# Canine Olfactory Optimizer (COO)
"""
# Canine Olfactory Optimization (COO)
A nature-inspired metaheuristic optimization algorithm based on the olfactory tracking behavior of canines.
## Features
- Bio-inspired algorithm based on canine pack hunting
- Surrogate-assisted optimization for expensive functions
- Multi-pack architecture for diverse exploration
- Adaptive population sizing
- Easy-to-use API
## Installation
```bash
pip install coo-optimizer
```
## Quick Start
```python
from coo_optimizer import CanineOlfactoryOptimization
import numpy as np
# Define your objective function (to maximize)
def sphere(x):
return -np.sum(x**2)
# Set bounds
bounds = [(-5, 5)] * 10 # 10-dimensional problem
# Create optimizer
coo = CanineOlfactoryOptimization(
bounds=bounds,
n_packs=2,
init_pack_size=10,
max_iterations=100,
random_state=42
)
# Optimize
best_position, best_fitness, history, diagnostics = coo.optimize(sphere)
print(f"Best fitness: {best_fitness}")
print(f"Best position: {best_position}")
```
## Testing
Run tests with pytest:
```bash
pytest tests/ -v --cov=coo_optimizer
```
## License
MIT License
"""
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"description": "# Canine Olfactory Optimizer (COO)\r\n\r\n\"\"\"\r\n\r\n# Canine Olfactory Optimization (COO)\r\n\r\nA nature-inspired metaheuristic optimization algorithm based on the olfactory tracking behavior of canines.\r\n\r\n## Features\r\n\r\n- Bio-inspired algorithm based on canine pack hunting\r\n- Surrogate-assisted optimization for expensive functions\r\n- Multi-pack architecture for diverse exploration\r\n- Adaptive population sizing\r\n- Easy-to-use API\r\n\r\n## Installation\r\n\r\n```bash\r\npip install coo-optimizer\r\n```\r\n\r\n## Quick Start\r\n\r\n```python\r\nfrom coo_optimizer import CanineOlfactoryOptimization\r\nimport numpy as np\r\n\r\n# Define your objective function (to maximize)\r\ndef sphere(x):\r\n return -np.sum(x**2)\r\n\r\n# Set bounds\r\nbounds = [(-5, 5)] * 10 # 10-dimensional problem\r\n\r\n# Create optimizer\r\ncoo = CanineOlfactoryOptimization(\r\n bounds=bounds,\r\n n_packs=2,\r\n init_pack_size=10,\r\n max_iterations=100,\r\n random_state=42\r\n)\r\n\r\n# Optimize\r\nbest_position, best_fitness, history, diagnostics = coo.optimize(sphere)\r\n\r\nprint(f\"Best fitness: {best_fitness}\")\r\nprint(f\"Best position: {best_position}\")\r\n```\r\n\r\n## Testing\r\n\r\nRun tests with pytest:\r\n\r\n```bash\r\npytest tests/ -v --cov=coo_optimizer\r\n```\r\n\r\n## License\r\n\r\nMIT License\r\n\"\"\"\r\n",
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