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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.
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基于 SOC 职业分类
Geospatial Active Inference framework with 44 modules for ecological, civic, and commercial spatial analysis. Use when working with geospatial data, Active Inference, Bayesian modeling, H3 hexagonal indexing, spatial statistics, or any domain-specific geographic analysis (agriculture, health, economics, risk, climate, energy, transport, marine, forestry, water).
Statistical Parametric Mapping for geospatial data. Use when performing GLM-based spatial analysis, random field theory corrections, cluster-level inference, or neuroimaging-style statistical mapping on geographic datasets.
H3 hexagonal spatial indexing and multi-backend spatial operations. Use when working with H3 cells, spatial indexing, coordinate systems, raster/vector operations, or any spatial backend dispatch (H3, SRAI, PostGIS).
Unified test runner and testing infrastructure for the GEO-INFER ecosystem. Use when running cross-module tests, configuring test categories, setting up test fixtures for spatial data, or analyzing test results.
Generative geospatial art and cartographic visualization. Use when creating artistic map visualizations, generative spatial art, interactive cartographic displays, animation sequences, or aesthetically-focused geographic rendering.
Bayesian inference and probabilistic modeling for geospatial data. Use when building hierarchical models, computing posteriors with PyMC or TFP, performing variational inference, model comparison (LOO/WAIC/DIC), or spatial Gaussian processes.
| 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/ |
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
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}")
uv run python -m pytest GEO-INFER-ANT/tests/ -v