Local optimization and the traveling salesman problem
Proceedings of the seventeenth international colloquium on Automata, languages and programming
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
A Framework for Distributed Evolutionary Algorithms
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
A Multilevel Approach to the Travelling Salesman Problem
Operations Research
Finding Cuts in the TSP (A preliminary report)
Finding Cuts in the TSP (A preliminary report)
Chained Lin-Kernighan for Large Traveling Salesman Problems
INFORMS Journal on Computing
Tour Merging via Branch-Decomposition
INFORMS Journal on Computing
An Algorithm for Finding Nearest Neighbors
IEEE Transactions on Computers
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
Multiagent optimization system for solving the traveling salesman problem (TSP)
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A backbone based TSP heuristic for large instances
Journal of Heuristics
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The Traveling Salesman Problem (TSP) is a well-known NP-hard combinatorial optimization problem, for which a large variety of evolutionary algorithms are known. However, these heuristics fail to find solutions for large instances due to time and memory constraints. Here, we discuss a set of edge fixing heuristics to transform large TSP problems into smaller problems, which can be solved easily with existing algorithms. We argue, that after expanding a reduced tour back to the original instance, the result is nearly as good as applying the used solver to the original problem instance, but requiring significantly less time to be achieved. We claim that with these reductions, very large TSP instances can be tackled with current state-of-the-art evolutionary local search heuristics.