/AntColonyVRP

Primary LanguageJupyter Notebook

Ant Colony Optimisation Algorithm
for solving Vehicle Routing Problem

Structure of the repo

  • report with analysis of the ACO algorithm
  • implementation of the ACO algorithm
  • input and output data
  • tools for data loading, testing, comparing and other

Fitness function of ACO

Obtained result

Solutions

benchmark epochs n_ants alpha beta rho init_pher mean_time found_cost opt_cost error
A-n32-k5 100 100 1.5 0.3 0.95 1000 31.0902 840.7085 784.0 0.0723
A-n33-k5 100 50 1.5 0.3 0.95 1000 17.45445 759.7473 661.0 0.1493
A-n33-k6 100 100 1.5 0.3 0.95 1000 39.4328 824.0234 742.0 0.1105
A-n34-k5 100 100 1.5 0.1 0.95 1000 36.65715 865.7229 778.0 0.1127
A-n36-k5 100 100 1.5 0.1 0.95 1000 37.42615 941.048 799.0 0.1777
A-n37-k5 100 100 1.5 0.3 0.95 1000 34.72325 827.0217 669.0 0.2362
A-n37-k6 100 100 1.5 0.3 0.95 1000 45.1742 1098.5397 949.0 0.1575
A-n38-k5 100 100 1.5 0.3 0.95 1000 41.6064 831.4388 730.0 0.1389
A-n39-k5 100 100 1.5 0.3 0.95 1000 42.3003 922.5199 822.0 0.1222
A-n39-k6 100 100 1.5 0.3 0.95 1000 47.4383 938.2681 831.0 0.1290
A-n44-k6 100 100 1.5 0.3 0.95 1000 54.8001 1071.6804 937.0 0.1437
A-n45-k6 100 100 1.5 0.1 0.95 1000 57.4604 1123.2443 944.0 0.1898
A-n45-k7 100 100 1.5 0.3 0.95 1000 58.953 1321.2444 1146.0 0.1529
A-n46-k7 100 100 1.5 0.1 0.95 1000 58.96205 1121.8012 914.0 0.2273
A-n48-k7 100 100 1.5 0.3 0.95 1000 64.0681 1292.9723 1073.0 0.2050
A-n53-k7 100 100 1.5 0.3 0.95 1000 72.03915 1230.8324 1010.0 0.2186
A-n54-k7 100 100 1.5 0.1 0.95 1000 72.0944 1366.4645 1167.0 0.1709
A-n55-k9 100 100 1.5 0.3 0.95 1000 90.30875 1286.7343 1073.0 0.1991
A-n60-k9 100 100 1.5 0.3 0.95 1000 95.5338 1654.2528 1354.0 0.2217
A-n61-k9 100 100 1.5 0.3 0.95 1000 106.98715 1261.4101 1034.0 0.2199
A-n62-k8 100 100 1.5 0.3 0.95 1000 90.25915 1602.115 1288.0 0.2438
A-n63-k10 100 100 1.5 0.3 0.95 1000 114.5476 1640.7818 1314.0 0.2486
A-n63-k9 100 50 1.5 0.3 0.95 1000 55.53265 1884.8819 1616.0 0.1663
A-n64-k9 100 100 1.5 0.3 0.95 1000 110.1165 1677.3008 1401.0 0.1972
A-n65-k9 100 100 1.5 0.3 0.95 1000 112.73695 1424.3889 1174.0 0.2132
A-n69-k9 100 50 1.5 0.3 0.95 1000 59.1424 1416.3391 1159.0 0.2220
A-n80-k10 100 100 1.5 0.3 0.95 1000 148.4076 2227.146 1763.0 0.2632
B-n31-k5 100 50 1.5 0.1 0.95 10 16.34135 714.3029 672.0 0.0629
B-n34-k5 100 100 1.5 0.3 0.95 1000 34.2074 830.59 788.0 0.0540
B-n35-k5 100 100 1.5 0.1 0.95 10 36.32685 1011.7098 955.0 0.0593
B-n38-k6 100 100 1.5 0.1 0.95 1000 44.6124 895.1143 805.0 0.1119
B-n39-k5 100 50 1.5 0.3 0.95 10 20.8248 634.6591 549.0 0.1560
B-n41-k6 100 100 1.5 0.3 0.95 1000 49.2865 892.5873 829.0 0.0767
B-n43-k6 100 100 1.5 0.1 0.95 1000 47.91175 820.7286 742.0 0.1061
B-n44-k7 100 50 1.5 0.1 0.95 1000 28.34285 1006.9213 909.0 0.1077
B-n45-k5 100 100 1.5 0.3 0.95 1000 48.9257 839.7059 751.0 0.1181
B-n45-k6 100 100 1.5 0.3 0.95 1000 57.04055 724.3774 678.0 0.0684
B-n50-k7 100 100 1.5 0.3 0.95 1000 62.14785 868.6793 741.0 0.1723
B-n50-k8 100 50 1.5 0.1 0.95 1000 35.81775 1409.6985 1312.0 0.0744
B-n51-k7 100 100 1.5 0.3 0.95 1000 71.9902 1059.7313 1032.0 0.0268
B-n52-k7 100 100 1.5 0.3 0.95 1000 70.2877 832.6004 747.0 0.1145
B-n56-k7 100 100 1.5 0.3 0.95 10 77.8671 873.309 707.0 0.2352
B-n57-k7 100 100 1.5 0.3 0.95 1000 80.5894 1235.8289 1153.0 0.0718
B-n57-k9 100 100 1.5 0.3 0.95 1000 91.4484 1771.8331 1598.0 0.1087
B-n63-k10 100 100 1.5 0.3 0.95 1000 116.1213 1747.8 1496.0 0.1683
B-n64-k9 100 100 1.5 0.1 0.95 1000 111.46845 990.3758 861.0 0.1502
B-n66-k9 100 100 1.5 0.3 0.95 1000 116.3869 1477.4993 1316.0 0.1227
B-n67-k10 100 100 1.5 0.3 0.95 10 126.58145 1250.5999 1032.0 0.2118
B-n68-k9 100 100 1.5 0.3 0.95 10 118.7391 1479.7776 1272.0 0.1633
B-n78-k10 100 100 1.5 0.3 0.95 1000 142.44425 1511.2385 1221.0 0.2377