The cardinality constrained covering traveling salesman problem
Computers and Operations Research
Very large-scale vehicle routing: new test problems, algorithms, and results
Computers and Operations Research
Solving the one-dimensional bin packing problem with a weight annealing heuristic
Computers and Operations Research
Multistart tabu search and diversification strategies for the quadratic assignment problem
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Computers and Operations Research
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In this paper, we introduce the concept of fine-tuned learning which relies on the notion of data approximation followed by sequential data refinement. We seek to determine whether fine-tuned learning is a viable approach to use when trying to solve combinatorial optimization problems. In particular, we conduct an extensive computational experiment to study the performance of fine-tuned-learning-based heuristics for the traveling salesman problem (TSP). We provide important insight that reveals how fine-tuned learning works and why it works well, and conclude that it is a meritorious concept that deserves serious consideration by researchers solving difficult problems