L ECTURE 27: S WARM I NTELLIGENCE 8 / A NT C OLONY O PTIMIZATION 4 T - - PowerPoint PPT Presentation

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L ECTURE 27: S WARM I NTELLIGENCE 8 / A NT C OLONY O PTIMIZATION 4 T - - PowerPoint PPT Presentation

May 25, 2023 208 likes •375 views

15-382 C OLLECTIVE I NTELLIGENCE - S19 L ECTURE 27: S WARM I NTELLIGENCE 8 / A NT C OLONY O PTIMIZATION 4 T EACHER : G IANNI A. D I C ARO 2-OPT LOCAL SEARCH 2 3-OPT LOCAL SEARCH 3 LOCAL SEARCH, ITERATED LOCAL SEARCH Check additional slides:

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15-382 COLLECTIVE INTELLIGENCE - S19

LECTURE 27: SWARM INTELLIGENCE 8 / ANT COLONY OPTIMIZATION 4

TEACHER: GIANNI A. DI CARO

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2-OPT LOCAL SEARCH

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3-OPT LOCAL SEARCH

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LOCAL SEARCH, ITERATED LOCAL SEARCH

Check additional slides: local-search.pdf

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PHEROMONE AND HEURISTIC ARE BOTH IMPORTANT!

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ANTS + LOCAL SEARCH

  • As a matter of fact, the best instances of ACO algorithms for (static/centralized)

combinatorial problems are those making use of a problem-specific local search daemon procedure (iterative solution modification) interleaved with ants’ solution construction

  • It is conjectured that ACO’s ants can provide good starting points for local search. More

in general, a construction heuristic can be used to quickly build up a complete solution of good quality, and then a solution modification procedure can take this solution as a starting point, trying to further improve it by iteratively modifying some of its parts

  • This hybrid two-phases search can be iterated and can be very effective if each phase can

produce a solution which is locally optimal within a different class of feasible solutions, with the intersection between the two classes being minimal

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MAX-MIN-AS (1999): LIMITS AND RESTARTS

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MMAS (1999): LIMITS AND RESTARTS

n

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ASRANK (1999): ELITISM BY RANK

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ANT-TABU (2001)

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DYNAMIC, DISTRIBUTED ENVIRONMENTS?

  • Routing in wired networks, AntNet (1998)
  • Routing in mobile ad hoc networks (AntHocNet, 2005)
  • Routing / Foraging in mobile robotic networks

Challenges: Distributed, path evaluation, non-stationary, errors, “unpredictable” traffic demands, interference of ants with normal traffic, limited bandwidth, when/where send ants, where to store pheromone?

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DECISIONS TO TAKE DESIGNING ACO ALGORITHMS…

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A FEW COMMON DESIGN CHOICES

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THEORETICAL RESULTS?

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ACO SUMMARY

  • Reverse engineering of stigmergic pheromone laying-following in ant colonies
  • Construction meta-heuristic biased by pheromone + heuristic
  • Ants: Monte Carlo sampling of solutions
  • Generalized policy iteration for learning pheromone parameters for decision-making:

policy evaluation (sampling of solutions) + policy improvement (pheromone updating)

  • Different strategies for sampling and updating
  • A number of different heuristic recipes (common in SI, heuristic optimization domains)
  • In physically distributed problems (e.g., networks, robotics) agents hops from one

decision node to another and then have to retrace their path back, if feasible

  • State of the art performance (when coupled with LS, for centralized problems)
  • Guaranteed performance: yes, in the probabilistic limit
  • Applied to a large variety of CO problems
  • Large number of scientific publications
  • Applied in the real world: Barilla, Migros, port management, logistics, ….