1 / 22

Tabu Search

Presenter: Leo, Shih-Chang, Lin Advisor: Frank, Yeong-Sung, Lin. Tabu Search. Agenda. What is Tabu search? Heuristic search Tabu search Characteristic Elements definition Tabu search process Algorithm Application ： TSP Related study. What is Tabu Search?.

wang-reilly
Download Presentation

Tabu Search

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Presenter: Leo, Shih-Chang, Lin Advisor: Frank, Yeong-Sung, Lin Tabu Search

  2. Agenda • What is Tabu search? • Heuristic search • Tabu search • Characteristic • Elements definition • Tabu search process • Algorithm • Application：TSP • Related study

  3. What is Tabu Search? • Proposed by Fred Glover in 1989 • A kind of heuristic search • Used for solving combinatorial optimization problems • Short term • Get the local optimum • Long term • Intensification and diversification • Leave the local optimum to get global optimum

  4. Heuristic Search(1/2) • Characteristic: • or “experienced search” • not always find the best solution • guarantee to find a good solution in reasonable time. • By sacrificing completeness it increases efficiency. • Useful in solving tough problems

  5. Heuristic Search(2/2) • Steps • Generate a possible solution which can either be a point in the problem space or a path from the initial state. • Test to see if this possible solution is a real solution by comparing the state reached with the set of goal states. • If it is a real solution, return. Otherwise repeat from 1.

  6. Tabu Search(1/7) • Characteristic • Capability of getting global solution instead of local solution • Tabu list can avoid repeating trivial search • Update tabu list to speed up searching

  7. Tabu Search(2/7) • Elements Definition • Neighborhood solution：a solution which must exist in a set of feasible solution, and which is not in the tabu list. • Move：change the current solution to its neighborhood solution.

  8. Tabu Search(3/7) • Tabu List：a short-term memory which records the solutions that have been visited in the recent past.In this way, we can avoid repeating search. In general, tabu list has a fixed size to memorize,and it follows FIFO to maintain the list. • Aspiration Criteria：when a solution in the tabu list is better than the currently-known best solution, the solution is permitted to replace the currently-known solution with the best solution.

  9. Tabu Search(4/7) • Stopping Criteria：the stopping conditions。 • Maximum iterative numbers • Maximum times which counts when object function’s value doesn’t improve • The longest default execution time of CPU • When object function’s output is acceptable

  10. Tabu Search(5/7) • Algorithm

  11. Tabu Search(6 / 7) • Process

  12. Tabu Search( 7 / 7)

  13. Application(1/7) • Traveling Salesman Problem (A Comparative Study of Tabu Search and Simulated Annealing for Traveling Salesman Problem by Sachin Jayaswal, University of Waterloo) • a problem where starting from a node it is required to visit every other node only once in a way that the total distance covered is minimized.

  14. Application(2/7) • Tabu Search for TSP • Solution Representation ： • A feasible solution is represented as a sequence of nodes, each node appearing only once and in the order it is visited. The first and the last visited nodes are fixed to 1.

  15. Application(3/7) • Initial Solution • A good feasible, yet not-optimal, solution to the TSP can be found quickly using a greedy approach. • Starting with the first node in the tour, find the nearest node. • Each time find the nearest unvisited node from the current node until all the nodes are visited.

  16. Application(4/7) • Neighborhood solution • A neighborhood solution to a given solution is defined as any other solution that is obtained by a pair wise exchange of any two nodes in the solution. • If we fix node 1 as the start and the end node, for a problem of N nodes, there are Cn-12 such neighborhoods to a given solution.

  17. Application(5/7) • Tabu List • Initially, it is empty • the attribute stored in the Tabu list is a pair of nodes that have been exchanged recently. • Aspiration criteria • The criterion used for this to happen in the present problem of TSP is to allow a move, even if it is in tabu list, if it results in a solution with an objective value better than that of the current best-known solution.

  18. Application(6/7) • Termination criteria • The algorithm terminates if a pre-specified number of iterations is reached .

  19. Application(7/7) • Computational Experience

  20. Related study (禁忌搜尋法則求解推銷員旅行問題, 吳泰熙 and 張欽智,1997) • Different parameters set in Tabu search affect the quality of optimum • The size of Tabu list： • n is the amount of cities, x is the coefficient of Tabu list • 0.5n <(0.5+(2.5x)/4)n < 3n • 2.375n as x = 3 • The maximum of iteration： • If n <50, iteration >= 2000 • If n >50 , iteration >= 4000

  21. Thanks for your attention.

  22. Q & A

More Related