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Swarm Intelligence Part 1 Ants Algorithm

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Title: Swarm Intelligence Part 1 Ants Algorithm


1
Swarm IntelligencePart 1Ants Algorithm
2
BIONICS
  • ????? ???? ????? ? ???? ????? ???? ?????? ??
    ??????? ? ?????? ??? ???? ???? ???
  • ?? ????? ????
  • ???????? ??? ????? Genetic Algorithms
  • ???? ??? ???? Neural Networks
  • ????? ??? ????????? ?? Self-organizing Systems
  • ????? ?? ??? ????? ?? ???? ????? ???? ?? ?????
    ??? ????? ?? ? ????? ??? ?? ????? ??????? ?????
    ???? ???
  • ?? ????? ???? ?? ??? ???? ???????? ??? ????????
    ???? ?????????? ??? ???????? ? ????? ??? ?????
    ??? ???
  • BIONICS Application of biological principles to
    the study and design of engineering systems.

3
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
4
??? ???? ? ????? ????????
5
Ants ????? ??
  • ????? ?? ??? ?? ???? ????????? ??????? ????
    ?????.
  • ????? ?? ?? ???? ????? ?????? ??? ?????? ?? ??
    ????? ?? ????
  • ??? ? ?????? ??? ?? ??? ?? ?? ????
  • ????? ???? ???
  • ???? ???? ??????
  • ???????? ????
  • ?????? ?? ??? ??
  • ????? ?? ???? ???? ?? ??
  • ???????
  • ??? ????
  • .........

6
????? ?????
  • ?? ?? ????? ?? ?????? ???? ? ???? ???? ??? ???
    ????? ?? ????
  • ??? ?? ????? ?? ?????? ????? ???? ???? ??? ?????
    ????? ?????? ???? ?? ???.
  • ??? ??? ????? ? ????? ?? ??? ??? ????? ?????.

7
Emergence
Unique global behavior arising from the
interaction of many agents ????? ??? ?? ?? ??????
????? ????? ????? ?? ????? ??? ???? ?? ???
  • Emergence ???? ????? ?? ???? ?? ?????? ????? ????
    ????? ?? ?? ?? ???? ?? ?????? ??? ?? ???? ?? ????
    ?? ????? ?????? ?????? ?? ?? ???.
  • ??? ?? ?????? ?? ????? ?? ???? ??? Emergence ??
    ??? ????? ????? ???? ?? ??? ? ??????? ????? ?
    ??????? ?? ?? ???? ??? ????.

8
Emergence
Unique global behavior arising from the
interaction of many agents ????? ??? ?? ?? ??????
????? ????? ????? ?? ????? ??? ???? ?? ???
  • Emergence ?? ????? ??? ?????? ????? ?? ????
    ????? ???? ??????? ? ???? ???? ?? ?????? ???
    ?????? ????? ????? ??? ?????????????? ???????
    ????? ??? ???? ??????? ? ????? ???? ????? ?.....
  • ?? ?? ?? ?? ??? ????? ?? ????? ????? ?? ?? ???
    ???? ?? ???? ????? ?? ????? ?? ??? ?????? ?? ????.

"EMERGENCE The connected lives of ants, brains,
cities, and software" by Steven Johnson
9
????? ????? ??
  • ?? ????? ???????? ???? ?? ?? ???? ?? ???
  • ???????? ????? ?????? ??? ?? ??? ?????? ????????
    ????? ?? ???? ?? ???

10
Swarm Intelligence
  • Swarm Intelligence ?? ??? ???? ????? ???? ?????
    ????? ????? ???? ???? ???? ?? ??? ??? ???
  • ??????? ??? ???? ??????? ??
  • ????? ???? ???
  • ????? ?? ???? ????????? ?? ?? ?????? ?????? ??
    ????
  • ????? ??? ?????? ?? ???????? ???? ???? ????? ????
    ?? ???
  • ??? ??????? ????????
  • ?? ?? ????? ?? ???? ????? ??? ?? ???????

11
???? ???????? ???? ????? ??
  • ????? ?? ????? ?? ??? ???? ??????? ????? ???? ??
    ??? ???? ? ?? ?? ?? ??? ?? ??? ?? ????
  • ??? ??? ?? ??? ????? ???? ?? ???
  • ?? ????? ???? ?? ???? ?? ???? ?????? ? ?? ??? ??
    ??? ???? ?? ???
  • ??? ????? ?? ?? ??? ????? ?????? ??? ?? ?? ?? ??
    ????
  • ????? ?? ???? ?????? ??? ????? ?? ???
  • ??? ????? ?? ?? ??? ????? ???????? ??? ???? ?? ??
    ???? ?? ???? ?? ???
  • ? ??? ?? ???? ??? ????? ?? ???
  • ??? ?? ???? ?????? ?? ??? ?? ????? ?? ?? ????
    ???? ???? ??? ??? ???.

12
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.

13
Ant Colony Optimization
  • Ant Colony Optimization ?? ?????? ????? ?? ????
    ???? ???? ??????? ????? ?? ????? ???? ?????? ????
    ???????? ?? ???? ??????? ?? ???

14
????? ?? ????? ?? ?????? ????? ???? ???? ?? ????
???? ?
15
????? ????? ?? ?????? ?????? ?? ????
  • ????? ?? ??????? ???? ? ????? ?? ?????? ? ??? ??
    ?? ?????? ??????? ?? ????
  • ????? ?? ??? ??? ??????
  • ?? ??? ????? ????? ?? ??? ?????? ?? ??? ? ?????
    ?? ?? ?????? ????? ????
  • ??? ?? ??????? ?? ?? ?????? ?? ?????? ??????? ??
    ????? ????
  • ??? ??? ?????? ?? stigmergy ?? ?????
  • ????? ?? ?? ??? ????? ?? ?? ?? ?? ?????? ?? ???
    ???? ?????
  • ?? ??? ???? pheromone ???? ?? ???
  • ????? ?? pheromone ?? ?? ????? ?? ???? ????? ??
    ?? ?? ?? ????
  • ????? ????? ????? ?? ??? ??? pheromone ?? ???
    ????? ?? ????

16
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

17
The Shortest Path (2)
  • The one taken the shorter path returns first and
    arrives back to the nest first

18
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

19
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

20
(No Transcript)
21
Simple Stochastic Algorithm
  • ???? ?? ????? ???? ?? ???? ?? ???
  • ?? ?????? ????? ?? ???? ???? ?? ???? ?? ???.
  • ?? ?????? ?????? ??? ?? ??????? ????? ?????
    pheromone ?? ?????? ?? ???.
  • ??? ?? ?????? ??????? ????? ?? ?? ??? pheromone
    ?? ????????? ???? ?????? ?????? ?? ??? ??????
    ????? ???.

22
??? ??????? ?????
  • ????? ??? ???? ?????? ????? ???? ?????? ??????
    ????? ???
  • ?????? ??????? ???? ?? ????? ???? ?? ?????
    ??????? ????? ?? ????
  • ?? ????? ??? ????? ?? ????? ????? ???? ???? ?? ???

23
pheromone ????? ? ??? ??????
  • Pheromone ?? ????? ????? ?? ???.
  • ??? ??? ?? ??????? ???? ???? ???????? ???.
  • ?????? ?? ??????? ????? ?? ??????? ????? ?? ????
    ????? pheromone ?? ??????? ????? ????? ????
    ?????? ???.

24
???????? ????
  • ???????? ?? ???? ???? ??? ?? ???.
  • ??? ??? ??? ?????? ???? ??????? ???? ? ?????
    ?????? ?? ??????? ???? ???.

25
?????? ?? ????? ? ??????? ????
  • ???????? ?? ???? ????? ???? ? ???? ?? ????? ??
    ???? ???? ????? ?? ???? ?? ????? ???? ??????? ??
    ?? ?????? ???.
  • ?????? ??? ????? ?? ???? ????? ??? ???????? ???
    ?? ?? ??????? ?????? ?? ???.
  • ???? ???? ??? ???? ?? ???? ????? ?? ???? ???? ???
    ????? ?? ?? ?? ??? ??? ? ???? ????? ????? ?? ??
    ???? ?? ???? ?? ????.

Disclaimer May not work with a cube of ice
26
??? ?????
  • ????? ?? ?? ??? ?????? ????? ?? ???? ????? ????
    ????? ????? ???? ???? ?? ????? ?? ????.
  • ??? ?? ??? ????? ???????? ???
  • ??? ????? ?? ????? ????????? ????? ????? ????

27
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

28
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

29
Modeling the Ants Behavior
DEMO
Dont go away We will continue after the demo
30
Part 2Ants in Networks
31
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)

32
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()
33
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

34
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

35
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
36
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

37
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

38
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

39
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.

40
Example
41
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

42
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)

43
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

44
Performance
  • AntNet reports better performance in terms of
  • Delay
  • Throughput
  • Robustness
  • Reaction to changes
  • Traffic overhead is higher than OSPF

45
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

46
Router Architecture
47
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
48
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

49
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
50
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
51
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

52
THE END
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