G5BAIM%20Artificial%20Intelligence%20Methods

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G5BAIMArtificial Intelligence Methods
Tabu Search
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Characteristics of SA (review)

• Random selection of a neighbouring solution
• Probabilistic acceptance of non-improving
  solutions
• The best solution is recorded
• Lack of memory of history of search
• All the information found during the search is
  lost

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Tabu Search

• Proposed independently by Glover(1986) and
  Hansen(1986)
• Tabu search is a meta-heuristic superimposed on
  another heuristic. The overall approach is to
  avoid entrapment in cycles by forbidding or
  penalizing moves which take the solution, in the
  next iteration, to points in the solution space
  previously visited (hence tabu).

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Tabu Search (continued)

• Accepts non-improving solutions deterministically
  in order to escape from local optima (where all
  the neighbouring solutions are non-improving) by
  guiding a steepest descent local seach (or
  steepest ascent hill climbing ) algorithm
• Uses of memory to
• prevent the search from revisiting previously
  visited solutions
• explore the unvisited areas of the solution
  space

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Is memory useful during the search?
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Uses of memory during the search?

• Intelligence needs memory!
• Discouraging some patterns in solution e.g. in
  clustering example, selection of some stores as
  warehouses or their assignment of some stores to
  a specific warehouse may be considered taboo
  (forbidden).
• Information on characteristics of good solutions
  (or bad solutions!)

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Dangers of memory

• Exhaustive usage of memory resources
• Design of efficient data structures to record and
  access the recorded data efficiently
• Collecting more data than could be handled
• Clear understanding of which attributes of
  solutions are crucial
• Limited selection of attributes of solutions to
  be memorised
• Clear strategy on usage of information or their
  disposal when not needed
• Memorising information which should not be
  remembered
• Misguiding patterns in local optima which are
  very different from global optimum

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Tabu Search algorithm

• Function TABU_SEARCH(Problem) returns a solution
  state
• Inputs Problem, a problem
• Local Variables Current, a state
• Next, a state
• BestSolutionSeen, a state
• H, a history of visited states

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Tabu Search algorithm (continued)

• Current MAKE-NODE(INITIAL-STATEProblem)
• While not terminte
• Next a highest-valued successor of Current
• If(not Move_Tabu(H,Next) or Aspiration(Next))
  then
• Current Next
• Update BestSolutionSeen
• H Recency(H Current)
• Endif
• End-While
• Return BestSolutionSeen

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Elements of Tabu Search

• Tabu List (short term memory) to record a
  limited number of attributes of solutions (moves,
  selections, assignments, etc) to be discouraged
  in order to prevent revisiting a visited
  solution
• Tabu tenure number of iterations a tabu move is
  considered to remain tabu
• Aspiration criteria accepting an improving
  solution even if generated by a tabu move
• Similar to SA in always accepting improving
  solutions, but accepting non-improving ones when
  there is no improving solution in te
  neighbourhood

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Elements of Tabu Search (continued)

• Long term memory) to record attributes of elite
  solutions to be used in
• Intensification giving priority to attributes of
  a set of elite solutions (usually in weighted
  probability manner)
• Diversification Discouraging attributes of elite
  solutions in selection functions in order to
  diversify the search to other areas of solution
  space

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Example of use of memory

• In our example of selecting warehouses, following
  information could be recorded
• Short term memory
• Maintain a list of t stores and prevent them from
  being selected as warehouse or prevent their
  assignments to specific selection of warehouses
  for a number of iterations
• Long term memory
• Maintain a list of t stores which have been
  selected as warehouses in the last k best
  solution and encourage (or discourage) their
  selection in future solutions by using their
  frequency of appearance in set of elite solutions
  and the quality of solutions which they have
  appeared in our selection function
• Which stores have been selected as warehouses in
  our elite solutions?
• Which assignment of stores to warehouses in our
  elite solutions

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References

• 1. Glover, F. 1989. Tabu Search Part I. ORSA
  Journal on Computing, Vol. 1, No. 3, pp 190-206.
• 2. Glover, F. 1990. Tabu Search Part II. ORSA
  Journal on Computing, Vol. 2, No. 1, pp 4-32.
• 3. Glover, F., Laguna, M. 1998. Tabu Search.
  Kluwer Academic Publishers
• 4. Rayward-Smith, V.J., Osman, I.H., Reeves,
  C.R., Smith, G.D. 1996. Modern Heuristic Search
  Methods, John Wiley Sons.
• 5. Russell, S., Norvig, P. 1995. Artificial
  Intelligence A Modern Approach. Prentice-Hall

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G5BAIMArtificial Intelligence Methods
End of Tabu Search