Publication Type
Book Chapter
Version
publishedVersion
Publication Date
12-2005
Abstract
Presently, most tabu search designers devise their applications without considering the potential of design and code reuse, which consequently prolong the development of subsequent applications. In this paper, we propose a software solution known as Tabu Search Framework (TSF), which is a generic C++ software framework for tabu search implementation. The framework excels in code recycling through the use of a well- designed set of generic abstract classes that clearly define their collaborative roles in the algorithm. Additionally, the framework incorporates a centralized process and control mechanism that enhances the search with intelligence. This results in a generic framework that is capable of solving a wide range of combinatorial optimization problems using various tabu search techniques and adaptive strategies. The applications of TSF are demonstrated on the implementation of two NP-hard problems, the Vehicle Routing Problem with Time Windows (VRPTW) and Quadratic Assignment Problem (QAP). We show that TSF is able to obtain quality solutions within reasonable implementation as well as computation time.
Keywords
Combinatorial optimization, Reusability, Software framework, Tabu search
Discipline
Artificial Intelligence and Robotics | Operations Research, Systems Engineering and Industrial Engineering
Publication
Metaheuristics: Progress as real problem solvers
Volume
32
Editor
T. Ibaraki, K. Nonobe, and M. Yagiura
First Page
203
Last Page
226
ISBN
9780387253831
Identifier
10.1007/0-387-25383-1_9
Publisher
Springer Verlag
City or Country
New York
Citation
LAU, Hoong Chuin; JIA, Xiaomin; and WAN, Wee Chong.
A generic object-oriented Tabu Search Framework. (2005). Metaheuristics: Progress as real problem solvers. 32, 203-226.
Available at: https://ink.library.smu.edu.sg/sis_research/843
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
http://doi.org/10.1007/0-387-25383-1_9
Included in
Artificial Intelligence and Robotics Commons, Operations Research, Systems Engineering and Industrial Engineering Commons