Traveling Salesman Problem (TSP) using Simulated Annealing
simulatedannealing() is an optimization routine for traveling salesman problem. Any dataset from the TSPLIB can be suitably modified and can be used with this routine. A detailed description about the function is included in "Simulated_Annealing_Support_Document.pdf."
A GUI is used with the core function to visualize and to vary annealing parameters.
Four sample data set from TSPLIB is provided. You can create your own data set by following a simple procedure given in the supporting document.
You can access the TSPLIB by following this link
http://www.iwr.uni-heidelberg.de/groups/comopt/software/TSPLIB95/
Feel free to drop in your comments, suggestions, bugs and mistakes in the algorithm.
Note: Simulated Annealing is a memory intensive and processor intensive algorithm. A big data set may take hours depending on your computer configuration.
Zitieren als
Aravind Seshadri (2024). Traveling Salesman Problem (TSP) using Simulated Annealing (https://www.mathworks.com/matlabcentral/fileexchange/9612-traveling-salesman-problem-tsp-using-simulated-annealing), MATLAB Central File Exchange. Abgerufen.
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- Mathematics and Optimization > Global Optimization Toolbox > Simulated Annealing >
- AI and Statistics > Statistics and Machine Learning Toolbox > Cluster Analysis and Anomaly Detection > Nearest Neighbors >
- MATLAB > Mathematics > Graph and Network Algorithms > Shortest Path > Traveling Salesman (TSP) >
- Mathematics and Optimization > Optimization Toolbox > Linear Programming and Mixed-Integer Linear Programming > Problem-Based Linear Programming >
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Inspiriert: TSP2024
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SA_TSP/
Version | Veröffentlicht | Versionshinweise | |
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1.0.0.0 | 1. Added a few more benchmark problems.
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