Lecture24-3-20-2002

# Lecture24-3-20-2002 - MAE 552 Heuristic Optimization...

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MAE 552 – Heuristic Optimization Lecture 24 March 20, 2002 Topic: Tabu Search

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Tabu Search – Modifications What happens if we come upon a very good solution and pass it by because it is Tabu? Perhaps we should incorporate more flexibility into the search. Maybe one of the Tabu neighbors, x 6 for instance provides an excellent evaluation score, much better than any of the solutions previously visited. In order to make the search more flexible, Tabu search evaluates the ‘whole’ neighborhood, and under normal circumstances selects a non-tabu move. But if circumstances are not normal i.e. one of the tabu solutions is outstanding, then take the tabu point as the solution.
Tabu Search –Modifications 1. Overriding the Tabu classification occurs when the ‘aspiration criteria’ is met. 1. There are other possibilities for increasing the flexibility of the Tabu Search. 1. Use a probabilistic strategy for selecting from the candidate solutions. Better solutions have a higher probability of being chosen. 2. The memory horizon could change during the search process. 3. The memory could be connected to the size of the problem (e.g. remembering the last n 1/2 moves) where n is the number of design variables in the problem.

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Tabu Search – Modifications 4. Incorporate a ‘long-term’ memory in addition to the short term memory that we have already introduced. The memory that we are using can be called a recency-based memory because it records some actions of the last few iterations. We might introduce a frequency-based memory that operation on a much longer horizon. A vector H might be introduced as a long term memory structure.
Tabu Search – Example 1: SAT Problem cont. The vector H is initialized to zero and at each stage of the search the entry H(i)=j is interpreted as ‘during the last h iterations of the algorithm the i-th bit was flipped j times.’ Usually the value of h is set quite high in comparison to the length of the short-term memory. For example after 100 iterations with h =50 the long term memory H might have the following values displayed. H: 5 7 11 3 9 8 1 6

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Tabu Search – H shows the distribution of moves during the last 50 iterations.How can we use this information? This could be used to diversify the search. For example H provides information as to which flips have been underrepresented or not represented at all, and we can diversify the search by exploring these possibilities.
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## This note was uploaded on 07/09/2011 for the course MAE 522 taught by Professor Hacker during the Spring '10 term at SUNY Buffalo.

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Lecture24-3-20-2002 - MAE 552 Heuristic Optimization...

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