⚡ Smart TSP Benchmark is a professional algorithm testing infrastructure with customizable scenarios and detailed metrics.
pip install smart-tsp-solverLaunch using Smart TSP Solver
pip install smart-tsp-solver
from smart_tsp_benchmark.tsp_benchmark import TSPBenchmark, AlgorithmConfig
from smart_tsp_solver import hierarchical_tsp_solver_v2
from smart_tsp_solver.algorithms.angular_radial.v1 import angular_radial_tsp_v1
from smart_tsp_solver.algorithms.angular_radial.v2 import angular_radial_tsp_v2
from smart_tsp_solver.algorithms.dynamic_gravity.v1 import dynamic_gravity_tsp_v1
from smart_tsp_solver.algorithms.dynamic_gravity.v2 import dynamic_gravity_tsp_v2
from smart_tsp_solver.algorithms.other.greedy.v2 import greedy_tsp_v2
def main():
config = {
'n_points': 100,
'seed': 123,
'point_generation': 'random',
'use_post_optimization': False,
'plot_results': True,
'verbose': True
}
benchmark = TSPBenchmark(config=config)
benchmark.add_algorithm(
name='Angular-radial v1',
config=AlgorithmConfig(
function=angular_radial_tsp_v1,
params={
"sort_by": "angle_distance",
"look_ahead": 100,
"max_2opt_iter": 100
},
post_optimize=True,
description="Angular-radial v1",
is_class=False
)
)
benchmark.add_algorithm(
name='Angular-radial v2',
config=AlgorithmConfig(
function=angular_radial_tsp_v2,
params={
"sort_by": "angle_distance",
"look_ahead": 100,
"max_2opt_iter": 100
},
post_optimize=True,
description="Angular-radial v2",
is_class=False
)
)
benchmark.add_algorithm(
name='Dynamic-gravity v1',
config=AlgorithmConfig(
function=dynamic_gravity_tsp_v1,
params={
"delta": 0.5,
"fast_2opt_iter": 100
},
post_optimize=True,
description="Dynamic-gravity v1",
is_class=False
)
)
benchmark.add_algorithm(
name='Dynamic-gravity v2',
config=AlgorithmConfig(
function=dynamic_gravity_tsp_v2,
params={
"delta": 0.5,
"fast_2opt_iter": 100
},
post_optimize=True,
description="Dynamic-gravity v2",
is_class=False
)
)
benchmark.add_algorithm(
name='Greedy v2',
config=AlgorithmConfig(
function=greedy_tsp_v2,
params={},
post_optimize=False,
description="Classic greedy TSP algorithm",
is_class=False,
)
)
benchmark.add_algorithm(
name='Hierarchical TSP',
config=AlgorithmConfig(
function=hierarchical_tsp_solver_v2,
params={
"cluster_size": 100,
"post_optimize": True
},
post_optimize=False,
description="Hierarchical clustering TSP solver",
is_class=False
)
)
benchmark.run_benchmark()
if __name__ == '__main__':
main()An example of testing TSP algorithms here
A.A. Suvorov
- Researcher specializing in computational optimization and high-performance algorithms
- Focused on bridging theoretical computer science with practical engineering applications
- This project represents extensive research into spatial optimization techniques
Explore other projects on GitHub.
BSD 3-Clause License
Copyright (c) 2025, Alexander Suvorov
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
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OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
(This is a summary of the full disclaimer, which is legally binding and located in sections 15 and 16 of the AGPLv3 license).
For commercial use that is not compatible with the AGPLv3 terms (e.g., including this software in a proprietary product without disclosing the source code), a commercial license is required. Please contact me at smartlegiondev@gmail.com to discuss terms.
For those interested in the theoretical foundations:
- smart-tsp-solver - My Python library featuring advanced heuristics (
Dynamic Gravity,Angular Radial) for solving large TSP instances where finding the exact optimum is impractical. - Exact TSP Solutions (TSP ORACLE): exact-tsp-solver - Optimal solutions for small instances
- Spatial Optimization: Computational geometry approaches for large-scale problems
- Heuristic Analysis: Comparative study of modern TSP approaches
Visual analysis showing Angular-radial's optimal sector-based routing, Dynamic-gravity's smooth trajectories, Greedy's suboptimal clustering
==================================================
SMART TSP ALGORITHMS BENCHMARK
==================================================
Cities: 100
Seed: 123
Generation: cluster
Post-opt: OFF
Algorithms:
- Angular-radial v1: sort_by=angle_distance, look_ahead=1001, max_2opt_iter=1001
- Angular-radial v2: sort_by=angle_distance, look_ahead=1000, max_2opt_iter=1001
- Dynamic-gravity v1: delta=0.5, fast_2opt_iter=1001
- Dynamic-gravity v2: delta=0.5, fast_2opt_iter=1001
- Greedy v2: start_point=0
==================================================
==================================================
Running Angular-radial v1 algorithm...
Description: Angular-radial v1
Parameters: sort_by=angle_distance, look_ahead=1001, max_2opt_iter=1001
Completed in 0.0842 seconds
Route length: 553.66
==================================================
==================================================
Running Angular-radial v2 algorithm...
Description: Angular-radial v2
Parameters: sort_by=angle_distance, look_ahead=1000, max_2opt_iter=1001
Completed in 0.0088 seconds
Route length: 553.66
==================================================
==================================================
Running Dynamic-gravity v1 algorithm...
Description: Dynamic gravity v1
Parameters: delta=0.5, fast_2opt_iter=1001
Completed in 0.0075 seconds
Route length: 567.00
==================================================
==================================================
Running Dynamic-gravity v2 algorithm...
Description: Dynamic gravity v2
Parameters: delta=0.5, fast_2opt_iter=1001
Completed in 0.0073 seconds
Route length: 534.90
==================================================
==================================================
Running Greedy v2 algorithm...
Description: Classic greedy TSP algorithm
Parameters: start_point=0
Completed in 0.0016 seconds
Route length: 609.21
==================================================
==============================================================================================================================
DETAILED ALGORITHM COMPARISON
==============================================================================================================================
Algorithm | Time (s) | vs Best | Length | vs Best | Params
------------------------------------------------------------------------------------------------------------------------------
Greedy v2 | 0.0016 | BEST | 609.21 | +13.89% | start_point=0
Dynamic-gravity v2 | 0.0073 | +348.65% | 534.90 | BEST | delta=0.5, fast_2opt_iter=1001
Dynamic-gravity v1 | 0.0075 | +361.28% | 567.00 | +6.00% | delta=0.5, fast_2opt_iter=1001
Angular-radial v2 | 0.0088 | +441.26% | 553.66 | +3.51% | sort_by=angle_distance, look_ahead=1000, max_2opt_iter=1001
Angular-radial v1 | 0.0842 | +5064.72% | 553.66 | +3.51% | sort_by=angle_distance, look_ahead=1001, max_2opt_iter=1001
==============================================================================================================================
PERFORMANCE ANALYSIS:
- Fastest algorithm(s): Greedy v2 (0.0016 sec)
- Shortest route(s): Dynamic-gravity v2 (534.90 units)
==================================================
SMART TSP ALGORITHMS BENCHMARK
==================================================
Cities: 1001
Seed: 0
Generation: cluster
Post-opt: OFF
Algorithms:
- Angular-radial v1: sort_by=angle_distance, look_ahead=1001, max_2opt_iter=1001
- Angular-radial v2: sort_by=angle_distance, look_ahead=1000, max_2opt_iter=1001
- Dynamic-gravity v1: delta=0.5, fast_2opt_iter=1001
- Dynamic-gravity v2: delta=0.5, fast_2opt_iter=1001
- Greedy v2: start_point=0
==================================================
==================================================
Running Angular-radial v1 algorithm...
Description: Angular-radial v1
Parameters: sort_by=angle_distance, look_ahead=1001, max_2opt_iter=1001
Completed in 0.2531 seconds
Route length: 1388.82
==================================================
==================================================
Running Angular-radial v2 algorithm...
Description: Angular-radial v2
Parameters: sort_by=angle_distance, look_ahead=1000, max_2opt_iter=1001
Completed in 0.1263 seconds
Route length: 1388.82
==================================================
==================================================
Running Dynamic-gravity v1 algorithm...
Description: Dynamic gravity v1
Parameters: delta=0.5, fast_2opt_iter=1001
Completed in 0.0279 seconds
Route length: 1486.12
==================================================
==================================================
Running Dynamic-gravity v2 algorithm...
Description: Dynamic gravity v2
Parameters: delta=0.5, fast_2opt_iter=1001
Completed in 0.0248 seconds
Route length: 1485.03
==================================================
==================================================
Running Greedy v2 algorithm...
Description: Classic greedy TSP algorithm
Parameters: start_point=0
Completed in 0.0022 seconds
Route length: 1612.74
==================================================
================================================================================================================================
DETAILED ALGORITHM COMPARISON
================================================================================================================================
Algorithm | Time (s) | vs Best | Length | vs Best | Params
--------------------------------------------------------------------------------------------------------------------------------
Greedy v2 | 0.0022 | BEST | 1612.74 | +16.12% | start_point=0
Dynamic-gravity v2 | 0.0248 | +1016.15% | 1485.03 | +6.93% | delta=0.5, fast_2opt_iter=1001
Dynamic-gravity v1 | 0.0279 | +1156.99% | 1486.12 | +7.01% | delta=0.5, fast_2opt_iter=1001
Angular-radial v2 | 0.1263 | +5590.97% | 1388.82 | BEST | sort_by=angle_distance, look_ahead=1000, max_2opt_iter=1001
Angular-radial v1 | 0.2531 | +11304.79% | 1388.82 | BEST | sort_by=angle_distance, look_ahead=1001, max_2opt_iter=1001
================================================================================================================================
PERFORMANCE ANALYSIS:
- Fastest algorithm(s): Greedy v2 (0.0022 sec)
- Shortest route(s): Angular-radial v1, Angular-radial v2 (1388.82 units)
Disclaimer: Performance results shown are for clustered distributions. Results may vary based on spatial characteristics. Always evaluate algorithms on your specific problem domains.