Single-Objective Optimization Problems
The pycellga.problems.single_objective package includes a diverse collection of single-objective optimization problems. These problems are commonly used to benchmark and evaluate the performance of optimization algorithms. The package is organized into two main subpackages, each addressing different types of problem structures: continuous and discrete.
Continuous Optimization Problems
The continuous subpackage provides a set of continuous benchmark functions designed to evaluate algorithms on continuous search spaces. These functions are ideal for testing an algorithm’s performance in terms of accuracy, convergence speed, and robustness on smooth, differentiable landscapes.
- Continuous Optimization Problems
- Ackley Function
- Bent Cigar Function
- Bohachevsky Function
- Chichinadze Function
- Drop Wave Function
- Frequency Modulation Sound Function (FMS)
- Griewank Function
- Holzman Function
- Levy Function
- Matyas Function
- Pow Function
- Powell Function
- Rastrigin Function
- Rosenbrock Function
- Rothellipsoid Function
- Schaffer Function
- Schaffer2 Function
- Schwefel Function
- Sphere Function
- Styblinskitang Function
- Sumofdifferentpowers Function
- Threehumps Function
- Zakharov Function
- Zettle Function
Discrete Optimization Problems
The discrete subpackage includes binary and permutation-based problems where solutions are represented as discrete values or permutations. This subpackage is particularly useful for assessing algorithms that handle combinatorial and discrete optimization challenges.