Publication Type
Conference Proceeding Article
Version
acceptedVersion
Publication Date
2008
Abstract
In this work, we liken the solving of combinatorial optimization problems under a prescribed computational budget as hunting for oil in an unexplored ground. Using this generic model, we instantiate an iterative deepening genetic annealing (IDGA) algorithm, which is a variant of memetic algorithms. Computational results on the traveling salesman problem show that IDGA is more effective than standard genetic algorithms or simulated annealing algorithms or a straightforward hybrid of them. Our model is readily applicable to solve other combinatorial optimization problems.
Discipline
Artificial Intelligence and Robotics | Operations Research, Systems Engineering and Industrial Engineering
Publication
Advances in Computational Intelligence in Transport, Logistics, and Supply Chain Management
Volume
144
First Page
169
Last Page
184
ISBN
9783540690245
Identifier
10.1007/978-3-540-69390-1_9
Publisher
Springer Verlag
City or Country
Berlin
Citation
LAU, Hoong Chuin and XIAO, Fei.
The oil drilling model and iterative deepening genetic annealing algorithm for the traveling salesman problem. (2008). Advances in Computational Intelligence in Transport, Logistics, and Supply Chain Management. 144, 169-184.
Available at: https://ink.library.smu.edu.sg/sis_research/240
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/10.1007/978-3-540-69390-1_9
Included in
Artificial Intelligence and Robotics Commons, Operations Research, Systems Engineering and Industrial Engineering Commons