Conference Proceeding Article
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.
Artificial Intelligence and Robotics | Operations Research, Systems Engineering and Industrial Engineering
Intelligent Systems and Decision Analytics
Advances in Computational Intelligence in Transport, Logistics, and Supply Chain Management
City or Country
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. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/240
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