A man-machine approach toward solving the traveling salesman problem
Communications of the ACM
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
A concurrent neural network algorithm for the traveling salesman problem
C3P Proceedings of the third conference on Hypercube concurrent computers and applications - Volume 2
A Cost Minimization Approach to Edge Detection Using Simulated Annealing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fuzzy measure on vehicle routing problem of hospital materials
Expert Systems with Applications: An International Journal
Comparison between heterogeneous mesh-based and tree-based application specific FPGA
ARC'11 Proceedings of the 7th international conference on Reconfigurable computing: architectures, tools and applications
Exploration of heterogeneous FPGA architectures
International Journal of Reconfigurable Computing - Special issue on selected papers from the international workshop on reconfigurable communication-centric systems on chips (ReCoSoC' 2010)
Distributed tuning of machine learning algorithms using MapReduce Clusters
Proceedings of the Third Workshop on Large Scale Data Mining: Theory and Applications
Exploration and optimization of a homogeneous tree-based application specific inflexible FPGA
Microelectronics Journal
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In recent papers by Kirkpatrick et al.(1982,1983), an analogy between the statistical mechanics of large multivariate physical systems and combinatorial optimization is presented and used to develop a general strategy for solving discrete optimization problems. The method relies on probabilistically accepting intermediate increases in the objective function through a set of user-controlled parameters. It is argued that by taking such controlled uphill steps, from time to time, a high quality solution can be found in a moderate amount of computer time. This paper applies an implementation of the proposed algorithm to the TSP for various size networks. The results show the algorithm to be inferior to several well-known heuristics in terms of both solution quality and computer time expended. In addition, set-up time for parameter selection constitutes a major burden for the user. Sensitivity of the algorithm to changes in stopping rules and parameter selection is demonstrated through extensive computational experiments.