Single Objective Optimization

This guide demonstrates how to use BOCoDe for single-objective optimization problems.

Basic Example

Here’s a complete example of using BOCoDe with a single-objective optimization problem:

import bocode
import numpy as np
import torch
from scipy.optimize import minimize

# Create a benchmark problem
problem = bocode.Synthetics.Michalewicz(dim=2)

problem.visualize_function()

# Get problem bounds
bounds = problem.bounds

# Define objective function for optimizer
def objective(x):
    x = torch.Tensor([x])
    fx, _ = problem.evaluate(x)
    fx = -fx # Negate the objective function for MINIMIZATION
    return fx.numpy()[0][0]

# Starting point (2-dimensional)
x0 = np.zeros(2)

# Optimize using SciPy
result = minimize(objective, x0, method='Powell', bounds=bounds)

print(f"Optimal value found: {result.fun}")
print(f"Optimal point found: {result.x}")

print(f"Actual optimal value: {-problem.optimum[0]}")
print(f"Actual optimal point: {problem.x_opt[0]}")

Advanced Features

• Custom callback functions

• Progress tracking

• Multiple starting points

• Integration with other optimization frameworks