Swarm Intelligence Part 1 Ants Algorithm

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Swarm IntelligencePart 1Ants Algorithm
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BIONICS

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• ???????? ??? ????? Genetic Algorithms
• ???? ??? ???? Neural Networks
• ????? ??? ????????? ?? Self-organizing Systems
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• BIONICS Application of biological principles to
  the study and design of engineering systems.

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Swarm Intelligence
any attempt to design algorithms or distributed
problem-solving devices inspired by the
collective behavior of social insect colonies and
other animal societies Bonabeau, Dorigo,
Theraulaz Swarm Intelligence, p. 7
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Ants ????? ??

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• .........

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Emergence
Unique global behavior arising from the
interaction of many agents ????? ??? ?? ?? ??????
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• Emergence ???? ????? ?? ???? ?? ?????? ????? ????
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• ??? ?? ?????? ?? ????? ?? ???? ??? Emergence ??
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Emergence
Unique global behavior arising from the
interaction of many agents ????? ??? ?? ?? ??????
????? ????? ????? ?? ????? ??? ???? ?? ???

• Emergence ?? ????? ??? ?????? ????? ?? ????
  ????? ???? ??????? ? ???? ???? ?? ?????? ???
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  ????? ??? ???? ??????? ? ????? ???? ????? ?.....
• ?? ?? ?? ?? ??? ????? ?? ????? ????? ?? ?? ???
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"EMERGENCE The connected lives of ants, brains,
cities, and software" by Steven Johnson
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????? ????? ??

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Swarm Intelligence

• Swarm Intelligence ?? ??? ???? ????? ???? ?????
  ????? ????? ???? ???? ???? ?? ??? ??? ???
• ??????? ??? ???? ??????? ??
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  ???? ???? ??? ??? ???.

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Ant Colony Optimization

• Marco Dorigo is research director of the IRIDIA
  lab at the Université Libre de Bruxelles
• Inspired by the remarkable ability of social
  insects to solve problems, Dorigo and Stützle
  introduce new technological design principles for
  seeking optimized solutions to difficult
  real-world problems, such as network routing and
  task scheduling.

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Ant Colony Optimization

• Ant Colony Optimization ?? ?????? ????? ?? ????
  ???? ???? ??????? ????? ?? ????? ???? ?????? ????
  ???????? ?? ???? ??????? ?? ???

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????? ?? ????? ?? ?????? ????? ???? ???? ?? ????
???? ?
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????? ????? ?? ?????? ?????? ?? ????

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  ?? ?????? ??????? ?? ????
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• ?? ??? ????? ????? ?? ??? ?????? ?? ??? ? ?????
  ?? ?? ?????? ????? ????
• ??? ?? ??????? ?? ?? ?????? ?? ?????? ??????? ??
  ????? ????
• ??? ??? ?????? ?? stigmergy ?? ?????
• ????? ?? ?? ??? ????? ?? ?? ?? ?? ?????? ?? ???
  ???? ?????
• ?? ??? ???? pheromone ???? ?? ???
• ????? ?? pheromone ?? ?? ????? ?? ???? ????? ??
  ?? ?? ?? ????
• ????? ????? ????? ?? ??? ??? pheromone ?? ???
  ????? ?? ????

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The Shortest Path (1)

• Two ants start their random walk
• They both eventually find the food
• The one taking the shorter path finds the food
  first
• Each ant leaves a trail of pheromones behind
• Once taken the food the ants follow their
  pheromone trail towards the nest

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The Shortest Path (2)

• The one taken the shorter path returns first and
  arrives back to the nest first

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The Shortest Path (3)

• Now a third ants wants to search for food
• The ant realizes the trials left behind by its
  predecessors
• Most likely it follows one of the existing trials
  rather than initiating a new trial
• Most likely it follows the trial with the higher
  density of pheromones

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The Shortest Path (4)

• This results in even denser pheromone trial on
  the shorter path
• In long term this results in most ants using the
  shortest path

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Simple Stochastic Algorithm

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  pheromone ?? ?????? ?? ???.
• ??? ?? ?????? ??????? ????? ?? ?? ??? pheromone
  ?? ????????? ???? ?????? ?????? ?? ??? ??????
  ????? ???.

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pheromone ????? ? ??? ??????

• Pheromone ?? ????? ????? ?? ???.
• ??? ??? ?? ??????? ???? ???? ???????? ???.
• ?????? ?? ??????? ????? ?? ??????? ????? ?? ????
  ????? pheromone ?? ??????? ????? ????? ????
  ?????? ???.

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Disclaimer May not work with a cube of ice
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StarLogo

• http//education.mit.edu/starlogo/
• StarLogo is a programmable modeling environment
  for exploring the behaviors of decentralized
  systems, such as ant colonies.
• In decentralized systems, orderly patterns can
  arise without centralized control.
• Increasingly, researchers are choosing
  decentralized models for the organizations and
  technologies that they construct in the world,
  and for the theories that they construct about
  the world.
• StarLogo visualizes the behavior of the
  decentralized system

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StarLogo

• StarLogo consists of graphic turtles and an
  environment
• The behavior of the turtles can be programmed
• All turtles run the same program in parallel
• A turtle can represent almost any type of object
  an ant in a colony, a car in a traffic jam, an
  antibody in an immune system, a molecule in a gas
  
• Also the effect of the environment on turtles can
  be programmed
• You can write programs for thousands of "patches"
  that make up the turtles' environment.
• Turtles and patches can interact with one another
• Turtles can be programmed to "sniff" around the
  environment, and change their behaviors based on
  what they sense in the patches

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Modeling the Ants Behavior
DEMO
Dont go away We will continue after the demo
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Part 2Ants in Networks
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History

• ABC routing (Schoonderwoerd et al., 96)
• Regular and Uniform ant routing (Subramanian et
  al., 97)
• Antnet (Dorigo et al., 98)
• Antnet (Dorigo et al., 02)
• Improved Antnet (Boyan et al., 02)
• Modified Antnet (Tekiner et al., 04)
• Antnet with evaporation (Tekiner et al., 04)
• Ants algorithm with QoS (Carrillo et al., 04)

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What Are Ants

• Ants are emulated by mobile agents
• Mobile agents are carried by packets
• Especial packets can be used as mobile agents
  (ants)

initialize_ant () while (current_state ?
target_state) A read_local_pheromone-table() P
compute_transition_probabilities (A, M,
problem_constraints) next_state
apply_ant_decision_policy (P, problem_constraints)
move_to_next_state (next_state) if
(step-by-step_pheromone_update) update_pheromone_t
able() // deposit pheromone on visited
arc update_ant_memory() if (delayed_pheromone_upda
te) evaluate_solution() update_pheromone_tables()
// deposit pheromone on ALL visited arcs die()
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What About Pheromones?

• Pheromones pass the information about the length
  of the path (time) to other ants
• The agents can pass the same information to data
  packets at the nodes
• Ants decide based on the density of the
  pheromones and some probability values
• The probability values can be calculated based on
  the path information and listed in routing tables
  in the nodes

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AntNet

• First application of ants algorithm for routing
  in (datagram) packet networks
• Ants are sent between source-destination pairs to
  create a test and feedback signal system
• Ants discover and maintain routes
• Inter-node trip times are used to adjust next-hop
  probabilities
• Packets are forwarded based on next-hop
  probabilities

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Routing Table

• Start with a static routing table for each node.
• Each routing table stores the probabilities of
  using the next hops to reach all possible
  destinations
• Sum of probabilities at each row equals one

Ports (Neighboring Nodes)
Destinations
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Routing Table Updates

• To create a dynamic routing table, create ants
  as agents that will go back and forth to random
  destinations.
• These ants will then update the probabilities in
  the routing table.
• Packets will be transferred to paths based on the
  probabilities listed in the routing table

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The Agents

• Two types of agents (ants)
• Forward Ants (to collect information)
• Backward Ants (to update probability table)
• Two types of queues
• Low priority queue (data packets and forward
  ants)
• High priority queue (backward ants)
• Forward ants are routed at the same priority as
  data packets
• Forward Ants experience the same congestion and
  delay as data
• Backward ants are routed with higher priority
  than other packets

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Forward Ants

• At regular intervals every node creates a forward
  ant to randomly selected destinations.
• Destinations are selected to match current
  traffic patterns
• Forward ant uses probabilistic routing tables at
  every intermediate node to choose output port
  from unvisited list of nodes.
• Elapsed time and node identifier is pushed to
  ants stack.
• If a cycle is detected , cycle is deleted from
  ants memory.
• When a forward ant reaches to its destination It
  transforms itself to a backward ant

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Backward Ants

• A backward ant visits the list of the nodes in
  its stack in a reverse order,
• Updates corresponding entries in the routing
  tables and array on its way back to source by
  using its values stored on its stack.

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Example
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Statistics

• Except for the routing table, each node also
  keeps a table with records of the mean and
  variance of the trip time to every destination
• At each node, backward ants update the trip time
  statistics to the destination in addition to the
  next-hop probability

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Routing Table Updates

• Reinforcement Factor (Based on the trip time and
  the statistics)
• r f (1 - Wbest/T) g (mean,var)
• 0 lt r lt 1,
• Increase the probability of the channel that
  backward ant comes from
• P P r (1 - P) P (1 r) r
• Decrease the probability of the other channels
• P P (1 - r)

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Data Packets

• Data packets are routed using the next-hop
  probabilities
• The packets are distributed over the paths
  proportional to their probabilities
• A probability threshold level can be used to
  avoid selection of not-so-good paths
• Achieve some degree of load balancing over all
  existing good paths

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Performance

• AntNet reports better performance in terms of
• Delay
• Throughput
• Robustness
• Reaction to changes
• Traffic overhead is higher than OSPF

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Variations

• Different varieties of the antnet routing
  algorithm will be resulted depending on the
• Forward ant routing mechanism
• Routing table update mechanism
• Packet forwarding criterion

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Router Architecture
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Evaporation

• Link usage statistics are used to evaporate the
  pheromone laid by the ants.
• It is the proportion of number of forward ants
  destined to the node x over the total ants
  received by the current node in the given time
  window.
• By evaporating the links probabilities in a
  predefined rate, average delay experienced per
  packet is reduced

Improved antnet routing algorithm with link
probability evaporation F. Tekiner, F. Z.
Gassemlooy, and S. Al-Khayatt
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Congestion

• Agents are delayed if congestion occurs
• Has the same effect of a longer path
• Pheromones evaporate more
• Less pheromone if agent is delayed more

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Quality of Service

• M-Class ants are used for M-class type of
  services
• Probabilities in the routing table represent the
  probability that packets can reach the required
  level of QoS
• The probabilities are updated based on the delay
  statistics per QoS class and the available
  bandwidth
• Considering other QoS parameters, such as
  availability etc., can be studied

A Quality of Service Routing Scheme for Packet
Switched Networks based on Ant Colony Behavior
Liliana Carrillo and J.L. Marzo
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Security Issues

• Threats
• Untrustworthy hosts
• Forward data/ant packets to a wrong direction
• Delay data packets
• Generate bursts of ant packets
• Malicious Agents
• Carry false trip time information
• Attack Goals
• Increase the packet latency
• Mislead packets to a longer path
• Break down a critical node
• Mislead packets to a certain node to overload it
• Drop Data Packets
• Mislead packets to a malicious node

Security Issues in Ant Routing Weilin Zhong
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Conclusion

• Application of swarm intelligence in network
  routing problems
• Inspired by the stigmergy model in ant colonies
• Using mobile multi-agent systems
• A distributed adaptive routing algorithm
• Autonomous

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THE END