pyglobalsearch.PyOQNLPParams

class pyglobalsearch.PyOQNLPParams(iterations=300, population_size=1000, wait_cycle=15, threshold_factor=0.2, distance_factor=0.75)

Bases: object

Parameters for the OQNLP global optimization algorithm.

The OQNLP algorithm combines scatter search metaheuristics with local optimization to find global minima in nonlinear optimization problems. These parameters control the behavior of the algorithm.

Parameters:

• iterations (int) – Maximum number of global iterations

• population_size (int) – Size of the scatter search population

• wait_cycle (int) – Iterations to wait without improvement before termination

• threshold_factor (float) – Controls acceptance threshold for new solutions

• distance_factor (float) – Controls minimum distance between solutions

Examples

Default parameters:

>>> params = gs.PyOQNLPParams()

Custom parameters for difficult problems:

>>> params = gs.PyOQNLPParams(
...     iterations=500,
...     population_size=2000,
...     wait_cycle=25,
...     threshold_factor=0.1,  # More exploration
...     distance_factor=0.2    # Allow closer solutions
... )

__init__()

Methods

__init__()

Attributes

distance_factor	Controls minimum distance between solutions
iterations	Maximum number of stage two iterations
population_size	Size of the scatter search population
threshold_factor	Controls acceptance threshold for new solutions
wait_cycle	Iterations to wait without improvement before termination

distance_factor

Controls minimum distance between solutions

iterations

Maximum number of stage two iterations

population_size

Size of the scatter search population

threshold_factor

Controls acceptance threshold for new solutions

wait_cycle

Iterations to wait without improvement before termination