| name | geo-infer-ant |
| description | Ant Colony Optimization and swarm intelligence for geospatial problems. Use when solving spatial optimization with ACO, PSO, ABC algorithms, implementing stigmergic coordination, or optimizing geographic routing and resource allocation with bio-inspired methods. |
| prerequisites | {"required":["geo-infer-space"],"recommended":["geo-infer-math"]} |
| difficulty | advanced |
| estimated_time | 60min |
| examples_dir | ../GEO-INFER-EXAMPLES/examples/ |
GEO-INFER-ANT
Instructions
Core Capabilities
- ACO: Ant Colony Optimization for spatial routing and TSP
- PSO: Particle Swarm Optimization for continuous spatial problems
- ABC: Artificial Bee Colony for facility location optimization
- Stigmergy: Pheromone-based coordination on spatial grids
- Colony convergence: Iteration tracking, solution quality metrics
Key Imports
from geo_infer_ant.core.aco import AntColonyOptimizer
from geo_infer_ant.core.pso import ParticleSwarmOptimizer
from geo_infer_ant.core.abc import ArtificialBeeColony
from geo_infer_ant.core.pheromone import PheromoneGrid
Examples
from geo_infer_ant.core.aco import AntColonyOptimizer
optimizer = AntColonyOptimizer(
n_ants=50, alpha=1.0, beta=2.0, rho=0.5
)
best_route = optimizer.solve(distance_matrix, n_iterations=100)
print(f"Best route cost: {best_route.cost}")
Guidelines
- Tests have long runtime (~213s) due to convergence iterations
- Convergence verification in development (Alpha)
Integrations
- Integrates with AGENT for multi-agent swarm coordination
- Test:
uv run python -m pytest GEO-INFER-ANT/tests/ -v
