increased swarm aggregate life
11/10/2005
Department Of Computer Science, BU, Waco.

44. Simulations, Results and Emergent Patterns
Prey herding predators
11/10/2005
Department Of Computer Science, BU, Waco.

45. Simulations, Results and Emergent Patterns
Prey circular motion
Prey move in circles
Cannot be cornered
Increased swarm aggregate life
11/10/2005
Department Of Computer Science, BU, Waco.

46. Simulations, Results and Emergent Patterns
Prey circular motion
11/10/2005
Department Of Computer Science, BU, Waco.

47. Simulations, Results and Emergent Patterns
Robustness of the evolved swarm is tested
Varying parameters
Number of predators 5 to 100 in steps of 5
Number of prey 25 to 80 in steps of 5
11/10/2005
Department Of Computer Science, BU, Waco.

48. Simulations, Results and Emergent Patterns
Gradual degradation
11/10/2005
Department Of Computer Science, BU, Waco.

49. Simulations, Results and Emergent Patterns
Robustness of disjunctive aggregation in rule failures is studied
Cumulative dropping of rules
Different predator prey population sizes
Number of swarm simulations 10
Predator step size=10, prey step size = 6
11/10/2005
Department Of Computer Science, BU, Waco.

50. Simulations, Results and Emergent Patterns
11/10/2005
Department Of Computer Science, BU, Waco.

51. Simulations, Results and Emergent Patterns
There is a gradual decrease in fitness of swarm aggregate
There is no sudden collapse of the swarm
Robustness
Disjoint agents
Disjoint rules
11/10/2005
Department Of Computer Science, BU, Waco.

52. Simulations, Results and Emergent Patterns
11/10/2005
Department Of Computer Science, BU, Waco.

53. Simulations, Results and Emergent Patterns
Analysis of single prey against one or multiple predators
Robustness only due to disjoint aggregation
Agent does not collapse under rule failures
11/10/2005
Department Of Computer Science, BU, Waco.

54. Department Of Computer Science, BU, Waco.
Conclusions
Disjunctive Aggregation
Disjoint at two levels
Two fold robustness
Robust under agent failure
Robust under rule failure
Ability to evolve
Emergent patterns
11/10/2005
Department Of Computer Science, BU, Waco.

55. Possibilities for future work
Further investigation into robustness of evolved swarm for varying parameter values
Field size, step size etc.
Incorporation of weak conjunctive logic between a subset of rules
11/10/2005
Department Of Computer Science, BU, Waco.

56. Department Of Computer Science, BU, Waco.
“with all human achievements in science and engineering, nature still provides the best systems that can ever be fashioned” – A philosopher
Questions
11/10/2005
Department Of Computer Science, BU, Waco.