ZDT Benchmarks

The ZDT (Zitzler, Deb, and Thiele) benchmark collection contains all functions from the ZDT benchmark suite. Python implementation is derived from the optproblems python library.

Sources:

Zitzler, E., Deb, K., and Thiele, L. (2000). Comparison of Multiobjective Evolutionary Algorithms: Empirical Results. Evolutionary Computation 8(2).

Available Problems

  • bocode.Synthetics.ZDT.ZDT1

  • bocode.Synthetics.ZDT.ZDT2

  • bocode.Synthetics.ZDT.ZDT3

  • bocode.Synthetics.ZDT.ZDT4

  • bocode.Synthetics.ZDT.ZDT5

    • ZDT 5 accepts 80 bits as input, automatically splitting it into the necessary sublists. See example below.

  • bocode.Synthetics.ZDT.ZDT6

Example Usage

import bocode
import torch

# Retrieve available dimensions for instantiation
available_dimensions = bocode.Synthetics.ZDT.ZDT1.available_dimensions

# Create a ZDT benchmark problem
problem = bocode.Synthetics.ZDT.ZDT1(dim=5)

# Get problem information
bounds = problem.bounds

# Evaluate at a point
x = torch.Tensor([[0.5] * problem.dim])
values, constraints = problem.evaluate(x)

print(f"First ZDT function values at [0.5]*5: {values[0]}")

Output:

First ZDT function values at [0.5]*5: tensor([0.5000, 3.8417])

Example Usage of ZDT5

import bocode
import torch

# Create a ZDT5 benchmark problem
problem = bocode.Synthetics.ZDT.ZDT5()

# Get problem information
bounds = problem.bounds

# Evaluate using 80 random bits of 0s and 1s
x = torch.randint(0, 2, (1, 80))
values, constraints = problem.evaluate(x)

print(f"ZDT5 function values at x: {values[0]}")

Output:

ZDT5 function values at x: tensor([10.0000,  4.5000])