Biobjective TSP

This page contains computational results for the following benchmark instances of the Biobjective TSP:

- Euclidean Instances from TSPLIB with 2 objectives
- Euclidean Instances generated from DIMACS code with 2 objectives
- Random Instances generated from DIMACS code with 2 objectives
- Mixed Instances generated from DIMACS code with 2 objectives

See publications

Stochastic local search algorithms for multiobjective combinatorial optimization: Methods and analysis, L. Paquete, Dissertations in Artificial Intelligence, Vol. 295, AKA Verlag/ IOS Press, 2006.
Pareto local optimum sets in the biobjective traveling salesman problem: An experimental study, L. Paquete, M. Chiarandini, T. Stützle, Metaheuristics for Multiobjective Optimisation, Vol. 535 of Lecture Notes in Economics and Mathematical Systems, Springer, 2004.
A two-phase local search for the biobjective traveling salesman problem, L. Paquete, T. Stützle, Proc. of the 2nd International Conference on Evolutionary Multi-criterion Optimization (EMO 2003), Vol. 2632 of Lecture Notes in Computer Science, pages 479-493, Springer, 2003.

Euclidean Instances from TSPLIB with 2 objectives

100 cities
kroAB100: kroA100.tsp - kroB100.tsp	kroAC100: kroA100.tsp - kroC100.tsp
kroAD100: kroA100.tsp - kroD100.tsp	kroAE100: kroA100.tsp - kroE100.tsp
kroBC100: kroB100.tsp - kroC100.tsp	kroBD100: kroB100.tsp - kroD100.tsp
kroBE100: kroB100.tsp - kroE100.tsp	kroCD100: kroC100.tsp - kroD100.tsp
kroCE100: kroC100.tsp - kroE100.tsp	kroDE100: kroD100.tsp - kroE100.tsp
150 cities kroAB150: kroA150.tsp - kroB150.tsp	200 cities kroAB200: kroA200.tsp - kroB200.tsp

Results from PD-TPLS

Results from PLS

Euclidean Instances generated from DIMACS code with 2 objectives

Instances	Best non-dominated set found
100 cities
euclidAB100: euclidA100.tsp - euclidB100.tsp	best.euclidAB100.tsp
euclidCD100: euclidC100.tsp - euclidD100.tsp	best.euclidCD100.tsp
euclidEF100: euclidE100.tsp - euclidF100.tsp	best.euclidEF100.tsp

300 cities
euclidAB300: euclidA300.tsp - euclidB300.tsp	best.euclidAB300.tsp
euclidCD300: euclidC300.tsp - euclidD300.tsp	best.euclidCD300.tsp
euclidEF300: euclidE300.tsp - euclidF300.tsp	best.euclidEF300.tsp
500 cities
euclidAB500: euclidA500.tsp - euclidB500.tsp	best.euclidAB500.tsp
euclidCD500: euclidC500.tsp - euclidD500.tsp	best.euclidCD500.tsp
euclidEF500: euclidE500.tsp - euclidF500.tsp	best.euclidEF500.tsp

Random Distance Matrix Instances generated from DIMACS code with 2 objectives

Instances

Best non-dominated set found

100 cities
randomAB100: randomA100.tsp - randomB100.tsp	best.randomAB100.tsp
randomCD100: randomC100.tsp - randomD100.tsp	best.randomCD100.tsp
randomEF100: randomE100.tsp - randomF100.tsp	best.randomEF100.tsp

300 cities
randomAB300: randomA300.tsp - randomB300.tsp	best.randomAB300.tsp
randomCD300: randomC300.tsp - randomD300.tsp	best.randomCD300.tsp
randomEF300: randomE300.tsp - randomF300.tsp	best.randomEF300.tsp
500 cities
randomAB500: randomA500.tsp - randomB500.tsp	best.randomAB500.tsp
randomCD500: randomC500.tsp - randomD500.tsp	best.randomCD500.tsp
randomEF500: randomE500.tsp - randomF500.tsp	best.randomEF500.tsp

Mixed Instances generated from DIMACS code with 2 objectives

Instances

Best non-dominated set found

100 cities
mixedGG100: euclidG100.tsp - randomG100.tsp	best.mixedGG100.tsp
mixedHH100: euclidH100.tsp - randomH100.tsp	best.mixedHH100.tsp
mixedII100: euclidI100.tsp - randomI100.tsp	best.mixedII100.tsp

300 cities
mixedGG300: euclidG300.tsp - randomG300.tsp	best.mixedGG300.tsp
mixedHH300: euclidH300.tsp - randomH300.tsp	best.mixedHH300.tsp
mixedII300: euclidI300.tsp - randomI300.tsp	best.mixedII300.tsp
500 cities
mixedGG500: euclidG500.tsp - randomG500.tsp	best.mixedGG500.tsp
mixedHH500: euclidH500.tsp - randomH500.tsp	best.mixedHH500.tsp
mixedII500: euclidI500.tsp - randomI500.tsp	best.mixedII500.tsp