WFG Benchmarks¶
The WFG (Walking Fish Group) benchmark collection contains all functions from the WFG benchmark suite. Python implementation of the original C++ code is derived from the optproblems python library.
Sources:
Huband, S.; Hingston, P.; Barone, L.; While, L. (2006). A review of multiobjective test problems and a scalable test problem toolkit. IEEE Transactions on Evolutionary Computation, vol.10, no.5, pp. 477-506.
Available Problems¶
bocode.Synthetics.WFG.WFG1bocode.Synthetics.WFG.WFG2bocode.Synthetics.WFG.WFG3bocode.Synthetics.WFG.WFG4bocode.Synthetics.WFG.WFG5bocode.Synthetics.WFG.WFG6bocode.Synthetics.WFG.WFG7bocode.Synthetics.WFG.WFG8bocode.Synthetics.WFG.WFG9
Example Usage¶
import bocode
import torch
# Retrieve available dimensions for instantiation
available_dimensions = bocode.Synthetics.WFG.WFG1.available_dimensions
# Create a WFG benchmark problem
problem = bocode.Synthetics.WFG.WFG1(dim=5)
# Get problem information
bounds = problem.bounds
# Evaluate at a point
x = torch.Tensor([[0.0] * problem.dim])
values, constraints = problem.evaluate(x)
print(f"First WFG function values at origin: {values[0]}")
Output:
First WFG function values at origin: tensor([1., 5.])