The N-City Travelling Salesman Problem: Statistical Mechanics and the Metropolis Algorithm

By E. Bonomi, J. Lutton. 1984

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

GADMM: Fast and Communication Efficient Framework for Distributed Machine Learning (2019, methodology): “ties in TSP, and the communication cost between each pair of workers in our problem is the distance between each pair of cities in TSP. Hence, proposed heuristics to solve TSP (Lenstra and Kan, 1975; Bonomi and Lutton, 1984) can be used to construct the chain in our problem with the aid of a central”
Discrete cuckoo search algorithm for the travelling salesman problem (2014, methodology): “Many of metaheuristic algorithms have been applied to solve TSP by various researchers, such as SA [4], TS [20], GA [17], ACO [8], Discrete particle swarm optimisation (DPSO) [27], genetic –simulated annealing ant colony system with particle swarm optimisation techniques (GSAACS-PSOT) [6] and fuzzy ”
How to Solve It: Modern Heuristics (2000, methodology): “Bonomi and Lutton [51] suggest using k = 0.”
Endmember bundles: a new approach to incorporating endmember variability into spectral mixture analysis (2000, methodology): “The parameter , often referred to as temperature in analogy with the annealing of metals [ 36 ], changes during the course of the search.”
Solving a combinatorial problem via self-organizing process: An application of the Kohonen algorithm to the traveling salesman problem (1988, methodology): “As mentioned before, Bonomi and Lutton have exhaustively studied an "annealing" method and compare the results to the asymptotical values.”