Traveling Salesman Problem Solver
ECSL, Department of Computer Science and Engineering, University of Nevada - Reno
Based upon
Interactive Genetic Algorithms for the Traveling Salesman Problem
by Sushil Louis and Rilun Tang, Genetic Adaptive Systems Lab, University of Nevada - Reno
and
An Improved Adaptive Multi-Start Approach to Finding Near-Optimal Solutions to the Euclidean TSP
by Dan Bonachea, Eugene Ingerman, Joshua Levy, and Scott McPeak, University of California - Berkeley
Input your TSP data here.
Florida State University TSP Datasets
Temporary Test Problem
Follow link above for formatting instructions. Will update with format later
NAME: berlin52 TYPE: TSP COMMENT: 52 locations in Berlin (Groetschel) DIMENSION: 52 EDGE_WEIGHT_TYPE: EUC_2D NODE_COORD_SECTION 1 565.0 575.0 2 25.0 185.0 3 345.0 750.0 4 945.0 685.0 5 845.0 655.0 6 880.0 660.0 7 25.0 230.0 8 525.0 1000.0 9 580.0 1175.0 10 650.0 1130.0 11 1605.0 620.0 12 1220.0 580.0 13 1465.0 200.0 14 1530.0 5.0 15 845.0 680.0 16 725.0 370.0 17 145.0 665.0 18 415.0 635.0 19 510.0 875.0 20 560.0 365.0 21 300.0 465.0 22 520.0 585.0 23 480.0 415.0 24 835.0 625.0 25 975.0 580.0 26 1215.0 245.0 27 1320.0 315.0 28 1250.0 400.0 29 660.0 180.0 30 410.0 250.0 31 420.0 555.0 32 575.0 665.0 33 1150.0 1160.0 34 700.0 580.0 35 685.0 595.0 36 685.0 610.0 37 770.0 610.0 38 795.0 645.0 39 720.0 635.0 40 760.0 650.0 41 475.0 960.0 42 95.0 260.0 43 875.0 920.0 44 700.0 500.0 45 555.0 815.0 46 830.0 485.0 47 1170.0 65.0 48 830.0 610.0 49 605.0 625.0 50 595.0 360.0 51 1340.0 725.0 52 1740.0 245.0 EOF
Run
Clear
Genetic Algorithm Parameters
Population Size:
Probability of Crossover:
Probability of Mutation:
Genetic Algorithm Progress
Map