coo-algorithm 1.0.0

Canine Olfactory Optimization Algorithm

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

pip install coo-optimizer

Quick Start

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:

pytest tests/ -v --cov=coo_optimizer

License

MIT License """