Lecture23-3-18-2002_Web

Lecture23-3-18-2002_Web - MAE 552 Heuristic Optimization...

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MAE 552 – Heuristic Optimization Lecture 23 March 18, 2002 Topic: Tabu Search
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http://unisci.com/stories/20021/0315023.htm
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Tabu Search The Tabu search begins by marching to a local minima.To avoid retracing the steps used, the method records recent moves in one or more Tabu lists. The role of the memory can change as the algorithm proceeds. At initialization the goal is make a coarse examination of the solution space, known as 'diversification’. As candidate locations are identified the search is more focused to produce local optimal solutions in a process of 'intensification'.
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Tabu Search In many cases the differences between the various implementations of the Tabu method have to do with the size, variability, and adaptability of the Tabu memory to a particular problem domain . The Tabu search has traditionally been used on combinatorial optimization problems. The technique is straightforwardly applied to continuous functions by choosing a discrete encoding of the problem. Many of the applications in the literature involve integer programming problems, scheduling, routing, traveling salesman and related problems.
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Tabu Search –Basic Ingredients Many solution approaches are characterized by identifying a neighborhood of a given solution which contains other so-called transformed solutions that can be reached in a single iteration. A transition from a feasible solution to a transformed feasible solution is referred to as a move. A starting point for Tabu search is to note that such a move may be described by a set of one or more attributes (or elements). These attributes (properly chosen) can become the foundation for creating an attribute based memory.
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Tabu Search Following a steepest descent / mildest ascent approach, a move may either result in a best possible improvement or a least possible deterioration of the objective function value. Without additional control, however, such a process can cause a locally optimal solution to be re-visited immediately after moving to a neighbor, or in a future stage of the search process, respectively. To prevent the search from endlessly cycling between the same solutions, a tabu list is created which operates like a short term memory.
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Tabu Search – Attributes of all explored moves are stored in a list named a running list representing the trajectory of solutions encountered. Then, related to a sublist of the running list a so- called tabu list may be introduced. The tabu list implicitly keeps track of moves (or more precisely, salient features of these moves) by recording attributes complementary to those of the running list.
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Tabu Search – These attributes will be forbidden from being embodied in moves selected in at least one subsequent iteration because their inclusion might lead back to a previously visited solution.
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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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Lecture23-3-18-2002_Web - MAE 552 Heuristic Optimization...

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