Noise strategies for improving local search
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
Phase transitions and the search problem
Artificial Intelligence - Special volume on frontiers in problem solving: phase transitions and complexity
Models for iterative global optimization
Models for iterative global optimization
Tour Merging via Branch-Decomposition
INFORMS Journal on Computing
A backbone-search heuristic for efficient solving of hard 3-SAT formulae
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
How autonomy oriented computing (AOC) tackles a computationally hard optimization problem
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Effective Tour Searching for TSP by Contraction of Pseudo Backbone Edges
AAIM '09 Proceedings of the 5th International Conference on Algorithmic Aspects in Information and Management
How the landscape of random job shop scheduling instances depends on the ratio of jobs to machines
Journal of Artificial Intelligence Research
Multiagent optimization system for solving the traveling salesman problem (TSP)
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Improving the efficiency of Helsgaun's Lin-Kernighan Heuristic for the symmetric TSP
CAAN'07 Proceedings of the 4th conference on Combinatorial and algorithmic aspects of networking
On Computing Backbones of Propositional Theories
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
Finding good tours for huge Euclidean TSP instances by iterative backbone contraction
AAIM'10 Proceedings of the 6th international conference on Algorithmic aspects in information and management
C-strategy: a dynamic adaptive strategy for the CLONALG algorithm
Transactions on computational science VIII
C-strategy: a dynamic adaptive strategy for the CLONALG algorithm
Transactions on computational science VIII
A backbone based TSP heuristic for large instances
Journal of Heuristics
Hi-index | 0.00 |
We present and investigate a new method for the Traveling Salesman Problem (TSP) that incorporates backbone information into the well known and widely applied Lin-Kernighan (LK) local search family of algorithms for the problem. We consider how heuristic backbone information can be obtained and develop methods to make biased local perturbations in the LK algorithm and its variants by exploiting heuristic backbone information to improve their efficacy. We present extensive experimental results, using large instances from the TSP Challenge suite and real-world instances in TSPLIB, showing the significant improvement that the new method can provide over the original algorithms.