Exploiting Tabu Search Memory in Constrained Problems

This paper puts forth a general method to optimize constrained problems effectively when using tabu search. An adaptive penalty approach is used that exploits the short-term memory structure of the tabu list along with the long-term memory of the search results. It is shown to be effective on a vari...

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Published inINFORMS journal on computing Vol. 16; no. 3; pp. 241 - 254
Main Authors Kulturel-Konak, Sadan, Norman, Bryan A, Coit, David W, Smith, Alice E
Format Journal Article
LanguageEnglish
Published Linthicum INFORMS 01.08.2004
Institute for Operations Research and the Management Sciences
Subjects
Computer memory
constraint optimization
facility layout
Job shops
Optimization
orienteering
reliability optimization
Studies
tabu search
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Abstract This paper puts forth a general method to optimize constrained problems effectively when using tabu search. An adaptive penalty approach is used that exploits the short-term memory structure of the tabu list along with the long-term memory of the search results. It is shown to be effective on a variety of combinatorial problems with different degrees and numbers of constraints. The approach requires few parameters, is robust to their setting, and encourages search in promising regions of the feasible and infeasible regions before converging to a final feasible solution. The method is tested on three diverse NP-hard problems, facility layout, system reliability optimization, and orienteering, and is compared with two other penalty approaches developed explicitly for tabu search. The proposed memory-based approach shows consistent strong performance.
AbstractList This paper puts forth a general method to optimize constrained problems effectively when using tabu search. An adaptive penalty approach is used that exploits the short-term memory structure of the tabu list along with the long-term memory of the search results. It is shown to be effective on a variety of combinatorial problems with different degrees and numbers of constraints. The approach requires few parameters, is robust to their setting, and encourages search in promising regions of the feasible and infeasible regions before converging to a final feasible solution. The method is tested on three diverse NP-hard problems, facility layout, system reliability optimization, and orienteering, and is compared with two other penalty approaches developed explicitly for tabu search. The proposed memory-based approach shows consistent strong performance.
This paper puts forth a general method to optimize constrained problems effectively when using tabu search. An adaptive penalty approach is used that exploits the short-term memory structure of the tabu list along with the long-term memory of the search results. It is shown to be effective on a variety of combinatorial problems with different degrees and numbers of constraints. The approach requires few parameters, is robust to their setting, and encourages search in promising regions of the feasible and infeasible regions before converging to a final feasible solution. The method is tested on three diverse NP-hard problems, facility layout, system reliability optimization, and orienteering, and is compared with two other penalty approaches developed explicitly for tabu search. The proposed memory-based approach shows consistent strong performance. [PUBLICATION ABSTRACT]
Audience Academic
Author Norman, Bryan A
Smith, Alice E
Coit, David W
Kulturel-Konak, Sadan
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Snippet This paper puts forth a general method to optimize constrained problems effectively when using tabu search. An adaptive penalty approach is used that exploits...
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SubjectTerms Computer memory
constraint optimization
facility layout
Job shops
Optimization
orienteering
reliability optimization
Studies
tabu search
Title Exploiting Tabu Search Memory in Constrained Problems
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