TEI 4

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Kajian literatur tentang penerapan Ant System dan hasil evolusinya dalam pengembangan algoritma komputasi cerdas

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TEI 4

  1. 1. Bahan Presentasi Teknik Elektro dan Informatika Lanjut 1 dan 2Multi-Agent Intrusion Detection System in Industrial Network using Ant Colony Clustering Approach and Unsupervised Feature Extraction Oleh : Chi-Ho Tsang and Sam Kwong Company LOGO
  2. 2. SCADA Network
  3. 3. Agents ACCM Monitor Agents (M)Registration Decisionagents (R) agents (D) User Action Interface agents (A)agents (UI) Coordination agents (C)
  4. 4. Inside Monitor Agent (M)Raw network packets Feature type Packet capture enginecaptured from subnets construction Pre-processed data sent to communication PCA dimensionality ICA feature extraction module of its reduction associiated Decission Agent
  5. 5. Inside Decission Agent (D)ACCM (Ant Colony Clustering Models)?
  6. 6. Evolving ACO-MH • Deneubourg • Dorigo dkk dkk • Dorigo dkk Binary • Goss dkk • Addition of Bridge SACO • Double Ant System heuristic Experiment • Path Bridge (AS) information Selection Experiment (β) Process • Maniezo & Ant • Gambardella Colorni, 1999 & Dorigo Modified Colony Max-Min • Ellitis AS • 4 difference AS System aspects from AS • Use only α (ACS) AS Fast Ant Ant-Q System Antabu (FANT) AS-Fundamentals of Computational Swarm Intelligence Rank ANTSAndries P. EngelbrechtWiley & Sons @2005
  7. 7. Perkembangan Ant SystemBINARY BRIDGE EXPERIMENT
  8. 8. Binary Bridge Experiment The probability of the next ant to choose path A at time step t + 1 is given as, where c quantifies the degree of attraction of an unexplored branch, α is the bias to using pheromone deposits in the decision process This algorithm is executed at each point where the ant needs to make a decision.Goss et al. extended the it is assumed that ants deposit the same amount of pheromonebinary bridge experiment and that pheromone does not evaporate
  9. 9. Perkembangan Ant SystemSIMPLE ANT COLONYOPTIMIZATION - SACO
  10. 10. Graph for Shortest Path Problem
  11. 11. SACO - Transition ProbabilityIf ant k is currently located at node i, it selects the next node j ∈ Nki , based on thetransition probability: ij is pheromone concentration associtated with edge (i,j)A number of ants, k = 1, . . . , nk, are placed on the source node.Nki is the set of feasible nodes connected to node i, with respect to ant k.α is a positive constant used to amplify the influence of pheromone concentrations.
  12. 12. SACO – Amount of deposit pheromoneAfter a complete path from the origin node to the destination node is accomplished,and all loops have been removed, each ant retraces its path to the source nodedeterministically, and deposits a pheromone amount, to each link, (i, j), of the corresponding path; Lk(t) is the length of the path constructed by ant k at time step t. That is, (17.4) Where nk is the number of ants
  13. 13. SACO – evaporation of pheromone intensitiesAnts rapidly converge to a solution, and that little time is spent exploring alternativepaths.To explore more, and to prevent premature convergence, pheromone intensities onlinks are allowed to “evaporate” at each iteration of the algorithm before beingreinforced on the basis of the newly constructed paths.For each link, (i, j), letwith ρ ∈ [0, 1].The constant, ρ, specifies the rate at which pheromones evaporate.The large values of ρ, pheromone evaporates rapidly, while small values of ρ resultin slower evaporation rates.The more pheromones evaporate, the more random the search becomes, facilitatingbetter exploration. For ρ = 1, the search is completely random.
  14. 14. First Ant Algorithm (by Dorigo, Maniezo & Colorni)ANT SYSTEM - AS
  15. 15. AS – Adding the heuristic (17.6) ij = aposteriori effectiveness of the move from i to j (pheromone intensity)  explorationηij = apriori effectiveness of the move from i to j (desirability/attractiveness/visibility)  exploitation k , defines the set of feasible nodes for ant k when located on node i. i To prevent loops, Nki may include all nodes not yet visited by ant k. For this purpose, a tabu list is usually maintained for each ant. As an ant visits a new node, that node is added to the ant’s tabu list. Nodes in the tabu list are removed from Nki , ensuring that no node is visited more than once.
  16. 16. AS – ModifiedManiezzo and Colorni:Pheromone evaporation: (17.5)After completion of a path by each ant, the pheromone on each link is updated as with (17.10) the amount of pheromone deposited by ant k on link (i, j) and k at time step t. (17.14)
  17. 17. AS – Modified (17.11) (17.13)
  18. 18. AS – Modified (Elitist) (17.4)Dorigo dkk, introduced elitist strategy using some elite ants, so the pheromoneupdate changes to: (17.15) (17.16)
  19. 19. AS – Algorithm
  20. 20. Improving Ant System (by Dorigo & Gambardella)ANT COLONY SYSTEM - ACS
  21. 21. ACS - A different transition ruler0 to balance explore-exploit processSmaller r0 exploration more emphasized.
  22. 22. ACS - A different pheromone update rule Pheromone is updated using the global update rule2 methods implemented in selecting the path x+(t)
  23. 23. ACS – Local pheromone updates are introduced
  24. 24. ACS - candidate lists are used to favor specific nodes
  25. 25. ACS - Algorithm

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