1. Swarm Intelligence and Bio-Inspired Computing
2007 Fall Comp Lecture
Sang Woo Lee
Hello, I’m Sang Woo Lee. I will give a lecture about swarm intelligence and bio-inspired computing.

2. Table of Content What is Biologically Inspired Algorithm?
Swarm Intelligence
Evolutionary Computation
Application in Motion Planning
Trail-Laying Robots for Robust Terrain Coverage
Dynamic Redistribution of a Swarm of Robots
Evolving Schooling Behaviors to Escape from Predator
Here is this lecture’s agenda. Firstly, I will introduce that what is the biologically inspired algorithm. There are many field of biologically inspired algorithm, but I will focused on algorithm related to robot motion planning. Therefore, I will give two main branch of the biologically inspired algorithm. I will talk about swarm intelligence and evolutionary computation. Also, I will show the application of these approach in application in motion planning. I will present about trail-laying robots, which designed for terrain coverage, inspired by ant path finding, and dynamic redistribution of robot swarm, inspired by ant house hunting. Lastly, I will present evolving schooling behaviors to escape from predator, using genetic algorithm to develop behavior.

3. What is Biologically Inspired Algorithm?
Simulate biological phenomena or model
Working algorithm in nature
Proven its efficiency and robustness by natural selection
Biologically inspired algorithms are algorithms that inspired by natural biological phenomena or model. It is a simulation of working algorithms in the nature, which is proven to be efficient and robust by natural selection.
Especially, insect communities are endless sources of this algorithms because they produce complex group behaviors from interaction of millions individuals. For example, cooperation in foraging, house hunting.

4. Motivation Dealing too complex problems
Incapable to solve by human proposed solution
Absence of complete mathematical model
Existing of similar problem in nature
Adaptation
Self-organization
Communication
Optimization
The motivation of this biologically inspired approach is two in general.
Firstly, it is motivated when we are dealing too complex problems to make efficient algorithms. Because there is no sound and complete mathematical model of the relevant phenomena, human proposed solution strategies are not properly deals the complex problem.
Secondly, it is motivated when we realize that many complex problems that we tackle have similar versions in the natural world. For example, adaptation, self-organization, set-point control, prediction, communication, and optimization.

5. Application Robotic Network Social Organization Computer Immunology
Multi-Robot Motion Planning
Self-configuration
Network
Distributed autonomous system
Routing algorithm
Social Organization
Traffic control
Urban planning
Computer Immunology
There are so many applications of this approach. From robotics to computer immunology, there are many similarity between our problems and natural world. Because of its characteristics, which is self-organization, simplicity and producing emergent result, it is very useful in robotics. It can be applicable to multi-robot motion planning, and self configuration robots.
It also can be used in network for distributed autonomous system, and routing algorithm.
Moreover, it can be applicable to organizing human society, such as traffic control and urban planning.
By simulating biological immune system, we can protect our systems. Actually, computer virus are originated from biological virus, it is very natural approach to use bio-inspired algorithm in computer immunology.

6. Application in previous lecture
Boids in Nick’s lecture
Well known flocking algorithm
Flocking
Separation
Alignment
Cohesion
Machine Learning in Dave’s lecture
Neural Network
Supervised learning Method
We can easily find some bio-inspired algorithms in previous lectures in this course. Boids, which introduced in Nick’s lecture, developed by Craig Reynolds simulating the flocking behavior of birds. This is also an application of biologically inspired algorithm in robot motion planning. In Dave’s lecture, neural networks was one of the learning method. Neural network is a general approach to simulate human brains, which is also biologically inspired.

7. Swarm Intelligence Population of simple agents Decentralized
Self-Organized
No or local communication
Example
Ant/Bee colonies
Bird flocking
Fish schooling
At first, I will introduce swarm intelligence. As I described before, we can simulate many algorithms from the insect colonies, because they produce complex behavior by simple algorithms. Swarm intelligence is artificial intelligence based on decentralized, self-organized system. Similar to natural swarms, it produce emergent global behavior from population of simple agents’ local behaviors. Its characteristics, decentralized, self-organized, no or local communication, and simplicity makes it very useful.
We can see swarm intelligence in ant and bee colonies, bird flocking and fish schooling.

8. Ant Colony Optimization
Meta-heuristic Optimization
Inspired from the behavior of ant colonies
Shortest paths between the nest and a food source
One of the most well know algorithms which inspired by swarm intelligence is ant colony optimization. This algorithms is meta-heuristic optimization algorithm, which is general approach to apply to many problems. It is inspired from the foraging behavior of real ant colonies, to identify shortest paths between the nest and a food source.

9. Ant Path Algorithm Evaporating pheromone trail
Probabilistic path decision
Biased by the amount of pheromone
Converge to shortest path
Ant trips on shorter path returns quicker
Longer path lose pheromone by evaporating
Ant’s finding shortest path while foraging describes like this.
While walking between their ant nest and the food source, the ants deposit a substance called pheromone.
When ants arrive to a path intersection, they need to choose the path to follow.
They select it applying a probabilistic decision biased by the amount of pheromone: stronger pheromone trails
are preferred.
The most promising paths receive a greater pheromone after some time. This is due to the fact that, because these paths are shorter, the ants following them are able to reach the goal quicker and to start the trip back soon.
Finally, the pheromone is evaporated by the environment, and makes less promising paths lose pheromones because they are progressively visited by fewer ants.
At last, it is converged to the shortest path to food source.

10. Ant Path Algorithm
Emergent behavior of the colony that ends by obtaining the shortest path between two points (mass recruitment)
All ants are in the nest and will begin to search for food
Ants choose a random path since they do not know which one is shorter (better)
Ants following shorter path return faster to the nest, depositing more pheromone on their way back
(D)Shortest path has more pheromone and makes ants follow it with a higher probability

11. Ant Colony Optimization
Solved problems
Traveling Salesman Problem
Quadratic Assignment Problem
Job Shop Scheduling
Vehicle and Network Routing
There are many solved problems by ant colony optimization.
Traveling sales man problem, Quadratic Assignment Problem, Job Shop Scheduling, Vehicle and Network Routing.
All these problems are all NP-hard.
Quadratic Assignment Problem : There are a set of n facilities and a set of n locations. For each pair of locations, a distance is specified and for each pair of facilities a weight or flow is specified (e.g., the amount of supplies transported between the two facilities). The problem is to assign all facilities to different locations with the goal of minimizing the sum of the distances multiplied by the corresponding flows.
The problem statement resembles that of the assignment problem, only the cost function is expressed in terms of quadratic inequalities, hence the name.
NP-hard
Job Shop Scheduling : small manufacturing operations that handle specialized manufacturing processes such as small customer orders or small batch jobs.

12. Ant Traffic Organization
Dussutour, A., Fourcassié, V., Helbing, D. & Deneubourg, J. L. Optimal traffic organization in ants under crowded conditions. Nature 428, (2004)
Research on ant path selection in bottle-neck situation
Maximizing traffic volume
If ant find shortest path as described previously, how about the traffic volume? Does it also optimized? What if there is the bottleneck in the path?
This paper researched on that problem. As result, ant also optimize traffic volume, as we easily expected.

13. Ant Traffic Organization
Their experiment setup is like this. They made two branches from nest to sugar source. They used 5 queenless colonies and each has 500 ants.
They changes the branches width as a control factor.

14. Ant Traffic Organization
Symmetrical traffic in narrow path
Threshold width between 10.0 and 6.0mm
Pushed Ant is redirected to other path
Symmetry restored before the maximum flow
Benefits of using a single trail
Condensed trail - Better orientation guidance and stronger stimulus
High-density - Good information exchange
Optimize the rate of food return
Proved by analytical model and experiment
With this experiment, they could conclude these things. When the branch is so narrow to make many collision in traffic, they adapted to become symmetrical traffic in two branches. The threshold width is between 6 to 10 mm. They found that pushed ant is redirected to other path, so this result the adaptation to symmetric traffic.
They also found that symmetry of traffic restored before the maximum flow is reached, which means they prefer the single trail path to two trails. It is due to benefits of using a single trail. Because single trail is more concentrated than two, it gave a better orientation guidance, and stronger arousal stimulus of pheromone. Also because higher density of ants make good information exchange and helps to their group defense.
As a result, they proved that ants optimized the rate of food return, which means they maximize traffic volume.
It is proved by analytical model and experiement.

15. Ant Traffic Organization
a & b. Symmetric transition between 6 and 10.
c. Contradict opposite one-way flows on both branches.
d. Number of pushing events
This algorithm used by network routing papers. I introduce this paper because I think it is interesting and maybe used for swarm simulation. However, I could not find any paper that use this in the swarm simulation.

16. Terrain-Covering Ant Robots
J. Svennebring and S. Koenig, “Trail-laying robots. for robust terrain coverage,”, Proc. of IEEE International Conference on Robotics and Automation 2003, Volume: 1,  On page(s): vol.1
Inspired by Ant forage
Exploration & Coverage
Here is an application of bio-inspired algorithm in robotics, which adapt ant foraging pattern to exploration and coverage behavior of real robot.
Like Ants, they laying trail of artificial pheromone.

17. Terrain-Covering Ant Robots
Pebbles III robot
6 infrared proximeter
Bump sensor, 2 motors
Lay trails – Black pen to track trail
8 Trail sensor
Here is their real robot specification. Their pebbles 3 robot, which has 6 infra-red proximeter to detect obstacles, bump sensor, 2 motors, and 8 trail sensor. They make trail with some fluorescence eventually, but in this paper they use black pen to track the trail.

18. Theoretical Foundation
Node Counting
Robot repeated enter cells
Counting by markers in cell
Move to smallest number
Their theoretical foundation of this ant robots’ coverage is node counting. When the robots are repeated enter the cells, it lay the marker at each time in the cell. By counting all the markers, we can move to the adjacent cell which has the smallest number of markers. They break the tie probabilistically.

19. Ant Robots No communication Very limited sensing
Very limited computing power
Marking current cell
Sensing markers of neighbor cells
This ant robot has very good characteristics to manufacture and cooperate with numerous ant robots.
This robot need no communication between each robots, has very limited sensing and computing power. All the needed operation is marking current cell and sensing the markers of neighbor cells. Working with numerous ant robots has good properties.
It is robust and cost less for its simplicity, and many redundant robots increase the system tolerance, and easy to cooperation with simple algorithm.

20. Restriction Assumptions on theoretical foundation
Move discrete step
Mark cell uniformly
No noise in sensor
By the way, it works even
Uneven quality trail
Some missing trail
Pushed to other location
This ant robot has some restriction caused by assumptions on its theoretical foundation. They assume that it moves discrete step in one cell, and should mark cell uniformly, and they detect the marker with no noise. This assumption seems to make it is impossible to use this algorithm to real ant robots, but they show it work well even with breaking the assumption by experiments with real robot. They tested with uneven quality trail, missing some parts of trails. Also, they test the robot to push to other location. In all cases, ant robot works well with a increase coverage time.

21. Ant Robot Behavior Obstacle Avoidance Behavior
Inversely proportional to the distance
Weight for each direction sensor
Trail Avoidance Behavior
Fixed length
Trail sensor with recent past information
Weight Balancing of two behavior
Need to well balanced
This ant robot behavior is very simple. It has only two behavior: obstacle avoidance and trail avoidance. With obstacle avoidance behavior, it calculate avoidance vector by inversely proportional to the distance, with weighting for each direction sensor. With trail avoidance behavior, it calculate with fixed length vector. Its direction is calculated by weighting of trail sensors and recent past trail sensor information.
Finally, they weight two behaviors, and balancing it. Weight should be well adjusted to result good coverage, because too large weight avoidance result limited coverage region, and too low weight avoidance result larger coverage time.

22. Experiment Result
Their result with one real ant robot experiment. The real robot works very fine for coverage.

23. Experiment Result Work well with Uneven quality trail
Move another location
Removing patches of trail
As I described previously, the experiment result with real ant robot also result that it works well with out of the assumption situation.

24. Simulation Faster than random walk With no cleaning of Trail
Until some threshold
Too many trails result large coverage time
With no cleaning of Trail
Coverage time grow steeply
With cleaning of Trail
Same as ant pheromone
Works good with many coverage number
They simulate ant robot, to simulate cooperation working situation.
Ant robot works faster than random walk, until some threshold of coverage number. Because too many trail interrupt coverage, it because larger and larger with increased coverage number after the threshold.
Therefore they introduced cleaning of the trail, which simulate the real ant pheromone evaporation, result efficient coverage pattern. Even with increase coverage number, it works finely.

25. Simulation Result
This figure’s shows the result of the simulation. Figure 8 on right top shows trails of one robot, in the case of with cleaning and no cleaning of trail. Figure 9 on right bottom shows that map is saturated by repeated trail, which cause ant robot coverage time increasing. Figure 10 shows that multi cooperated pebbles simulation result. Its coverage time is increasing very fast after the threshold without cleaning. With cleaning, it shows constant coverage time.

26. Video

27. Dynamic Redistribution of a Swarm of Robots
A. Halasz, M. Ani Hsieh, S. Berman, V. Kumar. Dynamic Redistribution of a Swarm of Robots Among Multiple Sites, 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems.
Inspired by Ant house-hunting
Here is an another application of bio-inspired algorithm. It deals with dynamic redistribution of a swarm of robots among multiple sites, which inspired by an house-hunting algorithm in nature.

28. Ant house hunting
Probability of initiating recruitment depends on the site’s quality
Superior site scout has shorter latency to recruit
Recruitment type
Summon fellow by tandem run
Passive majority by transport
Transport recruitment of new site triggered by population (Over the quorum)
Recruitment speed difference amplified by quorum requirement
Biology research on ant’s house hunting describes the algorithm like this.
When scout are go to new sites, the probability of initiating recruitment depends on the site’s quality. That means superior site scout has shorter latency to start recruit. There are two recruitment type in ant colony. First one is summon fellow by tandem run, which is sequential moving lead by one ant. Second one is transport passive majority, which is much faster than first one.
Transport recruitment, which is second one, of new site triggered by population threshold, quorum. This quorum requirement of transport recruitment amplifies recruitment speed difference between superior and inferior site.

29. Ant house hunting
This figure describe that process. Superior site has less latency, and increases speed of recruiting when the population is over the quorum.
* The number of ants not yet recruiting as a function of time since each ant first entered the nest-site.
Ants at a mediocre site are indicated by triangles and those at a superior site by circles. Analysis of such ‘survivorship curves’ shows that individual ants hesitate for less time before recruiting to a superior site than they do for a mediocre one.

30. Dynamic Redistribution of a Swarm of Robots
Collectively distributes itself to multiple sites
Predefined proportion
No inter-agent communication
Similar to task/resource allocation
This paper deals about the distribution a swarm of robots in multiple sites. They want to build an algorithm which collectively distributes the robot to multiple sites. They assume that there is predefined proportion between sites, and no inter-agent communication. This problem is similar to task and resource allocation.

31. Simple system model Scalable Graph G All agents know Graph G
Using fraction rather than agent number
Graph G
Strongly connected graph
Edge
Transition rate Kij
Transition time Tij
Maximum transition capacity
All agents know Graph G
They start with build up simple system model. It is scalable because they dealing with fraction of the agent number, not the absolute number. There is a graph G ,graph of sites, which is strongly connected graph, and edge has transition rate, transition time, and maximum transition capacity.
Also, All the agents should know this graph G.

32. Simple system model Property Stability Convergence
To a unique stable equilibrium point
Proved analistically
They proved this model’s properties with an analystic method. They proved it is stable and converges to unique stable equilibrium point.

33. Result
This is simple system model simulation result. This simulation uses 10,000 agents in the 42-dot display network.
The initial configuration forms the letter “T” with the design specification for a letter “C”. Agents at sites above design specs are shown in red, agents at sites below design specs are shown in green, and travelers in light blue.

34. Result
The result show that it converge to the unique solution. Occupancy fractions at one site for different total agent numbers compared to the ODE model.

35. Result
It converge to the unique solution. Total number of misplaced agents for different N agent numbers compared to the ODE model.

36. Problem in first system
Transition in equilibrium state
Fast transition makes more idle trips
Extension
Inject Quorum sensing
Fast converge, less idle transition
There are some problems in this simple system. With fast transition rate, it converges fast. However, because there is always transition even in equilibrium state, fast transition rate makes more idle, useless trips.
Therefore they extended their model with injection quorum sensing, which result fast converge and less idle transition by switching edge. This quorum requirement is inspired by the ants’ house hunting algorithm, which I described before.

37. System with quorum sensing
Adjacent sites communication
Quorum information instantly available
Transition rate switch
Above quorum to below quorum
Set to maximum transition rate
Stable
Converges asymptotically
This extended system with quorum sensing has also some based assumption. Adjacent sites communicate quorum information each other with no delay, which is ideal case. With this quorum information, transition rate switch between maximum value and original value. If the site A is above the quorum and site B is below the quorum, transition rate sets to maximum transition rate. They proved that this system is also stable and converges much faster.

38. System with quorum sensing
Global ’distance’ from the desired configuration as measured by the total number of misplaced agents for different quorum values in the ODE model, compared to the quorum-less model. This shows it converge much fast with high quorum value.

39. System with quorum sensing
Problem
Increasing quorum increase convergence speed
Too big quorum make system stuck by high transition rates
This system has also a problem. Increasing quorum increase convergence speed, but if quorum is too big, the system is stuck by high transition rates by below quorum sites, because of the travel time.

40. System with quorum sensing
Snapshots from a simulation redeploying 200 agents, initially located at two sites, to three sites in an urban environment. Agents leaving sites are denoted by square markers and agents entering sites are denoted by diamond-shaped markers. Agents located at sites above quorum are shown in red while agents located at sites below quorum are shown in blue.

41. Evolutionary Computation
Inspired from the natural processes that involve evolution
Genetic algorithm
Evolution strategies, evolutionary programming, genetic programming
Use a population of competing candidate solutions
Reproduce and evolve themselves
Evolutionary Computation is the term that denotes the group of algorithms and techniques that have inspiration from the natural processes that involve evolution. Evolutionary Algorithms constitute a class of search and optimization methods that share some generic concepts.
Some of the techniques that can be included in this class are genetic algorithms, evolution strategies, evolutionary programming and genetic programming.
All of these techniques share some properties: they use a population of individuals with the ability to reproduce, these individuals suffer some genetic variations (usually mutations and crossovers) and there exists some kind of natural selection among them.

42. Evolutionary Computation
Combination
Alteration
Selection
Increases the proportion of better solutions in the population
Better one survives!
Evolutionary computation evolve candidate solutions by means of combinations and alterations, and a selection mechanism that increases the proportion of better solutions in the population.
Every different approach has its own genetic structures and own genetic operators that manipulate and generate new candidate solutions.

43. Evolutionary Computation
Since evolutionary algorithms emulate natural evolution, they adopt a biological terminology to describe their structural elements and algorithmic operations.
However, we should keep in mind that these terms are much more simple than their biological analogs. In Table 1, we describe a mapping of some of the most common terms in evolutionary computation from nature to computer science.

44. Evolving Schooling Behaviors
T. Oboshi, S. Kato, A. Mutoh and H. Itoh, Collective or Scattering: Evolving Schooling Behaviors to Escape from Predator, edited by R. Standish, M. A. Bedau and Abbass, H. A., Artificial Life VIII (MIT Press, Cambridge, MA, 2002), p. 386.
Evolving schooling behavior by Genetic Algorithm
Here is a application of genetic algorithm to build schooling behaviors for escaping from predator. They evolve schooling behavior using genetic algorithm.

45. The basic behavior model
Fish’s schooling behavior
Use Aoki’s model
Assuming 2-D world
Movement
Speed and Direction
Their basic behavior model is Aoki’s model of fish’s schooling behavior, which assume 2-D world. They just dealing with movement, with speed and direction in 2D.

46. The basic behavior model
Four basic behavior patterns
Repulsion behavior
Move with a high parallel orientation
Biosocial attraction
Searching behavior
Reference individual
Nearer one selected with greater probability
This model has 4 basic behavior patterns, which are repulsion, moving with a high parallel orientation, biosocial attraction, searching behavior patterns. With repulsion and parallel moving and attraction, they chose a reference individual. They chose nearer one with greater probability.

47. The basic behavior model
This is ranges of the basic behavior patterns.

48. The basic behavior model
Direction determined by
Previous direction
Four basic behavior patterns
Wobbling with normal distribution
Speed
Gamma distribution
In this model, direction is determined by three factors : previous direction, four basic behavior patterns, and random wobbling with normal distribution. Its speed is also random variable with Gamma distribution.

49. Extension Considering predator‘s existence Urgent mode
Sensing predator approaching
Direction determined by
Lerp with 4 variables
Parallel to neighbor
Attracted to neighbor
Averting from predator
Away from predator
From this basic model, they extended with predator’s existence situation. When the fish individual sense the predator approaching, its direction is determined by some other 4 variables. They are parallel moving to neighbor, attraction to neighbor, averting form predator, away from predator. Direction is determined by weighting of this 4 variables.

50. Extension
You can see patterns in urgent mode in this figure. A is parallel moving, B is attracting, C is averting, D is away behavior.

51. Predator's behavior Chase detected prey Random search
Predator's sensory field
k times larger
Distribution of predator's speed
n times faster
Predator’s behavior is defined very simple. It chases detected prey. If there is no detection of prey, it searches randomly.
Predator is stronger than prey, so it has k times larger sensory field and n time faster speed than prey’s.

52. Ecology The artificial ecology BL – body length of individual
Size : 40BL * 40BL
N prey, 1 predator
If prey < N
Create next generation prey
They made an artificial ecology as an test environment. The size of ecology is 40BL * 40BL, and BL is body length of individual.
Their initial configuration is N prey and 1 predator. If number of prey is reduce under N, create next generation prey with genetic algorithms.

53. Evolution Genetic algorithm Gene of individual Selection Evolution
Weight of urgent mode pattern
Each of 10 bit
Selection
Surviving time
Evolution
Crossover each weight region of two parent
5% mutation for each bit
Parent selection probability
Proposition to surviving time
They make evolution with gentic algorithm. They define gene of individual with weight of urgent mode patterns, and each has 10 bit string.
Natural seletion is done by surviving time.
Evolution is done by one-point crossover and mutation. Crossover of each weight region over two parent, and 5% mutation for each bit.
Parent selection is probability with proposition to surviving time.
* A single crossover point on both parents' organism strings is selected. All data beyond that point in either organism string is swapped between the two parent organisms. The resulting organisms are the children.
* r1 = 0.5BL, r2 = 2.0BL, r3 = 5.0BL, and w = 30 for the ranges of their behavior patterns, wobble = 15.
300 generations, with 1,000 moves in each, and total of 10 runs were made

54. Result Average Proposition of 4 weight B(attract) becomes lower
D(away) becomes higher
A(parallel) becomes higher
Evolve for schooling
More for evading
Good gene value selected by artificial selection has this tendency. Attraction weight becomes lower, and away weight becomes higher. Especially, parallel becomes higher, but less than away. That means they evolve for schooling pattern, with more evading pattern.

55. Result
Average proportion of A,B,C,D in escaping behavior. B(Attraction) weight becomes lower, and D(away) weight becomes higher. A(parallel) becomes higher, but less than away.

56. Result Average Proposition of 4 weight against n (predator’s speed)
More schooling when lower risk
More evading when higher risk
Scattered evasion is more efficient with high risk
If predator is too fast, no strategy survives
If we see average proposition of 4 weight against n, which is predator’s speed factor, we can see this patterns.
When n is small, which means low risk, there are more schooling behavior. When n is large, means high risk, there are more evading pattern.
This means scattered evasion pattern is more efficient when there is high risk.
Finally, if predator is too fast (n is over 2.0), no strategy survives.

57. Result
Average proportions of A,B,C,D against change n. At low risk, more A(schooling behavior). At high risk, more D(evading pattern).

58. Result comparison with real fish schooling
They made comparison with real fish experiement.

59. Result comparison with real fish schooling
Polarization
Average angle deviation
Polarization means the average of the angle deviation of each fish to the mean swimming direction of the school.
In this graph, we can see that simulation graph pattern is quite similar to real one.

60. Conclusion Bio-inspired algorithms has many application
Simple algorithm becomes emergent logic
Useful for too complex problems
Very useful for swarm of robot control
Simple computation
Decentralized and self-organized
Need no or local communication
Useful to establish group behavior
Conclusively, there are many application in bio-inspired algorithms. The characteristic that simple algorithms becomes emergent logic emergent logic make it very useful solving to complex problems.
It is most suitable to swarm of robot control, because it need simple computation, decentralized and self-organized, need no or local communication. Also, it is useful for establishing group behavior, which is hard to design from the zero.
I tried to find some application of bio-inspired algorithms in human crowd simulation, but I had not found. Because it is very similar to control swarm of robot in problem’s character, it seems to be not tried to use this approach in human crowd simulation. However, human crowd simulation, e.g. RVO or Helbing, has same properties of swarm intelligence – decentralized, self-organized, local communication. In the high level, I think the two approach has some cross section.

61. Reference
de Castro, Leandro N. Recent Developments in Biologically Inspired Computing, Hershey, PA, USA: Idea Group Publishing, p vii.
Dussutour, A., Fourcassié, V., Helbing, D. & Deneubourg, J. L. Optimal traffic organization in ants under crowded conditions. Nature 428, (2004)
J. Svennebring and S. Koenig, “Trail-laying robots. for robust terrain coverage,”, Proc. of IEEE International Conference on Robotics and Automation 2003, Volume: 1,  On page(s): vol.1
A. Halasz, M. Ani Hsieh, S. Berman, V. Kumar. Dynamic Redistribution of a Swarm of Robots Among Multiple Sites, 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems.
T. Oboshi, S. Kato, A. Mutoh and H. Itoh, Collective or Scattering: Evolving Schooling Behaviors to Escape from Predator, edited by R. Standish, M. A. Bedau and Abbass, H. A., Artificial Life VIII (MIT Press, Cambridge, MA, 2002), p. 386.