aprender-tsp

Local TSP (Traveling Salesman Problem) optimization with personalized .apr models.

Features

• Multiple Solvers: ACO (Ant Colony), Tabu Search, Genetic Algorithm, Hybrid
• TSPLIB Support: Standard benchmark instance format (berlin52, att48, eil51, etc.)
• Model Persistence: Train once, solve fast with .apr binary format
• CLI & Library: Use as command-line tool or Rust library
• Deterministic: Seed-based reproducibility for benchmarking

Quick Start

# Install
cargo install aprender-tsp

# Train a model
aprender-tsp train instances/berlin52.tsp -o berlin52.apr --algorithm aco

# Solve new instances
aprender-tsp solve -m berlin52.apr instances/new-instance.tsp

# View model info
aprender-tsp info berlin52.apr

# Benchmark against known optimum
aprender-tsp benchmark berlin52.apr --instances instances/berlin52.tsp

Algorithms

Library Usage

use aprender_tsp::{TspInstance, AcoSolver, TspSolver, Budget};

// Load instance
let instance = TspInstance::from_tsplib("berlin52.tsp")?;

// Solve with ACO
let mut solver = AcoSolver::new()
    .with_num_ants(20)
    .with_alpha(1.0)
    .with_beta(2.5);

let solution = solver.solve(&instance, Budget::iterations(1000))?;

println!("Tour length: {}", solution.length);
println!("Gap from optimal: {:.2}%", solution.optimality_gap(&instance)?);

Pre-trained Models

POC models available on Hugging Face: paiml/aprender-tsp-poc

Instance Format

Supports TSPLIB format (.tsp):

NAME: example
TYPE: TSP
DIMENSION: 4
EDGE_WEIGHT_TYPE: EUC_2D
NODE_COORD_SECTION
1 0.0 0.0
2 1.0 0.0
3 1.0 1.0
4 0.0 1.0
EOF

Also supports CSV format with optional header.

Model Format

.apr files are compact binary models (~77 bytes):

• Magic bytes: APR\0
• Version: 1
• CRC32 checksum for integrity
• Algorithm-specific parameters

Part of Aprender

This crate uses aprender::metaheuristics for core optimization algorithms. See aprender for the full ML library.

License

MIT