This code shows how the simulated annealing optimization algorithm can be applied to the traveling salesman problem.
First, create a build directory
$ mkdir build
$ cd build
Run cmake
$ cmake -DCMAKE_BUILD_TYPE=Release ..
Run make
$ make
The executable is located in the bin/ subdirectory and is named "sa". Run the program as follows
$ ./sa
All important parameters are defined in the main.cpp file. There you can adjust the number of iterations, the cooling schedule, and the screen update cycle. Remember to recompile the project once you are finished updating the parameters.
- new-york street map, this is for benchmark purposes because we know the exact solution here
- generate a random map
- read in a tsp-file (discussed below)
If you run the program without any parameters, then a new-york street map is generated.
Call the program from console with parameter --help to get more information.
You can test different datasets from the LIBTSP repository [1]. Simply provide the filename of the .tsp file as the first argument. You almost surely have to adjust the parameters in order to get a good approximation.
You can use berlin52.tsp for a quick test. The example should run well without needing to adjust the parameters.
You can compare your results (using your parameters settings) to the optimal result [2].
The program only works with instances of type TSP and edge weight type EUC_2D.
##Implemented cooling schedules
- Geometric schedule
- logarithmic schedule [8]
- constant thermodynamic speed in order 1 [6-10]
The yellow line shows the shortest cycle that has been found so far. The purple line shows the current state.
[1]: TSPLIB95, University of Heidelberg. http://comopt.ifi.uni-heidelberg.de/software/TSPLIB95/tsp/
[2]: TSPLIB95 best solutions found, University of Heidelberg. http://comopt.ifi.uni-heidelberg.de/software/TSPLIB95/STSP.html
[3]: D. Bertsimas and J. Tsitsiklis. Simulated Annealing. Statistical Science Vol. 8 No. 1, pages 10-15, 1993
[4]: D. Stroock. An Introduction to Markov Processes. Graduate texts in mathematics, Springer, 2005.
[5]: C. Geyer. Markov Chain Monte Carlo Lecture Notes. http://www.stat.umn.edu/geyer/f05/8931/n1995.pdf
[6]: Yaghout Nourani and Bjarne Andresen 1998 J. Phys. A: Math. Gen. 31 8373. https://iopscience.iop.org/article/10.1088/0305-4470/31/41/011
[7]: Andresen B., Gordon J.M. (1996) Constant thermodynamic speed simulated annealing. In: Jacobsen B.H., Mosegaard K., Sibani P. (eds) Inverse Methods. Lecture Notes in Earth Sciences, vol 63. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0011789
[8]: Performance Analysis of Simulated Annealing Cooling Schedules in the Context of Dense Image Matching https://www.doi.org/10.13053/CyS-21-3-2553
[9]: Salamon et al., Simulated annealing with constant thermodynamic speed. https://doi.org/10.1016/0010-4655(88)90003-3
[10]: Abdellatif El Afia et al., A Self Controlled Simulated Annealing Algorithm using Hidden Markov Model State Classification. https://doi.org/10.1016/j.procs.2019.01.024
#License This is a fork of https://github.com/TobyPDE/simulated-annealing-tsp
Thanks a lot!
The MIT License (MIT) Copyright (c) 2021 Babalion
The MIT License (MIT) Copyright (c) 2016 Tobias Pohlen
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