Multi swarm problem solving in networks tony white email tony@sce carleton ca
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Multi-swarm Problem Solving in Networks Tony White email: Overview . Introduction What is it and why is it interesting? Problem solving and stigmergy. Swarm problem solving Swarm agent architecture Connection-oriented routing Simple diagnosis Extensions and Future.

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Multi swarm problem solving in networks tony white email tony@sce carleton ca

Multi-swarm Problem Solving in NetworksTony Whiteemail:


  • Introduction

    • What is it and why is it interesting?

    • Problem solving and stigmergy.

  • Swarm problem solving

    • Swarm agent architecture

    • Connection-oriented routing

    • Simple diagnosis

  • Extensions and Future

What is swarm intelligence
What is Swarm Intelligence?

  • “Swarm Intelligence is a property of systems of non-intelligent robots exhibiting collectively intelligent behavior.” [Beni, 89]

  • Characteristics of a swarm:

    • distributed, no central control or data source;

    • no (explicit) model of the environment;

    • perception of environment, i.e. sensing;

    • ability to change environment.

What is swarm intelligence cont
What is Swarm Intelligence (cont.)?

  • Swarm systems are examples of behavior-based systems exhibiting:

    • multiple lower level competencies;

    • situated in environment;

    • limited time to act;

    • autonomous with no explicit control provided;

    • problem solving is emergent behavior;

    • strong emphasis on reaction and adaptation;


  • Robust nature of animal problem-solving

    • simple creatures exhibit complex behavior;

    • behavior modified by dynamic environment.

  • Emergent behavior observed in:

    • bacteria

    • ants

    • bees

    • ...

Emergent problem solving
Emergent Problem Solving

  • For Lasius Niger ants, [Franks, 89] observed:

    • regulation of 1 degree celcius range;

    • forming bridges;

    • raiding specific areas for food;

    • building and protecting nest;

    • sorting brood and food items;

    • cooperating in carrying large items;

    • emigration of a colony;

    • finding shortest route from nest to food source;

    • preferentially exploiting the richest food source available.


  • Indirect communication via interaction with environment [Grassé, 59]

    • Sematonic [Wilson, 75] stigmergy

      • action of agent directly related to problem solving and affects behavior of other agents.

    • Sign-based stigmergy

      • action of agent affects environment not directly related to problem solving activity.

Ant colony
Ant Colony

  • Ants are behaviorally unsophisticated; collectively perform complex tasks.

  • Ants have highly developed sophisticated sign-based stigmergy

    • communicate using pheromones;

    • trails are laid that can be followed by other ants.

Pheromone trails
Pheromone Trails

  • Species lay pheromone trails travelling from nest, to nest or possibly in both directions.

  • pheromones evaporate.

  • pheromones accumulate with multiple ants using path.

Food source


Pheromone trails continued



30 ants

30 ants
















Pheromone Trails continued


T = 1

T = 0

10 ants

20 ants

15 ants

15 ants





10 ants

20 ants

15 ants

15 ants

30 ants

30 ants

Swarm operation
Swarm operation

My Agent

  • Swarm agents:

    • arrive at a node,

    • sense environment,

    • undertake local activity,

    • modify environment,

    • use sensory input to make migration decision

Swarm agent architecture
Swarm Agent Architecture


  • Agents have a uniform architecture consisting of five components:

    • emitters (E),

    • receptors (R),

    • chemistry (C),

    • a migration decision function (MDF),

    • memory (m)

Multi swarm architecture
Multi-swarm Architecture





My Agent


  • Generators of chemical messages (E)

Chemical modification

of environment


My Agent


  • Sensors of chemical messages (R) from local environment

Detector for local



  • Chemicals are digitally-encoded using a {1, 0, #} alphabet.

  • The # symbol unifies with 1 or 0.

  • Chemicals have two attributes:

    • encoding

    • concentration

  • Chemicals participate in reactions.


My Agent


  • The set of chemical reactions (C) that can operate on sensed and locally stored chemicals.

Chemical interactions

Examples of chemical reactions
Examples of chemical reactions

Catalytic breakdown of 011

Exothermic reactions

Endothermic reactions

Migration decision function
Migration Decision Function

  • The MDF is used to determine the next node to visit in the network

    pijk (t) = Pp[Tijkp(t) ]-akp[C(i,j)]-b/ Nk(i,j,t)

    Nk(i,j,t) = Sj in A(i)Pp[Tijkp(t) ]- akp[C(i,j)]-b

    akp, b are control parameters

    Nk(i,j,t) is a normalization term

    A(i) is the set of available egress links

Multi swarm scenario
Multi-Swarm scenario

  • Distributed Network Management employing delegation [Yemini, 91]

  • Three interacting swarms:

    • connection finding,

    • connection monitoring,

    • connection fault diagnosis






Routing problem
Routing Problem

  • Idea

    • Ants dropping different pheromones used to compute “shortest” path from source to destination(s);

    • more flexible adaptation to failures and network congestion;

    • use only local knowledge for routing and avoid costly communication of state to all network nodes.

Why routing
Why Routing?

  • Conventional routing often relies on:

    • global state available at all nodes;

    • centralized control;

    • fixed “shortest path” (Dijkstra) algorithms;

    • limited ability to deal with congestion or failure.

  • Ideally, would like to have network adapt routing patterns to take advantage of free resources and move existing traffic if possible.

Routing research
Routing Research

  • Three approaches so far investigated:

    • [White et al, 96+]

    • [Schoonderwoerd et al, 97] (*)

    • [Di Caro and Dorigo, 97]

  • Differences

    • Link cost metric constant in *

    • Point to point traffic only in *

    • AS parameter settings constant in *

Routing agents
Routing agents

  • Agent types:

    • explorer

      • used for route determination

    • allocator

      • allocates resources in network when route emerged

    • deallocator

      • deallocates resources in network at end of call

Point 2 point connections
Point-2-Point Connections

  • For explorer agents:

    • At each node, they choose path with probability proportional to f(ce, pe);

    • explorers visit edges once only (achieved through use of tabu list);

    • when destination reached, ants return along the path explored laying down pheromone trail;

    • when explorers return a decision is made regarding path emergence

Path emergence
Path Emergence

  • At source node (“nest”):

    • store paths for previous m explorer agents;

    • when p% follow same path allocator agent is sent to allocate bandwidth in network;

    • explorer agents continue to look for new (possibly better) paths.

  • Applies for one or many pt-2-pt connections:

    • ants use different, non-reacting pheromones.

Explorer agent algorithm

1. Initialize

set t:= 0

For every edge (i,j) set an initial value Tij(t) for trail intensity. Place m ants on the source node. [Generate new explorers at freq. ef] ]

2. Set s:= 1 { tabu list index)

for k:= 1 to m do

Place starting town of the kth

ant in tabuk(s).

3. Repeat until dest’n reached:

Set s := s + 1

for k:=1 to m do

Choose the node j to move to

with probability pijk (t)

Move the kth ant to node j.

Update explorer route cost:

rk = rk + c(i,j)

if (rk > rmax)

kill explorerk

Insert town j in tabuk(s).

At destination go to 4.

4. While s > 1

traverse edge (i,j)

T(i,j) = T(i,j) + pe

s := s - 1

5. At source node do:

if (pathe = pathBuffer * d)

create and send allocator

if t > Tmax

create and send allocator

Explorer agent algorithm

Evaporation occurs concurrently with exploration

Point 2 multipoint connections
Point-2-Multipoint Connections

  • For j destinations, consider as j pt-2-pt connections with:

    • same pheromone, i.e. all explorers communicate;

    • j allocator agents only allocate bandwidth once;

    • allocator send decision made when % of all j explorer ants agree on spanning tree.

Routing function
Routing Function

Transition probability (mdf):

pijk (t) = [Tijk(t) ]-a[C(i,j)]-b/ Nk(i,j,t)

Nk (i,j,t) = Sj in (S-Tabu(k)) [Tijk(t) ]-a[C(i,j)]-b

a, bare control parameters that determine the sensitivity of the algorithm to link cost and pheromone.

C(i,j) a function that depends upon the type of traffic, the length and utilization of the link.

Allocator agents
Allocator agents

  • Allocator agents can fail:

    • bandwidth already allocated by time allocator is sent;

    • allocator agent backtracks to source rolling back resource allocation and decreases pheromone levels;

    • decision to re-send allocator made at a later time (a backoff period is observed);

    • explorer ants continue to search for routes.

Connection monitoring agents
Connection monitoring agents

  • Connection’s quality of service monitored.

  • Changes in QoS cause Connection monitoring agents to be sent into network, laying down q-chemical.







Connection diagnosis agents
Connection diagnosis agents

  • Examples of netlets

  • Senseq-chemicalconcentrations








Experimental parameters fixed
Experimental Parameters (fixed)

  • Number of ants to create = 20

  • Frequency of creation = every 10 cycles

  • Amount of pheromone dropped = 10

  • pheromone evaporation rate = 0.9

  • Sample window size = 50

  • Emergence criterion = 0.9

Routing results
Routing Results

  • Shortest paths emerged quickly

  • Mixed pt-2-pt, pt-2-mpt routes emerged

  • Routing responds to changes in environment:

    • node failure;

    • link failure;

    • link cost changes

Parameter sensitivity
Parameter Sensitivity

  • Bad solutions and stagnation

    • For high values of a the algorithm enters stagnation behavior very quickly without finding very good solutions.

  • Bad solutions and no stagnation

    • a too low, insufficient importance associated with trail.

  • Good solutions

    • a , b in the central area (1,1), (1,2), (1,5), (0.5, 5)

Routing results1
Routing Results

  • Link cost functions: C(i,j)



Five functions studied experimentally:

  • constant

  • linear

  • linear threshold

  • quadratic

  • server (1/1-u)

    At high occupancy (> 50%)

    server appeared to give the best

    results. At low occupancy (<25%)

    function relatively unimportant.





  • Diagnosis agent is ‘hill climbing’ in space of q-chemical.

  • Very quickly finds high q-chemical concentrations and initiates diagnostic activity.

  • An example of distributed diagnosis through constructive chemical interference.

Self adaptation
Self Adaptation

  • pheromone and cost sensitivities should vary during search:

    • avoid premature convergence;

    • speed up search considerably.

  • Explorers encode sensitivity values:

    • fitness of encoding is cost of route;

    • new agents are created with and use genetically-manipulated values for route finding.

Extensions to current m swarm system
Extensions to current m-Swarm system

  • Multi-class routing

    • Higher priority traffic causes pheromone levels of lower priority traffic to decay;

    • automated re-routing of traffic performed.

  • Swarms learn to avoid regions of network providing low quality connections.

Other potential applications of m swarm systems
Other (potential) applications of m-Swarm systems

  • Behavior-based Network Management;

    • explorer agents allocate routes;

    • parent agents monitor “health” of explorers;

    • congestion agents identify global network congestion;

    • congestion agents signal re-planning of network;

    • congestion agent pheromones react with routing agent pheromones in order to cause re-routing after network re-planned.


  • Active networks are being researched at:

    • M.I.T.

    • U. Penn.

    • CMU

    • Georgia Tech.

  • Management by delegation [Yemini, 91] considered an essential design criterion for next generation network management systems.